Organochlorine pesticides disrupted reproduction and killed many raptorial birds, and contributed to population declines during the 1940s to 1970s. We sought to discern whether and to what extent territory occupancy and breeding success changed from the pesticide era to recent years in a resident population of Peregrine Falcons Falco peregrinus in southern Scotland using long-term (1964–2015) field data and multi-state, multi-season occupancy models. Peregrine territories that were occupied with successful reproduction in one year were much more likely to be occupied and experience reproductive success in the following year, compared with those that were unoccupied or occupied by unsuccessful breeders in the previous year. Probability of territory occupancy differed between territories in the eastern and western parts of the study area, and varied over time. The probability of occupancy of territories that were unoccupied and those that were occupied with successful reproduction during the previous breeding season generally increased over time, whereas the probability of occupancy of territories that were occupied after failed reproduction decreased. The probability of reproductive success (conditional on occupancy) in territories that were occupied during the previous breeding season increased over time. Specifically, for territories that had been successful in the previous year, the probability of occupancy as well as reproductive success increased steadily over time; these probabilities were substantially higher in recent years than earlier, when the population was still exposed to direct or residual effects of organochlorine pesticides. These results are consistent with the hypothesis that progressive reduction, followed by a complete ban, in the use of organochlorine pesticides improved reproductive success of Peregrines in southern Scotland. Differences in the temporal pattern of probability of reproductive success between south-eastern and south-western Scotland suggest that the effect of organochlorine pesticides on Peregrine reproductive success and/or the recovery from pesticide effects varied geographically and was possibly affected by other factors such as persecution.
","language":"English","publisher":"Ibis Society","publisherLocation":"London","doi":"10.1111/ibi.12443","usgsCitation":"McGrady, M.J., Hines, J.E., Rollie, C., Smith, G.D., Morton, E.R., Moore, J.F., Mearns, R.M., Newton, I., Murillo-Garcia, O.E., and Oli, M.K., 2017, Territory occupancy and breeding success of Peregrine Falcons Falco peregrinus at various stages of population recovery: Ibis, v. 159, no. 2, p. 285-296, https://doi.org/10.1111/ibi.12443.","productDescription":"12 p.","startPage":"285","endPage":"296","ipdsId":"IP-077722","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470018,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/ibi.12443","text":"External Repository"},{"id":337439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"159","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-02","publicationStatus":"PW","scienceBaseUri":"58c7af95e4b0849ce9795e68","contributors":{"authors":[{"text":"McGrady, Michael J.","contributorId":189117,"corporation":false,"usgs":false,"family":"McGrady","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":683934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":683895,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rollie, Chris","contributorId":189118,"corporation":false,"usgs":false,"family":"Rollie","given":"Chris","email":"","affiliations":[],"preferred":false,"id":683935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, George D.","contributorId":189119,"corporation":false,"usgs":false,"family":"Smith","given":"George","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":683936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morton, Elise R.","contributorId":189121,"corporation":false,"usgs":false,"family":"Morton","given":"Elise","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":683937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":683938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mearns, Richard M.","contributorId":189123,"corporation":false,"usgs":false,"family":"Mearns","given":"Richard","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":683939,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Newton, Ian","contributorId":111901,"corporation":false,"usgs":true,"family":"Newton","given":"Ian","email":"","affiliations":[],"preferred":false,"id":683903,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murillo-Garcia, Oscar E.","contributorId":189120,"corporation":false,"usgs":false,"family":"Murillo-Garcia","given":"Oscar","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":683940,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Oli, Madan K.","contributorId":86089,"corporation":false,"usgs":true,"family":"Oli","given":"Madan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":683904,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70185060,"text":"70185060 - 2017 - Geochemistry of host rocks in the Howards Pass district, Yukon-Northwest Territories, Canada: implications for sedimentary environments of Zn-Pb and phosphate mineralization","interactions":[],"lastModifiedDate":"2017-03-22T14:41:40","indexId":"70185060","displayToPublicDate":"2017-03-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of host rocks in the Howards Pass district, Yukon-Northwest Territories, Canada: implications for sedimentary environments of Zn-Pb and phosphate mineralization","docAbstract":"Detailed lithogeochemical data are reported here on early Paleozoic sedimentary rocks that host the large Howards Pass stratiform Zn-Pb deposits in Yukon-Northwest Territories. Redox-sensitive trace elements (Mo, Re, V, U) and Ce anomalies in members of the Duo Lake Formation record significant environmental changes. During the deposition of lower footwall units (Pyritic siliceous and Calcareous mudstone members), bottom waters were anoxic and sulphidic, respectively; these members formed in a marginal basin that may have become increasingly restricted with time. Relative to lower members, a major environmental change is proposed for deposition of the overlying Lower cherty mudstone member, which contains phosphorite beds up to ∼0.8 m thick in the upper part, near the base of the Zn-Pb deposits. The presence of these beds, together with models for modern phosphorite formation, suggests P input from an upwelling system and phosphorite deposition in an upper slope or outer shelf setting. The overlying Active mudstone member contains stratabound to stratiform Zn-Pb deposits within black mudstone and gray calcareous mudstone. Data for unmineralized black mudstone in this member indicate deposition under diverse redox conditions from suboxic to sulphidic. Especially distinctive in this member are uniformly low ratios of light to heavy rare earth elements that are unique within the Duo Lake Formation, attributed here to the dissolution of sedimentary apatite by downward-percolating acidic metalliferous brines. Strata that overlie the Active member (Upper siliceous mudstone member) consist mainly of black mudstone with thin (0.5–1.5 cm) laminae of fine-grained apatite, recording continued deposition on an upper slope or outer shelf under predominantly suboxic bottom waters. Results of this study suggest that exploration for similar stratiform sediment-hosted Zn-Pb deposits should include the outer parts of ancient continental margins, especially at and near stratigraphic transitions from marginal basin facies to overlying slope or shelf facies.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-016-0680-x","usgsCitation":"Slack, J.F., Falck, H., Kelley, K.D., and Xue, G.G., 2017, Geochemistry of host rocks in the Howards Pass district, Yukon-Northwest Territories, Canada: implications for sedimentary environments of Zn-Pb and phosphate mineralization: Mineralium Deposita, v. 52, no. 4, p. 565-593, https://doi.org/10.1007/s00126-016-0680-x.","productDescription":"29 p.","startPage":"565","endPage":"593","ipdsId":"IP-076693","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":337465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-05","publicationStatus":"PW","scienceBaseUri":"58c7af9be4b0849ce9795e72","contributors":{"authors":[{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":684113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falck, Hendrik","contributorId":167705,"corporation":false,"usgs":false,"family":"Falck","given":"Hendrik","email":"","affiliations":[{"id":24811,"text":"NWT Geoscience Office, Yellowknife, Canada","active":true,"usgs":false}],"preferred":false,"id":684114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelley, Karen D. kdkelley@usgs.gov","contributorId":431,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":684115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xue, Gabriel G.","contributorId":189206,"corporation":false,"usgs":false,"family":"Xue","given":"Gabriel","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":684116,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184964,"text":"70184964 - 2017 - Relationships between maternal engorgement weight and the number, size, and fat content of larval Ixodes scapularis (Acari: Ixodidae)","interactions":[],"lastModifiedDate":"2017-05-31T16:29:08","indexId":"70184964","displayToPublicDate":"2017-03-13T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2385,"text":"Journal of Medical Entomology","active":true,"publicationSubtype":{"id":10}},"title":"Relationships between maternal engorgement weight and the number, size, and fat content of larval Ixodes scapularis (Acari: Ixodidae)","docAbstract":"The relationship between engorgement weight of female Ixodes scapularis Say and characteristics of offspring was studied using field-collected females fed on rabbits in the laboratory. The number of eggs laid was positively related to maternal engorgement weight in one trial, and larval size (estimated by scutal area) was positively related to maternal engorgement weight in the other. These results suggest a trade-off in number of eggs produced versus average size of offspring, possibly determined during late engorgement. The adults for the two trials were collected from different sites in southern Rhode Island and in different seasons (the fall adults were newly emerged, while the spring adults had presumably lived through the winter), so it is not clear whether these results reflect genetic differences or subtle environmental differences between trials. Percent egg hatch and average fat content of larvae were not related to female engorgement weight. We present a modified method to measure lipid content of pooled larval ticks.
