Population consequences of endangered species interacting as predators and prey have been considered theoretically and legally, but rarely investigated in the field. We examined relationships between spatially variable populations of a predator, the California sea otter, Enhydra lutris nereis, and a prey species, the black abalone, Haliotis cracherodii. Both species are federally listed under the Endangered Species Act and co-occur along the coast of California. We compared the local abundance and habitat distribution of black abalone at 12 sites with varying densities of sea otters. All of the populations of abalone we examined were in the geographic area currently unaffected by withering disease, which has decimated populations south of the study area. Surprisingly, our findings indicate that sea otter density is positively associated with increased black abalone density. The presence of sea otters also correlated with a shift in black abalone to habitat conferring greater refuge, which could decrease illegal human harvest. These results highlight the need for a multi-species approach to conservation management of the two species, and demonstrate the importance of using field-collected data rather than simple trophic assumptions to understand relationships between jointly vulnerable predator and prey populations.
Bi-annual newsletter for the John Wesley Powell Center for Analysis and Synthesis, covering news through July of 2015
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","usgsCitation":"Baron, J., and Goldhaber, M., 2015, Powell Center Newsletter, Volume 2, Issue 1, 2 p.","productDescription":"2 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067592","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":311740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":311739,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://powellcenter.usgs.gov/newsletter"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"565d732ce4b071e7ea54344d","contributors":{"authors":[{"text":"Baron, Jill S. 0000-0002-5902-6251 jill_baron@usgs.gov","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":822,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","email":"jill_baron@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":579548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldhaber, Marty","contributorId":49657,"corporation":false,"usgs":true,"family":"Goldhaber","given":"Marty","email":"","affiliations":[],"preferred":false,"id":580615,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159483,"text":"70159483 - 2015 - Book review: Bats: A world of science and mystery.","interactions":[],"lastModifiedDate":"2015-12-11T12:11:15","indexId":"70159483","displayToPublicDate":"2015-11-30T09:15:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Bats: A world of science and mystery.","docAbstract":"This book has something for everyone, from casual seekers of fascinating eye candy to professional scientists interested in the latest discoveries. Without losing sight of how mysterious bats remain despite decades of research, the authors deftly introduce readers to bats and the people who study them. The book is nice to look at, easy to understand, and interesting in many ways. These stories stick in the reader's memory long after being read—a sign of great scientific communication.
\nReview info: Bats: A world of science and mystery. By M. Brock Fenton, Nancy B. Simmons (Eds.), 2015. ISBN 978-0226065120, 240 pp.
","language":"English","publisher":"Elsevier Science Ltd.","publisherLocation":"Kidlington, Oxford","doi":"10.1016/j.biocon.2015.10.005","usgsCitation":"Cryan, P.M., 2015, Book review: Bats: A world of science and mystery.: Biological Conservation, v. 192, p. 323-323, https://doi.org/10.1016/j.biocon.2015.10.005.","productDescription":"1 p.","startPage":"323","endPage":"323","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069570","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":311738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"192","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"565d7328e4b071e7ea543449","contributors":{"authors":[{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":579157,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220207,"text":"70220207 - 2015 - A comparison of thermal infrared to fiber-optic distributed temperature sensing for evaluation of groundwater discharge to surface water","interactions":[],"lastModifiedDate":"2021-04-27T14:29:52.674829","indexId":"70220207","displayToPublicDate":"2015-11-30T08:31:30","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of thermal infrared to fiber-optic distributed temperature sensing for evaluation of groundwater discharge to surface water","docAbstract":"Groundwater has a predictable thermal signature that can be used to locate discrete zones of discharge to surface water. As climate warms, surface water with strong groundwater influence will provide habitat stability and refuge for thermally stressed aquatic species, and is therefore critical to locate and protect. Alternatively, these discrete seepage locations may serve as potential point sources of contaminants from polluted aquifers. This study compares two increasingly common heat tracing methods to locate discrete groundwater discharge: direct-contact measurements made with fiber-optic distributed temperature sensing (FO-DTS) and remote sensing measurements collected with thermal infrared (TIR) cameras. FO-DTS is used to make high spatial resolution (typically m) thermal measurements through time within the water column using temperature-sensitive cables. The spatialtemporal data can be analyzed with statistical measures to reveal zones of groundwater influence, however, the personnel requirements, time to install, and time to georeference the cables can be burdensome, and the control units need constant calibration. In contrast, TIR data collection, either from handheld, airborne, or satellite platforms, can quickly capture point-in-time evaluations of groundwater seepage zones across large scales. However the remote nature of TIR measurements means they can be adversely influenced by a number of environmental and physical factors, and the measurements are limited to the surface skin temperature of water features. We present case studies from a range of lentic to lotic aquatic systems to identify capabilities and limitations of both technologies and highlight situations in which one or the other might be a better instrument choice for locating groundwater discharge. FO-DTS performs well in all systems across seasons, but data collection was limited spatially by practical considerations of cable installation. TIR is found to consistently locate groundwater seepage zones above and along the streambank, but submerged seepage zones are only well identified in shallow systems (e.g. <0.5 m depth) with moderate flow. Winter data collection, when groundwater is relatively warm and buoyant, increases the water surface expression of discharge zones in shallow systems.