Coastal forested hummocks support clusters of trees in the saltwater–freshwater transition zone. To examine how hummocks support trees in mesohaline sites that are beyond physiological limits of the trees, we used salinity and stable isotopes (2H and 18O) of water as tracers to understand water fluxes in hummocks and uptake by baldcypress (Taxodium distichum (L.) Rich.), which is the most abundant tree species in coastal freshwater forests of the southeastern U.S. Hummocks were always partially submerged and were completely submerged 1 to 8% of the time during the two studied growing seasons, in association with high water in the estuary. Salinity, δ18O, and δ2H varied more in the shallow open water than in groundwater. Surface water and shallow groundwater were similar to throughfall in isotopic composition, which suggested dominance by rainfall. Salinity of groundwater in hummocks increased with depth, was higher than in swales, and fluctuated little over time. Isotopic composition of xylem water in baldcypress was similar to the vadose zone and unlike other measured sources, indicating that trees preferentially use unsaturated hummock tops as refugia from higher salinity and saturated soil in swales and the lower portions of hummocks. Sustained upward gradients of salinity from groundwater to surface water and vadose water, and low variation in groundwater salinity and isotopic composition, suggested long residence time, limited exchange with surface water, and that the shallow subsurface of hummocks is characterized by episodic salinization and slow dilution.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1738","usgsCitation":"Hsueh, Y., Chambers, J., Krauss, K.W., Allen, S.T., and Keim, R., 2016, Hydrologic exchanges and baldcypress water use on deltaic hummocks, Louisiana, USA: Ecohydrology, v. 9, no. 8, p. 1452-1463, https://doi.org/10.1002/eco.1738.","productDescription":"12 p.","startPage":"1452","endPage":"1463","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067366","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":320812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.14,\n 29.75\n ],\n [\n -90.14,\n 29.76\n ],\n [\n -90.15,\n 29.76\n ],\n [\n -90.15,\n 29.75\n ],\n [\n -90.14,\n 29.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-28","publicationStatus":"PW","scienceBaseUri":"57286c1be4b0b13d3917ce0e","contributors":{"authors":[{"text":"Hsueh, Yu-Hsin","contributorId":169051,"corporation":false,"usgs":false,"family":"Hsueh","given":"Yu-Hsin","email":"","affiliations":[],"preferred":false,"id":628310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chambers, Jim L.","contributorId":16498,"corporation":false,"usgs":true,"family":"Chambers","given":"Jim L.","affiliations":[],"preferred":false,"id":628311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken W. 0000-0003-2195-0729 kraussk@usgs.gov","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":2017,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"kraussk@usgs.gov","middleInitial":"W.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":628298,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allen, Scott T.","contributorId":168409,"corporation":false,"usgs":false,"family":"Allen","given":"Scott","email":"","middleInitial":"T.","affiliations":[{"id":25282,"text":"School of Renewable Natural Resources, Louisiana State University, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":628312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keim, Richard F.","contributorId":21858,"corporation":false,"usgs":true,"family":"Keim","given":"Richard F.","affiliations":[],"preferred":false,"id":628313,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170760,"text":"70170760 - 2016 - Female gonadal hormones and reproductive behaviors as key determinants of successful reproductive output of breeding whooping cranes (Grus americana)","interactions":[],"lastModifiedDate":"2016-05-02T10:19:20","indexId":"70170760","displayToPublicDate":"2016-05-02T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1738,"text":"General and Comparative Endocrinology","active":true,"publicationSubtype":{"id":10}},"title":"Female gonadal hormones and reproductive behaviors as key determinants of successful reproductive output of breeding whooping cranes (Grus americana)","docAbstract":"Reproductive success of endangered whooping cranes (Grus americana) maintained ex situ is poor. As part of an effort to identify potential causes of poor reproductive success in a captive colony, we used non-invasive endocrine monitoring to assess gonadal and adrenal steroids of bird pairs with various reproductive outcomes and evaluated the relationships of hormones and behaviors to reproductive performance. Overall, reproductively successful (i.e., egg laying) females had significantly higher mean estrogen levels but lower mean progestogen concentrations than did unsuccessful females. Other hormones, including glucocorticoids and androgens, were not significantly different between successful and unsuccessful individuals. Observations of specific behaviors such as unison calling, marching, and the number of copulation attempts, along with overall time spent performing reproductive behaviors, were significantly higher in successful pairs. Our findings indicate that overall reproductive performance of whooping crane pairs is linked to female gonadal hormone excretion and reproductive behaviors, but not to altered adrenal hormone production.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ygcen.2016.04.009","usgsCitation":"Brown, M.E., Converse, S.J., Chandler, J.N., Shafer, C., Brown, J.L., Keefer, C., and Songsasen, N., 2016, Female gonadal hormones and reproductive behaviors as key determinants of successful reproductive output of breeding whooping cranes (Grus americana): General and Comparative Endocrinology, v. 230-231, p. 158-165, https://doi.org/10.1016/j.ygcen.2016.04.009.","productDescription":"8 p.","startPage":"158","endPage":"165","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074740","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471032,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ygcen.2016.04.009","text":"Publisher Index Page"},{"id":320811,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"230-231","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57286c19e4b0b13d3917ce08","contributors":{"authors":[{"text":"Brown, Megan E.","contributorId":169048,"corporation":false,"usgs":false,"family":"Brown","given":"Megan","email":"","middleInitial":"E.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":628300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":3513,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":628299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandler, Jane N. 0000-0002-6131-2396 jchandler@usgs.gov","orcid":"https://orcid.org/0000-0002-6131-2396","contributorId":3512,"corporation":false,"usgs":true,"family":"Chandler","given":"Jane","email":"jchandler@usgs.gov","middleInitial":"N.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628301,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafer, Charles cshafer@usgs.gov","contributorId":3510,"corporation":false,"usgs":true,"family":"Shafer","given":"Charles","email":"cshafer@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":628302,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, Janine L","contributorId":169049,"corporation":false,"usgs":false,"family":"Brown","given":"Janine","email":"","middleInitial":"L","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":628303,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keefer, Carol L","contributorId":146370,"corporation":false,"usgs":false,"family":"Keefer","given":"Carol L","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":628304,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Songsasen, Nucharin","contributorId":146371,"corporation":false,"usgs":false,"family":"Songsasen","given":"Nucharin","email":"","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":628305,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70162383,"text":"sir20165008 - 2016 - Geology of tight oil and potential tight oil reservoirs in the lower part of the Green River Formation, Uinta, Piceance, and Greater Green River Basins, Utah, Colorado, and Wyoming","interactions":[],"lastModifiedDate":"2016-05-02T10:42:55","indexId":"sir20165008","displayToPublicDate":"2016-05-02T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5008","title":"Geology of tight oil and potential tight oil reservoirs in the lower part of the Green River Formation, Uinta, Piceance, and Greater Green River Basins, Utah, Colorado, and Wyoming","docAbstract":"The recent successful development of a tight oil play in the Eocene-age informal Uteland Butte member of the lacustrine Green River Formation in the Uinta Basin, Utah, using modern horizontal drilling and hydraulic fracturing techniques has spurred a renewed interest in the tight oil potential of lacustrine rocks. The Green River Formation was deposited by two large lakes, Lake Uinta in the Uinta and Piceance Basins and Lake Gosiute in the Greater Green River Basin. These three basins contain the world’s largest in-place oil shale resources with recent estimates of 1.53 trillion, 1.33 trillion, and 1.44 trillion barrels of oil in place in the Piceance, Uinta, and Greater Green River Basins, respectively. The Uteland Butte member was deposited during an early freshwater stage of the lake in the Uinta Basin prior to deposition of the assessed oil shale intervals. This report only presents information on the early freshwater interval and overlying brackish-water interval in all three basins because these intervals are most likely to have tight oil potential. Burial histories of the