","language":"English","publisher":"Bernice Pauahi Bishop Museum","publisherLocation":"Honolulu, HI","doi":"10.1093/jme/tjw191","usgsCitation":"Ginsberg, H., Lee, C., Volson, B., Dyer, M.C., and LeBrun, R.A., 2017, Relationships between maternal engorgement weight and the number, size, and fat content of larval Ixodes scapularis (Acari: Ixodidae): Journal of Medical Entomology, v. 54, no. 2, p. 275-280, https://doi.org/10.1093/jme/tjw191.","productDescription":"6 p.","startPage":"275","endPage":"280","ipdsId":"IP-074914","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470017,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjw191","text":"Publisher Index Page"},{"id":337414,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-27","publicationStatus":"PW","scienceBaseUri":"58c7af9be4b0849ce9795e70","contributors":{"authors":[{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":683720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Chong","contributorId":189052,"corporation":false,"usgs":false,"family":"Lee","given":"Chong","email":"","affiliations":[],"preferred":false,"id":683721,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Volson, Barry","contributorId":189053,"corporation":false,"usgs":false,"family":"Volson","given":"Barry","email":"","affiliations":[],"preferred":false,"id":683722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dyer, Megan C.","contributorId":178309,"corporation":false,"usgs":false,"family":"Dyer","given":"Megan","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":683723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeBrun, Roger A.","contributorId":70907,"corporation":false,"usgs":false,"family":"LeBrun","given":"Roger","email":"","middleInitial":"A.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":683724,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184375,"text":"ds1041 - 2017 - Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana","interactions":[],"lastModifiedDate":"2017-03-10T10:36:47","indexId":"ds1041","displayToPublicDate":"2017-03-10T09:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1041","title":"Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana","docAbstract":"As part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the south-central coast of Louisiana, from Raccoon Point to Point Au Fer Island, in July 2015. The goal of the BICM program is to provide long-term data on Louisiana’s coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report will provide baseline bathymetric information for future research investigating island evolution, sediment transport, and recent and long-term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 300 square kilometers of nearshore environment from Raccoon Point to Point Au Fer Island. This data series serves as an archive of processed single-beam bathymetry data, collected from July 22–29, 2015, under USGS Field Activity Number 2015-320-FA. Geographic information system data products include a 200-meter-cell-size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1041","usgsCitation":"Stalk, C.A., DeWitt, N.T., Kindinger, J.L., Flocks, J.G., Reynolds, B.J., Kelso, K.W., Fredericks, J.J., and Tuten, T.M., 2017, Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana: U.S. Geological Survey Data Series 1041, https://doi.org/10.3133/ds1041.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076353","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":337140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1041/coverthb.jpg"},{"id":337141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1041/","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1041"}],"country":"United States ","state":"Louisiana","otherGeospatial":"Point Au Fer Island, Raccoon Point","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.0384521484375,\n 30.90693797089463\n ],\n [\n -92.0819091796875,\n 30.888083515609047\n ],\n [\n -92.1258544921875,\n 28.85429649869795\n ],\n [\n -90.0604248046875,\n 28.849485201023\n ],\n [\n -90.0384521484375,\n 30.90693797089463\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.
","language":"English","publisher":"Taylor & Francis Online","doi":"10.1080/15481603.2017.1295514","usgsCitation":"DeWitt, J.D., Warner, T.A., Chirico, P.G., and Bergstresser, S.E., 2017, Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering: GIScience and Remote Sensing, v. 54, no. 4, p. 552-572, https://doi.org/10.1080/15481603.2017.1295514.","productDescription":"21 p.","startPage":"552","endPage":"572","ipdsId":"IP-079221","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":499872,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/584b1176a9f44398859e796820e90170","text":"External Repository"},{"id":344785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-10","publicationStatus":"PW","scienceBaseUri":"59901399e4b09fa1cb178929","contributors":{"authors":[{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":707426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warner, Timothy A. 0000-0002-0414-9748","orcid":"https://orcid.org/0000-0002-0414-9748","contributorId":195554,"corporation":false,"usgs":false,"family":"Warner","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":707427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chirico, Peter G. 0000-0001-8375-5342 pchirico@usgs.gov","orcid":"https://orcid.org/0000-0001-8375-5342","contributorId":195555,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter","email":"pchirico@usgs.gov","middleInitial":"G.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":707428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergstresser, Sarah E. 0000-0003-0182-5779 sbergstresser@usgs.gov","orcid":"https://orcid.org/0000-0003-0182-5779","contributorId":195556,"corporation":false,"usgs":true,"family":"Bergstresser","given":"Sarah","email":"sbergstresser@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":707429,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184445,"text":"70184445 - 2017 - Putting flow-ecology relationships into practice: A decision-support system to assess fish community response to water-management scenarios","interactions":[],"lastModifiedDate":"2017-03-09T11:42:19","indexId":"70184445","displayToPublicDate":"2017-03-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Putting flow-ecology relationships into practice: A decision-support system to assess fish community response to water-management scenarios","docAbstract":"This paper presents a conceptual framework to operationalize flow–ecology relationships into decision-support systems of practical use to water-resource managers, who are commonly tasked with balancing multiple competing socioeconomic and environmental priorities. We illustrate this framework with a case study, whereby fish community responses to various water-management scenarios were predicted in a partially regulated river system at a local watershed scale. This case study simulates management scenarios based on interactive effects of dam operation protocols, withdrawals for municipal water supply, effluent discharges from wastewater treatment, and inter-basin water transfers. Modeled streamflow was integrated with flow–ecology relationships relating hydrologic departure from reference conditions to fish species richness, stratified by trophic, reproductive, and habitat characteristics. Adding a hypothetical new water-withdrawal site was predicted to increase the frequency of low-flow conditions with adverse effects for several fish groups. Imposition of new reservoir release requirements was predicted to enhance flow and fish species richness immediately downstream of the reservoir, but these effects were dissipated further downstream. The framework presented here can be used to translate flow–ecology relationships into evidence-based management by developing decision-support systems for conservation of riverine biodiversity while optimizing water availability for human use.
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The initial sampling was designed to provide a spatially unbiased assessment of the quality of untreated groundwater used for public water supplies in the 12 study units. In these study units, 550 sampling sites were selected by using a spatially distributed, randomized, grid-based method to provide spatially unbiased representation of the areas assessed (grid sites, also called “status sites”). After the initial sampling period, 76 of the previously sampled status sites (approximately 10 percent in each study unit) were randomly selected for trend sampling (“trend sites”). The 12 study units sampled both during the initial sampling and during the trend sampling period were distributed among 6 hydrogeologic provinces: Coastal (Northern and Southern), Transverse Ranges and Selected Peninsular Ranges, Klamath, Modoc Plateau and Cascades, and Sierra Nevada Hydrogeologic Provinces. For the purposes of this trend report, the six hydrogeologic provinces were grouped into two hydrogeologic regions based on location: Coastal and Mountain.
The groundwater samples were analyzed for a number of synthetic organic constituents (volatile organic compounds, pesticides, and pesticide degradates), constituents of special interest (perchlorate and 1,2,3-trichloropropane), and natural inorganic constituents (nutrients, major and minor ions, and trace elements). Isotopic tracers (tritium, carbon-14, and stable isotopes of hydrogen and oxygen in water) also were measured to help identify processes affecting groundwater quality and the sources and ages of the sampled groundwater. More than 200 constituents and water-quality indicators were measured during the trend sampling period.
Quality-control samples (blanks, replicates, matrix-spikes, and surrogate compounds) were collected at about one-third of the trend sites, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. On the basis of detections in laboratory and field blank samples collected by GAMA-PBP study units, including the 12 study units presented here, reporting levels for some groundwater results were adjusted in this report. Differences between replicate samples were mostly within acceptable ranges, indicating low variability in analytical results. Matrix-spike recoveries were largely within the acceptable range (70 to 130 percent).
This study did not attempt to evaluate the quality of water delivered to consumers. After withdrawal, groundwater used for drinking water typically is treated, disinfected, and blended with other waters to achieve acceptable water quality. The comparison benchmarks used in this report apply to treated water that is served to the consumer, not to untreated groundwater. To provide some context for the results, however, concentrations of constituents measured in these groundwater samples were compared with benchmarks established by the U.S. Environmental Protection Agency and the State of California. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.
Most organic constituents that were detected in groundwater samples from the trend sites were found at concentrations less than health-based benchmarks. One volatile organic compound—perchloroethene—was detected at a concentration greater than the health-based benchmark in samples from one trend site during the initial and trend sampling periods. Chloroform was detected in at least 10 percent of the samples at trend sites in both sampling periods. Methyl tert-butyl ether was detected in samples from more than 10 percent of the trend sites during the initial sampling period. No pesticide or pesticide degradate was detected in greater than 10 percent of the samples from trend sites or at concentrations greater than their health-based benchmarks during either sampling period. Nutrients were not detected at concentrations greater than their health-based benchmarks during either sampling period.
