Air pollution has been identified as one of the most challenging health issues in urban areas worldwide.
The aim of this study was to investigate the association between short-term exposure to ambient air pollution and respiratory disease over a long-term period in Shiraz, one of the largest cities in Iran. Methods: hospital admissions due to respiratory diseases (asthma, pneumonia, chronic obstructive pulmonary disease(COPD) and pleural effusion) in residents of Shiraz between March 21, 2009 and March 20, 2015 were included. Demographics for each patient and meta data to include principal meteorological variables (temperature and relative humidity) and five ambient pollutants (CO, O3, SO2, NO2, and PM10) were also included. Statistical analysis was performed by Poisson regression in single-pollutant generalized linear model with principal component analysis (GLPCA) to analyze the relationship between pollutants and hospital admissions at the 0–9 cumulative lag day period. Pearson correlation test was used to determine the relationship between different pollutants, temperature and humidity.
It was found that the highest increase in asthma admission was related to PM10(relative risk (RR) = 1.31, 95% CI = 1.17, 1.47). For COPD, the rate of hospital visits significantly increased with the increase in NO2 concentration (RR = 1.17, 95% CI = 1.09.1.27). In the children's hospital, O3 (RR = 1.25, 95% CI = 1.06, 1.47) and SO2(RR = 1.17, 95% CI = 1.07, 1.28) affected the asthma admissions and all contaminants (highest RR observed was for NO2 (RR = 1.28 95% CI = 1.18, 1.40) affected pneumonia admissions on cumulative lag days of 0–9. Conclusions: These data confirm an association between ambient air pollutants and hospital admissions due to respiratory disease.
Thiamine Deficiency Complex (TDC) limits early life stage survival of salmonines. Consuming fatty prey has been hypothesized as a cause of thiamine deficiency; however, this relationship has not been evaluated in the Laurentian Great Lakes where TDC occurs. We found that alewife (Alosa pseudoharengus) have higher lipid content than other common Lake Ontario prey fish. In addition, alewife were predicted as the most consumed prey for brown trout (Salmo trutta), Chinook salmon (Oncorhynchus tshawytscha), coho salmon (O. kisutch), lake trout (Salvelinus namaycush), and steelhead trout (O. mykiss); however, the relative importance of alewife in diet composition varied within and among species. Overall, species with greater predicted consumption of alewife had lower egg and muscle thiamine concentrations. Negative correlations between thiamine concentrations and both lipid content and fatty acid concentrations (mg/mg of wet tissue) were limited to brown trout. Similarly, negative correlations between fatty acid proportions (i.e., cumulative proportions of polyunsaturated fatty acids [PUFA] and monounsaturated fatty acids [MUFA]) and thiamine concentrations were only observed for brown and lake trout. Combining data from all species produced curvilinear correlations between thiamine concentrations (egg and muscle) and fatty acid composition (eggs and belly flap). Proportions of PUFAs had negative correlations with thiamine concentrations while proportions of MUFAs had positive correlations. These results provide evidence that, in some cases, salmonine fatty acid composition negatively correlates with thiamine concentrations in Lake Ontario; however, additional research is needed to confirm that this mechanism causes TDC in salmonines, and to understand additional factors potentially associated with TDC.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2019.08.005","usgsCitation":"Futia, M.H., Connerton, M., Weidel, B., and Rinchard, J., 2019, Diet predictions of Lake Ontario salmonines based on fatty acids and correlations between their fat content and thiamine concentrations: Journal of Great Lakes Research, v. 45, no. 5, p. 934-948, https://doi.org/10.1016/j.jglr.2019.08.005.","productDescription":"15 p.","startPage":"934","endPage":"948","ipdsId":"IP-105375","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":373572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.256103515625,\n 44.134913443750726\n ],\n [\n -75.9814453125,\n 44.25700308645885\n ],\n [\n -76.1627197265625,\n 44.402391829093915\n ],\n [\n -76.871337890625,\n 44.20583500104184\n ],\n [\n -77.156982421875,\n 44.01257086123085\n ],\n [\n -77.5799560546875,\n 44.08758502824516\n ],\n [\n -78.936767578125,\n 43.929549935614595\n ],\n [\n -79.5135498046875,\n 43.6599240747891\n ],\n [\n -79.9969482421875,\n 43.27720532212024\n ],\n [\n -80.0244140625,\n 43.205175817237304\n ],\n [\n -79.793701171875,\n 43.197167282501276\n ],\n [\n -79.2828369140625,\n 43.13306116240612\n ],\n [\n -78.673095703125,\n 43.29320031385282\n ],\n [\n -78.046875,\n 43.345154990451135\n ],\n [\n -77.574462890625,\n 43.21718664827096\n ],\n [\n -77.025146484375,\n 43.23319741022136\n ],\n [\n -76.6845703125,\n 43.28920196020127\n ],\n [\n -76.387939453125,\n 43.49676775343911\n ],\n [\n -76.1956787109375,\n 43.492782808225\n ],\n [\n -76.13525390624999,\n 43.65197548731187\n ],\n [\n -76.17919921875,\n 43.84245116699039\n ],\n [\n -75.98693847656249,\n 44.000717834282774\n ],\n [\n -76.256103515625,\n 44.134913443750726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"5","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Futia, Matthew H.","contributorId":208498,"corporation":false,"usgs":false,"family":"Futia","given":"Matthew","email":"","middleInitial":"H.","affiliations":[{"id":37810,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, 350 New Campus Drive, Brockport, New York","active":true,"usgs":false}],"preferred":false,"id":785834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connerton, Michael J.","contributorId":25495,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael J.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":785835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":785833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rinchard, Jacques","contributorId":208500,"corporation":false,"usgs":false,"family":"Rinchard","given":"Jacques","affiliations":[{"id":37810,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, 350 New Campus Drive, Brockport, New York","active":true,"usgs":false}],"preferred":false,"id":785836,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205170,"text":"70205170 - 2019 - Interseismic quiescence and triggered slip of active normal faults of Kīlauea Volcano’s south flank during 2001-2018","interactions":[],"lastModifiedDate":"2019-10-28T10:16:29","indexId":"70205170","displayToPublicDate":"2019-08-16T08:53:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Interseismic quiescence and triggered slip of active normal faults of Kīlauea Volcano’s south flank during 2001-2018","docAbstract":"The mobile south flank of Kīlauea Volcano hosts two normal fault systems, the Koa'e fault system (KFS) and the Hilina fault system (HFS). In historical time, at least three M>6.5 earthquakes\nhave occurred on the basal detachment of the Kīlauea Volcano's south flank, with the most recent being the 4 May 2018 M6.9 earthquake. Here we analyze kinematic Global Positioning System data collected from 2001 to 2017 and interferometric synthetic aperture radar data before, during, and after the 2018 M6.9 earthquake to determine the crustal motion across the HFS and KFS faults. Our results indicate that the HFS faults did not significantly slip during the interseismic period from 2007 to 2011. Despite its substantial magnitude, interferometric synthetic aperture radar (InSAR) data show that the 2018 M6.9 earthquake triggered subcentimeter level slip along sections of the previously mapped HFS branches. Up to 20 cm of offset occurred on what appears to be a newly formed (or previously unknown) fault near the eastern end of the HFS. During the 3 months following the M6.9 earthquake, up to ~30 cm of slip occurred along the KFS, which helps accommodate rapid large‐scale subsidence of Kīlauea's summit region as large volumes of summit reservoir magma fed the lower East Rift Zone eruption. The HFS appears to activate only in concert with large earthquakes on the basal detachment. The KFS, on the other hand, moves both seismically during small local earthquakes and aseismically in response to nearby earthquakes and caldera subsidence.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB017419","usgsCitation":"Wang, K., MacArthur, H., Johanson, I.A., Montgomery-Brown, E.K., Poland, M.P., Cannon, E., d’Alessio, M., and Bürgmann, R., 2019, Interseismic quiescence and triggered slip of active normal faults of Kīlauea Volcano’s south flank during 2001-2018: Journal of Geophysical Research B: Solid Earth, v. 124, no. 9, p. 9780-9794, https://doi.org/10.1029/2019JB017419.","productDescription":"15 p.","startPage":"9780","endPage":"9794","ipdsId":"IP-104626","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467364,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019jb017419","text":"External Repository"},{"id":367210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.4393768310547,\n 19.18862319930604\n ],\n [\n -154.99717712402344,\n 19.18862319930604\n ],\n [\n -154.99717712402344,\n 19.445874298215937\n ],\n [\n -155.4393768310547,\n 19.445874298215937\n ],\n [\n -155.4393768310547,\n 19.18862319930604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"9","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Kang","contributorId":197483,"corporation":false,"usgs":false,"family":"Wang","given":"Kang","email":"","affiliations":[],"preferred":false,"id":770210,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacArthur, Hayden","contributorId":218774,"corporation":false,"usgs":false,"family":"MacArthur","given":"Hayden","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":770211,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":770209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montgomery-Brown, Emily K. 0000-0001-6787-2055","orcid":"https://orcid.org/0000-0001-6787-2055","contributorId":214074,"corporation":false,"usgs":true,"family":"Montgomery-Brown","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":770212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":770213,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cannon, Eric","contributorId":218775,"corporation":false,"usgs":false,"family":"Cannon","given":"Eric","affiliations":[{"id":34755,"text":"Golder Associates Inc.","active":true,"usgs":false}],"preferred":false,"id":770214,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"d’Alessio, Matthew","contributorId":218776,"corporation":false,"usgs":false,"family":"d’Alessio","given":"Matthew","email":"","affiliations":[{"id":39477,"text":"California State University Northridge","active":true,"usgs":false}],"preferred":false,"id":770215,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bürgmann, Roland","contributorId":195087,"corporation":false,"usgs":false,"family":"Bürgmann","given":"Roland","affiliations":[],"preferred":false,"id":770216,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70204786,"text":"70204786 - 2019 - Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","interactions":[],"lastModifiedDate":"2019-08-19T13:52:04","indexId":"70204786","displayToPublicDate":"2019-08-16T06:59:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests","docAbstract":"Abstract \nWildfires raise risks of floods, debris flows, major geomorphologic and sedimentologic change, and water quality and quantity shifts. A principal control on the magnitude of these changes is field-saturated hydraulic conductivity (Kfs), which dictates surface runoff generation and is a key input into numerical models. This work synthesizes 73 Kfs datasets from the literature in the first year following fire at the plot scale (≤ 10 m2). A meta-analysis using a random effects analysis showed significant differences between burned and unburned Kfs. The reductions in Kfs after fire, expressed by the ratio of Kfs Burned / Kfs Unburned, were 0.46 (95% confidence interval of 0.31-0.70) combining wildfire and prescribed fire and 0.3 (95% confidence interval of 0.13-0.71) for wildfire. No significant differences for Kfs were observed between wildfire and prescribed fire or moderate and high fire severity. Both Kfs magnitude and variability depended more on measurement method than measurement support area at the plot scale, with methods applying head ≥0.5 cm producing larger estimates of Kfs. It is recommended that post-fire efforts to characterize Kfs for modeling or process-based interpretations use methods that reflect the dominant infiltration processes: tension infiltrometers and simulated rainfall methods when soil matrix flow dominates and ponded head methods when macropore flow is critical.","language":"English","publisher":"Wiley","doi":"10.1002/esp.4621","usgsCitation":"Ebel, B.A., 2019, Measurement method has a larger impact than spatial scale for plot-scale field-saturated hydraulic conductivity (Kfs) after wildfire and prescribed fire in forests: Earth Surface Processes and Landforms, v. 44, no. 10, p. 1945-1956, https://doi.org/10.1002/esp.4621.","productDescription":"12 p.","startPage":"1945","endPage":"1956","ipdsId":"IP-101329","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":366581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"10","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":218151,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":768477,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215778,"text":"70215778 - 2019 - Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture","interactions":[],"lastModifiedDate":"2020-10-29T21:50:50.764784","indexId":"70215778","displayToPublicDate":"2019-08-15T16:39:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture","docAbstract":"Hennig Brandt's discovery of phosphorus (P) occurred during the early European colonization of the Chesapeake Bay region. Today, P, an essential nutrient on land and water alike, is one of the principal threats to the health of the bay. Despite widespread implementation of best management practices across the Chesapeake Bay watershed following the implementation in 2010 of a total maximum daily load (TMDL) to improve the health of the bay, P load reductions across the bay's 166,000‐km2 watershed have been uneven, and dissolved P loads have increased in a number of the bay's tributaries. As the midpoint of the 15‐yr TMDL process has now passed, some of the more stubborn sources of P must now be tackled. For nonpoint agricultural sources, strategies that not only address particulate P but also mitigate dissolved P losses are essential. Lingering concerns include legacy P stored in soils and reservoir sediments, mitigation of P in artificial drainage and stormwater from hotspots and converted farmland, manure management and animal heavy use areas, and critical source areas of P in agricultural landscapes. While opportunities exist to curtail transport of all forms of P, greater attention is required toward adapting P management to new hydrologic regimes and transport pathways imposed by climate change.
","language":"English","publisher":"Wiley","doi":"10.2134/jeq2019.03.0112","usgsCitation":"Kleinman, P., Fanelli, R., Hirsch, R.M., Buda, A.R., Easton, Z.M., Wainger, L.A., Brosch, C., Lowenfish, M., Collick, A.S., Shirmohammadi, A., Boomer, K., Hubbart, J.A., Bryant, R.B., and Shenk, G., 2019, Phosphorus and the Chesapeake Bay: Lingering issues and emerging concerns for agriculture: Journal of Environmental Quality, v. 48, no. 5, p. 1191-1203, https://doi.org/10.2134/jeq2019.03.0112.","productDescription":"13 p.","startPage":"1191","endPage":"1203","ipdsId":"IP-106511","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":467365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2134/jeq2019.03.0112","text":"Publisher Index Page"},{"id":379941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 36.92793899776678\n ],\n [\n -75.948486328125,\n 37.23470197166817\n ],\n [\n -75.673828125,\n 37.896530447543\n ],\n [\n -75.816650390625,\n 38.28993659801203\n ],\n [\n -75.8221435546875,\n 38.436379603\n ],\n [\n -76.0858154296875,\n 38.44498466889473\n ],\n [\n -76.0308837890625,\n 38.71980474264237\n ],\n [\n -75.7781982421875,\n 39.614152077002664\n ],\n [\n -76.1956787109375,\n 39.592990390285024\n ],\n [\n -76.7230224609375,\n 39.21948715423953\n ],\n [\n -76.629638671875,\n 38.565347844885466\n ],\n [\n -76.629638671875,\n 38.40194908237822\n ],\n [\n -77.0635986328125,\n 38.487994609214795\n ],\n [\n -77.05810546875,\n 38.21660403859855\n ],\n [\n -76.4373779296875,\n 37.92686760148135\n ],\n [\n -77.04711914062499,\n 38.190704293996504\n ],\n [\n -77.156982421875,\n 38.043765107439675\n ],\n [\n -76.497802734375,\n 37.501010429493284\n ],\n [\n -76.4813232421875,\n 37.322120359451766\n ],\n [\n -76.4813232421875,\n 37.14718209972376\n ],\n [\n -76.234130859375,\n 36.85764758564407\n ],\n [\n -76.11328125,\n 36.92793899776678\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kleinman, Peter","contributorId":244141,"corporation":false,"usgs":false,"family":"Kleinman","given":"Peter","email":"","affiliations":[{"id":48855,"text":"USDA-ARS, Pasture Syst. and Watershed Mgmt. Res. 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B.","contributorId":191824,"corporation":false,"usgs":false,"family":"Bryant","given":"R.","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":803488,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Shenk, Gary","contributorId":244194,"corporation":false,"usgs":false,"family":"Shenk","given":"Gary","affiliations":[],"preferred":false,"id":803489,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70227922,"text":"70227922 - 2019 - Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","interactions":[],"lastModifiedDate":"2022-02-03T12:08:31.632737","indexId":"70227922","displayToPublicDate":"2019-08-15T14:38:12","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context","docAbstract":"Hybridization can profoundly affect the genomic composition and phenotypes of closely related species, and provides an opportunity to identify mechanisms that maintain reproductive isolation between species. Recent evidence suggests that hybridization outcomes within a species pair can vary across locations. However, we still don’t know how extensive variation in outcomes of hybridization is across geographic replicates, and what mechanisms drive that variation. In this study, we described hybridization outcomes across 27 locations in the North Fork Shoshone River basin (Wyoming, USA) where native Yellowstone cutthroat trout and introduced rainbow trout co-occur. We used genomic data and hierarchical Bayesian models to precisely identify ancestry of hybrid individuals. Hybridization outcomes varied across locations. In some locations, only rainbow trout and advanced backcrossed hybrids towards rainbow trout were present, while other locations had a broader range of ancestry, including both parental species and first-generation hybrids. Using an individual-based simulation, we found that outcomes of hybridization in the North Fork Shoshone River basin deviate substantially from what we would expect under assumptions of random mating and no selection against hybrids. Since this implies that some mechanisms of reproductive isolation function to maintain parental taxa and a diversity of hybrid types, we then modeled hybridization outcomes as a function of environmental variables and stocking history that are likely to affect prezygotic barriers to hybridization. Variables associated with history of fish stocking were the strongest predictors of hybridization outcomes, followed by environmental variables that might affect overlap in spawning time and location.","language":"English","publisher":"Wiley","doi":"10.1111/mec.15175","usgsCitation":"Mandeville, E., Walters, A.W., Nordberg, B.J., Higgins, K.H., Burckhardt, J.C., and Wagner, C.E., 2019, Variable hybridization outcomes in trout are predicted by historical fish stocking and environmental context: Molecular Ecology, v. 28, no. 16, p. 3738-3755, https://doi.org/10.1111/mec.15175.","productDescription":"18 p.","startPage":"3738","endPage":"3755","ipdsId":"IP-099309","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467366,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6775767","text":"External Repository"},{"id":395304,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Buffalo Bill Reservoir, North Fork Shoshone River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.57077026367188,\n 44.26683800273895\n ],\n [\n -108.96102905273438,\n 44.26683800273895\n ],\n [\n -108.96102905273438,\n 44.6579085850145\n ],\n [\n -109.57077026367188,\n 44.6579085850145\n ],\n [\n -109.57077026367188,\n 44.26683800273895\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"16","noUsgsAuthors":false,"publicationDate":"2019-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nordberg, Brittany J.","contributorId":270690,"corporation":false,"usgs":false,"family":"Nordberg","given":"Brittany","email":"","middleInitial":"J.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Higgins, Karly H.","contributorId":273111,"corporation":false,"usgs":false,"family":"Higgins","given":"Karly","email":"","middleInitial":"H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":832580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burckhardt, Jason C.","contributorId":270692,"corporation":false,"usgs":false,"family":"Burckhardt","given":"Jason","email":"","middleInitial":"C.","affiliations":[{"id":56161,"text":"wygf","active":true,"usgs":false}],"preferred":false,"id":832581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Catherine E.","contributorId":270693,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":832582,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204253,"text":"sir20195068 - 2019 - Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","interactions":[],"lastModifiedDate":"2019-08-16T06:55:10","indexId":"sir20195068","displayToPublicDate":"2019-08-15T13:46:58","publicationYear":"2019","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":"2019-5068","displayTitle":"Flood-Inundation Maps for Joachim Creek, De Soto, Missouri, 2018","title":" Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018","docAbstract":"Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek, De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the city of De Soto and Jefferson County, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&gage=desm7, which also forecasts flood hydrographs at this site (site DESM7).
Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013.
The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.
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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Demand for freshwater on the Island of Maui is expected to increase by 45 percent between 2015 and 2035. Groundwater availability on Maui is affected by changes in climate and agricultural irrigation. To evaluate the availability of fresh groundwater under projected future climate conditions and changing agricultural irrigation practices, estimates of groundwater recharge are needed. A water-budget model with a daily computation interval was used to estimate the spatial distribution of recharge on Maui for one present-day and two future-climate scenarios. All three scenarios used 2017 land cover. The two future-climate scenarios, including one wetter than the present-day scenario and one drier than the present-day scenario, were developed using available high-resolution downscaled climate projections. The drier future scenario was developed using projections for a Representative Concentration Pathway warming scenario during 2071–99 with total radiative forcing of 8.5 Watts per square meter by the year 2100 (RCP8.5 2071–99 scenario), whereas the wetter future scenario was developed using projections for a “Special Report on Emissions Scenarios” A1B emission scenario during 2080–99 (A1B 2080–99 scenario). For the RCP8.5 2071–99 scenario, projected mean annual recharge decrease for Maui is about 172 million gallons per day, or about 14 percent less than present-day recharge, which is estimated to be 1,232 million gallons per day. Recharge for the RCP8.5 2071–99 scenario is projected to decrease in 22 of Maui’s 25 aquifer systems, which are defined by the Hawaiʻi Commission on Water Resource Management. For the A1B 2080–99 future scenario, projected mean annual recharge increase for Maui is about 144 million gallons per day, or about 12 percent more than present-day recharge. Recharge for the A1B 2080–99 scenario is projected to increase in 17 of Maui’s 25 aquifer systems. Between the two future scenarios, a total of 11 aquifer systems show similar direction in drying (Kahului, Kama‘ole, Lualaʻilua, Makawao, Olowalu, Pāʻia, Ukumehame, Waikapū) or wetting (Honopou, Kawaipapa, and Waikamoi) changes for recharge. Selectively modifying the climate inputs for the A1B 2080–99 scenario indicates that the projected changes in rainfall account for most of the projected changes in recharge for Maui’s 25 aquifer systems. However, projected changes in reference evapotranspiration and forest-canopy evaporation also can account for a substantial part of the projected changes in recharge where changes in reference evapotranspiration are relatively large and where changes in forest-canopy evaporation extend across large forested areas. Projected changes in daily rainfall frequency have a relatively small but non-negligible impact on recharge estimates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195064","collaboration":"Prepared in cooperation with the County of Maui Department of Water Supply and the Pacific Regional Integrated Sciences and Assessments Program","usgsCitation":"Mair, A., Johnson A.G., Rotzoll, Kolja, and Oki, D.S., 2019, Estimated groundwater recharge from a water-budget model incorporating selected climate projections, Island of Maui, Hawai‘i: U.S. Geological Survey Scientific Investigations Report 2019–5064, 46 p., https://doi.org/10.3133/sir20195064.","productDescription":"Report: vi, 46 p., 3 data releases","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-100732","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":366544,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98W9ABX","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP5 RCP8.5 2071-99 scenario rainfall and 2017 land cover"},{"id":366541,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5064/sir20195064.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5064"},{"id":366542,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WSOFO","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for average climate conditions, 1978-2007 rainfall and 2017 land cover"},{"id":366540,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5064/coverthb.jpg"},{"id":366543,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9437T2F","linkHelpText":"Mean annual water-budget components for the Island of Maui, Hawaii, for projected climate conditions, CMIP3 A1B 2080-99 scenario climate and 2017 land cover"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.7474365234375,\n 20.52478875041428\n ],\n [\n -155.8685302734375,\n 20.52478875041428\n ],\n [\n -155.8685302734375,\n 21.099875492701216\n ],\n [\n -156.7474365234375,\n 21.099875492701216\n ],\n [\n -156.7474365234375,\n 20.52478875041428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
ubsurface coal environments, where biogenic coal-to-methane conversion occurs, are difficult to access, resulting in inherent challenges and expenses for in situexperiments. Previous batch reactor studies provided insights into specific processes, pathways, kinetics, and engineering strategies, but field-relevance is restricted due to limited substrate availability or byproduct accumulation that may influence reactions or metabolisms. In this study, continuous-flow column reactors were used to overcome some batch limitations, improve the understanding of in situconditions, and increase field-relevance for subsurface engineering technology development. The bench-scale reactor system was constructed to investigate the addition of algal amendment for enhancing microbial coal-to-methane conversion previously developed in batch systems. Four reactor columns were packed with coal and inoculated with a microbial consortium from the same Flowers-Goodale coal bed. Two reactors were amended with 13C-labeled algal amendment on day 0, and two were unamended. On day 61, one previously amended and one previously unamended reactor were re-amended. Produced gases were captured in a gas trap, and CH4 and CO2 were quantified. The reactor amended twice produced 1712.6 µmol CH4 (4.6% as 13CH4). The reactor amended only on day 0 produced 1485.5 µmol CH4 (2.6% as 13CH4). The reactor amended only on day 61 produced 278.9 µmol CH4 (3.9% as 13CH4). The reactor with no amendment produced no measurable gases for the duration of the 172-day experiment. Amendment increased the rate of coal-to-methane conversion and total gas production; most of the produced gases were due to coal conversion with only small contributions (<7%) from amendment conversion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2019.115905","usgsCitation":"Davis, K.J., Platt, G.A., Barnhart, E.P., Hiebart, R., Hyatt, R., Fields, M.W., and Gerlach, R., 2019, Biogenic coal-to-methane conversion can be enhanced with small additions of algal amendment in field-relevant upflow column reactors: Fuel, v. 256, 115905, 8 p., https://doi.org/10.1016/j.fuel.2019.115905.","productDescription":"115905, 8 p.","ipdsId":"IP-106712","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":467367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1557363","text":"Publisher Index Page"},{"id":366902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"256","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Katherine J.","contributorId":203246,"corporation":false,"usgs":false,"family":"Davis","given":"Katherine","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Platt, George A.","contributorId":218404,"corporation":false,"usgs":false,"family":"Platt","given":"George","email":"","middleInitial":"A.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":769183,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hiebart, Randy","contributorId":218422,"corporation":false,"usgs":false,"family":"Hiebart","given":"Randy","email":"","affiliations":[],"preferred":false,"id":769186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hyatt, Robert","contributorId":218406,"corporation":false,"usgs":false,"family":"Hyatt","given":"Robert","email":"","affiliations":[{"id":39839,"text":"Montana Emergent Technologies","active":true,"usgs":false}],"preferred":false,"id":769187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":769188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":769189,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70207520,"text":"70207520 - 2019 - Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons","interactions":[],"lastModifiedDate":"2019-12-21T10:30:56","indexId":"70207520","displayToPublicDate":"2019-08-15T10:27:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"title":"Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons","docAbstract":"Viability selection yields adult populations that are more genetically variable than those of juveniles, producing a positive correlation between heterozygosity and survival. Viability selection could be the result of decreased heterozygosity across many loci in inbred individuals and a subsequent decrease in survivorship resulting from the expression of the deleterious alleles. Alternatively, locus-specific differences in genetic variability between adults and juveniles may be driven by forms of balancing selection, including heterozygote advantage, frequency-dependent selection, or selection across temporal and spatial scales. We use a pooled-sequencing approach to compare genome-wide and locus-specific genetic variability between 74 golden eagle (Aquila chrysaetos), 62 imperial eagle (Aquila heliaca), and 69 prairie falcon (Falco mexicanus) juveniles and adults. Although genome-wide genetic variability is comparable between juvenile and adult golden eagles and prairie falcons, imperial eagle adults are significantly more heterozygous than juveniles. This evidence of viability selection may stem from a relatively smaller imperial eagle effective population size and potentially greater genetic load. We additionally identify ~2000 single-nucleotide polymorphisms across the 3 species with extreme differences in heterozygosity between juveniles and adults. Many of these markers are associated with genes implicated in immune function or olfaction. These loci represent potential targets for studies of how heterozygote advantage, frequency-dependent selection, and selection over spatial and temporal scales influence survivorship in avian species. Overall, our genome-wide data extend previous studies that used allozyme or microsatellite markers and indicate that viability selection may be a more common evolutionary phenomenon than often appreciated.