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2015.09.059","usgsCitation":"Hare, D.K., Briggs, M., Rosenberry, D., Boutt, D., and Lane, J., 2015, A comparison of thermal infrared to fiber-optic distributed temperature sensing for evaluation of groundwater discharge to surface water: Journal of Hydrology, v. 530, p. 153-166, https://doi.org/10.1016/j.jhydrol.2015.09.059.","productDescription":"14 p.","startPage":"153","endPage":"166","ipdsId":"IP-068976","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":471620,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2015.09.059","text":"Publisher Index Page"},{"id":385322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, Michigan, Montana, New York, Pennsylvania","otherGeospatial":"Delaware River, Higgins, Lake, Quashnet River, Red Rocks Lake, Tidmarsh Farms","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.79179382324219,\n 44.41269287945535\n ],\n [\n -84.64210510253906,\n 44.41269287945535\n ],\n [\n -84.64210510253906,\n 44.520989167323734\n ],\n [\n -84.79179382324219,\n 44.520989167323734\n ],\n [\n -84.79179382324219,\n 44.41269287945535\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.91154479980467,\n 44.58582159355544\n ],\n [\n -111.66229248046874,\n 44.58582159355544\n ],\n [\n -111.66229248046874,\n 44.67182693970573\n ],\n [\n -111.91154479980467,\n 44.67182693970573\n ],\n [\n -111.91154479980467,\n 44.58582159355544\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.37513732910156,\n 41.83733944214672\n ],\n [\n -75.16159057617188,\n 41.83733944214672\n ],\n [\n -75.16159057617188,\n 41.99624282178583\n ],\n [\n -75.37513732910156,\n 41.99624282178583\n ],\n [\n -75.37513732910156,\n 41.83733944214672\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.64105987548828,\n 41.828386176902555\n ],\n [\n -70.54630279541016,\n 41.828386176902555\n ],\n [\n -70.54630279541016,\n 41.908664970834444\n ],\n [\n -70.64105987548828,\n 41.908664970834444\n ],\n [\n -70.64105987548828,\n 41.828386176902555\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.55746078491211,\n 41.58026831154093\n ],\n [\n -70.50750732421875,\n 41.58026831154093\n ],\n [\n -70.50750732421875,\n 41.62044728626882\n ],\n [\n -70.55746078491211,\n 41.62044728626882\n ],\n [\n -70.55746078491211,\n 41.58026831154093\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"530","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hare, Danielle K","contributorId":257636,"corporation":false,"usgs":false,"family":"Hare","given":"Danielle","email":"","middleInitial":"K","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":814761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":257638,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":814763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boutt, Dave","contributorId":257639,"corporation":false,"usgs":false,"family":"Boutt","given":"Dave","affiliations":[{"id":52076,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":814764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lane, John W. Jr. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":210076,"corporation":false,"usgs":true,"family":"Lane","given":"John W.","suffix":"Jr.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814766,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70164522,"text":"70164522 - 2015 - Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions","interactions":[],"lastModifiedDate":"2018-08-09T12:27:41","indexId":"70164522","displayToPublicDate":"2015-11-30T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions","docAbstract":"Observations of elemental mercury (Hg0) at sites in North America and Europe show large decreases (∼1–2% y−1) from 1990 to present. Observations in background northern hemisphere air, including Mauna Loa Observatory (Hawaii) and CARIBIC (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) aircraft flights, show weaker decreases (<1% y−1). These decreases are inconsistent with current global emission inventories indicating flat or increasing emissions over that period. However, the inventories have three major flaws: (i) they do not account for the decline in atmospheric release of Hg from commercial products; (ii) they are biased in their estimate of artisanal and small-scale gold mining emissions; and (iii) they do not properly account for the change in Hg0/HgII speciation of emissions from coal-fired utilities after implementation of emission controls targeted at SO2 and NOx. We construct an improved global emission inventory for the period 1990 to 2010 accounting for the above factors and find a 20% decrease in total Hg emissions and a 30% decrease in anthropogenic Hg0 emissions, with much larger decreases in North America and Europe offsetting the effect of increasing emissions in Asia. Implementation of our inventory in a global 3D atmospheric Hg simulation [GEOS-Chem (Goddard Earth Observing System-Chemistry)] coupled to land and ocean reservoirs reproduces the observed large-scale trends in atmospheric Hg0 concentrations and in HgII wet deposition. The large trends observed in North America and Europe reflect the phase-out of Hg from commercial products as well as the cobenefit from SO2 and NOx emission controls on coal-fired utilities.
\n","language":"English","publisher":"The Academy","publisherLocation":"Washington, D.C.","doi":"10.1073/pnas.1516312113","usgsCitation":"Zhang, Y., Jacob, D.J., Horowitz, H.M., Chen, L., Amos, H.M., Krabbenhoft, D.P., Slemr, F., St. Louis, V.L., and Elsie M. Sunderland, 2015, Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions: Proceedings of the National Academy of Sciences, v. 133, no. 3, p. 526-531, https://doi.org/10.1073/pnas.1516312113.","productDescription":"6 p.","startPage":"526","endPage":"531","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070993","costCenters":[{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":471622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1516312113","text":"Publisher Index Page"},{"id":316735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"133","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-04","publicationStatus":"PW","scienceBaseUri":"56bb1bc8e4b08d617f654e36","contributors":{"authors":[{"text":"Zhang, Yanxu","contributorId":156387,"corporation":false,"usgs":false,"family":"Zhang","given":"Yanxu","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":597723,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jacob, Daniel J.","contributorId":156388,"corporation":false,"usgs":false,"family":"Jacob","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":597724,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horowitz, Hannah M.","contributorId":156389,"corporation":false,"usgs":false,"family":"Horowitz","given":"Hannah","email":"","middleInitial":"M.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":597725,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Long","contributorId":156390,"corporation":false,"usgs":false,"family":"Chen","given":"Long","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":597726,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amos, Helen M.","contributorId":156391,"corporation":false,"usgs":false,"family":"Amos","given":"Helen","email":"","middleInitial":"M.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":597727,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":597722,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Slemr, Franz","contributorId":156392,"corporation":false,"usgs":false,"family":"Slemr","given":"Franz","email":"","affiliations":[{"id":12534,"text":"Max-Planck-Institute for Chemistry, Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":597728,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"St. Louis, Vincent L.","contributorId":156393,"corporation":false,"usgs":false,"family":"St. Louis","given":"Vincent","email":"","middleInitial":"L.","affiliations":[{"id":12980,"text":"Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":597729,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elsie M. Sunderland","contributorId":156394,"corporation":false,"usgs":false,"family":"Elsie M. Sunderland","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":597730,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70164514,"text":"70164514 - 2015 - What is the Anthropocene?","interactions":[],"lastModifiedDate":"2016-02-09T12:09:27","indexId":"70164514","displayToPublicDate":"2015-11-30T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3879,"text":"Eos, Earth and Space Science News","active":true,"publicationSubtype":{"id":10}},"title":"What is the Anthropocene?","docAbstract":"
Since Paul Crutzen and Eugene Stoermer introduced the word “Anthropocene” in 2000, scientists and nonscientists alike have used the word to highlight the concept that we are now living in a time when the global environment, at some level, is shaped by humankind rather than vice versa. Humans have significantly altered Earth’s land surface, oceans, rivers, atmosphere, flora, and fauna. By its emphasis on the here and now and on what humans have done and can do in the future, the word “Anthropocene” has served as a call to action for environmental sustainability and responsibility [Crutzen and Stoermer, 2000; Waters et al., 2014; Ruddiman et al., 2015].