three basins were reconstructed to study (1) variations in subsidence and lake development, and (2) post deposition burial that led to the development of a petroleum system in only the Uinta Basin. The Uteland Butte member is a successful tight oil play because it is thermally mature for hydrocarbon generation and contains organic-rich shale, brittle carbonate, and porous dolomite. Abnormally high pressure in parts of the Uteland Butte is also important to production. Variations in organic richness of the Uteland Butte were studied using Fischer assay analysis from oil shale assessments, and pressures were studied using drill-stem tests. Freshwater lacustrine intervals in the Piceance and Greater Green River Basins are immature for hydrocarbon generation and contain much less carbonate than the Uteland Butte member. The brackish-water interval in the Uinta Basin is thermally mature for hydrocarbon generation but is clay-rich and contains little carbonate, and thus is a poor prospect for tight oil development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165008","usgsCitation":"Johnson, R.C., Birdwell, J.E., Mercier, T.J., and Brownfield, M.E., 2016, Geology of tight oil and potential tight oil reservoirs in the lower part of the Green River Formation, Uinta, Piceance, and Greater Green River Basins, Utah, Colorado, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2016–5008, 63 p., https://dx.doi.org/10.3133/sir20165008.","productDescription":"Report: vii, 63 p.; Table 1; Figure 29","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059890","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":320649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5008/coverthb.jpg"},{"id":320650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5008/sir20165008.pdf","text":"Report","size":"53.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5008"},{"id":320651,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5008/sir20165008_fig29.pdf","text":"Figure 29","size":"1.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5008 Figure 29"},{"id":320672,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5008/sir20165008_Table1UtelandFischerassay.xlsx","text":"Table 1","size":"68.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5008 Table 1"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Green River Basin, Piceance River Basin, Uinta River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.566162109375,\n 38.62545397209084\n ],\n [\n -111.566162109375,\n 43.30119623257966\n ],\n [\n -106.336669921875,\n 43.30119623257966\n ],\n [\n -106.336669921875,\n 38.62545397209084\n ],\n [\n -111.566162109375,\n 38.62545397209084\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
Taiyangshan is a poorly studied copper–molybdenum deposit located in the Triassic Western Qinling collisional belt of northwest China. The intrusions exposed in the vicinity of the Taiyangshan deposit record episodic magmatism over 20–30 million years. Pre-mineralization quartz diorite porphyries, which host some of the deposit, were emplaced at 226.6 ± 6.2 Ma. Syn-collisional monzonite and quartz monzonite porphyries, which also host mineralization, were emplaced at 218.0 ± 6.1 Ma and 215.0 ± 5.8 Ma, respectively. Mineralization occurred during the transition from a syn-collisional to a post-collisional setting at ca. 208 Ma. A barren post-mineralization granite porphyry marked the end of post-collisional magmatism at 200.7 ± 5.1 Ma. The ore-bearing monzonite and quartz monzonite porphyries have a εHf(t) range from − 2.0 to + 12.5, which is much more variable than that of the slightly older quartz diorite porphyries, with TDM2 of 1.15–1.23 Ga corresponding to the positive εHf(t) values and TDM1 of 0.62–0.90 Ga corresponding to the negative εHf(t) values. Molybdenite in the Taiyangshan deposit with 27.70 to 38.43 ppm Re suggests metal sourced from a mantle–crust mixture or from mafic and ultramafic rocks in the lower crust. The δ34S values obtained for pyrite, chalcopyrite, and molybdenite from the deposit range from + 1.3‰ to + 4.0‰, + 0.2‰ to + 1.1‰, and + 5.3‰ to + 5.9‰, respectively, suggesting a magmatic source for the sulfur. Calculated δ18Ofluid values for magmatic K-feldspar from porphyries (+ 13.3‰), hydrothermal K-feldspar from stockwork veins related to potassic alteration (+ 11.6‰), and hydrothermal sericite from quartz–pyrite veins (+ 8.6 to + 10.6‰) indicate the Taiyangshan deposit formed dominantly from magmatic water. Hydrogen isotope values for hydrothermal sericite ranging from − 85 to − 50‰ may indicate that magma degassing progressively depleted residual liquid in deuterium during the life of the magmatic–hydrothermal system. Alternatively, δD variability may have been caused by a minor amount of mixing with meteoric waters. We propose that the ore-related magma was derived from partial melting of the ancient Mesoproterozoic to Neoproterozoic middle to lower continental crust. This crust was likely metasomatized during earlier subduction, and the crustal magmas may have been contaminated with lithospheric mantle derived magma triggered by MASH (e.g., melting, assimilation, storage, and homogenization) processes during collisional orogeny. In addition, a significant proportion of the metals and sulfur supplied from mafic magma were simultaneously incorporated into the resultant hybrid magmas.
","language":"English","publisher":"International Association for Gondwana Research","doi":"10.1016/j.gr.2016.03.014","usgsCitation":"Kun-Feng Qiu, Taylor, R.D., Song, Y., Yu, H., Kai-Rui Song, and Li, N., 2016, Geologic and geochemical insights into the formation of the Taiyangshan porphyry copper–molybdenum deposit, Western Qinling Orogenic Belt, China: Gondwana Research, v. 35, p. 40-58, https://doi.org/10.1016/j.gr.2016.03.014.","productDescription":"19 p.","startPage":"40","endPage":"58","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072464","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":322044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 102,\n 32\n ],\n [\n 102,\n 36\n ],\n [\n 107,\n 36\n ],\n [\n 107,\n 32\n ],\n [\n 102,\n 32\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500763e4b0ee97d51bb609","contributors":{"authors":[{"text":"Kun-Feng Qiu","contributorId":169784,"corporation":false,"usgs":false,"family":"Kun-Feng Qiu","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":631055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Ryan D. 0000-0002-8845-5290 rtaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":3412,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"rtaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":631054,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Song, Yao-Hui","contributorId":169785,"corporation":false,"usgs":false,"family":"Song","given":"Yao-Hui","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":631056,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yu, Hao-Cheng","contributorId":169788,"corporation":false,"usgs":false,"family":"Yu","given":"Hao-Cheng","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":631059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kai-Rui Song","contributorId":169786,"corporation":false,"usgs":false,"family":"Kai-Rui Song","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":631057,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Nan","contributorId":169787,"corporation":false,"usgs":false,"family":"Li","given":"Nan","email":"","affiliations":[{"id":24737,"text":"China University of Geosciences, Beijing","active":true,"usgs":false}],"preferred":false,"id":631058,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70174339,"text":"70174339 - 2016 - A millennial-scale record of Pb and Hg contamination in peatlands of the Sacramento-San Joaquin Delta of California, USA","interactions":[],"lastModifiedDate":"2016-07-08T13:22:05","indexId":"70174339","displayToPublicDate":"2016-05-01T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A millennial-scale record of Pb and Hg contamination in peatlands of the Sacramento-San Joaquin Delta of California, USA","docAbstract":"In this paper, we provide the first record of millennial patterns of Pb and Hg concentrations on the west coast of the United States. Peat cores were collected from two micro-tidal marshes in the Sacramento-San Joaquin Delta of California. Core samples were analyzed for Pb, Hg, and Ti concentrations and dated using radiocarbon, 210Pb, and 137Cs. Pre-anthropogenic concentrations of Pb and Hg in peat ranged from 0.60 to 13.0 µg g-1and from 6.9 to 71 ng g-1, respectively. For much of the past 6000+ years, the Delta was free from anthropogenic pollution, however, beginning in ~1425 CE, Hg and Pb concentrations, Pb/Ti ratios, Pb enrichment factors (EFs), and HgEFs all increased. Pb isotope compositions of the peat suggest that this uptick was likely caused by smelting activities originating in Asia. The next increases in Pb and Hg contamination occurred during the California Gold Rush (beginning ~1850 CE), when concentrations reached their highest levels (74 µg g-1 Pb, 990 ng g-1 Hg; PbEF = 12 and HgEF = 28). Lead concentrations increased again beginning in the ~1920s with the incorporation of Pb additives in gasoline. The phase-out of lead additives in the late 1980s was reflected in Pb isotope ratios and reductions in Pb concentrations in the surface layers of the peat. The rise and fall of Hg contamination was also tracked by the peat archive, with the highest Hg concentrations occurring just before 1963 CE and then decreasing during the post-1963 period. Overall, the results show that the Delta was a pristine region for most of its ~6700-year existence; however, since ~1425 CE, it has received Pb and Hg contamination from both global and regional sources.