Most detections of major ions and trace elements in samples from trend sites were less than health-based benchmarks during both sampling periods. Arsenic and boron each were detected at concentrations greater than the health-based benchmark in samples from four trend sites during the initial and trend sampling periods. Molybdenum was detected in samples from four trend sites at concentrations greater than the health-based benchmark during both sampling periods. Samples from two of these trend sites had similar molybdenum concentrations, and two had substantially different concentrations during the initial and trend sampling periods. Uranium was detected at a concentration greater than the health-based benchmark only at two trend sites.
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\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
http://ca.water.usgs.gov
Breton Island, located at the southern end of the Chandeleur Islands, supports one of Louisiana’s largest historical brown pelican (Pelecanus occidentalis) nesting colonies. Although the brown pelican was delisted as an endangered species in 2009, nesting areas are threatened by continued land loss and are extremely vulnerable to storm impacts. The U.S. Fish and Wildlife Service proposed to restore Breton Island to pre-Hurricane Katrina conditions through rebuilding the shoreface, dune, and back-barrier marsh environments. Prior to restoration, scientists from the U.S. Geological Survey’s (USGS) St. Petersburg Coastal and Marine Science Center Geologic and Morphologic Evolution of Coastal Margins project collected high-resolution geophysical (topography, bathymetry, and sub-bottom profiles) and sedimentologic data from around Breton Island to characterize the geologic framework of the island platform, nearshore, and shelf environments. These data will be used to characterize the geologic framework around Breton Island, identify potential borrow areas for restoration efforts, quantify seafloor change, and provide information for sediment transport and morphologic change models to assess island response to restoration and natural processes.
This report, along with the accompanying USGS data release, serves as an archive of sediment data from vibracores, push cores, and submerged grab samples collected from around Breton and Gosier Islands, Louisiana, during two surveys conducted in July 2014 and January 2015 (USGS Field Activity Numbers 2014–314–FA and 2014–336–FA, respectively). Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to ground-truth the geophysical data and characterize potential sand resources or can be incorporated into sediment transport or morphologic change models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, and geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the accompanying data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1037","usgsCitation":"Bernier, J.C., Kelso, K.W., Tuten, T.M., Stalk, C.A., and Flocks, J.G., 2017, Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana: U.S. Geological Survey Data Series 1037, https://doi.org/10.3133/ds1037.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-081095","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":336951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1037/coverthb.jpg"},{"id":336945,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1037","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1037"},{"id":336959,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79C6VKF","text":"USGS data release ","linkHelpText":"Archive of Sediment Data Collected in 2014 and 2015 From Around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Islands, Breton National Wildlife Refuge, Gosier Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.241943359375,\n 29.450360671054415\n ],\n [\n -88.97140502929688,\n 29.450360671054415\n ],\n [\n -88.97140502929688,\n 29.60987920220905\n ],\n [\n -89.241943359375,\n 29.60987920220905\n ],\n [\n -89.241943359375,\n 29.450360671054415\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 4th Street South
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https://coastal.er.usgs.gov/
This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R (http://www.r-project.org/). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer.
Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/.
Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3377","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., Gronberg, J.M., 2017, Predicted pH at the domestic and public supply drinking water depths, Central Valley, California: U.S. Geological Survey Scientific Investigations Map 3377, 1 sheet, scale 1:2,400,000, https://doi.org/10.3133/sim3377.","productDescription":"Sheet: 19.00 x 21.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-079912","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336887,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX77K4","text":"USGS data release","description":"USGS data release","linkHelpText":"Ascii grids of predicted pH in depth zones used by domestic and public drinking water supply depths, Central Valley, California."},{"id":336878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3377/coverthb.jpg"},{"id":336879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3377/sim3377.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3377"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.22290039062499,\n 40.75557964275589\n ],\n [\n -122.958984375,\n 40.38839687388361\n ],\n [\n -122.574462890625,\n 39.32579941789298\n ],\n [\n -122.08007812499999,\n 38.07404145941957\n ],\n [\n -120.7177734375,\n 36.77409249464195\n ],\n [\n -119.83886718750001,\n 35.33529320309328\n ],\n [\n -119.267578125,\n 34.912962495216966\n ],\n [\n -118.740234375,\n 35.110921809704756\n ],\n [\n -118.740234375,\n 35.8356283888737\n ],\n [\n -118.91601562499999,\n 36.359374956015856\n ],\n [\n -119.84985351562499,\n 37.32648861334206\n ],\n [\n -120.82763671875,\n 38.24680876017446\n ],\n [\n -121.39892578125,\n 39.2492708462234\n ],\n [\n -122.1240234375,\n 40.53050177574321\n ],\n [\n -122.22290039062499,\n 40.75557964275589\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
http://ca.water.usgs.gov/
Carbon dioxide (CO2) has shown promise as a tool to control movements of invasive Asian carp, but its effects on native freshwater biota have not been well studied. The authors evaluated lethal and sublethal responses of juvenile fatmucket (Lampsilis siliquoidea) mussels to CO2 at levels (43–269 mg/L, mean concentration) that bracket concentrations effective for deterring carp movement. The 28-d lethal concentration to 50% of the mussels was 87.0 mg/L (95% confidence interval [CI] 78.4–95.9) and at 16-d postexposure, 76.0 mg/L (95% CI 62.9–90.3). A proportional hazards regression model predicted that juveniles could not survive CO2 concentrations >160 mg/L for more than 2 wk or >100 mg/L CO2 for more than 30 d. Mean shell growth was significantly lower for mussels that survived CO2 treatments. Growth during the postexposure period did not differ among treatments, indicating recovery of the mussels. Also, CO2 caused shell pitting and erosion. Behavioral effects of CO2 included movement of mussels to the substrate surface and narcotization at the highest concentrations. Mussels in the 110 mg/L mean CO2treatment had the most movements in the first 3 d of exposure. If CO2 is infused continuously as a fish deterrent, concentrations <76 mg/L are recommended to prevent juvenile mussel mortality and shell damage. Mussels may survive and recover from brief exposure to higher concentrations.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3567","usgsCitation":"Waller, D.L., Bartsch, M.R., Fredricks, K.T., Bartsch, L., Schleis, S.M., and Lee, S., 2017, Effects of carbon dioxide on juveniles of the freshwater mussel (Lampsilis siliquoidea [Unionidae]): Environmental Toxicology and Chemistry, v. 36, no. 3, p. 671-681, https://doi.org/10.1002/etc.3567.","productDescription":"11 p.","startPage":"671","endPage":"681","ipdsId":"IP-074542","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":337067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-28","publicationStatus":"PW","scienceBaseUri":"58c12634e4b014cc3a3d344e","contributors":{"authors":[{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartsch, Michelle R. 0000-0002-9571-5564 mbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-9571-5564","contributorId":149359,"corporation":false,"usgs":true,"family":"Bartsch","given":"Michelle","email":"mbartsch@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fredricks, Kim T. 0000-0003-2363-7891 kfredricks@usgs.gov","orcid":"https://orcid.org/0000-0003-2363-7891","contributorId":173994,"corporation":false,"usgs":true,"family":"Fredricks","given":"Kim","email":"kfredricks@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartsch, Lynn A. 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":149360,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn A.","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681185,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schleis, Susan M. 0000-0002-9396-7856 sschleis@usgs.gov","orcid":"https://orcid.org/0000-0002-9396-7856","contributorId":2858,"corporation":false,"usgs":true,"family":"Schleis","given":"Susan","email":"sschleis@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":681186,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, Sheldon","contributorId":173995,"corporation":false,"usgs":false,"family":"Lee","given":"Sheldon","email":"","affiliations":[{"id":25359,"text":"Viterbo University","active":true,"usgs":false}],"preferred":false,"id":681187,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184389,"text":"70184389 - 2017 - Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior","interactions":[],"lastModifiedDate":"2018-08-06T13:07:37","indexId":"70184389","displayToPublicDate":"2017-03-08T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior","docAbstract":"The Lower Colorado River provides critical riparian areas in an otherwise arid region and is an important stopover site for migrating landbirds. In order to reverse ongoing habitat degradation due to drought and human-altered hydrology, a pulse flow was released from Morelos Dam in spring of 2014, which brought surface flow to dry stretches of the Colorado River in Mexico. To assess the potential effects of habitat modification resulting from the pulse flow, we used foraging behavior of spring migrants from past and current studies to assess the relative importance of different riparian habitats. We observed foraging birds in 2000 and 2014 at five riparian sites along the Lower Colorado River in Mexico to quantify prey attack rates, prey attack maneuvers, vegetation use patterns, and degree of preference for fully leafed-out or flowering plants. Prey attack rate was highest in mesquite (Prosopis spp.) in 2000 and in willow (Salix gooddingii) in 2014; correspondingly, migrants predominantly used mesquite in 2000 and willow in 2014 and showed a preference for willows in flower or fruit in 2014. Wilson’s warbler (Cardellina pusilla) used relatively more low-energy foraging maneuvers in willow than in tamarisk (Tamarix spp.) or mesquite. Those patterns in foraging behavior suggest native riparian vegetation, and especially willow, are important resources for spring migrants along the lower Colorado River. Willow is a relatively short-lived tree dependent on spring floods for dispersal and establishment and thus spring migrants are likely to benefit from controlled pulse flows.