\nSo far, however, the term “Anthropocene” has not been integrated into the official Geologic Time Scale, which geologists use to divide the past into named blocks based on the rock record. In 2016 or thereabouts, the International Commission on Stratigraphy—the scientific body that maintains the official Geologic Time Scale—will consider a proposal to formalize a definition of this term. It’s a decision that has both semantic and scientific implications and may have legal implications as well.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2015EO040297","usgsCitation":"Edwards, L.E., 2015, What is the Anthropocene?: Eos, Earth and Space Science News, v. 97, no. 2, p. 6-7, https://doi.org/10.1029/2015EO040297.","productDescription":"2 p.","startPage":"6","endPage":"7","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065862","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":471623,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2015eo040297","text":"Publisher Index Page"},{"id":316736,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56bb1bd4e4b08d617f654e8d","contributors":{"authors":[{"text":"Edwards, Lucy E. 0000-0003-4075-3317 leedward@usgs.gov","orcid":"https://orcid.org/0000-0003-4075-3317","contributorId":2647,"corporation":false,"usgs":true,"family":"Edwards","given":"Lucy","email":"leedward@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":597698,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70187177,"text":"70187177 - 2015 - Exploring drought controls on spring phenology","interactions":[],"lastModifiedDate":"2018-12-13T09:16:10","indexId":"70187177","displayToPublicDate":"2015-11-26T14:39:43","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Exploring drought controls on spring phenology","docAbstract":"The timing of spring phenology can be influenced by several drivers. Many studies have shown the effect of temperature on spring vegetation growth, but the role of moisture is complex and not as well researched. We explored drivers for aspen spring phenology in the mountains of the western U.S. While temperature exerted control over the timing of aspen green-up in the spring, snow moisture as measured by April 1 snow water equivalent (SWE), played a significant role especially in southern locations bordering the Colorado Plateau. Maximum spring temperatures (March-May) were significantly (p<0.01) correlated with the start of the growing season across the entire Wasatch and Uinta Mountains and most of the western and northern Southern Rocky Mountains. Spring SWE was significantly (p<0.01) correlated with the growing season start across all of the Wasatch and Uinta Mountains and more than half of the Southern Rockies. The locations that experienced a larger snow influence, given by linear regression and R2 values, were located adjacent to the drier Colorado Plateau and south of 40oN latitude. Historical spatial patterns of regional snow accumulations and anomalies in the western U.S. have been chiefly explained by decadal antiphasing patterns across a north-south dipole. Anomalously low SWE co-occurs with a Pacific/North American teleconnection winter circulation associated with strong high pressure over the Pacific Northwest. The pattern shown in aspen phenology in this study, where the timing of spring green-up in the southern half of the intermountain West (south of a zone from 40oN - 42oN and mainly west of the continental divide in Colorado) showed higher sensitivity to winter snow moisture, and this spatial pattern was supported by other studies. Although winter moisture was not as consistent a factor as temperature in driving the start of the season, this study shows evidence that possible future winter drought could shift the growing season earlier than temperature increases alone.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Evaluation of drought and drought impacts through interdisciplinary methods","language":"English","publisher":" Global Change Research Centre","isbn":"9788087902127","usgsCitation":"Brown, J.F., and Meier, G., 2015, Exploring drought controls on spring phenology, chap. of Evaluation of drought and drought impacts through interdisciplinary methods, p. 92-96.","productDescription":"5 p.","startPage":"92","endPage":"96","ipdsId":"IP-066844","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":359677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bfd1472e4b0815414ca390a","contributors":{"editors":[{"text":"Hayes, M.","contributorId":68138,"corporation":false,"usgs":true,"family":"Hayes","given":"M.","affiliations":[],"preferred":false,"id":752020,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Trnka, M.","contributorId":210803,"corporation":false,"usgs":false,"family":"Trnka","given":"M.","email":"","affiliations":[],"preferred":false,"id":752022,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":692937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meier, Gretchen","contributorId":191405,"corporation":false,"usgs":false,"family":"Meier","given":"Gretchen","affiliations":[],"preferred":false,"id":692938,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168434,"text":"70168434 - 2015 - Observations of net soil exchange of CO2 in a dryland show experimental warming increases carbon losses in biocrust soils","interactions":[],"lastModifiedDate":"2016-02-12T13:23:36","indexId":"70168434","displayToPublicDate":"2015-11-26T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Observations of net soil exchange of CO2 in a dryland show experimental warming increases carbon losses in biocrust soils","docAbstract":"Many arid and semiarid ecosystems have soils covered with well-developed biological soil crust communities (biocrusts) made up of mosses, lichens, cyanobacteria, and heterotrophs living at the soil surface. These communities are a fundamental component of dryland ecosystems, and are critical to dryland carbon (C) cycling. To examine the effects of warming temperatures on soil C balance in a dryland ecosystem, we used infrared heaters to warm biocrust-dominated soils to 2 °C above control conditions at a field site on the Colorado Plateau, USA. We monitored net soil exchange (NSE) of CO2 every hour for 21 months using automated flux chambers (5 control and 5 warmed chambers), which included the CO2 fluxes of the biocrusts and the soil beneath them. We observed measurable photosynthesis in biocrust soils on 12 % of measurement days, which correlated well with precipitation events and soil wet-up. These days included several snow events, providing what we believe to be the first evidence of substantial photosynthesis underneath snow by biocrust organisms in drylands. Overall, biocrust soils in both control and warmed plots were net CO2 sources to the atmosphere, with control plots losing 62 ± 8 g C m−2 (mean ± SE) over the first year of measurement and warmed plots losing 74 ± 9 g C m−2. Between control and warmed plots, the difference in soil C loss was uncertain over the course of the entire year due to large and variable rates in spring, but on days during which soils were wet and crusts were actively photosynthesizing, biocrusts that were warmed by 2 °C had a substantially more negative C balance (i.e., biocrust soils took up less C and/or lost more C in warmed plots). Taken together, our data suggest a substantial risk of increased C loss from biocrust soils with higher future temperatures, and highlight a robust capacity to predict CO2 exchange in biocrust soils using easily measured environmental parameters.