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jhered/esz048","usgsCitation":"Doyle, J.M., Willoughby, J.R., Bell, D.A., Bloom, P.H., Bragin, E.A., Fernandez, N.B., Katzner, T., Leonard, K., and DeWoody, J., 2019, Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons: Journal of Heredity, v. 110, no. 6, p. 696-706, https://doi.org/10.1093/jhered/esz048.","productDescription":"11 p.","startPage":"696","endPage":"706","ipdsId":"IP-105731","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/esz048","text":"Publisher Index Page"},{"id":370601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"110","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Doyle, Jacqueline M.","contributorId":175099,"corporation":false,"usgs":false,"family":"Doyle","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":778350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willoughby, Janna R 0000-0002-0176-1878","orcid":"https://orcid.org/0000-0002-0176-1878","contributorId":221484,"corporation":false,"usgs":false,"family":"Willoughby","given":"Janna","email":"","middleInitial":"R","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":778351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Douglas A.","contributorId":199739,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":24634,"text":"East Bay Regional Park District","active":true,"usgs":false}],"preferred":false,"id":778352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bloom, Peter H.","contributorId":191356,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":778353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bragin, Evgeny A.","contributorId":194894,"corporation":false,"usgs":false,"family":"Bragin","given":"Evgeny","email":"","middleInitial":"A.","affiliations":[{"id":35656,"text":"Science Department, Naurzum National Nature Reserve, Kostanay Oblast, Naurzumski Raijon, Karamendy, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":778354,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fernandez, Nadia B.","contributorId":175100,"corporation":false,"usgs":false,"family":"Fernandez","given":"Nadia","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":778355,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778349,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leonard, Kolbe","contributorId":204166,"corporation":false,"usgs":false,"family":"Leonard","given":"Kolbe","email":"","affiliations":[{"id":36867,"text":"Department of Computer and Information Sciences, Towson University","active":true,"usgs":false}],"preferred":false,"id":778356,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeWoody, J Andrew","contributorId":221485,"corporation":false,"usgs":false,"family":"DeWoody","given":"J Andrew","affiliations":[{"id":33107,"text":"Towson University","active":true,"usgs":false}],"preferred":false,"id":778357,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211576,"text":"70211576 - 2019 - Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","interactions":[],"lastModifiedDate":"2020-07-31T15:01:25.745893","indexId":"70211576","displayToPublicDate":"2019-08-15T09:24:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","docAbstract":"The Rio Grande Cutthroat Trout Oncorhynchus clarkii virginalis (RGCT ) occupies just 12% of its ancestral range. As the southernmost subspecies of Cutthroat Trout, we expect a warming climate to bring additional stressors to RGCT populations, such as increased stream temperatures, reduced streamflows, and increased incidence of wildfire. We developed a Bayesian network (BN ) model using site‐specific data, empirical research, and expert knowledge to estimate the probability of persistence for each of the 121 remaining RGCT conservation populations and to rank the severity of the threats they face. These inputs quantified the genetic risks (e.g., inbreeding risk and hybridization risk), population demographics (disease risk, habitat suitability, and survival), and probability of stochastic disturbances (stream drying risk and wildfire risk) in an uncertain future. We also created stream temperature and base flow discharge models coupled with regionally downscaled climate projections to predict future abiotic conditions at short‐term (2040s) and long‐term (2080s) time horizons. In the absence of active management, we predicted a decrease in the average probability of population persistence from 0.53 (current) to 0.31 (2040s) and 0.26 (2080s). Only 11% of these populations were predicted to have a greater than 75% chance of persisting to the 2080s. Threat of invasion by nonnative trout had the strongest effect on population persistence. Of the 78 populations that are already invaded or lacking complete barriers, 60% were estimated to be extirpated by 2080 and the remainder averaged only a 10% chance of persistence. In contrast, the effects of increased stream temperatures were predicted to affect the future persistence of only 9% of the 121 RGCT populations remaining, as most have been restricted to high‐elevation habitats that are cold enough to buffer against some stream warming. Our BN model provides a framework for evaluating threats and will be useful to guide management actions that are likely to provide the most benefit for long‐term conservation.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10320","usgsCitation":"Zeigler, M.P., Rogers, K., Roberts, J., Todd, A., and Fausch, K., 2019, Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future: North American Journal of Fisheries Management, v. 39, no. 5, p. 819-848, https://doi.org/10.1002/nafm.10320.","productDescription":"30 p.","startPage":"819","endPage":"848","ipdsId":"IP-080586","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.677001953125,\n 33.578014746143985\n ],\n [\n -103.7548828125,\n 33.578014746143985\n ],\n [\n -103.7548828125,\n 38.47079371120379\n ],\n [\n -107.677001953125,\n 38.47079371120379\n ],\n [\n -107.677001953125,\n 33.578014746143985\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigler, Mathew P.","contributorId":91006,"corporation":false,"usgs":true,"family":"Zeigler","given":"Mathew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Kevin B.","contributorId":220104,"corporation":false,"usgs":false,"family":"Rogers","given":"Kevin B.","affiliations":[],"preferred":false,"id":794699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Todd, Andrew atodd@usgs.gov","contributorId":149790,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew","email":"atodd@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":794700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fausch, Kurt D. 0000-0001-5825-7560","orcid":"https://orcid.org/0000-0001-5825-7560","contributorId":198488,"corporation":false,"usgs":false,"family":"Fausch","given":"Kurt D.","affiliations":[],"preferred":false,"id":794701,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215168,"text":"70215168 - 2019 - Phytoplankton community structure response to groundwater-borne nutrients in the inland bays, Delaware","interactions":[],"lastModifiedDate":"2020-10-09T12:48:32.877319","indexId":"70215168","displayToPublicDate":"2019-08-15T07:46:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton community structure response to groundwater-borne nutrients in the inland bays, Delaware","docAbstract":"To determine the impacts of groundwater-borne nutrients on phytoplankton biomass and community structure, we conducted a series of mesocosm experiments in the Inland Bays of Delaware. Four treatments were tested, including mesocosms coupled directly to submarine groundwater seepage, mesocosms with the addition of pumped submarine groundwater, mesocosms with the addition of phosphate, and control mesocosms with no nutrient addition. We measured chlorophyll a concentrations as a proxy for overall biomass and used genetic sequencing techniques to characterize the phytoplankton community structure. Groundwater carried a high N load to the estuary with NO3- up to 295 µmol l-1 and NH4+ up to 55 µmol l-1. As a result, treatment mesocosms had elevated NO3- and NH4+, while control mesocosms were relatively low in nutrients. In June, the highest chlorophyll a concentrations occurred in mesocosms attached to seepage meters after 3.5 d, with significant differences across all treatments. In August, groundwater-amended mesocosms reached the highest biomass concentrations, which peaked after 3 d. There were significant differences across all treatments, except control and phosphate-amended mesocosms which remained unchanged. Community sequence data showed that species assemblage was also impacted by availability of nutrients, with significant differences in community structure for mesocosms receiving nutrients vs. control mesocosms in both June and August experiments. Harmful algal species proliferated in high nutrient treatments, including Cylindrotheca closterium, Karlodinium veneficum, Nitzschia spp., and Heterocapsa spp. While the general relationship between nutrient supply and biomass production is well known, we demonstrate the role groundwater-borne nutrients and sediment processes play in shaping community structure in estuarine primary producers and in promoting harmful algal blooms.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps13012","usgsCitation":"Torre, D., Coyne, K., Kroeger, K.D., and York, J.K., 2019, Phytoplankton community structure response to groundwater-borne nutrients in the inland bays, Delaware: Marine Ecology Progress Series, v. 624, p. 51-63, https://doi.org/10.3354/meps13012.","productDescription":"12 p.","startPage":"51","endPage":"63","ipdsId":"IP-105254","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":379268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":801061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"York, Joanna K.","contributorId":140023,"corporation":false,"usgs":false,"family":"York","given":"Joanna","email":"","middleInitial":"K.