","language":"English","publisher":"Elsevier B.V.","doi":"10.1016/j.scitotenv.2016.01.201","collaboration":"(REPEAT II project)","usgsCitation":"Drexler, J.Z., Alpers, C.N., Neymark, L., Paces, J.B., Taylor, H.E., and Fuller, C.C., 2016, A millennial-scale record of Pb and Hg contamination in peatlands of the Sacramento-San Joaquin Delta of California, USA: Science of the Total Environment, v. 551-552, p. 738-751, https://doi.org/10.1016/j.scitotenv.2016.01.201.","productDescription":"13 p.","startPage":"738","endPage":"751","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071457","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":324937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta of California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.25537109375,\n 41.94314874732696\n ],\n [\n -124.3212890625,\n 41.73852846935917\n ],\n [\n -124.20043945312499,\n 41.65649719441145\n ],\n [\n -124.178466796875,\n 41.36856413680967\n ],\n [\n -124.31030273437499,\n 41.16211393939692\n ],\n [\n -124.222412109375,\n 40.95501133048621\n ],\n [\n -124.53002929687499,\n 40.463666324587685\n ],\n [\n -124.45312499999999,\n 40.245991504199026\n ],\n [\n -123.90380859374999,\n 39.69873414348139\n ],\n [\n -123.914794921875,\n 39.317300373271024\n ],\n [\n -123.85986328124999,\n 38.89103282648846\n ],\n [\n -123.6181640625,\n 38.7283759182398\n ],\n [\n -123.11279296875001,\n 38.08268954483802\n ],\n [\n -123.07983398437499,\n 37.94419750075404\n ],\n [\n -122.84912109375,\n 37.90953361677018\n ],\n [\n -122.78320312499999,\n 37.57070524233116\n ],\n [\n -122.56347656249999,\n 37.59682400108367\n ],\n [\n -121.10229492187501,\n 37.75334401310656\n ],\n [\n -121.322021484375,\n 38.59970036588819\n ],\n [\n -121.79443359375,\n 39.78321267821705\n ],\n [\n -121.728515625,\n 40.830436877649255\n ],\n [\n -121.89331054687499,\n 41.623655390686395\n ],\n [\n -121.92626953124999,\n 41.97582726102573\n ],\n [\n -124.25537109375,\n 41.94314874732696\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"551-552","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5780ceaee4b0811616822296","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - 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Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. 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Understanding the spatial variability of nutrient concentrations in flowing waters and the apparent contributions that human and nonhuman factors have on different sizes of streams and rivers is critical to the development of effective water quality assessment and management plans. This semi-quantitative analysis is also intended to identify areas within which more detailed quantitative work can be conducted to determine specific regional factors associated with variations in stream N concentrations.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.71.3.167","usgsCitation":"Omernik, J.M., Paulsen, S.G., Griffith, G.E., and Weber, M.H., 2016, Regional patterns of total nitrogen concentrations in the National Rivers and Streams Assessment: Journal of Soil and Water Conservation, v. 71, no. 3, p. 167-181, https://doi.org/10.2489/jswc.71.3.167.","productDescription":"15 p.","startPage":"167","endPage":"181","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057215","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":322053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-09","publicationStatus":"PW","scienceBaseUri":"57500771e4b0ee97d51bb70e","contributors":{"authors":[{"text":"Omernik, James M.","contributorId":169740,"corporation":false,"usgs":false,"family":"Omernik","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":25578,"text":"USGS -Volunteer","active":true,"usgs":false}],"preferred":false,"id":630982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paulsen, Steven G.","contributorId":169741,"corporation":false,"usgs":false,"family":"Paulsen","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":630983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Griffith, Glenn E. 0000-0001-7966-4720 ggriffith@usgs.gov","orcid":"https://orcid.org/0000-0001-7966-4720","contributorId":4053,"corporation":false,"usgs":true,"family":"Griffith","given":"Glenn","email":"ggriffith@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":630981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weber, Marc H.","contributorId":169742,"corporation":false,"usgs":false,"family":"Weber","given":"Marc","email":"","middleInitial":"H.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":630984,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171382,"text":"70171382 - 2016 - Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes","interactions":[],"lastModifiedDate":"2022-11-03T16:08:51.655969","indexId":"70171382","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes","docAbstract":"The National Marine Fisheries Service (NMFS), the U.S. Geological Survey (USGS) and the U.S. Fish and Wildlife Service (USFWS) have collaborated to develop passage design guidance for use by engineers and other restoration practitioners considering and designing nature‐like fishways (NLFs). The primary purpose of these guidelines is to provide a summary of existing fish swimming and leaping performance data and the best available scientific information on safe, timely and effective passage for 14 diadromous fish species using Atlantic Coast rivers and streams. These guidelines apply to passage sites where complete barrier removal is not possible. This technical memorandum presents seven key physical design parameters based on the biometrics and swimming mode and performance of each target fishes for application in the design of NLFs addressing passage of a species or an assemblage of these species. The passage parameters include six dimensional guidelines recommended for minimum weir opening width and depth, minimum pool length, width and depth, and maximum channel slope, along with a maximum flow velocity guideline for each species. While\r\nthese guidelines are targeted for the design of step‐pool NLFs, the information may also have application in the design of other NLF types being considered at passage restoration sites and grade control necessary for infrastructure protection upstream of some dam removals, and in considering passage performance at sites such as natural bedrock features.","language":"English","publisher":"NOAA National Marine Fisheries Service","usgsCitation":"Turek, J., Haro, A.J., and Towler, B., 2016, Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes, iii., 48 p.","productDescription":"iii., 48 p.","ipdsId":"IP-064934","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":336275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321862,"type":{"id":15,"text":"Index Page"},"url":"https://repository.library.noaa.gov/view/noaa/28919"}],"country":"United States","otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.53543042272173,\n 25.604386278077925\n ],\n [\n -78.9738695738202,\n 26.2643242769976\n ],\n [\n -77.65563187117027,\n 27.236312736346505\n ],\n [\n -77.71124176588978,\n 30.200566890880694\n ],\n [\n -75.63764563171657,\n 31.546671317157745\n ],\n [\n -72.88778473754125,\n 34.107153920647505\n ],\n [\n -71.80667946744646,\n 38.30485849898287\n ],\n [\n -68.12544832929673,\n 40.622781401735494\n ],\n [\n -66.51825275575436,\n 43.557615229438966\n ],\n [\n -66.93056853663478,\n 44.9924864367367\n ],\n [\n -70.50837479109403,\n 44.130583189469974\n ],\n [\n -71.56583190151437,\n 42.4531846455987\n ],\n [\n -71.04268393869013,\n 41.91302433499763\n ],\n [\n -73.12852497871336,\n 41.628116403040906\n ],\n [\n -74.47837257632011,\n 40.75996139502365\n ],\n [\n -74.85717819557229,\n 39.49615977300374\n ],\n [\n -75.36275241829512,\n 40.14818872121907\n ],\n [\n -76.70262042023853,\n 39.66328529391899\n ],\n [\n -77.26910128913559,\n 38.52431672198591\n ],\n [\n -76.80124506434308,\n 36.52548541174258\n ],\n [\n -77.48886662326828,\n 34.86340904706914\n ],\n [\n -78.19352283187868,\n 34.321846961811204\n ],\n [\n -79.02627770062944,\n 34.25764313694468\n ],\n [\n -79.67952029102847,\n 33.43439936645508\n ],\n [\n -81.32614282245044,\n 32.564707556457805\n ],\n [\n -82.03773749487134,\n 30.379248385899615\n ],\n [\n -80.44494631692717,\n 26.744769560981013\n ],\n [\n -80.53543042272173,\n 25.604386278077925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b548c1e4b01ccd54fddfb8","contributors":{"authors":[{"text":"Turek, James","contributorId":169721,"corporation":false,"usgs":false,"family":"Turek","given":"James","email":"","affiliations":[{"id":25574,"text":"NOAA/NMFS Restoration Center","active":true,"usgs":false}],"preferred":false,"id":630824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haro, Alexander J. 0000-0002-7188-9172 