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- 2015 - Aerial-broadcast application of diphacinone bait for rodent control in Hawai`i: Efficacy and non-target species risk assessment","interactions":[],"lastModifiedDate":"2018-01-04T12:41:35","indexId":"70160826","displayToPublicDate":"2015-11-26T14:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":414,"text":"Technical Report","active":false,"publicationSubtype":{"id":9}},"seriesNumber":"HCSU-071","title":"Aerial-broadcast application of diphacinone bait for rodent control in Hawai`i: Efficacy and non-target species risk assessment","docAbstract":"Introduced rats (Rattus rattus, R. exulans, and R. norvegicus) have been implicated in the decline or extinction of numerous species of plants and animals in Hawai‘i. This study investigated the efficacy of aerial-broadcast application of Ramik® Green baits containing 50 ppm (0.005%) diphacinone in reducing rat and mouse populations and the risk to non-target species. The study was undertaken in paired 45.56-ha treatment and non-treatment plots in Hawai‘i Volcanoes National Park. All 21 radio-collared rats in the treatment plot died within nine days of bait application, whereas none of the 18 radio-collared rats in the non-treatment plot died. There was a 99% drop in both the rat capture rate and percentage of non-toxic census bait blocks gnawed by rats in the treatment plot relative to the non-treatment plot three weeks after bait application. The only rat captured in the treatment plot three weeks after bait application was not ear-tagged (i.e., it was not a recapture), whereas 44% of the 52 rats captured in the non-treatment plot were ear-tagged. Most of the bait had disappeared from the forest floor within about one month of application. No birds likely to have eaten bait were found dead, although residues of diphacinone were found in the livers of three species of introduced seed-eating/omnivorous birds captured alive after bait application. No predatory birds were found dead one month or three months after bait application. The remains of a Hawaiian hawk (Buteo solitarius) were found six months after bait application, but it was not possible to determine the cause of death. This study demonstrated the efficacy of aerially broadcast diphacinone bait for control of rats and mice in Hawaiian montane forests, and was part of the dataset submitted to the U.S. Environmental Protection Agency for the national registration of a diphacinone bait for the control of rat populations in conservation areas.
","language":"English","publisher":"University of Hawaii at Hilo","publisherLocation":"Hilo, HI","usgsCitation":"Foote, D., Spurr, E.B., Lindsey, G.D., and Forbes Perry, C., 2015, Aerial-broadcast application of diphacinone bait for rodent control in Hawai`i: Efficacy and non-target species risk assessment: Technical Report HCSU-071, Report: iii, 24 p.","productDescription":"Report: iii, 24 p.","startPage":"1","endPage":"24","numberOfPages":"28","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070629","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":326260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a9ad2de4b05e859bdfb7c7","contributors":{"authors":[{"text":"Foote, David dfoote@usgs.gov","contributorId":375,"corporation":false,"usgs":true,"family":"Foote","given":"David","email":"dfoote@usgs.gov","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":584018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spurr, Eric B.","contributorId":151021,"corporation":false,"usgs":false,"family":"Spurr","given":"Eric","email":"","middleInitial":"B.","affiliations":[{"id":12679,"text":"Landcare Research","active":true,"usgs":false}],"preferred":false,"id":584019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Gerald D.","contributorId":102534,"corporation":false,"usgs":true,"family":"Lindsey","given":"Gerald","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":584020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forbes Perry, Charlotte","contributorId":151022,"corporation":false,"usgs":false,"family":"Forbes Perry","given":"Charlotte","email":"","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":584021,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70175780,"text":"70175780 - 2015 - Habitat influences distribution of chronic wasting disease in white-tailed deer","interactions":[],"lastModifiedDate":"2016-08-19T10:01:15","indexId":"70175780","displayToPublicDate":"2015-11-26T11:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Habitat influences distribution of chronic wasting disease in white-tailed deer","docAbstract":"Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in 1967 in a captive research facility in Colorado. In the northeastern United States, CWD was first confirmed in white-tailed deer (Odocoileus virginianus) in 2005. Because CWD is a new and emerging disease with a spatial distribution that had yet to be assessed in the Northeast, we examined demographic, environmental, and spatial effects to determine how each related to this spatial distribution. The objectives of our study were to identify environmental and spatial effects that best described the spatial distribution of CWD in free-ranging white-tailed deer and identify areas that support deer that are at risk for CWD infection in the Northeast. We used Bayesian hierarchical modeling that incorporated demographic covariates, such as sex and age, along with environmental covariates, which included elevation, slope, riparian corridor, percent clay, and 3 landscapes (i.e., developed, forested, open). The model with the most support contained landscape covariates and spatial effects that represented clustering of CWD in adjacent grid cells. Forested landscapes had the strongest relationship with the distribution of CWD, with increased risk of CWD occurring in areas that had lesser amounts of forest. Our results will assist resource managers in understanding the spatial distribution of CWD within the study area, and in surrounding areas where CWD has yet to be found. Efficiency of disease surveillance and containment efforts can be improved by allocating resources used for surveillance in areas with deer populations that are at greatest risk for infection.