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2016.06.001","usgsCitation":"Darrah, A., Greeney, H.F., and van Riper, C., 2017, Importance of the 2014 Colorado River Delta pulse flow for migratory songbirds: Insights from foraging behavior: Ecological Engineering, v. 106, no. B, p. 784-790, https://doi.org/10.1016/j.ecoleng.2016.06.001.","productDescription":"7 p.","startPage":"784","endPage":"790","ipdsId":"IP-071691","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":470022,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoleng.2016.06.001","text":"Publisher Index Page"},{"id":337066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.1202392578125,\n 31.765537409484374\n ],\n [\n -113.70849609375,\n 31.765537409484374\n ],\n [\n -113.70849609375,\n 32.78265637602964\n ],\n [\n -115.1202392578125,\n 32.78265637602964\n ],\n [\n -115.1202392578125,\n 31.765537409484374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"B","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58c12633e4b014cc3a3d344c","contributors":{"authors":[{"text":"Darrah, Abigail J.","contributorId":187674,"corporation":false,"usgs":false,"family":"Darrah","given":"Abigail J.","affiliations":[{"id":12625,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, 85721, USA","active":true,"usgs":false},{"id":35720,"text":"Audubon Mississippi, Coastal Bird Stewardship ProgramMoss PointUSA","active":true,"usgs":false}],"preferred":false,"id":681275,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Greeney, Harold F.","contributorId":187675,"corporation":false,"usgs":false,"family":"Greeney","given":"Harold","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":681276,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Riper, Charles III 0000-0003-1084-5843 charles_van_riper@usgs.gov","orcid":"https://orcid.org/0000-0003-1084-5843","contributorId":169488,"corporation":false,"usgs":true,"family":"van Riper","given":"Charles","suffix":"III","email":"charles_van_riper@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":681274,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182547,"text":"tm11B8 - 2017 - Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions","interactions":[],"lastModifiedDate":"2022-04-26T18:52:10.673538","indexId":"tm11B8","displayToPublicDate":"2017-03-07T09:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"11-B8","title":"Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions","docAbstract":"Datum conversions from the National Geodetic Vertical Datum of 1929 to the North American Vertical Datum of 1988 among inland and coastal gages throughout the hydrologic regions of New England, the Mid-Atlantic, and the South Atlantic-Gulf have implications among river and storm surge forecasting, general commerce, and water-control operations. The process of data conversions may involve the application of a recovered National Geodetic Vertical Datum of 1929–North American Vertical Datum of 1988 offset, a simplistic datum transformation using VDatum or VERTCON software, or a survey, depending on a gaging network datum evaluation, anticipated uncertainties for data use among the cooperative water community, and methods used to derive the conversion. Datum transformations from National Geodetic Vertical Datum of 1929 to North American Vertical Datum of 1988 using VERTCON purport errors of ± 0.13 foot at the 95 percent confidence level among modeled points, claiming more consistency along the east coast. Survey methods involving differential and trigonometric leveling, along with observations using Global Navigation Satellite System technology, afford a variety of approaches to establish or perpetuate a datum during a survey. Uncertainties among leveling approaches are generally < 0.1 foot, and and Global Navigation Satellite System approaches may be categorized with uncertainties of ≤0.1 foot for a Level I quality category and ≥0.1 foot for Level II or III quality categories (defined by the U.S. Geological Survey) by observation and review of experienced practice. The conversion process is initiated with an evaluation of the inland and coastal gage network datum, beginning with altitude datum components and the history of those components queried through the U.S. Geological Survey Groundwater Site Inventory database. Subsequent edits to the Groundwater Site Inventory database may be required and a consensus reached among the U.S. Geological Survey Water Science Centers to identify the outstanding workload categorized as in-office datum transformations or offset applications versus out-of-office survey efforts. Datum conversions or datum establishment for the inland or coastal gaging network should meet datum uncertainty requirements among other Federal agencies. Datum uncertainty requirements are ±0.25 foot for U.S. Army Corps of Engineers water-control or construction projects and ±0.16 foot for Federal Emergency Management Agency field surveys and checkpoint surveys used for mapping. River level forecasts generally are defined as ± 0.10 foot among the National Oceanic and Atmospheric Administration–National Weather Service. Collaboration and communication among the cooperative water community is necessary during a datum conversion or datum change. Datum notification time-change requirements set by the National Oceanic and Atmospheric Administration–National Weather Service vary from 30 to 120 days, depending on datum conversion or datum-change case scenarios. Notification times associated with these case scenarios may be useful to the National Oceanic and Atmospheric Administration–National Weather Service and U.S. Army Corps of Engineers, because their daily operations are time sensitive, unlike the notification time change requirements of other entities that make up the cooperative water community. At the time of this writing, a future geopotential datum resulting from Gravity for the Redefinition of the American Vertical Datum is anticipated in 2022. A future version of VDatum and VERTCON is anticipated to provide a transformation among North American Vertical Datum of 1988 elevations to the new geopotential datum.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B8","usgsCitation":"Rydlund, P.H., Jr., and Noll, M.L., 2017, Vertical datum conversion process for the inland and coastal gage network located in the New England, Mid-Atlantic, and South Atlantic-Gulf hydrologic regions (ver. 1.1, July 2017) U.S. Geological Survey Techniques and Methods, book 11, chap. B8, 29 p., https://doi.org/10.3133/tm11B8.","productDescription":"ix, 29 p.","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-078726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":399692,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_105491.htm"},{"id":344212,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/tm/11/b08/versionHist.txt"},{"id":336266,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b08/coverthb2.jpg"},{"id":336267,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b08/tm11B8.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-8B"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6484375,\n 30.183121842195515\n ],\n [\n -89.23095703125,\n 30.278044377800153\n ],\n [\n -88.39599609375,\n 30.221101852485987\n ],\n [\n -87.07763671875,\n 30.35391637229704\n ],\n [\n -86.06689453125,\n 30.164126343161097\n ],\n [\n -85.23193359375,\n 29.49698759653577\n ],\n [\n -84.08935546875,\n 30.012030680358613\n ],\n [\n -83.60595703125,\n 29.630771207229\n ],\n [\n -82.77099609375,\n 28.671310915880834\n ],\n [\n -82.99072265625,\n 27.858503954841247\n ],\n [\n -81.93603515625,\n 26.017297563851745\n ],\n [\n -81.32080078125,\n 25.105497373014686\n ],\n [\n -80.15625,\n 25.045792240303445\n ],\n [\n -79.8486328125,\n 26.70635985763354\n ],\n [\n -80.39794921875,\n 28.013801376380712\n ],\n [\n -81.1669921875,\n 30.939924331023445\n ],\n [\n -80.22216796875,\n 32.194208672875384\n ],\n [\n -77.93701171875,\n 33.687781758439364\n ],\n [\n -75.322265625,\n 35.08395557927643\n ],\n [\n -75.1025390625,\n 35.51434313431818\n ],\n [\n -75.7177734375,\n 36.87962060502676\n ],\n [\n -73.6962890625,\n 40.36328834091583\n ],\n [\n -69.85107421874999,\n 41.178653972331674\n ],\n [\n -69.49951171875,\n 41.83682786072714\n ],\n [\n -70.51025390625,\n 42.24478535602799\n ],\n [\n -70.42236328125,\n 43.197167282501276\n ],\n [\n -69.6533203125,\n 43.644025847699496\n ],\n [\n -66.81884765625,\n 44.74673324024678\n ],\n [\n -67.7197265625,\n 45.72152152227954\n ],\n [\n -67.7197265625,\n 47.100044694025215\n ],\n [\n -68.26904296875,\n 47.35371061951363\n ],\n [\n -69.10400390625,\n 47.517200697839414\n ],\n [\n -69.9609375,\n 46.89023157359399\n ],\n [\n -70.51025390625,\n 45.767522962149876\n ],\n [\n -71.25732421875,\n 45.336701909968134\n ],\n [\n -71.71875,\n 45.02695045318546\n ],\n [\n -73.41064453125,\n 45.01141864227728\n ],\n [\n -74.5751953125,\n 44.63739123445585\n ],\n [\n -76.97021484375,\n 43.13306116240612\n ],\n [\n -78.3544921875,\n 42.114523952464246\n ],\n [\n -79.189453125,\n 39.791654835253425\n ],\n [\n -80.26611328125,\n 37.82280243352756\n ],\n [\n -80.88134765625,\n 36.65079252503471\n ],\n [\n -82.41943359375,\n 35.28150065789119\n ],\n [\n -89.49462890625,\n 35.02999636902566\n ],\n [\n -90.41748046874999,\n 32.47269502206151\n ],\n [\n -90.10986328125,\n 31.034108344903512\n ],\n [\n -89.6484375,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted March 2017; Version 1.1: July 2017","publicComments":"This report is Chapter 8 in Section B: U.S. Geological Survey Standards in Book 11: Collection and delineation of spatial data.","contact":"Director, Missouri Water Science Center
U.S. Geological Survey
1400 Independence Road, MS 100
Rolla, MO 65401
https://mo.water.usgs.gov/
Approximately 6.4 million tons of dissolved solids are discharged from the Upper Colorado River Basin (UCRB) to the Lower Colorado River Basin each year. This results in substantial economic damages, and tens of millions of dollars are spent annually on salinity control projects designed to reduce salinity loads in surface waters of the UCRB. Dissolved solids in surface water and groundwater have been studied extensively over the past century, and these studies have contributed to a conceptual understanding of sources and transport of dissolved solids. This conceptual understanding was incorporated into a Spatially Referenced Regressions on Watershed Attributes (SPARROW) model to examine sources and transport of dissolved solids in the UCRB. The results of this model were published in 2009. The present report documents the methods and data used to develop an updated dissolved-solids SPARROW model for the UCRB, and incorporates data defining current basin attributes not available in the previous model, including delineation of irrigated lands by irrigation type (sprinkler or flood irrigation), and calibration data from additional monitoring sites.