aharo@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-9172","contributorId":2917,"corporation":false,"usgs":true,"family":"Haro","given":"Alexander","email":"aharo@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":630823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Towler, Brett","contributorId":141164,"corporation":false,"usgs":false,"family":"Towler","given":"Brett","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":630825,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70191143,"text":"70191143 - 2016 - Economic impacts of a California tsunami","interactions":[],"lastModifiedDate":"2017-09-27T16:56:47","indexId":"70191143","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2823,"text":"Natural Hazards Review","active":true,"publicationSubtype":{"id":10}},"title":"Economic impacts of a California tsunami","docAbstract":"The economic consequences of a tsunami scenario for Southern California are estimated using computable general equilibrium analysis. The economy is modeled as a set of interconnected supply chains interacting through markets but with explicit constraints stemming from property damage and business downtime. Economic impacts are measured by the reduction of Gross Domestic Product for Southern California, Rest of California, and U.S. economies. For California, total economic impacts represent the general equilibrium (essentially quantity and price multiplier) effects of lost production in industries upstream and downstream in the supply-chain of sectors that are directly impacted by port cargo disruptions at Port of Los Angeles and Port of Long Beach (POLA/POLB), property damage along the coast, and evacuation of potentially inundated areas. These impacts are estimated to be $2.2 billion from port disruptions, $0.9 billion from property damages, and $2.8 billion from evacuations. Various economic-resilience tactics can potentially reduce the direct and total impacts by 80–85%.
We present ground motion prediction equations (GMPEs) for computing natural log means and standard deviations of vertical-component intensity measures (IMs) for shallow crustal earthquakes in active tectonic regions. The equations were derived from a global database with M 3.0–7.9 events. The functions are similar to those for our horizontal GMPEs. We derive equations for the primary M- and distance-dependence of peak acceleration, peak velocity, and 5%-damped pseudo-spectral accelerations at oscillator periods between 0.01–10 s. We observe pronounced M-dependent geometric spreading and region-dependent anelastic attenuation for high-frequency IMs. We do not observe significant region-dependence in site amplification. Aleatory uncertainty is found to decrease with increasing magnitude; within-event variability is independent of distance. Compared to our horizontal-component GMPEs, attenuation rates are broadly comparable (somewhat slower geometric spreading, faster apparent anelastic attenuation), VS30-scaling is reduced, nonlinear site response is much weaker, within-event variability is comparable, and between-event variability is greater.
","language":"English","publisher":"Earthquake Engineering Research Institute","doi":"10.1193/072114EQS116M","usgsCitation":"Stewart, J.P., Boore, D.M., Seyhan, E., and Atkinson, G.M., 2016, NGA-West2 equations for predicting vertical-component PGA, PGV, and 5%-damped PSA from shallow crustal earthquakes: Earthquake Spectra, v. 32, no. 2, p. 1005-1031, https://doi.org/10.1193/072114EQS116M.","productDescription":"27 p.","startPage":"1005","endPage":"1031","ipdsId":"IP-061692","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":338265,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-01","publicationStatus":"PW","scienceBaseUri":"58d63038e4b05ec7991310eb","contributors":{"authors":[{"text":"Stewart, Jonathan P.","contributorId":100110,"corporation":false,"usgs":false,"family":"Stewart","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":685959,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boore, David M. boore@usgs.gov","contributorId":2509,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","middleInitial":"M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":false,"id":685958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seyhan, Emel","contributorId":51193,"corporation":false,"usgs":false,"family":"Seyhan","given":"Emel","email":"","affiliations":[{"id":7081,"text":"University of California - Los Angeles","active":true,"usgs":false}],"preferred":false,"id":685960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atkinson, Gail M.","contributorId":60515,"corporation":false,"usgs":false,"family":"Atkinson","given":"Gail","email":"","middleInitial":"M.","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":685961,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184218,"text":"70184218 - 2016 - Megafloods and Clovis cache at Wenatchee, Washington","interactions":[],"lastModifiedDate":"2017-03-06T11:23:52","indexId":"70184218","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Megafloods and Clovis cache at Wenatchee, Washington","docAbstract":"Immense late Wisconsin floods from glacial Lake Missoula drowned the Wenatchee reach of Washington's Columbia valley by different routes. The earliest debacles, nearly 19,000 cal yr BP, raged 335 m deep down the Columbia and built high Pangborn bar at Wenatchee. As advancing ice blocked the northwest of Columbia valley, several giant floods descended Moses Coulee and backflooded up the Columbia past Wenatchee. Ice then blocked Moses Coulee, and Grand Coulee to Quincy basin became the westmost floodway. From Quincy basin many Missoula floods backflowed 50 km upvalley to Wenatchee 18,000 to 15,500 years ago. Receding ice dammed glacial Lake Columbia centuries more—till it burst about 15,000 years ago. After Glacier Peak ashfall about 13,600 years ago, smaller great flood(s) swept down the Columbia from glacial Lake Kootenay in British Columbia. The East Wenatchee cache of huge fluted Clovis points had been laid atop Pangborn bar after the Glacier Peak ashfall, then buried by loess. Clovis people came five and a half millennia after the early gigantic Missoula floods, two and a half millennia after the last small Missoula flood, and two millennia after the glacial Lake Columbia flood. People likely saw outburst flood(s) from glacial Lake Kootenay.
","language":"English","publisher":"Cambridge University Press","doi":"10.1016/j.yqres.2016.02.007","usgsCitation":"Waitt, R.B., 2016, Megafloods and Clovis cache at Wenatchee, Washington: Quaternary Research, v. 85, no. 3, p. 430-444, https://doi.org/10.1016/j.yqres.2016.02.007.","productDescription":"15 p.","startPage":"430","endPage":"444","ipdsId":"IP-022694","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":336869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","volume":"85","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"58be8338e4b014cc3a3a99e1","contributors":{"authors":[{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":680593,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70187263,"text":"70187263 - 2016 - Do transmitters affect survival and body condition of American beavers Castor canadensis?","interactions":[],"lastModifiedDate":"2017-04-27T10:43:33","indexId":"70187263","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Do transmitters affect survival and body condition of American beavers Castor canadensis?","docAbstract":"One key assumption often inferred with using radio-equipped individuals is that the transmitter has no effect on the metric of interest. To evaluate this assumption, we used a known fate model to assess the effect of transmitter type (i.e. tail-mounted or peritoneal implant) on short-term (one year) survival and a joint live—dead recovery model and results from a mark—recapture study to compare long-term (eight years) survival and body condition of ear-tagged only American beavers Castor canadensis to those equipped with radio transmitters in Voyageurs National Park, Minnesota, USA. Short-term (1-year) survival was not influenced by transmitter type (wi = 0.64). Over the 8-year study period, annual survival was similar between transmitter-equipped beavers (tail-mounted and implant transmitters combined; 0.76; 95% CI = 0.45–0.91) versus ear-tagged only (0.78; 95% CI = 0.45–0.93). Additionally, we found no difference in weight gain (t9 = 0.25, p = 0.80) or tail area (t11 = 1.25, p = 0.24) from spring to summer between the two groups. In contrast, winter weight loss (t22 = - 2.03, p = 0.05) and tail area decrease (t30 = - 3.04, p = 0.01) was greater for transmitterequipped (weight = - 3.09 kg, SE = 0.55; tail area = - 33.71 cm2, SE = 4.80) than ear-tagged only (weight = - 1.80 kg, SE = 0.33; tail area = - 12.38 cm2, SE = 5.13) beavers. Our results generally support the continued use of transmitters on beavers for estimating demographic parameters, although we recommend additional assessments of transmitter effects under different environmental conditions.