","language":"English","publisher":"Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/jwmg.1004","usgsCitation":"Evans, T.S., Kirchgessner, M.S., Eyler, B., Ryan, C.W., and Walter, W.D., 2015, Habitat influences distribution of chronic wasting disease in white-tailed deer: Journal of Wildlife Management, v. 80, no. 2, p. 284-291, https://doi.org/10.1002/jwmg.1004.","startPage":"284","endPage":"291","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058818","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":326911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-26","publicationStatus":"PW","scienceBaseUri":"57b82dc7e4b03fd6b7da377f","contributors":{"authors":[{"text":"Evans, Tyler S.","contributorId":172196,"corporation":false,"usgs":false,"family":"Evans","given":"Tyler","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":646354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirchgessner, Megan S.","contributorId":173866,"corporation":false,"usgs":false,"family":"Kirchgessner","given":"Megan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":646355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eyler, B.","contributorId":48674,"corporation":false,"usgs":true,"family":"Eyler","given":"B.","email":"","affiliations":[],"preferred":false,"id":646356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ryan, Christopher W.","contributorId":173867,"corporation":false,"usgs":false,"family":"Ryan","given":"Christopher","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":646357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter, W. David 0000-0003-3068-1073 wwalter@usgs.gov","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":5083,"corporation":false,"usgs":true,"family":"Walter","given":"W.","email":"wwalter@usgs.gov","middleInitial":"David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":646340,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70134260,"text":"70134260 - 2015 - Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR","interactions":[],"lastModifiedDate":"2023-01-02T14:52:45.247243","indexId":"70134260","displayToPublicDate":"2015-11-26T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"1","title":"Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR","docAbstract":"The American Society of Photogrammetry and Remote Sensing defined remote sensing as the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell et al., 1983). Environmental Systems Research Institute (ESRI) in its geographic information system (GIS) dictionary defines remote sensing as “collecting and interpreting information about the environment and the surface of the earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the earth’s surface or from the atmosphere, or by sending signals transmitted from a device and reflected back to it (ESRI, 2014).” The usual source of passive remote sensing data is the measurement of reflected or transmitted electromagnetic radiation (EMR) from the sun across the electromagnetic spectrum (EMS); this can also include acoustic or sound energy, gravity, or the magnetic field from or of the objects under consideration. In this context, the simple act of reading this text is considered remote sensing. In this case, the eye acts as a sensor and senses the light reflected from the object to obtain information about the object. It is the same technology used by a handheld camera to take a photograph of a person or a distant scenic view. Active remote sensing, however, involves sending a pulse of energy and then measuring the returned energy through a sensor (e.g., Radio Detection and Ranging [RADAR], Light Detection and Ranging [LiDAR]). Thermal sensors measure emitted energy by different objects. Thus, in general, passive remote sensing involves the measurement of solar energy reflected from the Earth’s surface, while active remote sensing involves synthetic (man-made) energy pulsed at the environment and the return signals are measured and recorded.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Remotely sensed data characterization, classification, and accuracies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"CRC Press","publisherLocation":"Boca Raton, FL","usgsCitation":"Panda, S.S., Rao, M.N., Thenkabail, P.S., and Fitzerald, J.E., 2015, Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR, chap. 1 of Remotely sensed data characterization, classification, and accuracies, p. 3-57.","productDescription":"55 p.","startPage":"3","endPage":"57","ipdsId":"IP-060641","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":342120,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411272,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.taylorfrancis.com/chapters/edit/10.1201/b19294-8/remote-sensing-systems%E2%80%94platforms-sensors-aerial-satellite-uav-optical-radar-lidar-sudhanshu-panda-mahesh-rao-prasad-%C2%82enkabail-james-fitzerald?context=ubx&refId=44b9585a-fc6b-4fa8-9f55-6c56552074ed"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59366da9e4b0f6c2d0d7d62c","contributors":{"authors":[{"text":"Panda, Sudhanshu S.","contributorId":127587,"corporation":false,"usgs":false,"family":"Panda","given":"Sudhanshu","email":"","middleInitial":"S.","affiliations":[{"id":7066,"text":"University of North Georgia","active":true,"usgs":false}],"preferred":false,"id":697141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rao, Mahesh N.","contributorId":127588,"corporation":false,"usgs":false,"family":"Rao","given":"Mahesh","email":"","middleInitial":"N.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":525767,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":525765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fitzerald, James E.","contributorId":127589,"corporation":false,"usgs":false,"family":"Fitzerald","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":7068,"text":"Cobb County Government, GA","active":true,"usgs":false}],"preferred":false,"id":697142,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168478,"text":"70168478 - 2015 - A broader definition of occupancy: A reply to Hayes and Monofils","interactions":[],"lastModifiedDate":"2016-02-16T14:09:30","indexId":"70168478","displayToPublicDate":"2015-11-26T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"A broader definition of occupancy: A reply to Hayes and Monofils","docAbstract":"Occupancy models are widely used to analyze presence–absence data for a variety of taxa while accounting for observation error (MacKenzie et al. 2002, 2006; Tyre et al. 2003; Royle and Dorazio 2008). Hayes and Monfils (2015) question their use for analyzing avian point count data based on purported violations of model assumptions incurred by avian mobility. Animal mobility is an important consideration, not just for occupancy models, but for a variety of population and habitat models (Boyce 2006, Royle et al. 2009, Manning and Goldberg 2010, Dormann et al. 2013, Renner et al. 2015). Nevertheless, we believe the ultimate conclusions of Hayes and Monfils are shortsighted mainly due to a narrow interpretation of occupancy. Rather than turn away from the use of occupancy models, we believe they remain an appropriate method for analyzing many data sets collected from avian point count surveys. Further, we suggest that there is value in having a broader and more nuanced interpretation of occupancy that incorporates the potential for animal movement.