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":801062,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207595,"text":"70207595 - 2019 - Predicting surf zone injuries along the Delaware coast using a Bayesian network","interactions":[],"lastModifiedDate":"2019-12-30T16:30:44","indexId":"70207595","displayToPublicDate":"2019-08-14T16:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Predicting surf zone injuries along the Delaware coast using a Bayesian network","docAbstract":"Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.","language":"English","publisher":"Springer","doi":"10.1007/s11069-019-03697-y","usgsCitation":"Doelp, M., Puleo, J., and Plant, N.G., 2019, Predicting surf zone injuries along the Delaware coast using a Bayesian network: Natural Hazards, v. 98, no. 2, p. 379-401, https://doi.org/10.1007/s11069-019-03697-y.","productDescription":"22 p.","startPage":"379","endPage":"401","ipdsId":"IP-100096","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":370880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"98","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Doelp, Matthew","contributorId":221558,"corporation":false,"usgs":false,"family":"Doelp","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":778659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puleo, Jack A.","contributorId":108287,"corporation":false,"usgs":true,"family":"Puleo","given":"Jack A.","affiliations":[],"preferred":false,"id":778660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":778661,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204836,"text":"70204836 - 2019 - Petrology and geochemistry of migrated hydrocarbons associated with the Albert Formation oil shale in New Brunswick, Canada","interactions":[],"lastModifiedDate":"2019-08-19T15:53:11","indexId":"70204836","displayToPublicDate":"2019-08-14T15:35:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1709,"text":"Fuel","active":true,"publicationSubtype":{"id":10}},"title":"Petrology and geochemistry of migrated hydrocarbons associated with the Albert Formation oil shale in New Brunswick, Canada","docAbstract":"Samples of the Carboniferous oil shale of the Albert Formation in New Brunswick, Canada, were examined using reflected white and fluorescence light microscopy, Rock-Eval pyrolysis, and ICP-MS (for elements). The presence of fractured filled solid bitumen in contact with, and within the Albert Formation oil shale, particularly in fractures at the right angle to the bedding of oil shale indicated that migrated oil had enough force to overcome the tensil strength of oil shale matrix, and penetrating the oil shale. Migrating fluid also caused thermal alteration of the oil shale matrix, as evident by the presence primary bitumen and oil droplets. The evidence of oil migration included the presence of solid bitumen and crystalline carbonates in contact with the immature oil shale. The low permeability oil shale acted as a seal/aquitard and created a diagenetic ‘front’ by reducing/slowing the advance of migrating oil, resulting in the formation of a reaction zone. Oil droplets were found in this reaction zone. Albertite was the only solid bitumen reported in the Albert Mine area previously. However, the present study found that migrated solid bitumen consisted of both soluble solid bitumen types such as gilsonite and glance-pitch, and non-soluble solid bitumen such as wurtzilite and albertite. The high hydrocarbon yield of oil shales in the Albert Mine area was due to the presence of various solid bitumen types associated with the oil shale and possibly slight thermal alteration that the oil shale experienced when it came in contact with migrating oil. The variation of Th/K ratio and TOC (wt%) indicates that most of oil shales from the Albert Mine area and within the vicinity of oil migration have higher content of TOC (17-25 wt%) as compared to the other Albert oil shales ( TOC=<10 %). The oil shales examined in this study were immature to marginally mature and had a wide range of hydrocarbon yield (2-213 L/Tonne). The highest hydrocarbon yield range (66-199 L/Tonne) was associated with oil shale outcrop samples collected in the Albert Mine itself, where the oil shale was heavily impregnated by migrated solid bitumen.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2019.115922","usgsCitation":"Goodarzi, F., T. Gentzis, Karacan, C.O., H. Sanei, and Pederson, P., 2019, Petrology and geochemistry of migrated hydrocarbons associated with the Albert Formation oil shale in New Brunswick, Canada: Fuel, v. 256, 115922, 17 p., https://doi.org/10.1016/j.fuel.2019.115922.","productDescription":"115922, 17 p.","ipdsId":"IP-105975","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":366661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"New Brunswick","otherGeospatial":"Alberta Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.7366943359375,\n 45.49094569262732\n ],\n [\n -63.74267578125,\n 45.49094569262732\n ],\n [\n -63.74267578125,\n 46.33175800051563\n ],\n [\n -65.7366943359375,\n 46.33175800051563\n ],\n [\n -65.7366943359375,\n 45.49094569262732\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"256","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goodarzi, F.","contributorId":218201,"corporation":false,"usgs":false,"family":"Goodarzi","given":"F.","email":"","affiliations":[{"id":39778,"text":"FG & Partners Ltd","active":true,"usgs":false}],"preferred":false,"id":768678,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"T. 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Sanei","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":768680,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pederson, P.K.","contributorId":218204,"corporation":false,"usgs":false,"family":"Pederson","given":"P.K.","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":768681,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216452,"text":"70216452 - 2019 - The Islands of Oceania – Political geography, biogeography, and terrestrial ecosystems","interactions":[],"lastModifiedDate":"2020-11-19T12:59:13.764691","indexId":"70216452","displayToPublicDate":"2019-08-14T10:52:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"The Islands of Oceania – Political geography, biogeography, and terrestrial ecosystems","docAbstract":"Humans are dependent upon ecosystems for the production of goods and services necessary for their well-being (Daily, 1997). As the service provider units (SPUs) for these benefits of nature (Anderson et al., 2015), ecosystems need to be managed in a way that maximizes their persistence on the planet. Part of that management effort includes knowing a) what the ecosystem types are, b) where they are located on the landscape and in the seascape, and c) what condition they are in. Mapping of ecosystem occurrences as the SPUs for ecosystems goods and services is therefore an important element of ecosystem accounting, an inherently spatial activity. Maps are also needed of the ecosystem accounting areas within which the accounting is conducted. Standardized maps of ecosystem accounting areas and terrestrial ecosystem SPUs have not previously been available for many islands of the Oceania region. We describe the availability of new island shorelines and ecosystems data for Oceania herein, and encourage consideration and evaluation of the data for ecosystem accounting and other applications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2019.100985","usgsCitation":"Sayre, R., Martin, M., Karagulle, D., Frye, C., Breyer, S., Wright, D., Butler, K., Van Graafeiland, K., and Maynard, S., 2019, The Islands of Oceania – Political geography, biogeography, and terrestrial ecosystems: Ecosystem Services, v. 39, 100985, 7 p., https://doi.org/10.1016/j.ecoser.2019.100985.","productDescription":"100985, 7 p.","ipdsId":"IP-110629","costCenters":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":380602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 91.0546875,\n -47.27922900257082\n ],\n [\n 178.59375,\n -47.27922900257082\n ],\n [\n 178.59375,\n 21.94304553343818\n ],\n [\n 91.0546875,\n 21.94304553343818\n ],\n [\n 91.0546875,\n -47.27922900257082\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sayre, Roger 0000-0001-6703-7105","orcid":"https://orcid.org/0000-0001-6703-7105","contributorId":245011,"corporation":false,"usgs":true,"family":"Sayre","given":"Roger","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":805165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Madeline Thomas 0000-0002-2704-1879","orcid":"https://orcid.org/0000-0002-2704-1879","contributorId":240612,"corporation":false,"usgs":true,"family":"Martin","given":"Madeline Thomas","affiliations":[{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":805167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karagulle, Deniz","contributorId":213676,"corporation":false,"usgs":false,"family":"Karagulle","given":"Deniz","email":"","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":805168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frye, Charlie","contributorId":191631,"corporation":false,"usgs":false,"family":"Frye","given":"Charlie","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":805169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Breyer, Sean","contributorId":213678,"corporation":false,"usgs":false,"family":"Breyer","given":"Sean","affiliations":[{"id":38832,"text":"Esri","active":true,"usgs":false}],"preferred":false,"id":805170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, Dawn","contributorId":200268,"corporation":false,"usgs":false,"family":"Wright","given":"Dawn","affiliations":[],"preferred":false,"id":805171,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, Kevin","contributorId":200270,"corporation":false,"usgs":false,"family":"Butler","given":"Kevin","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":805172,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Van Graafeiland, Keith","contributorId":200271,"corporation":false,"usgs":false,"family":"Van Graafeiland","given":"Keith","email":"","affiliations":[{"id":18946,"text":"Environmental Systems Research Institute, Inc. (ESRI), Redlands, CA","active":true,"usgs":false}],"preferred":false,"id":805166,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Maynard, Simone","contributorId":191652,"corporation":false,"usgs":false,"family":"Maynard","given":"Simone","email":"","affiliations":[],"preferred":false,"id":805173,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203767,"text":"sir20195057 - 2019 - Paleoliquefaction field reconnaissance in eastern North Carolina—Is there evidence for large magnitude earthquakes between the central Virginia seismic zone and Charleston seismic zone?","interactions":[],"lastModifiedDate":"2019-08-15T09:08:37","indexId":"sir20195057","displayToPublicDate":"2019-08-14T10:45:00","publicationYear":"2019","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":"2019-5057","displayTitle":"Paleoliquefaction Field Reconnaissance in Eastern North Carolina—Is There Evidence for Large Magnitude Earthquakes Between the Central Virginia Seismic Zone and Charleston Seismic Zone?","title":"Paleoliquefaction field reconnaissance in eastern North Carolina—Is there evidence for large magnitude earthquakes between the central Virginia seismic zone and Charleston seismic zone?","docAbstract":"In June 2016, approximately 64 kilometers (km) of riverbank were examined along the Tar and Neuse Rivers near Tarboro and Kinston, North Carolina, for evidence of liquefaction-forming earthquakes. The study area is in the vicinity of the Grainger’s fault zone in eastern North Carolina. The Grainger’s fault zone is a fault zone in the inner Coastal Plain Province that has well-documented Paleogene and younger deformation of Cretaceous to Eocene strata. Low-magnitude earthquakes near the fault zone (for example, magnitude [M] 2.1 in 2013, 13 km south-southwest of Kinston) suggest larger earthquakes may have struck this region in the past. The study area is about equidistant from newly documented Holocene paleoliquefaction sites in the Central Virginia Seismic Zone (CVSZ) and liquefaction sites formed during the 1886 M7.1 Charleston, South Carolina earthquake. The northernmost Holocene paleoliquefaction features associated with the Charleston Seismic Zone (CSZ) are in Southport, North Carolina.