","language":"English","publisher":"Nordic Board for Wildlife Research","doi":"10.2981/wlb.00160","usgsCitation":"Smith, J.B., Windels, S.K., Wolf, T., Klaver, R.W., and Belant, J.L., 2016, Do transmitters affect survival and body condition of American beavers Castor canadensis?: Wildlife Biology, v. 22, no. 3, p. 117-123, https://doi.org/10.2981/wlb.00160.","productDescription":"7 p.","startPage":"117","endPage":"123","ipdsId":"IP-069209","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":471036,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00160","text":"Publisher Index Page"},{"id":340493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030325e4b0e862d230f725","contributors":{"authors":[{"text":"Smith, Joshua B.","contributorId":71883,"corporation":false,"usgs":true,"family":"Smith","given":"Joshua","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":693143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windels, Steve K.","contributorId":182422,"corporation":false,"usgs":false,"family":"Windels","given":"Steve","email":"","middleInitial":"K.","affiliations":[{"id":18939,"text":"Voyageurs National Park","active":true,"usgs":false}],"preferred":false,"id":693144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolf, Tiffany","contributorId":191470,"corporation":false,"usgs":false,"family":"Wolf","given":"Tiffany","affiliations":[],"preferred":false,"id":693145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belant, Jerrold L.","contributorId":108394,"corporation":false,"usgs":false,"family":"Belant","given":"Jerrold","email":"","middleInitial":"L.","affiliations":[{"id":35599,"text":"Carnivore Ecology Laboratory, Mississippi State University, Mississippi State, MS","active":true,"usgs":false}],"preferred":false,"id":693146,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70186182,"text":"70186182 - 2016 - Demographic outcomes of diverse migration strategies assessed in a metapopulation of tundra swans","interactions":[],"lastModifiedDate":"2017-03-31T10:15:09","indexId":"70186182","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Demographic outcomes of diverse migration strategies assessed in a metapopulation of tundra swans","docAbstract":"Background\nMigration is a prominent aspect of the life history of many avian species, but the demographic consequences of variable migration strategies have only infrequently been investigated, and rarely when using modern technological and analytical methods for assessing survival, movement patterns, and long-term productivity in the context of life history theory. We monitored the fates of 50 satellite-implanted tundra swans (Cygnus columbianus) over 4 years from five disparate breeding areas in Alaska, and used known-fate analyses to estimate monthly survival probability relative to migration distance, breeding area, migratory flyway, breeding status, and age. We specifically tested whether migratory birds face a trade-off, whereby long-distance migrants realize higher survival rates at the cost of lower productivity because of reduced time on breeding areas relative to birds that migrate shorter distances and spend more time on breeding areas.\nResults\nAnnual migration distances varied significantly among breeding areas (1020 to 12720 km), and were strongly negatively correlated with time spent on breeding areas (r = −0.986). Estimates of annual survival probability varied by wintering area (Pacific coast, Alaska Peninsula, and Eastern seaboard) and ranged from 0.79 (95%CI: 0.70–0.88) to 1.0, depending on criteria used to discern mortalities from radio failures. We did not find evidence for a linear relationship between migration distance and survival as swans from the breeding areas with the shortest and longest migration distances had the highest survival probabilities. Survival was lower in the first year post-marking than in subsequent years, but there was not support for seasonal differences in survival. Productivity varied among breeding populations and was generally inversely correlated to survival, but not migration distance or time spent on breeding areas.\nConclusions\nTundra swans conformed to a major tenet of life history theory, as populations with the highest survival generally had the lowest productivity. The lack of a uniform relationship between time spent on breeding areas and productivity, or time spent on wintering areas and survival, indicates that factors other than temporal investment dictate demographic outcomes in this species. The tremendous diversity of migration strategies we identify in Alaskan tundra swans, without clear impacts on survival, underscores the ability of this species to adapt to different environments and climatic regimes.\nKeywords: Cygnus columbianus, Known fate, Life history, Metapopulation, Migration distance, Productivity, Satellite telemetry, Survival, Transmitter effects, Tundra swan","language":"English","publisher":"BioMed Central","doi":"10.1186/s40462-016-0075-8","usgsCitation":"Ely, C.R., and Meixell, B.W., 2016, Demographic outcomes of diverse migration strategies assessed in a metapopulation of tundra swans: Movement Ecology, v. 4, no. 10, HTML document , https://doi.org/10.1186/s40462-016-0075-8.","productDescription":"HTML document ","ipdsId":"IP-065806","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":471317,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-016-0075-8","text":"Publisher Index Page"},{"id":338915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":338873,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1186/s40462-016-0075-8"}],"volume":"4","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-01","publicationStatus":"PW","scienceBaseUri":"58df6ac1e4b02ff32c6aea39","chorus":{"doi":"10.1186/s40462-016-0075-8","url":"http://dx.doi.org/10.1186/s40462-016-0075-8","publisher":"Springer Nature","authors":"Ely Craig R., Meixell Brandt W.","journalName":"Movement Ecology","publicationDate":"5/1/2016"},"contributors":{"authors":[{"text":"Ely, Craig R. 0000-0003-4262-0892 cely@usgs.gov","orcid":"https://orcid.org/0000-0003-4262-0892","contributorId":3214,"corporation":false,"usgs":true,"family":"Ely","given":"Craig","email":"cely@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":687776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":687777,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70191257,"text":"70191257 - 2016 - Coesite in suevites from the Chesapeake Bay impact structure","interactions":[],"lastModifiedDate":"2017-10-02T13:57:51","indexId":"70191257","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2715,"text":"Meteoritics and Planetary Science","active":true,"publicationSubtype":{"id":10}},"title":"Coesite in suevites from the Chesapeake Bay impact structure","docAbstract":"The occurrence of coesite in suevites from the Chesapeake Bay impact structure is confirmed within a variety of textural domains in situ by Raman spectroscopy for the first time and in mechanically separated grains by X-ray diffraction. Microtextures of coesite identified in situ investigated under transmitted light and by scanning electron microscope reveal coesite as micrometer-sized grains (1–3 μm) within amorphous silica of impact-melt clasts and as submicrometer-sized grains and polycrystalline aggregates within shocked quartz grains. Coesite-bearing quartz grains are present both idiomorphically with original grain margins intact and as highly strained grains that underwent shock-produced plastic deformation. Coesite commonly occurs in plastically deformed quartz grains within domains that appear brown (toasted) in transmitted light and rarely within quartz of spheroidal texture. The coesite likely developed by a mechanism of solid-state transformation from precursor quartz. Raman spectroscopy also showed a series of unidentified peaks associated with shocked quartz grains that likely represent unidentified silica phases, possibly including a moganite-like phase that has not previously been associated with coesite.