\nThe critical role of rare earth elements (REEs), particularly heavy REEs (HREEs), in high-tech industries has created a surge in demand that is quickly outstripping known global supply and has triggered a worldwide scramble to discover new sources. The chemical analysis of 23 sedimentary phosphate deposits (phosphorites) in the United States demonstrates that they are significantly enriched in REEs. Leaching experiments using dilute H2SO4 and HCl, extracted nearly 100% of their total REE content and show that the extraction of REEs from phosphorites is not subject to the many technological and environmental challenges that vex the exploitation of many identified REE deposits. Our data suggest that phosphate rock currently mined in the United States has the potential to produce a significant proportion of the world's REE demand as a byproduct. Importantly, the size and concentration of HREEs in some unmined phosphorites dwarf the world's richest REE deposits. Secular variation in phosphate REE contents identifies geologic time periods favorable for the formation of currently unrecognized high-REE phosphates. The extraordinary endowment, combined with the ease of REE extraction, indicates that such phosphorites might be considered as a primary source of REEs with the potential to resolve the global REE (particularly for HREE) supply shortage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gr.2014.10.008","usgsCitation":"Emsbo, P., McLaughlin, P.I., Breit, G.N., du Bray, E.A., and Koenig, A.E., 2015, Rare earth elements in sedimentary phosphate deposits: Solution to the global REE crisis?: Gondwana Research, v. 27, no. 2, p. 776-785, https://doi.org/10.1016/j.gr.2014.10.008.","productDescription":"10 p.","startPage":"776","endPage":"785","ipdsId":"IP-053368","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":471624,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gr.2014.10.008","text":"Publisher Index Page"},{"id":342853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59521d20e4b062508e3c3676","contributors":{"authors":[{"text":"Emsbo, Poul 0000-0001-9421-201X pemsbo@usgs.gov","orcid":"https://orcid.org/0000-0001-9421-201X","contributorId":997,"corporation":false,"usgs":true,"family":"Emsbo","given":"Poul","email":"pemsbo@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":700467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McLaughlin, Patrick I.","contributorId":105165,"corporation":false,"usgs":true,"family":"McLaughlin","given":"Patrick","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":700473,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breit, George N. 0000-0003-2188-6798 gbreit@usgs.gov","orcid":"https://orcid.org/0000-0003-2188-6798","contributorId":1480,"corporation":false,"usgs":true,"family":"Breit","given":"George","email":"gbreit@usgs.gov","middleInitial":"N.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700474,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"du Bray, Edward A. 0000-0002-4383-8394 edubray@usgs.gov","orcid":"https://orcid.org/0000-0002-4383-8394","contributorId":755,"corporation":false,"usgs":true,"family":"du Bray","given":"Edward","email":"edubray@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700475,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koenig, Alan E. 0000-0002-5230-0924 akoenig@usgs.gov","orcid":"https://orcid.org/0000-0002-5230-0924","contributorId":1564,"corporation":false,"usgs":true,"family":"Koenig","given":"Alan","email":"akoenig@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":700476,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70157187,"text":"70157187 - 2015 - Coastal change from a massive sediment input: Dam removal, Elwha River, Washington, USA","interactions":[],"lastModifiedDate":"2017-04-24T13:13:00","indexId":"70157187","displayToPublicDate":"2015-11-26T00:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Coastal change from a massive sediment input: Dam removal, Elwha River, Washington, USA","docAbstract":"The removal of two large dams on the Elwha River, Washington, provides an ideal opportunity to study coastal morphodynamics during increased sediment supply. The dam removal project exposed ~21 million cubic meters (~30 million tonnes) of sediment in the former reservoirs, and this sediment was allowed to erode by natural river processes. Elevated rates of sand and gravel sediment transport in the river occurred during dam removal. Most of the sediment was transported to the coast, and this renewed sediment supply resulted in hundreds of meters of seaward expansion of the river delta since 2011. Our most recent survey in January 2015 revealed that a cumulative ~3.5 million m3 of sediment deposition occurred at the delta since the beginning of the dam removal project, and that aggradation had exceeded 8 m near the river mouth. Some of the newly deposited sediment has been shaped by waves and currents into a series of subaerial berms that appear to move shoreward with time.
On September 9, 2013, rain began to fall in eastern Colorado as a large low-pressure system pulled plumes of tropical moisture northward from the Pacific Ocean and the Gulf of Mexico. By September 16, 2013, as much as 12 to 20 inches of rain had fallen in the foothills of the Front Range of the Southern Rocky Mountains and adjacent plains near Colorado Springs, Colorado, north to the Colorado-Wyoming border. The rain caused major flooding during September 9–18, 2013, in a large part of the South Platte River Basin and in the Fountain Creek Basin. The floods resulted in several fatalities, more than 31,000 damaged or destroyed structures, and an estimated 3 billion dollars in damages. The U.S. Geological Survey (USGS) documented peak stage, streamflow, or both from the flood event for 80 sites located on selected rivers and streams in the South Platte River and Fountain Creek Basins and on the Platte River in Nebraska. The majority of flood-peak streamflows occurred on September 12 or 13, 2013, coinciding with the period of maximum rainfall. The flood resulted in new record peak streamflows at 17 streamgages having at least 10 years of record; 13 in the South Platte River Basin and 4 in the Fountain Creek Basin.
\nFlooding in the South Platte River Basin was primarily contained to select streams in Aurora and the Denver metropolitan area, most of the mountain tributaries joining the main stem South Platte River from Denver to Greeley, and in the main stem South Platte River from Denver to the Colorado-Nebraska State line. In Aurora, where about 15 inches of rain fell, streamflow peaked at 5,470 cubic feet per second (ft3/s) in Toll Gate Creek, a tributary to Sand Creek. Downstream from Aurora near the confluence with the South Platte River, Sand Creek peaked at 14,900 ft3/s, which was the highest streamflow since at least 1992, but less than the peak of 25,500 ft3/s in 1957 that occurred 4 miles upstream from the mouth. Flood-peak streamflows in the Denver metropolitan area were generally below historic records. The peak of 3,930 ft3/s on September 12 at the State of Colorado streamgage South Platte River at Denver ranked 59 out of 116 peaks and was less than the 1965 peak of 40,300 ft3/s. Ten of the 13 streamgages in the South Platte River Basin with new record peak streamflows were located on the mountain tributaries; Bear Creek, Fourmile Creek, Boulder Creek, St. Vrain Creek, the Big Thompson River, and the Cache la Poudre River. A daily average streamflow of 8,910 ft3/s on September 13 in Boulder Creek at the confluence with St. Vrain Creek was more than twice the previous instantaneous peak of 4,410 ft3/s from 1938. The USGS calculated a peak streamflow of 23,800 ft3/s for the St. Vrain Creek at Lyons; the highest streamflow on record at this State of Colorado streamgage (122 years of record) is 10,500 ft3/s from 1941. A peak streamflow of 16,200 ft3/s was calculated for the Big Thompson River at mouth of canyon near Drake streamgage, which is the second highest peak in 90 years of record and about one-half the magnitude of the peak of 31,200 ft3/s from July 31, 1976. A streamflow of 60,000 ft3/s in the South Platte River at Fort Morgan (September 15, 2013) suggests that a new record streamflow occurred in the main stem in the Greeley area, about 45 miles upstream from Fort Morgan. The current peak of record at a State of Colorado streamgage at Kersey, about 6.5 miles downstream from Greeley, is 31,500 ft3/s from 1973. Given that there was minimal inflow between Kersey and Fort Morgan, the USGS estimates there was probably at least 60,000 ft3/s at Kersey, which would be almost double the peak streamflow of record from 1973.