Dissolved-solids loads estimated for 312 monitoring sites were used to calibrate the SPARROW model, which predicted loads for each of 10,789 stream reaches in the UCRB. The calibrated model provided a good fit to the calibration data as evidenced by R2 and yield R2 values of 0.96 and 0.73, respectively, and a root-mean-square error of 0.47. The model included seven geologic sources that have estimated dissolved-solids yields ranging from approximately 1 to 45 tons per square mile (tons/mi2). Yields generated from irrigated agricultural lands are substantially greater than those from geologic sources, with sprinkler irrigated lands generating an average of approximately 150 tons/mi2 and flood irrigated lands generating between 770 and 2,300 tons/mi2 depending on underlying lithology. The coefficients estimated for six landscape transport characteristics that influence the delivery of dissolved solids from sources to streams, are consistent with the process understanding of dissolved-solids loading to streams in the UCRB.
Dissolved-solids loads and the proportion of those loads among sources in the entire UCRB as well as in major tributaries in the basin are reported, as are loads generated from irrigated lands, rangelands, Bureau of Land Management (BLM) lands, and grazing allotments on BLM lands. Model-predicted loads also are compared with load estimates from 1957 and 1991 at selected locations in three divisions of the UCRB. At the basin scale, the model estimates that 32 percent of the dissolved-solids loads are from irrigated agricultural land sources that compose less than 2 percent of the land area in the UCRB. This estimate is less than previously reported estimates of 40 to 45 percent of basin-scale dissolved-solids loads from irrigated agricultural land sources. This discrepancy could be a result of the implementation of salinity control projects in the basin. Notably, results indicate that the conversion of flood irrigated agricultural lands to sprinkler irrigated agricultural lands is a likely process contributing to the temporal decrease in dissolved-solids loads from irrigated lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175009","collaboration":"Prepared in cooperation with the Colorado River Basin Salinity Control Forum","usgsCitation":"Miller, M.P., Buto, S.G., Lambert, P.M., and Rumsey, C.A., 2017, Enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2017–5009, 23 p., https://doi.org/10.3133/sir20175009.","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-076357","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":438425,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LO3JV2","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions of total dissolved loads in streams of the Upper Colorado River Basin watershed"},{"id":336947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5009/coverthb.jpg"},{"id":336948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5009/sir20175009.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5009"},{"id":336949,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0JT4","text":"USGS data release","description":"USGS data release","linkHelpText":"Catchment-flowline network and selected model inputs for an enhanced and updated spatially referenced statistical assessment of dissolved-solids load sources and transport in streams of the Upper Colorado River Basin"}],"country":"United States","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.500244140625,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 35.53222622770337\n ],\n [\n -106.14990234375,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 43.27720532212024\n ],\n [\n -111.500244140625,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"When performing screening-level and baseline risk assessments, assessors usually compare estimated exposures of wildlife receptor species with toxicity reference values (TRVs). We modeled the exposure of American robins (Turdus migratorius) to 10 elements (As, Cd, Cr, Cu, Hg, Mn, Pb, Se, Zn, and V) in spring and early summer, a time when earthworms are the preferred prey. We calculated soil benchmarks associated with possible toxic effects to these robins from 6 sets of published TRVs. Several of the resulting soil screening-level benchmarks were inconsistent with each other and less than soil background concentrations. Accordingly, we examined the derivations of the TRVs as a possible source of error. In the case of V, a particularly toxic chemical compound (ammonium vanadate) containing V, not normally present in soil, had been used to estimate a TRV. In the cases of Zn and Cu, use of uncertainty values of 10 in estimating TRVs led to implausibly low soil screening values. In the case of Pb, a TRV was calculated from studies demonstrating reductions in egg production in Japanese quail (Coturnix coturnix japonica) exposed to Pb concentrations well below than those causing toxic effects in other species of birds. The results on quail, which were replicated in additional trials, are probably not applicable to other, unrelated species, although we acknowledge that only a small fraction of all species of birds has been tested. These examples underscore the importance of understanding the derivation and relevance of TRVs before selecting them for use in screening or in ecological risk assessment.
","language":"English","publisher":"Wiley","doi":"10.1002/ieam.1792","usgsCitation":"Beyer, W.N., and Sample, B.E., 2017, An evaluation of inorganic toxicity reference values for use in assessing hazards to American robins (Turdus migratorius): Integrated Environmental Assessment and Management, v. 13, no. 2, p. 352-359, https://doi.org/10.1002/ieam.1792.","productDescription":"8 p.","startPage":"352","endPage":"359","ipdsId":"IP-068783","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":336938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-07","publicationStatus":"PW","scienceBaseUri":"58bfd4efe4b014cc3a3ba47f","contributors":{"authors":[{"text":"Beyer, W. Nelson 0000-0002-8911-9141 nbeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8911-9141","contributorId":3301,"corporation":false,"usgs":true,"family":"Beyer","given":"W.","email":"nbeyer@usgs.gov","middleInitial":"Nelson","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":680906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sample, Bradley E.","contributorId":61135,"corporation":false,"usgs":true,"family":"Sample","given":"Bradley","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":680942,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70184286,"text":"70184286 - 2017 - Prediction and visualization of redox conditions in the groundwater of Central Valley, California","interactions":[],"lastModifiedDate":"2018-09-25T11:31:39","indexId":"70184286","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Prediction and visualization of redox conditions in the groundwater of Central Valley, California","docAbstract":"Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175427,"text":"70175427 - 2017 - Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska","interactions":[],"lastModifiedDate":"2021-04-19T17:06:54.450804","indexId":"70175427","displayToPublicDate":"2017-03-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska","docAbstract":"The distribution of volcanogenic massive sulfide (VMS), porphyry-epithermal, Alaska-type ultramafic-mafic complexes, intrusion-related Au, and granitoid Sn-W ore deposits in southwest Alaska supports current metallogenic models linking the formation of these deposit types to the emplacement of different suites of igneous rocks during the evolution of this convergent plate margin. Regional-scale aeromagnetic data provide a continuous set of observations over the deposits and show contrasting patterns over the igneous rock suites hosting the various deposit types. Combined with surface geologic data and regional metallogenic constraints, aeromagnetic data—filtered to enhance the anomalous magnetic field and map magnetic domains—were used to produce a mineral potential map across this accreted island-arc setting. The reduced-to-pole, upward continuation, and total horizontal gradient transform maps show anomalies that could represent porphyry-epithermal deposits within the intraoceanic- and continental-arc terranes. The tilt derivative transform highlights lineaments within the back arc that may represent zones with potential for VMS deposits. The truncations of tilt derivative lineaments outline a major magnetic domain boundary between the back-arc and craton margin, which is prospective for granitoid Sn-W deposits. Annular tilt derivative highs outline granitoids that could be associated with intrusion-related Au deposits within the craton margin. Shallow, magnetite-rich Alaska-type ultramafic-mafic complexes are mapped by their short-wavelength, high-amplitude anomalies. Successful mineral potential mapping across southwestern Alaska as performed in the present study suggests that filtered aeromagnetic data can be effectively used in mineral exploration in convergent continental margin settings.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.112.2.375","usgsCitation":"Anderson, E., Monecke, T., Hitzman, M.W., Zhou, W., and Bedrosian, P.A., 2017, Mineral potential mapping in an accreted island-arc setting using aeromagnetic data: An example from southwest Alaska: Economic Geology, v. 112, no. 2, p. 375-396, https://doi.org/10.2113/econgeo.112.2.375.","productDescription":"22 p.","startPage":"375","endPage":"396","ipdsId":"IP-069250","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":327805,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.23632812499997,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 56.992882804633986\n ],\n [\n -132.275390625,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 62.55285695857292\n ],\n [\n -157.23632812499997,\n 56.992882804633986\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"112","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-07","publicationStatus":"PW","scienceBaseUri":"57c6b175e4b0f2f0cebe6f32","contributors":{"authors":[{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":172766,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":645153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monecke, Thomas","contributorId":50423,"corporation":false,"usgs":true,"family":"Monecke","given":"Thomas","affiliations":[],"preferred":false,"id":645155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hitzman, Murray W. 0000-0002-3876-0537 mhitzman@usgs.gov","orcid":"https://orcid.org/0000-0002-3876-0537","contributorId":200913,"corporation":false,"usgs":true,"family":"Hitzman","given":"Murray","email":"mhitzman@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":false,"id":645156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Wendy","contributorId":205989,"corporation":false,"usgs":false,"family":"Zhou","given":"Wendy","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":814484,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70180905,"text":"sir20165143 - 2017 - Groundwater quality for 75 domestic wells in Lycoming County, Pennsylvania, 2014","interactions":[],"lastModifiedDate":"2017-03-06T09:10:37","indexId":"sir20165143","displayToPublicDate":"2017-03-06T09:00:00","publicationYear":"2017","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":"2016-5143","title":"Groundwater quality for 75 domestic wells in Lycoming County, Pennsylvania, 2014","docAbstract":"Groundwater is a major source of drinking water in Lycoming County and adjacent counties in north-central and northeastern Pennsylvania, which are largely forested and rural and are currently undergoing development for hydrocarbon gases. Water-quality data are needed for assessing the natural characteristics of the groundwater resource and the potential effects from energy and mineral extraction, timber harvesting, agriculture, sewage and septic systems, and other human influences.