Conditions suitable for liquefaction were identified at 38 sites on both rivers, but only one site was classified as highly susceptible. Stratigraphy consists of Paleozoic gneiss; Cretaceous sandstone/shale; Paleocene mudstone/claystone to Eocene fossiliferous limestone; Quaternary unconsolidated, crossbedded sand and gravel; and Holocene alluvium. Three sets of stratigraphic conditions suitable for liquefaction—unconsolidated source sand beneath capping strata—were identified in detailed examinations at 105 sites: (1) Holocene alluvial sand beneath alluvial silt and clay beds; (2) Quaternary terrace sand beneath beds of silt and clay; and (3) Holocene alluvial sand or Quaternary terrace sand capped by clay-rich Bt soil horizons. Weathered and unconsolidated Cretaceous sand capped by a Bt soil horizon was identified at one site, but the weathered sand is likely too compacted to liquefy readily. One outcrop containing three small sand dikes, and four outcrops of soft-sediment deformation features—mostly load casts—were observed, but none of these features could be conclusively established as seismogenic. A few examples of pseudo-sand-dikes were also identified: sand-filled cypress root casts and pedogenic weathering fronts created the appearance of sand dikes and sills.
A comparable survey in 2015 of 119 km of riverbank exposures in the CVSZ yielded 19 paleoliquefaction sites of probable earthquake origin; these features formed from at least one M~6 earthquake in the past 6,000 years (6 ka). This survey in eastern North Carolina revealed no definitive paleoliquefaction features; earthquakes of sufficient magnitude to produce liquefaction likely have not affected this region during the Holocene.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195057","usgsCitation":"Carter, M.W., and McLaurin, B.T., 2019, Paleoliquefaction field reconnaissance in eastern North Carolina—Is there evidence for large magnitude earthquakes between the Central Virginia Seismic Zone and Charleston Seismic Zone?: U.S. Geological Survey Scientific Investigations Report 2019–5057, 54 p., https://doi.org/10.3133/sir20195057. ","productDescription":"vi, 54 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092950","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":366514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5057/coverthb.jpg"},{"id":366515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5057/sir20195057.pdf","text":"Report","size":"18.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5057"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.7607421875,\n 32.37996146435729\n ],\n [\n -74.50927734375,\n 32.37996146435729\n ],\n [\n -74.50927734375,\n 37.61423141542417\n ],\n [\n -79.7607421875,\n 37.61423141542417\n ],\n [\n -79.7607421875,\n 32.37996146435729\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
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The Big Chino subbasin is located in central-northwest Arizona in the transition zone between the Colorado Plateau and the Basin and Range Province. The controlled source audio-frequency magnetotelluric (CSAMT) geophysical method, a low-impact, non-intrusive, electrical resistance sounding technique, was used to evaluate the subsurface hydrogeology of the southern third of the Big Chino subbasin. The Big Chino subbasin is a northwest-trending, late Tertiary graben bordered by the Big Chino Fault along its northeast flank where there is as much as 1,100 meters of displacement. The main water-bearing stratigraphic unit of the basin is Tertiary alluvial-fill sediment. The Devonian Martin Formation provides water to wells near Drake and the Mississippian Redwall Limestone provides water to wells east of the basin and in the Paulden area.
The purpose of the CSAMT surveys was to improve the conceptual model of the aquifer by constraining the basin geometry and identifying stratigraphic units and their subsurface extents. CSAMT methods were used to map the subsurface along 100 kilometers (62 miles) of survey lines across the southern third of the subbasin. Of 21 survey lines, 14 were west of the town of Paulden and another 7 were east of Paulden. Data were cleaned and prepared for entry into Zonge SCS2D software and then inverted to provide a two-dimensional resistivity profile for each survey line. Final inversion models representing the best fit to measured data were compared to driller’s logs or borehole data where present.
Data from the CSAMT lines west and north of Paulden are consistent with thicker alluvial basin deposits that range from 100 meters thick to a few hundred meters thick. Data from the CSAMT lines east of Paulden are consistent with thinner alluvial and basalt deposits overlying Paleozoic Martin Formation and Redwall Limestone, Tapeats Sandstone, and Precambrian granite and schist.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195082","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Macy, J.P., Gungle, B., and Mason, J.P., 2019, Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotellursurveys: U.S. Geological Survey Scientific Investigations Report 2019–5082, 39 p., https://doi.org/10.3133/sir20195082.\nic ","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-098264","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":366547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5082/sir20195082.pdf","text":"Report","size":"50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
From fine-scale foraging to broad-scale migration, animal movement is shaped by the distribution of resources. There is mounting evidence, however, that learning and memory also guide movement. Although migratory mammals commonly track resource waves, how resource tracking and memory guide long-distance migration has not been reconciled. We examined these hypotheses using movement data from four populations of migratory mule deer (n = 91). Spatial memory had an extraordinary influence on migration, affecting movement 2–28 times more strongly than tracking spring green-up or autumn snow depth. Importantly, with only an ability to track resources, simulated deer were unable to recreate empirical migratory routes. In contrast, simulated deer with memory of empirical routes used those routes and obtained higher foraging benefits. For migratory terrestrial mammals, spatial memory provides knowledge of where seasonal ranges and migratory routes exist, whereas resource tracking determines when to beneficially move within those areas.
Birds of prey are becoming increasingly common occupants of urbanized settings, but the comparative benefits or detriments they experience in urban environs compared to exurban areas are poorly understood. We examined the reproductive success and phenology of Mississippi Kites (Ictinia mississippiensis) nesting concurrently in urban and exurban areas in west Texas during the 2010–2012 and 2014 breeding seasons. Our objective was to better understand the reproductive advantages or disadvantages that kites may experience by nesting in urban areas rather than exurban areas. We calculated annual productivity, estimated daily survival rates of nests, and estimated hatch dates. Kites produced 0.52 fledglings per urban nesting attempt and 0.33 fledglings per exurban nesting attempt. We found no difference in the mean number of young that fledged from successful urban (1.06) and exurban (1.07) nests, but kites in urban areas had greater nesting success within years than those in exurban areas. Both heat and drought were associated with low productivity and success in both areas, but the urban landscape appears to have provided some buffer to their effects. These factors may have had direct (e.g., exposure) or indirect (e.g., negative influences on prey availability) influences on reproductive success. Counter to previous studies of urban raptors, we found that exurban kites nested significantly earlier than urban kites. This may be linked to differences in environmental conditions and prey availability between the areas. Our results suggest that urban areas provide a reproductive advantage for Mississippi Kites in terms of facilitating an overall higher nesting success, but this does not translate into increased productivity for successful pairs. Further, our data suggest that, although urban areas may buffer species from drought or heat, that buffer can be breached during severe conditions.