\nFlooding in the Fountain Creek Basin was primarily contained to Fountain Creek from southern Colorado Springs to its confluence with the Arkansas River in Pueblo, in lower Monument Creek, and in several mountain tributaries. New record peak streamflows occurred at four mountain tributary streamgages having at least 10 years of record; Bear Creek, Cheyenne Creek, Rock Creek, and Little Fountain Creek. Five streamgages with at least 10 years of record in a 32-mile reach of Fountain Creek extending from Colorado Springs to Piñon had peak streamflows in the top five for the period of record. A peak of 15,300 ft3/s at Fountain Creek near Fountain was the highest streamflow recorded in the Fountain Creek Basin during the September 2013 event and ranks the third highest peak in 46 years. Near the mouth of the basin, a peak of 11,800 ft3/s in Pueblo was only the thirteenth highest annual peak in 74 years. A new Colorado record for daily rainfall of 11.85 inches was recorded at a USGS rain gage in the Little Fountain Creek Basin on September 12, 2013.
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Since 1972, Landsat satellites have continuously acquired space-based images of the Earth’s land surface, providing data that serve as valuable resources for land use/land change research. The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education. Landsat is a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). NASA develops remote sensing instruments and the spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and data distribution. The result of this program is an unprecedented continuing record of natural and human-induced changes on the global landscape.
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Most mortality of endangered Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers in Upper Klamath Lake, Oregon, appears to occur within the first year of life. However, juvenile suckers in Clear Lake Reservoir, California, appear to survive longer and may even recruit to the spawning populations. Our goal in this study was to develop productive lines of inquiry into the causes of mortality of juvenile suckers, especially in Upper Klamath Lake, through comparison of sucker health and environmental conditions in both lakes. The health of juvenile suckers was associated with physical, biological, and chemical characteristics in each lake from July to September 2013 and 2014.
\nWater-quality dynamics differed substantially between lakes. Diel fluctuations were greater for dissolved-oxygen concentrations and pH in Upper Klamath Lake than in Clear Lake Reservoir, but diel temperature fluctuations were greater in Clear Lake Reservoir. Minimum dissolved-oxygen concentrations were as low as 1.0 milligram per liter (mg/L) in Upper Klamath Lake, but were no lower than 5.7 mg/L in Clear Lake Reservoir. Unionized ammonia (NH3) concentrations were near or less than the minimum reporting limit of 0.002 mg/L NH3 in Clear Lake Reservoir and generally were less than the concentration known to cause cellular changes to the gills or mortality of suckers in Upper Klamath Lake. Concentrations of microcystins were less than the detection limit (≤0.10 microgram per liter [μg/L]) in both dissolved (<63 micrometers [μm]) and particulate (≥63 μm) fractions of water samples collected from Clear Lake Reservoir in both years. In Upper Klamath Lake, concentrations of microcystins were relatively low in the 2013 particulate fractions and dissolved fractions of samples in both years, but as high as 44.30 μg/L, more than 44 times the World Health Organization recommended levels for drinking water (1 μg/L), in the 2014 particulate fraction. However, there was little histological evidence and no chemical evidence that these concentrations in water were harmful to suckers that we captured.
\nThe species and age compositions of juvenile sucker populations differed between lakes. A total of 98 percent of all juvenile suckers captured in Upper Klamath Lake were age-0. In contrast, as many as six age classes of suckers were represented in Clear Lake Reservoir, indicating much better juvenile survival than in Upper Klamath Lake. Based on genetic species identification, 98 percent of juvenile suckers collected from Clear Lake Reservoir were shortnose or Klamath largescale suckers (Catostomus snyderi). In contrast, there was a high degree of apparent genetic hybridization in juvenile suckers collected from Upper Klamath Lake, and both Lost River and non-Lost River juvenile suckers were nearly equally represented in samples.
\nNeither gross nor histological examination revealed a high prevalence of abnormalities in suckers that might indicate a mechanism for juvenile mortality in Upper Klamath Lake. Therefore, high mortality primarily may have occurred outside our study period (for example, in spring or over winter), or was owing to a factor that could not be detected with our methods (for example, predation). Alternatively, abnormalities in a small percentage of passively captured suckers in Upper Klamath Lake may indicate health-related issues that were more prevalent in populations than in our samples. Some apparent symptoms of stress or exposure to irritants, such as peribiliary cuffing in hepatocytes and mild inflammation and necrosis in gill tissues, were present in suckers from both lakes and do not appear to be clues to the cause of differential mortality between lakes. Seasonal trends in energy storage as glycogen and triglycerides were similar between lakes, indicating prey availability was not a factor in differential mortality.