","language":"English","publisher":"Wiley","doi":"10.1111/maps.12638","usgsCitation":"Jackson, J.C., Horton, J.W., Chou, I., and Belkin, H.E., 2016, Coesite in suevites from the Chesapeake Bay impact structure: Meteoritics and Planetary Science, v. 51, no. 5, p. 946-965, https://doi.org/10.1111/maps.12638.","productDescription":"20 p.","startPage":"946","endPage":"965","ipdsId":"IP-065888","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":346318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","volume":"51","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-24","publicationStatus":"PW","scienceBaseUri":"59d35028e4b05fe04cc34d62","contributors":{"authors":[{"text":"Jackson, John C. jjackson@usgs.gov","contributorId":2652,"corporation":false,"usgs":true,"family":"Jackson","given":"John","email":"jjackson@usgs.gov","middleInitial":"C.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, J. Wright Jr. 0000-0001-6756-6365 whorton@usgs.gov","orcid":"https://orcid.org/0000-0001-6756-6365","contributorId":173694,"corporation":false,"usgs":true,"family":"Horton","given":"J.","suffix":"Jr.","email":"whorton@usgs.gov","middleInitial":"Wright","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":711704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chou, I-Ming 0000-0001-5233-6479 imchou@usgs.gov","orcid":"https://orcid.org/0000-0001-5233-6479","contributorId":882,"corporation":false,"usgs":true,"family":"Chou","given":"I-Ming","email":"imchou@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belkin, Harvey E. 0000-0001-7879-6529 hbelkin@usgs.gov","orcid":"https://orcid.org/0000-0001-7879-6529","contributorId":581,"corporation":false,"usgs":true,"family":"Belkin","given":"Harvey","email":"hbelkin@usgs.gov","middleInitial":"E.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":711706,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192021,"text":"70192021 - 2016 - Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure","interactions":[],"lastModifiedDate":"2017-10-25T15:51:49","indexId":"70192021","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","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":"Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure","docAbstract":"Wind energy is a rapidly expanding industry with potential indirect effects to wildlife populations that are largely unexplored. In 2011 and 2012, we monitored 211 nests of 2 grassland songbirds, Horned Lark (Eremophila alpestris) and McCown's Longspur (Rhynchophanes mccownii), at 3 wind farms and 2 undeveloped reference sites in Wyoming, USA. We evaluated several indices of reproductive investment and success: clutch size, size-adjusted nestling mass, daily nest survival rate, and number of fledglings. We compared reproductive success between wind farms and undeveloped sites and modeled reproductive success within wind farms as a function of wind energy infrastructure and habitat. Size-adjusted nestling mass of Horned Lark was weakly negatively related to turbine density. In 2011, nest survival of Horned Lark decreased 55% as turbine density increased from 10 to 39 within 2 km of the nest. In 2012, however, nest survival of Horned Lark was best predicted by the combination of vegetation height, distance to shrub edge, and turbine density, with survival increasing weakly with increasing vegetation height. McCown's Longspur nest survival was weakly positively related to vegetation density at the nest site when considered with the amount of grassland habitat in the neighborhood and turbine density within 1 km of the nest. Habitat and distance to infrastructure did not explain clutch size or number of fledglings for either species, or size-adjusted nestling mass for McCown's Longspur. Our results suggest that the influence of wind energy infrastructure varies temporally and by species, even among species using similar habitats. Turbine density was repeatedly the most informative measure of wind energy development. Turbine density could influence wildlife responses to wind energy production and may become increasingly important to consider as development continues in areas with high-quality wind resources.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-15-25.1","usgsCitation":"Mahoney, A., and Chalfoun, A.D., 2016, Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure: The Condor, v. 118, no. 2, p. 360-375, https://doi.org/10.1650/CONDOR-15-25.1.","productDescription":"16 p.","startPage":"360","endPage":"375","ipdsId":"IP-064787","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-25.1","text":"Publisher Index Page"},{"id":347413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"118","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a7e4b0220bbd9d9f7c","contributors":{"authors":[{"text":"Mahoney, Anika","contributorId":198389,"corporation":false,"usgs":false,"family":"Mahoney","given":"Anika","email":"","affiliations":[],"preferred":false,"id":715909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. achalfoun@usgs.gov","contributorId":3735,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":713852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192507,"text":"70192507 - 2016 - Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States","interactions":[],"lastModifiedDate":"2017-10-26T10:27:36","indexId":"70192507","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3035,"text":"Pest Management Science","active":true,"publicationSubtype":{"id":10}},"title":"Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States","docAbstract":"BACKGROUND
Genetically modified (GM) varieties of soybean, corn and cotton have largely replaced conventional varieties in the United States. The most widely used applications of GM technology have been the development of crops that are resistant to a specific broad-spectrum herbicide (primarily glyphosate) or that produce insecticidal compounds within the plant itself. With the widespread adoption of GM crops, a decline in the use of conventional pesticides was expected.
RESULTS
There has been a reduction in the annual herbicide application rate to corn since the advent of GM crops, but the herbicide application rate is mostly unchanged for cotton. Herbicide use on soybean has increased. There has been a substantial reduction in the amount of insecticides used on both corn and cotton since the introduction of GM crops.
CONCLUSIONS
The observed changes in pesticide use are likely to be the result of many factors, including the introduction of GM crops, regulatory restrictions on some conventional pesticides, introduction of new pesticide technologies and changes in farming practices. In order to help protect human and environmental health and to help agriculture plan for the future, more detailed and complete documentation on pesticide use is needed on a frequent and ongoing basis.