This report, prepared in cooperation with Lycoming County, presents analytical data for groundwater samples from 75 domestic wells sampled throughout Lycoming County in June, July, and August 2014. The samples were collected using existing pumps and plumbing prior to any treatment and analyzed for physical and chemical characteristics, including nutrients, major ions, metals and trace elements, volatile organic compounds, gross-alpha particle and gross beta-particle activity, uranium, and dissolved gases, including methane and radon-222.
Results indicate groundwater quality generally met most drinking-water standards, but that some samples exceeded primary or secondary maximum contaminant levels (MCLs) for arsenic, iron, manganese, total dissolved solids (TDS), chloride, pH, bacteria, or radon-222. Arsenic concentrations were higher than the MCL of 10 micrograms per liter (µg/L) in 9 of the 75 (12 percent) well-water samples, with concentrations as high as 23.6 μg/L; arsenic concentrations were higher than the health advisory level (HAL) of 2 μg/L in 23 samples (31 percent). Total iron concentrations exceeded the secondary maximum contaminant level (SMCL) of 300 μg/L in 20 of the 75 samples. Total manganese concentrations exceeded the SMCL of 50 μg/L in 20 samples and the HAL of 300 μg/L in 2 of those samples. Three samples had chloride concentrations that exceeded the SMCL of 250 milligrams per liter (mg/L); two of those samples exceeded the SMCL of 500 mg/L for TDS. The pH ranged from 5.3 to 9.15 and did not meet the SMCL range of 6.5 to 8.5 in 22 samples, with 17 samples having a pH less than 6.5 and 8 samples having pH greater than 8.5. Generally, the samples that had elevated TDS, chloride, or arsenic concentrations had high pH.
Total coliform bacteria were detected in 39 of 75 samples (52 percent), with Escherichia coli detected in 10 of those 39 samples. Radon-222 activities ranged from non-detect to 7,420 picocuries per liter (pCi/L), with a median of 863 pCi/L, and exceeded the proposed drinking-water standard of 300 pCi/L in 50 (67 percent) of the 75 samples; radon-222 activities were higher than the alternative proposed standard of 4,000 pCi/L in 3 samples.
Water from 15 of 75 (20 percent) wells had concentrations of methane greater than the reporting level of 0.01 mg/L; detectable methane concentrations ranged from 0.04 to 16.8 mg/L. Two samples had methane concentrations (13.1 and 16.8 mg/L) exceeding the action level of 7 mg/L. Low levels of ethane (up to 0.12 mg/L) were present in the five samples with the highest methane concentrations (near or above 1 mg/L) that were analyzed for hydrocarbon compounds and isotopic composition. The isotopic composition of methane in four of these groundwater samples, from the Catskill and Lock Haven Formations and the Hamilton Group, have sample carbon isotopic ratio delta values (carbon-13/carbon-12) ranging from –42.36 to –36.08 parts per thousand (‰) and hydrogen isotopic ratio delta values (deuterium/protium) ranging from –212.0 to –188.4 ‰, which are consistent with the isotopic compositions reported for mud-gas logging samples from these geologic units and a thermogenic source of the methane. However, the isotopic composition and ratios of methane to ethane in a fifth sample indicate the methane in that sample may be of microbial origin that subsequently underwent oxidation. The fifth sample had the highest concentration of methane, 16.8 mg/L, with an carbon isotopic ratio delta values of -50.59 ‰ and a hydrogen isotopic ratio delta values of -209.7 ‰.
The six well-water samples with the highest methane concentrations also had among the highest pH values (8.25 to 9.15) and elevated concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide. Relatively elevated concentrations of some other constituents, such as barium, strontium, and chloride, commonly were present in, but not limited to, those well-water samples with elevated methane.
Three of the six groundwater samples with the highest methane concentrations had chloride/bromide ratios that indicate mixing with a small amount of brine (0.02 percent or less) similar in composition to those reported at undetermined depth below the freshwater aquifer and for gas and oil well brines in Pennsylvania. The sample with the highest methane concentration and most other samples with low methane concentrations (less than about 1 mg/L) have chloride/bromide ratios that indicate predominantly anthropogenic sources of chloride, such as road-deicing salt, septic systems, and (or) animal waste. Brines that are naturally present may originate from deeper parts of the aquifer system, while anthropogenic sources are more likely to affect shallow groundwater because they occur on or near the land-surface.
The spatial distribution of groundwater compositions generally indicate that (1) uplands along the western border of Lycoming County usually have dilute, slightly acidic oxygenated, calcium-bicarbonate type waters; (2) intermediate altitudes or areas of carbonate bedrock usually have water of near neutral pH, with highest amounts of hardness (calcium and magnesium); (3) stream valleys, low elevations where groundwater may be discharging, and deep wells in uplands usually have water with pH values greater than 8 and highest arsenic, sodium, lithium, bromide concentrations. Geochemical modeling indicated that for samples with elevated pH, sodium, lithium, bromide, and alkalinity, the water chemistry could have resulted by dissolution of calcite (calcium carbonate) combined with cation-exchange and mixing with a small amount of brine. Through cation-exchange reactions between water and bedrock, which are equivalent to processes in a water softener, calcium ions released by calcite dissolution are exchanged for sodium ions on clay minerals. Thus, the assessment of groundwater quality in Lycoming County indicates groundwater is generally of good quality, but various parts of Lycoming County can have groundwater with low to moderate concentrations of methane and other constituents that appear in naturally present brine and produced waters from gas and oil wells at high concentrations.\"
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165143","collaboration":"Prepared in cooperation with the County of Lycoming, Pennsylvania","usgsCitation":"Gross, E.L., and Cravotta, C.A., III, 2017, Groundwater quality for 75 domestic wells in Lycoming County, Pennsylvania, 2014: U.S. Geological Survey Scientific Investigations Report 2016–5143, 74 p., https://doi.org/10.3133/sir20165143.","productDescription":"Report: xi, 74 p.; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076071","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":336804,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5143/sir20165143_appendix2.xlsx","text":"Appendix 2 - Table 2-1 - ","size":"27.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5143","linkHelpText":"Spearman rank correlation coefficient (r) matrix 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Ontogenetic niche shifts (ONS) are important drivers of population and community dynamics, but they can be difficult to identify for species with prolonged larval or juvenile stages, or for species that inhabit continuous habitats. Most studies of ONS focus on single transitions among discrete habitat patches at local scales. However, for species with long larval or juvenile periods, affinity for particular locations within connected habitat networks may differ among cohorts. The resulting spatial patterns of distribution can result from a combination of landscape-scale habitat structure, position of a habitat patch within a network, and local habitat characteristics—all of which may interact and change as individuals grow. We estimated such spatial ONS for spring salamanders (Gyrinophilus porphyriticus), which have a larval period that can last 4 years or more. Using mixture models to identify larval cohorts from size frequency data, we fit occupancy models for each age class using two measures of the branching structure of stream networks and three measures of stream network position. Larval salamander cohorts showed different preferences for the position of a site within the stream network, and the strength of these responses depended on the basin-wide spatial structure of the stream network. The isolation of a site had a stronger effect on occupancy in watersheds with more isolated headwater streams, while the catchment area, which is associated with gradients in stream habitat, had a stronger effect on occupancy in watersheds with more paired headwater streams. Our results show that considering the spatial structure of habitat networks can provide new insights on ONS in long-lived species.