The use of data generated from long term monitoring efforts necessitates accurate authoritative source citations of those data to ensure credit for data collected, and accountability for the data quality to enable repeated retrieval of a given data set. Data sets used in published reports and articles are increasingly being considered objects that are required to be published and cited. Aggregating data into open access databases is becoming common and is the focus of the Coordinated Assessment for Salmon and Steelhead project (CA; https://www.pnamp.org/project/coordinated-assessments-for-salmon-and-steelhead; http://www.streamnet.org/data/coordinated-assessments/) and National Marine Fisheries Service, National Oceanic and Atmospheric Administration Salmon Population Summary (SPS; https://www.webapps.nwfsc.noaa.gov/apex/f?p=261:home:0) among others. Guidelines are needed for citing these long-term dynamic data sets that have many contributors. We explore best practices and provide recommendations for including robust metadata attributes within data sets to enable data publication and citation using the CA and SPS data repositories as case studies. From reviewing the current citations possible from the CA and the SPS we recommend at minimum that natural resource monitoring databases contain: metadata to identify organizations that generated the data; contact persons for each organization that contributes data to an aggregated data set; and that metadata be incorporated into databases to enable auto-generated citations that recognize all contributing organizations with time-stamped versions of the data delivered. Beyond those minimums, additional best practice recommendations include this suite of metadata elements that identify a given data set upon citation or publication: author(s); publication date; description of data; file format(s) of data - e.g. tiles, shapefile sets, images, text files; dates data were collected; locations where data were collected; producers/contributors to the data set version cited; date data set was downloaded; original data repository from which the data were obtained; version identifier to note significant change to a data set; and a persistent identifier that can be used to locate that version of the data.
","language":"English","publisher":"Pacific Northwest Aquatic Monitoring Partnership","usgsCitation":"Olson, S.J., Barnas, K.A., Williams, M., Wheaton, C., Banach, M., and Bayer, J.M., 2019, Citing aquatic monitoring data sets: Best practice recommendations for authoritative data citation, 38 p.","productDescription":"38 p.","ipdsId":"IP-096434","costCenters":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":367206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367184,"type":{"id":15,"text":"Index Page"},"url":"https://www.pnamp.org/document/15001"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Olson, Sheryn J. 0000-0002-6751-9013","orcid":"https://orcid.org/0000-0002-6751-9013","contributorId":204736,"corporation":false,"usgs":true,"family":"Olson","given":"Sheryn","email":"","middleInitial":"J.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":770136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnas, Katie A","contributorId":218747,"corporation":false,"usgs":false,"family":"Barnas","given":"Katie","email":"","middleInitial":"A","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":770137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Margaret","contributorId":218748,"corporation":false,"usgs":false,"family":"Williams","given":"Margaret","email":"","affiliations":[{"id":39899,"text":"NOAA - Affiliate","active":true,"usgs":false}],"preferred":false,"id":770138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheaton, Christopher","contributorId":218749,"corporation":false,"usgs":false,"family":"Wheaton","given":"Christopher","email":"","affiliations":[{"id":39900,"text":"PSMFC","active":true,"usgs":false}],"preferred":false,"id":770139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Banach, Michael","contributorId":218750,"corporation":false,"usgs":false,"family":"Banach","given":"Michael","email":"","affiliations":[{"id":39900,"text":"PSMFC","active":true,"usgs":false}],"preferred":false,"id":770141,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bayer, Jennifer M. 0000-0001-9564-3110 jbayer@usgs.gov","orcid":"https://orcid.org/0000-0001-9564-3110","contributorId":3393,"corporation":false,"usgs":true,"family":"Bayer","given":"Jennifer","email":"jbayer@usgs.gov","middleInitial":"M.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":770140,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227931,"text":"70227931 - 2019 - Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie","interactions":[],"lastModifiedDate":"2022-02-03T12:03:54.22088","indexId":"70227931","displayToPublicDate":"2019-08-13T15:14:36","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia ) in tallgrass prairie","title":"Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie","docAbstract":"The >2,570,000-ha Flint Hills ecoregion of Kansas, USA, harbors the largest remaining contiguous tract of tallgrass prairie in North America, a unique system, as the remainder of North America's tallgrass prairie has succumbed to development and conversion. Consequently, the loss and degradation of tallgrass prairie has reduced populations of many North American prairie-obligate species including the regal fritillary (Speyeria idalia) butterfly. Population abundance and occupied range of regal fritillary have declined >99%, restricting many populations to isolated, remnant patches of tallgrass prairie. Such extensive decline has resulted in consideration of the regal fritillary for protection under the Endangered Species Act. Although it is widely accepted that management practices such as fire, grazing, and haying are necessary to maintain prairie ecosystems, reported responses by regal fritillary to these management regimes have been ambiguous. We tested effects of prescribed fire across short, moderate, and long fire-return intervals as well as grazing and haying management treatments on regal fritillary density. We also tested the relative influence of habitat characteristics created by these management regimes by measuring density of an obligate host plant (Viola spp.) and canopy cover of woody vegetation, grasses, forbs/ferns, bare ground, and litter. We found density was at least 1.6 times greater in sites burned with a moderate fire-return interval vs. sites burned with short and long fire-return intervals. Overall management regardless of fire-return interval did not have an effect on density. Percent cover of grass had the strongest positive association, while percent cover of woody vegetation had the greatest negative effect on density. Our results indicate that patch-burning is a viable and perhaps even ideal management strategy for regal fritillary in tallgrass prairie landscapes. Additionally, these results elucidate the importance of fire, particularly when applied at moderate-return intervals to regal fritillary, and corroborate a growing suite of studies that suggest fire is perhaps not as detrimental to populations of regal fritillary as previously believed.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2845","usgsCitation":"McCullough, K., Albanese, G., Haukos, D.A., Ricketts, A., and Stratton, S., 2019, Management regime and habitat response influence abundance of regal fritillary (Speyeria idalia) in tallgrass prairie: Ecosphere, v. 10, no. 8, e02845, 18 p., https://doi.org/10.1002/ecs2.2845.","productDescription":"e02845, 18 p.","ipdsId":"IP-094927","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2845","text":"Publisher Index Page"},{"id":395312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Flint Hills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.18505859374999,\n 37.020098201368114\n ],\n [\n -96.3720703125,\n 36.96744946416934\n ],\n [\n -95.47119140625,\n 36.932330061503144\n ],\n [\n -95.47119140625,\n 39.80853604144591\n ],\n [\n -97.1630859375,\n 39.80853604144591\n ],\n [\n -97.18505859374999,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"8","noUsgsAuthors":false,"publicationDate":"2019-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"McCullough, K.","contributorId":273122,"corporation":false,"usgs":false,"family":"McCullough","given":"K.","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albanese, G.","contributorId":67722,"corporation":false,"usgs":true,"family":"Albanese","given":"G.","email":"","affiliations":[],"preferred":false,"id":832759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricketts, A.M.","contributorId":273124,"corporation":false,"usgs":false,"family":"Ricketts","given":"A.M.","email":"","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stratton, S.","contributorId":273125,"corporation":false,"usgs":false,"family":"Stratton","given":"S.","email":"","affiliations":[{"id":56426,"text":"fort riley","active":true,"usgs":false}],"preferred":false,"id":832761,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206000,"text":"70206000 - 2019 - Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","interactions":[],"lastModifiedDate":"2019-10-17T07:00:50","indexId":"70206000","displayToPublicDate":"2019-08-13T14:44:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","docAbstract":"The 9 month long 2011-2012 eruption of Cordon Caulle (Southern Andes, Chile) is the best instrumentally recorded rhyolitic eruption to date and the first time that the effusion of a rhyolitic flow has been observed in detail. We use Interferometric Synthetic Aperture Radar (InSAR), with time-lapse DEMs and numerical models to study the dynamics of coupled magma reservoir deflation and lava effusion. InSAR recorded ~2.2-2.5 m of deflation after the first three days of the eruption, which can be modeled using a spheroidal magma reservoir at a depth of ∼5 km, ∼20 km long, and with a pressure drop of ~20-30 MPa. The source is elongated in the NW-SE direction and its large dimensions imply a large plumbing system spanning neighboring volcanoes and active throughout the eruption, with a slight change halfway through the effusive phase. TanDEM-X DEMs record the extrusion of both the rhyolitic lava flow and the intrusion of a shallow laccolith around the eruptive vent, with a total volume of ~1.2 km3 DRE. The laccolith was emplaced during the first month of the eruption, during both the eruption explosive and effusive stages. Both the reservoir pressure drop and the extruded volume time series follow quasi-exponential trends, and can be explained by a model that couples the reservoir pres- sure decrease, time- and pressured ependent variations in the magma properties inside of the reservoir, and conduit flow. This model predicts both the temporal evolution and amplitude of both time series, and a magma compressibility of ∼10^−10 Pa−1, half the compressibility of the magma of the sub-Plinian explosive phase. Further, we estimate that the reservoir contained 1-3 wt.