","language":"English","publisher":"Wiley","doi":"10.1002/ps.4082","usgsCitation":"Coupe, R.H., and Capel, P.D., 2016, Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States: Pest Management Science, v. 72, no. 5, p. 1013-1022, https://doi.org/10.1002/ps.4082.","productDescription":"10 p.","startPage":"1013","endPage":"1022","ipdsId":"IP-066541","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":347436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"72","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-10","publicationStatus":"PW","scienceBaseUri":"5a07ea42e4b09af898c8cc70","contributors":{"authors":[{"text":"Coupe, Richard H. 0000-0001-8679-1015 rhcoupe@usgs.gov","orcid":"https://orcid.org/0000-0001-8679-1015","contributorId":551,"corporation":false,"usgs":true,"family":"Coupe","given":"Richard","email":"rhcoupe@usgs.gov","middleInitial":"H.","affiliations":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":716095,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159049,"text":"70159049 - 2016 - Estimating forest and woodland aboveground biomass using active and passive remote sensing","interactions":[],"lastModifiedDate":"2016-06-01T13:43:53","indexId":"70159049","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Estimating forest and woodland aboveground biomass using active and passive remote sensing","docAbstract":"Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.82.4.271","usgsCitation":"Wu, Z., Dye, D.G., Vogel, J.M., and Middleton, B.R., 2016, Estimating forest and woodland aboveground biomass using active and passive remote sensing: Photogrammetric Engineering and Remote Sensing, v. 82, no. 4, p. 271-281, https://doi.org/10.14358/PERS.82.4.271.","productDescription":"11 p.","startPage":"271","endPage":"281","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058621","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.82.4.271","text":"Publisher Index Page"},{"id":322022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500761e4b0ee97d51bb5ca","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":577541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dye, Dennis G. 0000-0002-7100-272X ddye@usgs.gov","orcid":"https://orcid.org/0000-0002-7100-272X","contributorId":4233,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","email":"ddye@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogel, John M. 0000-0002-8226-1188 jvogel@usgs.gov","orcid":"https://orcid.org/0000-0002-8226-1188","contributorId":3167,"corporation":false,"usgs":true,"family":"Vogel","given":"John","email":"jvogel@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577542,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577543,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70161743,"text":"70161743 - 2016 - Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","interactions":[],"lastModifiedDate":"2016-06-28T16:09:00","indexId":"70161743","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","docAbstract":"We present a revised ground‐motion prediction equation (GMPE) for computing medians and standard deviations of peak ground acceleration (PGA) and 5% damped pseudospectral acceleration (PSA) response ordinates of the horizontal component of randomly oriented ground motions to be used for seismic‐hazard analyses and engineering applications. This GMPE is derived from the expanded Next Generation Attenuation (NGA)‐West 1 database (see Data and Resources; Chiou et al., 2008). The revised model includes an anelastic attenuation term as a function of quality factor (Q0) to capture regional differences in far‐source (beyond 150 km) attenuation, and a new frequency‐dependent sedimentary‐basin scaling term as a function of depth to the 1.5 km/s shear‐wave velocity isosurface to improve ground‐motion predictions at sites located on deep sedimentary basins. The new Graizer–Kalkan 2015 (GK15) model, developed to be simple, is applicable for the western United States and other similar shallow crustal continental regions in active tectonic environments for earthquakes with moment magnitudes (M) 5.0–8.0, distances 0–250 km, average shear‐wave velocities in the upper 30 m (VS30) 200–1300 m/s, and spectral periods (T) 0.01–5 s. Our aleatory variability model captures interevent (between‐event) variability, which decreases with magnitude and increases with distance. The mixed‐effect residuals analysis reveals that the GK15 has no trend with respect to the independent predictor parameters. Compared to our 2007–2009 GMPE, the PGA values are very similar, whereas spectral ordinates predicted are larger at T<0.2 s and they are smaller at longer periods.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"El Cerito, CA","doi":"10.1785/0120150194","usgsCitation":"Graizer, V., and Kalkan, E., 2016, Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 687-707, https://doi.org/10.1785/0120150194.","productDescription":"21 p.","startPage":"687","endPage":"707","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065326","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":324563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Armenia, Georgia, Italy, Taiwan, Turkey, United States, Uzbekistan","state":"Alaska, California, Nevada","volume":"106","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-06","publicationStatus":"PW","scienceBaseUri":"57739fb7e4b07657d1a90d78","contributors":{"authors":[{"text":"Graizer, Vladimir;","contributorId":152040,"corporation":false,"usgs":false,"family":"Graizer","given":"Vladimir;","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":587625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":587624,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176210,"text":"70176210 - 2016 - Organic petrology and geochemistry of Eocene Suzak bituminous marl, north-central Afghanistan: Depositional environment and source rock potential","interactions":[],"lastModifiedDate":"2016-09-01T16:21:44","indexId":"70176210","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2682,"text":"Marine and Petroleum Geology","active":true,"publicationSubtype":{"id":10}},"title":"Organic petrology and geochemistry of Eocene Suzak bituminous marl, north-central Afghanistan: Depositional environment and source rock potential","docAbstract":"Organic geochemistry and petrology of Eocene Suzak bituminous marl outcrop samples from Madr village in north-central Afghanistan were characterized via an integrated analytical approach to evaluate depositional environment and source rock potential. Multiple proxies suggest the organic-rich (TOC ∼6 wt.%) bituminous marls are ‘immature’ for oil generation (e.g., vitrinite Ro < 0.4%, Tmax < 425 °C, PI ≤ 0.05, C29 ααα S/S + R ≤ 0.12, C29 ββS/ββS+ααR ≤ 0.10, others), yet oil seeps are present at outcrop and live oil and abundant solid bitumen were observed via optical microscopy. Whole rock sulfur content is ∼2.3 wt.% whereas sulfur content is ∼5.0–5.6 wt.% in whole rock extracts with high polar components, consistent with extraction from S-rich Type IIs organic matter which could generate hydrocarbons at low thermal maturity. Low Fe-sulfide mineral abundance and comparison of Pr/Ph ratios between saturate and whole extracts suggest limited Fe concentration resulted in sulfurization of organic matter during early diagenesis. From these observations, we infer that a Type IIs kerogen in ‘immature’ bituminous marl at Madr could be generating high sulfur viscous oil which is seeping from outcrop. However, oil-seep samples were not collected for correlation studies. Aluminum-normalized trace element concentrations indicate enrichment of redox sensitive trace elements Mo, U and V and suggest anoxic-euxinic conditions during sediment deposition. The bulk of organic matter observed via optical microscopy is strongly fluorescent amorphous bituminite grading to lamalginite, possibly representing microbial mat facies. Short chain n-alkanes peak at C14–C16 (n-C17/n-C29 > 1) indicating organic input from marine algae and/or bacterial biomass, and sterane/hopane ratios are low (0.12–0.14). Monoaromatic steroids are dominated by C28clearly indicating a marine setting. High gammacerane index values (∼0.9) are consistent with anoxia stratification and may indicate intermittent saline-hypersaline conditions. Stable C isotope ratios also suggest a marine depositional scenario for the Suzak samples, consistent with the presence of marine foraminifera including abundant planktic globigerinida(?) and rare benthic discocyclina(?) and nummulites(?). Biomarker 2α-methylhopane for photosynthetic cyanobacteria implies shallow photic zone deposition of Madr marls and 3β-methylhopane indicates presence of methanotrophic archaea in the microbial consortium. The data presented herein are consistent with deposition of Suzak bituminous marls in shallow stratified waters of a restricted marine basin associated with the southeastern incipient or proto-Paratethys. Geochemical proxies from Suzak rock extracts (S content, high polar content, C isotopes, normal (αααR) C27–29 steranes, and C29/C30 and C26/C25 hopane ratios) are similar to extant data from Paleogene oils produced to the north in the Afghan-Tajik Basin. This observation may indicate laterally equivalent strata are effective source rocks as suggested by previous workers; however, further work is needed to strengthen oil-source correlations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2016.02.029","usgsCitation":"Hackley, P.C., and Sanfilipo, J., 2016, Organic petrology and geochemistry of Eocene Suzak bituminous marl, north-central Afghanistan: Depositional environment and source rock potential: Marine and Petroleum Geology, v. 73, p. 572-589, https://doi.org/10.1016/j.marpetgeo.2016.02.029.","productDescription":"18 p.","startPage":"572","endPage":"589","ipdsId":"IP-069387","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":328205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"73","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57c95130e4b0f2f0cec15bfc","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":647807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanfilipo, John 0000-0002-8739-5628 jsan@usgs.gov","orcid":"https://orcid.org/0000-0002-8739-5628","contributorId":140236,"corporation":false,"usgs":true,"family":"Sanfilipo","given":"John","email":"jsan@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":647808,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176812,"text":"70176812 - 2016 - To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors","interactions":[],"lastModifiedDate":"2016-10-11T15:27:47","indexId":"70176812","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors","docAbstract":"Roads are significant barriers to landscape-scale movements of individuals or populations of many wildlife taxa. The decision by an animal near a road to either cross or not cross may be influenced by characteristics of the road, environmental conditions, traits of the individual animal, and other aspects of the context within which the decision is made. We considered such factors in a mixed-effects logistic regression model describing the nightly road crossing probabilities of invasive nocturnal Brown Treesnakes (Boiga irregularis) through short-term radiotracking of 691 snakes within close proximity to 50 road segments across the island of Guam. All measures of road magnitude (traffic volume, gap width, surface type, etc.) were significantly negatively correlated with crossing probabilities. Snake body size was the only intrinsic factor associated with crossing rates, with larger snakes crossing roads more frequently. Humidity was the only environmental variable affecting crossing rate. The distance of the snake from the road at the start of nightly movement trials was the most significant predictor of crossings. The presence of snake traps with live mouse lures during a portion of the trials indicated that localized prey cues reduced the probability of a snake crossing the road away from the traps, suggesting that a snake's decision to cross roads is influenced by local foraging opportunities. Per capita road crossing rates of Brown Treesnakes were very low, and comparisons to historical records suggest that crossing rates have declined in the 60+ yr since introduction to Guam. We report a simplified model that will allow managers to predict road crossing rates based on snake, road, and contextual characteristics. Road crossing simulations based on actual snake size distributions demonstrate that populations with size distributions skewed toward larger snakes will result in a higher number of road crossings. Our method of modeling per capita road crossing probabilities as a binary response variable, influenced by contextual factors, may be useful for describing or predicting road crossings by individuals of other taxa provided that appropriate spatial and temporal resolution can be achieved and that potentially influential covariate data can be obtained.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1292","usgsCitation":"Siers, S.R., Reed, R., and Savidge, J.A., 2016, To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors: Ecosphere, v. 7, no. 5, e01292; 19 p., https://doi.org/10.1002/ecs2.1292.","productDescription":"e01292; 19 p.","ipdsId":"IP-069215","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1292","text":"Publisher Index Page"},{"id":329465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-31","publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d143713","contributors":{"authors":[{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":650394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178653,"text":"70178653 - 2016 - @KarlTheFog has been mapped!","interactions":[],"lastModifiedDate":"2017-04-28T10:19:25","indexId":"70178653","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5024,"text":"BayGEO Journal","active":true,"publicationSubtype":{"id":10}},"title":"@KarlTheFog has been mapped!","docAbstract":"Within the world of mapping, clouds are a pesky interference to be removed from satellite remote sensed imagery. However, to many of us, that is a waste of pixels. Cloud maps are becoming increasingly valuable in the quest to understand land cover change and surface processes. In coastal California, the dynamic summertime interactions between air masses, the ocean, and topography result in blankets of fog and low clouds flowing into low lying areas of the San Francisco Bay Area. The low clouds and fog advected from the Pacific bring moisture and shade to coastal ecosystems. This acts to reduce temperatures and evapotranspiration stress during the otherwise arid Mediterranean climate season, in turn impacting vegetation distribution, irrigation needs, and urban energy consumption.