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Campbell, Lowe Winsor H.","journalName":"Ecosphere","publicationDate":"2/2017","publiclyAccessibleDate":"2/21/2017"},"contributors":{"authors":[{"text":"Fields, William R.","contributorId":152076,"corporation":false,"usgs":false,"family":"Fields","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":680743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Evan H. Campbell 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":167017,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","email":"ehgrant@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":680744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowe, Winsor H.","contributorId":126722,"corporation":false,"usgs":false,"family":"Lowe","given":"Winsor","email":"","middleInitial":"H.","affiliations":[{"id":6577,"text":"University of Montana, Division of Biological Sciences, Missoula, MT, 59812, USA.","active":true,"usgs":false}],"preferred":false,"id":680745,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70184205,"text":"fs20173014 - 2017 - The California stream quality assessment","interactions":[],"lastModifiedDate":"2017-03-07T08:07:21","indexId":"fs20173014","displayToPublicDate":"2017-03-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3014","title":"The California stream quality assessment","docAbstract":"In 2017, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project is assessing stream quality in coastal California, United States. The USGS California Stream Quality Assessment (CSQA) will sample streams over most of the Central California Foothills and Coastal Mountains ecoregion (modified from Griffith and others, 2016), where rapid urban growth and intensive agriculture in the larger river valleys are raising concerns that stream health is being degraded. Findings will provide the public and policy-makers with information regarding which human and natural factors are the most critical in affecting stream quality and, thus, provide insights about possible approaches to protect the health of streams in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173014","issn":"2327-6916 (print)","usgsCitation":"Van Metre, P.C., Egler, A.L., and May, Jason, 2017, The California Stream Quality Assessment: U.S. Geological Survey Fact Sheet 2017–3014, 2 p., https://doi.org/10.3133/fs20173014.","productDescription":"2 p.","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":336797,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3014/cover/coverthb.jpg"},{"id":336798,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3014/fs20173014.pdf","text":"Report","size":"733 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2017-3014"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.45312499999999,\n 42.00032514831621\n ],\n [\n -124.365234375,\n 41.86956082699455\n ],\n [\n -124.23339843749999,\n 41.623655390686395\n ],\n [\n -124.18945312500001,\n 41.27780646738183\n ],\n [\n -124.27734374999999,\n 40.896905775860006\n ],\n [\n -124.365234375,\n 40.697299008636755\n ],\n [\n -124.56298828125001,\n 40.329795743702064\n ],\n [\n -123.90380859374999,\n 39.740986355883564\n ],\n [\n -123.92578125,\n 39.30029918615029\n ],\n [\n -123.662109375,\n 38.788345355085625\n ],\n [\n -123.1787109375,\n 38.151837403006766\n ],\n [\n -123.02490234375,\n 37.85750715625203\n ],\n [\n -122.67333984374999,\n 37.666429212090605\n ],\n [\n -122.4755859375,\n 37.28279464911045\n ],\n [\n -122.32177734375,\n 37.020098201368114\n ],\n [\n -121.904296875,\n 36.82687474287728\n ],\n [\n -122.2119140625,\n 36.59788913307022\n ],\n [\n -121.86035156249999,\n 36.03133177633187\n ],\n [\n -121.33300781249999,\n 35.53222622770337\n ],\n [\n -121.00341796874999,\n 35.29943548054545\n ],\n [\n -120.80566406250001,\n 35.08395557927643\n ],\n [\n -120.7177734375,\n 34.52466147177172\n ],\n [\n -119.44335937499999,\n 34.161818161230386\n ],\n [\n -118.828125,\n 33.90689555128866\n ],\n [\n -118.47656249999999,\n 33.706062655101206\n ],\n [\n -118.0810546875,\n 33.65120829920497\n ],\n [\n -117.42187500000001,\n 33.15594830078649\n ],\n [\n -117.20214843749999,\n 32.45415593941475\n ],\n [\n -116.96044921875,\n 32.39851580247402\n ],\n [\n -116.08154296875001,\n 32.43561304116276\n ],\n [\n -115.4443359375,\n 32.54681317351514\n ],\n [\n -114.7412109375,\n 32.47269502206151\n ],\n [\n -114.54345703125,\n 33.04550781490999\n ],\n [\n -114.6533203125,\n 33.33970700424026\n ],\n [\n -114.49951171875,\n 33.54139466898275\n ],\n [\n -114.47753906249999,\n 33.797408767572485\n ],\n [\n -114.14794921875,\n 34.07086232376631\n ],\n [\n -113.92822265625,\n 34.34343606848294\n ],\n [\n -114.345703125,\n 34.470335121217474\n ],\n [\n -114.43359375,\n 34.77771580360469\n ],\n [\n -120.0146484375,\n 38.89103282648846\n ],\n [\n -120.1904296875,\n 42.09822241118974\n ],\n [\n -124.45312499999999,\n 42.00032514831621\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
http://water.usgs.gov/nawqa/
The El Niño-Southern Oscillation is the dominant mode of interannual climate variability across the Pacific Ocean basin, with influence on the global climate. The two end members of the cycle, El Niño and La Niña, force anomalous oceanographic conditions and coastal response along the Pacific margin, exposing many heavily populated regions to increased coastal flooding and erosion hazards. However, a quantitative record of coastal impacts is spatially limited and temporally restricted to only the most recent events. Here we report on the oceanographic forcing and coastal response of the 2015–2016 El Niño, one of the strongest of the last 145 years. We show that winter wave energy equalled or exceeded measured historical maxima across the US West Coast, corresponding to anomalously large beach erosion across the region. Shorelines in many areas retreated beyond previously measured landward extremes, particularly along the sediment-starved California coast.
","language":"English","publisher":"Nature Publishing Group","publisherLocation":"London","doi":"10.1038/ncomms14365","usgsCitation":"Barnard, P., Hoover, D.J., Hubbard, D.M., Snyder, A.G., Ludka, B., Allan, J., Kaminsky, G.M., Ruggiero, Gallien, T.W., Gabel, L., McCandless, D., Weiner, H.M., Cohn, N., Anderson, D.L., and Serafin, K.A., 2017, Extreme oceanographic forcing and coastal response due to the 2015–2016 El Niño: Nature Communications, v. 8, 14365: 8 p., https://doi.org/10.1038/ncomms14365.","productDescription":"14365: 8 p.","ipdsId":"IP-075724","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":470026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/ncomms14365","text":"Publisher Index Page"},{"id":336849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Pacific coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.75,\n 32.5\n ],\n [\n -114,\n 32.5\n ],\n [\n -114,\n 49\n ],\n [\n -125.75,\n 49\n ],\n [\n -125.75,\n 32.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-14","publicationStatus":"PW","scienceBaseUri":"58be8337e4b014cc3a3a99d9","contributors":{"authors":[{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science 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Diana","contributorId":187530,"corporation":false,"usgs":false,"family":"McCandless","given":"Diana","email":"","affiliations":[],"preferred":false,"id":680758,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Weiner, Heather M.","contributorId":177392,"corporation":false,"usgs":false,"family":"Weiner","given":"Heather","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":680759,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cohn, Nicholas","contributorId":172822,"corporation":false,"usgs":false,"family":"Cohn","given":"Nicholas","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":680736,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Anderson, Dylan 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The model of the fault system upon which we applied the simulator code was obtained from the DISS 3.2.0 database, selecting all the faults that are recognized on the Calabria region, for a total of 22 fault segments. The application of our simulation algorithm provides typical features in time, space and magnitude behavior of the seismicity, which can be compared with those of the real observations. The results of the physics-based simulator algorithm were compared with those obtained by an alternative method using a slip-rate balanced technique. Finally, as an example of a possible use of synthetic catalogs, an attenuation law has been applied to all the events reported in the synthetic catalog for the production of maps showing the exceedance probability of given values of PGA on the territory under investigation.