% dissolved H2O at the onset of lava effusion, with no exsolved CO2 and H2O in the reservoir throughout the eruption. This is in accord with a magma that was significantly degassed after the explosive phase. These remaining volatiles might have been responsible for magma fragmentation, consistent with the hybrid explosive and effusive style observed during the waning of the eruption.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.115736","usgsCitation":"Delgado, F., Julia Kubanek, Anderson, K.R., Paul Lundgren, and Pritchard, M.E., 2019, Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption: Earth and Planetary Science Letters, v. 524, p. 1-14, https://doi.org/10.1016/j.epsl.2019.115736.","productDescription":"115736, 14p.","startPage":"1","endPage":"14","ipdsId":"IP-102193","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2019.115736","text":"Publisher Index Page"},{"id":368337,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0012821X19304285"},{"id":368348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Cordón Caulle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.59765625,\n -42.779275360241904\n ],\n [\n -71.69677734375,\n -42.779275360241904\n ],\n [\n -71.69677734375,\n -41.4262531950727\n ],\n [\n -72.59765625,\n -41.4262531950727\n ],\n [\n -72.59765625,\n -42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"524","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Delgado, Francisco","contributorId":219817,"corporation":false,"usgs":false,"family":"Delgado","given":"Francisco","email":"","affiliations":[{"id":40075,"text":"Institute de Physique du Globe de Paris","active":true,"usgs":false}],"preferred":false,"id":773252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Julia Kubanek","contributorId":219818,"corporation":false,"usgs":false,"family":"Julia Kubanek","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":773253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":773251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul Lundgren","contributorId":219819,"corporation":false,"usgs":false,"family":"Paul Lundgren","affiliations":[{"id":32904,"text":"Jet Propulsion Lab, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":773254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pritchard, Matthew E.","contributorId":219820,"corporation":false,"usgs":false,"family":"Pritchard","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":773255,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208120,"text":"70208120 - 2019 - Global positioning system tracking devices can decrease Greater Sage-Grouse survival","interactions":[],"lastModifiedDate":"2020-01-29T16:27:32","indexId":"70208120","displayToPublicDate":"2019-08-13T13:06:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Global positioning system tracking devices can decrease Greater Sage-Grouse survival","docAbstract":"Reliable demographic estimates hinge on the assumption that marking animals does not alter their behavior, reproduction, or survival. Violations can bias inference and are especially egregious for species of high conservation concern. Global positioning system (GPS) devices represent a recent technological advancement that has contributed greatly to avian ecological studies compared with traditionally used very high frequency (VHF) radio transmitters, but may affect demographic rates differently than VHF transmitters. We compared survival between VHF (necklace attachment) and GPS (rump-mounted attachment) devices from >1,100 Greater Sage-Grouse (Centrocercus urophasianus), a species of high conservation concern, across multiple populations within California and Nevada. We found lower survival for GPS-marked compared to VHF-marked sage-grouse across most sex, age, and seasonal comparisons. Estimates of annual survival for GPS-marked sage-grouse were 0.55–0.86 times that of VHF-marked birds with considerable variation among sex and age classes. Differences in survival could be attributed to features associated with GPS devices, including greater weight, position of attachment (e.g., rump-mount harness), and a semi-reflective solar panel. In a post hoc analysis, we evaluated additive and interactive effects between device type (GPS vs. VHF) and transmitter mass as a proportion of body mass (PBM). While the device type effect alone was the best model, the PBM interaction also had support. For GPS devices, survival decreased with increasing PBM, whereas PBM effects were not found for VHF. We attributed differences in PBM effect to placement of transmitters on sage-grouse, as weight of GPS devices was positioned rearward. This information can help managers and researchers weigh costs and benefits of GPS-based monitoring. Our results indicate demographic data collected from GPS devices should be interpreted with caution, and use of these devices should be tailored to specific ecological questions. Future research aimed at investigating behavioral impacts and GPS designs that reduce adverse impacts on survival would be beneficial.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/condor/duz032","usgsCitation":"Severson, J.P., Coates, P.S., Prochazka, B.G., Ricca, M.A., Casazza, M.L., and Delahunty, D.J., 2019, Global positioning system tracking devices can decrease Greater Sage-Grouse survival: The Condor, v. 121, no. 3, duz032, 15 p., https://doi.org/10.1093/condor/duz032.","productDescription":"duz032, 15 p.","ipdsId":"IP-106346","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":460307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz032","text":"Publisher Index Page"},{"id":371649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, 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Here, we provide an update on the state of the knowledge of OHC, and also include mercury, exposure and/or associated effects in key Arctic marine and terrestrial mammal and bird species as well as in fish by reviewing the literature published since the last AMAP assessment in 2010. We aimed at updating the knowledge of how single but also combined health effects are or can be associated to the exposure to single compounds or mixtures of OHCs. We also focussed on assessing both potential individual as well as population health impacts using population-specific exposure data post 2000. We have identified quantifiable effects on vitamin metabolism, immune functioning, thyroid and steroid hormone balances, oxidative stress, tissue pathology, and reproduction. As with the previous assessment, a wealth of documentation is available for biological effects in marine mammals and seabirds, and sentinel species such as the sledge dog and Arctic fox, but information for terrestrial vertebrates and fish remain scarce. While hormones and vitamins are thoroughly studied, oxidative stress, immunotoxic and reproductive effects need further investigation. Depending on the species and population, some OHCs and mercury tissue contaminant burdens post 2000 were observed to be high enough to exceed putative risk threshold levels that have been previously estimated for non-target species or populations outside the Arctic. In this assessment, we made use of risk quotient calculations to summarize the cumulative effects of different OHC classes and mercury for which critical body burdens can be estimated for wildlife across the Arctic. As our ultimate goal is to better predict or estimate the effects of OHCs and mercury in Arctic wildlife at the individual, population and ecosystem level, there remain numerous knowledge gaps on the biological effects of exposure in Arctic biota. These knowledge gaps include the establishment of concentration thresholds for individual compounds as well as for realistic cocktail mixtures that in fact indicate biologically relevant, and not statistically determined, health effects for specific species and subpopulations. Finally, we provide future perspectives on understanding Arctic wildlife health using new in vivo, in vitro, and in silico techniques, and provide case studies on multiple stressors to show that future assessments would benefit from significant efforts to integrate human health, wildlife ecology and retrospective and forecasting aspects into assessing the biological effects of OHC and mercury exposure in Arctic wildlife and fish.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133792","usgsCitation":"Dietz, R., Letcher, R.J., Desforges, J., Eulaers, I., Sonne, C., Wilson, S., Andersen-Ranberg, E., Basu, N., Barst, B.D., Bustnes, J.O., Bytingsvik, J., Ciesielski, T.M., Drevnick, P.E., Gabrielsen, G.W., Haarr, A., Hylland, K., Jenssen, B.M., Levin, M., McKinney, M.A., Norregaard, R.D., Pedersen, K.E., Provencher, J., Styrishave, B., Tartu, S., Aars, J., Ackerman, J., Rosing-Asvid, A., Barrett, R., Bignert, A., Born, E.W., Branigan, M., Braune, B., Bryan, C.E., Dam, M., Eagles-Smith, C.A., Evans, M.S., Evans, T., Fisk, A.T., Gamberg, M., Gustavson, K., Hartman, C.A., Helander, B., Herzog, M.P., Hoekstra, P.F., Houde, M., Hoydal, K., Jackson, A., Kucklick, J., Lie, E., Loseto, L., Mallory, M.L., Miljeteig, C., Mosbech, A., Muir, D.C., Nielsen, S.T., Peacock, E., Pedro, S., Peterson, S.H., Polder, A., Riget, F.F., Roach, P., Saunes, H., Sinding, M.S., Skaare, J.U., Sondergaard, J., Stenson, G., Stern, G., Treu, G., Schuur, S.S., and Vikingsson, G., 2019, Current state of knowledge on biological effects from contaminants on arctic wildlife and fish: Science of the Total Environment, v. 696, 133792, 40 p., https://doi.org/10.1016/j.scitotenv.2019.133792.","productDescription":"133792, 40 p.","ipdsId":"IP-107367","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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To this end, we evaluated the efficacy of adding environmental (e)DNA surveillance to the U.S. Geological Survey (USGS) streamgage network, which consists of > 8,200 streamgages nationwide systemically visited by USGS hydrologic technicians. Incorporating strategic eDNA sample collection during routine streamgage visits could provide early detection surveillance of aquatic invasive species with minimal additional cost. For this evaluation, USGS hydrologic technicians collected monthly eDNA water samples, May – September 2018, from streamgages downstream of reservoirs in the Columbia River Basin thought to be vulnerable to invasive dreissenid mussel (Dreissenidae spp.) establishment. We tested water samples for dreissenid mussel DNA and also for kokanee (Oncorhynchus nerka) and yellow perch (Perca flavescens) DNA; the two fishes were used to assess if streamgages are adequately located to provide early-detection eDNA surveillance of taxa known to be present in upstream reservoirs. No Columbia River Basin streamgage samples met our criteria for being scored as positive for dreissenid DNA. We did detect kokanee and yellow perch DNA at all streamgages downstream of reservoirs where these species are known to occur. Field collection, laboratory analyses, and personnel time required for collection of four eDNA samples at a streamgage site cost US$500 -US$600 (net). Given these results, incorporating eDNA biosurveillance into routine streamgage visits might decrease costs associated with an invasion since early detection maximizes the potential for eradication, containment, and mitigation.
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