","language":"English","publisher":"BayGeo","usgsCitation":"Torregrosa, A.A., 2016, @KarlTheFog has been mapped!: BayGEO Journal, v. 9, no. 1, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-075388","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":331423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331424,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://journal.baygeo.org/karlthefog/"}],"volume":"9","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"584144e0e4b04fc80e5073b0","contributors":{"authors":[{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654724,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70177914,"text":"70177914 - 2016 - Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","interactions":[],"lastModifiedDate":"2016-10-26T11:50:49","indexId":"70177914","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","docAbstract":"Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2016.01.003","usgsCitation":"Groff, S.C., Loftin, C., Drummond, F., Bushmann, S., and McGill, B.J., 2016, Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA: Environmental Modelling and Software, v. 79, p. 1-9, https://doi.org/10.1016/j.envsoft.2016.01.003.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-064763","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":488536,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2016.01.003","text":"Publisher Index Page"},{"id":330404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.95294189453125,\n 43.98491011404692\n ],\n [\n -68.95294189453125,\n 44.904523389609324\n ],\n [\n -67.1978759765625,\n 44.904523389609324\n ],\n [\n -67.1978759765625,\n 43.98491011404692\n ],\n [\n -68.95294189453125,\n 43.98491011404692\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a6f","chorus":{"doi":"10.1016/j.envsoft.2016.01.003","url":"http://dx.doi.org/10.1016/j.envsoft.2016.01.003","publisher":"Elsevier BV","authors":"Groff Shannon C., Loftin Cynthia S., Drummond Frank, Bushmann Sara, McGill Brian","journalName":"Environmental Modelling & Software","publicationDate":"5/2016","auditedOn":"2/1/2017","publiclyAccessibleDate":"1/29/2017"},"contributors":{"authors":[{"text":"Groff, Shannon C.","contributorId":176308,"corporation":false,"usgs":false,"family":"Groff","given":"Shannon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":652153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":652139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drummond, Frank","contributorId":176309,"corporation":false,"usgs":false,"family":"Drummond","given":"Frank","email":"","affiliations":[],"preferred":false,"id":652154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bushmann, Sara","contributorId":176310,"corporation":false,"usgs":false,"family":"Bushmann","given":"Sara","email":"","affiliations":[],"preferred":false,"id":652155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGill, Brian J.","contributorId":146422,"corporation":false,"usgs":false,"family":"McGill","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":652156,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178062,"text":"70178062 - 2016 - Developing fish trophic interaction indicators of climate change for the Great Lakes","interactions":[],"lastModifiedDate":"2016-11-01T15:10:34","indexId":"70178062","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Developing fish trophic interaction indicators of climate change for the Great Lakes","docAbstract":"This project addressed regional climate change effects on aquatic food webs in the Great Lakes. We sought insights by examining Lake Erie as a representative system with a high level of anthropogenic impacts, strong nutrient gradients, seasonal hypoxia, and spatial overlap of cold- and cool-water fish guilds. In Lake Erie and in large embayments throughout the Great Lakes basin, this situation is a concern for fishery managers, as climate change may exacerbate hypoxia and reduce habitat volume for some species. We examined fish community composition, fine-scale distribution, prey availability, diets, and biochemical tracers for dominant fishes from study areas with medium-high nutrient levels (mesotrophic, Fairport study area), and low nutrient levels (oligotrophic, Erie study area). This multi-year database (2011-2013) provides the ability to contrast years with wide variation in rainfall, winter ice-cover, and thermal stratification. In addition, multiple indicators of dietary and distributional responses to environmental variability will allow resource managers to select the most informative approach for addressing specific climate change questions. Our results support the incorporation of some relatively simple and cost-efficient approaches into existing agency monitoring programs to track the near-term condition status of fish and fish community composition by functional groupings. Other metrics appear better suited for understanding longer-term changes, and may take more resources to implement on an ongoing basis. Although we hypothesized that dietary overlap and similarity in selected species would be sharply different during thermal stratification and hypoxic episodes, we found little evidence of this. Instead, to our surprise, this study found that fish tended to aggregate at the edges of hypoxia, highlighting potential spatial changes in catch efficiency of the fishery. This work has had several positive impacts on a wide range of resource management and stakeholder activities, most notably in Lake Erie. The results were instrumental in the development of an interim decision rule for dealing with data collected during hypoxic events to improve stock assessment of Yellow Perch. In addition, novel findings from this study regarding spatial and temporal variability in hypoxia have aided US-Environmental Protection Agency in the development of a modified sampling protocol to more accurately quantify the central basin hypoxic zone, and this directly addressed a goal of the Great Lakes Water Quality Agreement of 2012 to reduce the extent and severity of hypoxia. Finally, the study areas developed in this project formed the basis for food web sampling in the 2014 bi-national Coordinated Science and Monitoring Initiative work in Lake Erie.
","language":"English","publisher":"Northeast Climate Science Center","usgsCitation":"Kraus, R.T., Knight, C.T., Gorman, A.M., Kocovsky, P.M., Weidel, B., and Rogers, M.W., 2016, Developing fish trophic interaction indicators of climate change for the Great Lakes, 70 p.","productDescription":"70 p.","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":330639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330638,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necsc.umass.edu/biblio/developing-fish-trophic-interaction-indicators-climate-change-great-lakes"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5819a9c4e4b0bb36a4c91029","contributors":{"authors":[{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Carey T.","contributorId":56529,"corporation":false,"usgs":true,"family":"Knight","given":"Carey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":652684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Ann Marie","contributorId":145525,"corporation":false,"usgs":false,"family":"Gorman","given":"Ann","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":652685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":652687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":652688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}