","language":"English","publisher":"Springer-Verlag","publisherLocation":"Berlin","doi":"10.1007/s11600-017-0020-2","usgsCitation":"Console, R., Nardi, A., Carluccio, R., Murru, M., Falcone, G., and Parsons, T.E., 2017, A physics-based earthquake simulator and its application to seismic hazard assessment in Calabria (Southern Italy) region: Acta Geophysica, v. 65, no. 1, p. 243-257, https://doi.org/10.1007/s11600-017-0020-2.","productDescription":"15 p.","startPage":"243","endPage":"257","ipdsId":"IP-083545","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":336810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Calabria region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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Maura","contributorId":172714,"corporation":false,"usgs":false,"family":"Murru","given":"Maura","email":"","affiliations":[{"id":27088,"text":"Istituto Nazionale di Geofisica e Vulcanologia (INGV)","active":true,"usgs":false}],"preferred":false,"id":680518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Falcone, Giuseppe","contributorId":187466,"corporation":false,"usgs":false,"family":"Falcone","given":"Giuseppe","email":"","affiliations":[],"preferred":false,"id":680519,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":680514,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70184214,"text":"70184214 - 2017 - Pyrogenic carbon distribution in mineral topsoils of the northeastern United States","interactions":[],"lastModifiedDate":"2025-05-14T19:05:53.709545","indexId":"70184214","displayToPublicDate":"2017-03-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Pyrogenic carbon distribution in mineral topsoils of the northeastern United States","docAbstract":"Due to its slow turnover rates in soil, pyrogenic carbon (PyC) is considered an important C pool and relevant to climate change processes. Therefore, the amounts of soil PyC were compared to environmental covariates over an area of 327,757 km2 in the northeastern United States in order to understand the controls on PyC distribution over large areas. Topsoil (defined as the soil A horizon, after removal of any organic horizons) samples were collected at 165 field sites in a generalised random tessellation stratified design that corresponded to approximately 1 site per 1600 km2 and PyC was estimated from diffuse reflectance mid-infrared spectroscopy measurements using a partial least-squares regression analysis in conjunction with a large database of PyC measurements based on a solid-state 13C nuclear magnetic resonance spectroscopy technique. Three spatial models were applied to the data in order to relate critical environmental covariates to the changes in spatial density of PyC over the landscape. Regional mean density estimates of PyC were 11.0 g kg− 1 (0.84 Gg km− 2) for Ordinary Kriging, 25.8 g kg− 1(12.2 Gg km− 2) for Multivariate Linear Regression, and 26.1 g kg− 1 (12.4 Gg km− 2) for Bayesian Regression Kriging. Akaike Information Criterion (AIC) indicated that the Multivariate Linear Regression model performed best (AIC = 842.6; n = 165) compared to Ordinary Kriging (AIC = 982.4) and Bayesian Regression Kriging (AIC = 979.2). Soil PyC concentrations correlated well with total soil sulphur (P < 0.001; n = 165), plant tissue lignin (P = 0.003), and drainage class (P = 0.008). This suggests the opportunity of including related environmental parameters in the spatial assessment of PyC in soils. Better estimates of the contribution of PyC to the global carbon cycle will thus also require more accurate assessments of these covariates.
","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.geoderma.2017.02.022","usgsCitation":"Jauss, V., Sullivan, P.J., Sanderman, J., Smith, D.B., and Lehmann, J., 2017, Pyrogenic carbon distribution in mineral topsoils of the northeastern United States: Geoderma, v. 296, p. 69-78, https://doi.org/10.1016/j.geoderma.2017.02.022.","productDescription":"10 p.","startPage":"69","endPage":"78","ipdsId":"IP-081130","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":488415,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geoderma.2017.02.022","text":"Publisher Index Page"},{"id":336818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"296","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ba8ebbe4b0bcef64f0b92f","contributors":{"authors":[{"text":"Jauss, Verena","contributorId":187475,"corporation":false,"usgs":false,"family":"Jauss","given":"Verena","email":"","affiliations":[],"preferred":false,"id":680578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Patrick J.","contributorId":187476,"corporation":false,"usgs":false,"family":"Sullivan","given":"Patrick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":680579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanderman, Jonathan","contributorId":187477,"corporation":false,"usgs":false,"family":"Sanderman","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":680580,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, David B. 0000-0001-8396-9105 dsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8396-9105","contributorId":138565,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":680577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lehmann, Johannes","contributorId":187478,"corporation":false,"usgs":false,"family":"Lehmann","given":"Johannes","email":"","affiliations":[],"preferred":false,"id":680581,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198630,"text":"70198630 - 2017 - Walrus haul-out and in water activity levels relative to sea ice availability in the Chukchi Sea","interactions":[],"lastModifiedDate":"2019-06-11T11:43:29","indexId":"70198630","displayToPublicDate":"2017-03-01T13:27:06","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Walrus haul-out and in water activity levels relative to sea ice availability in the Chukchi Sea","docAbstract":"An animal’s energetic costs are dependent on the amount of time it allocates to various behavioral activities. For Arctic pinnipeds, the time allocated to active and resting behaviors could change with future reductions in sea ice cover and longer periods of open water. The Pacific walrus (Odobenus rosmarus divergens) is a large Arctic pinniped that rests on sea ice or land between foraging trips to feed on the seafloor. We used behavioral data collected from radiotagged walruses in the Chukchi Sea (2008–2014) in a Bayesian generalized linear mixed effects model to estimate the probability a walrus was in water foraging, in water not foraging, or hauled out, as a function of environmental covariates. The probability of a walrus being in water increased with wind speed and decreased with air temperature, and the probability a walrus was foraging, given it was in water, increased with available benthic macrofaunal biomass. The probability of each behavior was also related to the nature and availability of haul-out substrates. The amount of time walruses spent in water foraging and hauled out was greatest when only sea ice was available, which typically occurs when walruses occupy feeding areas during summer and early autumn. This situation may be most energy efficient for walruses because it allows the highest proportion of in water energy expenditure to be allocated to foraging. Conversely, the amount of time walruses spent in water foraging and hauled out was lowest when only land was available, which typically occurs in late autumn, in years when walruses were constrained to land haul-outs because sea ice was absent over the continental shelf.
","language":"English","publisher":"Oxford","doi":"10.1093/jmammal/gyw195","usgsCitation":"Jay, C.V., Taylor, R.L., Fischbach, A., Udevitz, M.S., and Beatty, W.S., 2017, Walrus haul-out and in water activity levels relative to sea ice availability in the Chukchi Sea: Journal of Mammalogy, v. 98, no. 2, p. 386-396, https://doi.org/10.1093/jmammal/gyw195.","productDescription":"11 p.","startPage":"386","endPage":"396","ipdsId":"IP-076658","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":470029,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyw195","text":"Publisher Index Page"},{"id":438428,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XD0ZTG","text":"USGS data release","linkHelpText":"Walrus Haulout and In-water Activity Levels Relative to Sea Ice Availability in the Chukchi Sea: 2008-2014"},{"id":356409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chukchi Sea","volume":"98","issue":"2","noUsgsAuthors":false,"publicationDate":"2017-02-08","publicationStatus":"PW","scienceBaseUri":"5b98a47ee4b0702d0e843086","contributors":{"authors":[{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742292,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742293,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Udevitz, Mark S. 0000-0003-4659-138X mudevitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4659-138X","contributorId":3189,"corporation":false,"usgs":true,"family":"Udevitz","given":"Mark","email":"mudevitz@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":742294,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beatty, William S. 0000-0003-0013-3113 wbeatty@usgs.gov","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":173946,"corporation":false,"usgs":true,"family":"Beatty","given":"William","email":"wbeatty@usgs.gov","middleInitial":"S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":742295,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197936,"text":"70197936 - 2017 - Climate change influences on pollinator, forest, and farm interactions across a climate gradient","interactions":[],"lastModifiedDate":"2018-08-07T12:10:57","indexId":"70197936","displayToPublicDate":"2017-03-01T12:10:50","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate change influences on pollinator, forest, and farm interactions across a climate gradient","docAbstract":"Climate impact models are often implemented at horizontal resolutions (“scales”) too coarse to be readily applied in local impact assessments. However, recent advancements in fine-scale modeling are allowing the creation of impact models that can be applied to landscape-scale adaptation planning. Here, we illustrate the use of fine-scale impact models for landscape-scale adaptation planning of pollination services for six sites in Central America. The strategies include the identification of (1) potential reservoir areas that may retain bee diversity and serve as a source of recolonization after climate shocks such as droughts; and (2) potential restoration areas, where improving forest cover is likely to lead to increases in pollinator services both in the present and in the future. Coarse-scale (>1-km horizontal resolution) climatic controls on pollinator diversity and forest cover determine the general location of these areas in our six landscapes. Fine-scale (<100-m horizontal resolution) variation in climatic water deficit provides an index of forest health which can help identify intervention strategies within these zones. All sites have significant areas in which protecting or restoring forest cover is likely to enhance pollination services. The gradient in rainfall change across the study sites dictates choice of adaptation strategies.
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