\nDifferences in sucker health and condition between lakes were considered the most promising clues to the causes of differential juvenile sucker morality between lakes. A low prevalence of petechial hemorrhaging of the skin (16 percent) and deformed opercula (8 percent) in Upper Klamath Lake suckers may indicate exposure to a toxin other than microcystin. Suckers grew slower in their first year of life, but had similar or greater triglyceride and glycogen levels in Upper Klamath Lake compared to Clear Lake Reservoir. These findings do not suggest a lack of prey quantity but may indicate lower prey quality in Upper Klamath Lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20151217","usgsCitation":"Burdick, S.M., Elliott, D.G., Ostberg, C.O., Conway, C.M., Dolan-Caret, A., Hoy, M.S., Feltz, K.P., and Echols, K.R., 2015, Health and condition of endangered juvenile Lost River and shortnose suckers relative to water quality and fish assemblages in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California: U.S. Geological Survey Open-File Report 2015-1217, 56 p., https://dx.doi.org/10.3133/ofr20151217.","productDescription":"vi, 56 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-068722","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":311688,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2015/1217/ofr20151217.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2015-1217 PDF"},{"id":311687,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2015/1217/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Upper Klamath Lake, Clear Lake Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.09130859375,\n 41.823525308461456\n ],\n [\n -121.03500366210936,\n 41.832735062152615\n ],\n [\n -121.03912353515626,\n 41.87774145109676\n ],\n [\n -121.07070922851564,\n 41.928846628183166\n ],\n [\n -121.1077880859375,\n 41.94110578381598\n ],\n [\n -121.16683959960938,\n 41.939062754848564\n ],\n [\n -121.22451782226561,\n 41.920672548686824\n ],\n [\n -121.27532958984375,\n 41.88592102814744\n ],\n [\n -121.23687744140624,\n 41.83171182161546\n ],\n [\n -121.1517333984375,\n 41.748775021355044\n ],\n [\n -121.11602783203124,\n 41.78257704086764\n ],\n [\n -121.08993530273438,\n 41.81943165932007\n ],\n [\n -121.09130859375,\n 41.823525308461456\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.06634521484374,\n 42.54195129663955\n ],\n [\n -121.9976806640625,\n 42.50956476517422\n ],\n [\n -121.95098876953125,\n 42.4893146571508\n ],\n [\n -121.88232421875,\n 42.48323834594139\n ],\n [\n -121.83563232421875,\n 42.44980808481614\n ],\n [\n -121.71890258789062,\n 42.291532494305976\n ],\n [\n -121.70379638671874,\n 42.216313604344776\n ],\n [\n -121.70928955078126,\n 42.14100501649703\n ],\n [\n -121.82327270507812,\n 42.1104489601222\n ],\n [\n -121.95236206054688,\n 42.132858175814626\n ],\n [\n -122.06634521484374,\n 42.180705855725115\n ],\n [\n -121.9757080078125,\n 42.274260416436476\n ],\n [\n -122.0306396484375,\n 42.35854391749705\n ],\n [\n -122.1075439453125,\n 42.40824865050679\n ],\n [\n -122.17071533203125,\n 42.465005871175755\n ],\n [\n -122.17208862304686,\n 42.51766297209017\n ],\n [\n -122.11578369140626,\n 42.556115124326766\n ],\n [\n -122.06634521484374,\n 42.54195129663955\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
As the source rocks from which petroleum is generated, organic-rich shales have always been considered an important component of petroleum systems. Over the last few years, it has been realized that in some mudrocks, sufficient hydrocarbons remain in place to allow for commercial development, although advanced drilling and completion technology is typically required to access hydrocarbons from these reservoirs. Tight oil reservoirs (also referred to as continuous oil accumulations) contain hydrocarbons migrated from source rocks that are geologically/stratigraphically interbedded with or occur immediately overlying/underlying them. Migration is minimal in charging these tight oil accumulations (Gaswirth and Marra 2014). Companies around the world are now successfully exploiting organic-rich shales and tight rocks for contained hydrocarbons, and the search for these types of unconventional petroleum reservoirs is growing. Unconventional reservoirs range in geologic age from Ordovician to Tertiary (Silverman et al. 2005; EIA 2013a).
\nFish consumption advisories are used to inform citizens in the United States about noncommercial game fish with hazardous levels of methylmercury (MeHg). The US Environmental Protection Agency (USEPA) suggests issuing a fish consumption advisory when concentrations of MeHg in fish exceed a human health screening value of 300 ng/g. However, states have authority to develop their own systems for issuing fish consumption advisories for MeHg. Five states in the south central United States (Arkansas, Louisiana, Mississippi, Oklahoma, and Texas) issue advisories for the general human population when concentrations of MeHg exceed 700 ng/g to 1000 ng/g. The objective of the present study was to estimate the increase in fish consumption advisories that would occur if these states followed USEPA recommendations. The authors used the National Descriptive Model of Mercury in Fish to estimate the mercury concentrations in 5 size categories of largemouth bass–equivalent fish at 766 lentic and lotic sites within the 5 states. The authors found that states in this region have not issued site‐specific fish consumption advisories for most of the water bodies that would have such advisories if USEPA recommendations were followed. One outcome of the present study may be to stimulate discussion between scientists and policy makers at the federal and state levels about appropriate screening values to protect the public from the health hazards of consuming MeHg‐contaminated game fish.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3185","usgsCitation":"Adams, K., Drenner, R.W., Chumchal, M.M., and Donato, D.I., 2015, Disparity between state fish consumption advisory systems for methylmercury and US Environmental Protection Agency recommendations: A case study of the South Central United States: Environmental Toxicology and Chemistry, v. 35, no. 1, p. 247-251, https://doi.org/10.1002/etc.3185.","productDescription":"5 p.","startPage":"247","endPage":"251","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064147","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":318156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.330078125,\n 30.391830328088137\n ],\n [\n -88.505859375,\n 32.0639555946604\n ],\n [\n -88.04443359375,\n 35.02999636902566\n ],\n [\n -90.17578124999999,\n 35.10193405724606\n ],\n [\n -89.6484375,\n 36.01356058518153\n ],\n [\n -90.3515625,\n 36.03133177633187\n ],\n [\n -90.02197265625,\n 36.36822190085111\n ],\n [\n -90.263671875,\n 36.527294814546245\n ],\n [\n -94.59228515625,\n 36.527294814546245\n ],\n [\n -94.6142578125,\n 37.020098201368114\n ],\n [\n -103.0078125,\n 37.03763967977139\n ],\n [\n -103.02978515625,\n 36.527294814546245\n ],\n [\n -103.1396484375,\n 31.970803930433096\n ],\n [\n -106.69921875,\n 31.98944183792288\n ],\n [\n -106.5673828125,\n 31.89621446335144\n ],\n [\n -104.87548828125,\n 30.486550842588485\n ],\n [\n -104.58984375,\n 29.668962525992505\n ],\n [\n -103.1396484375,\n 28.92163128242129\n ],\n [\n -102.67822265625,\n 29.80251790576445\n ],\n [\n -102.23876953125,\n 29.80251790576445\n ],\n [\n -101.337890625,\n 29.7453016622136\n ],\n [\n -100.5029296875,\n 28.844673680771795\n ],\n [\n -99.7119140625,\n 27.586197857692664\n ],\n [\n -99.49218749999999,\n 27.078691552927534\n ],\n [\n -98.96484375,\n 26.352497858154024\n ],\n [\n -98.02001953125,\n 26.05678288577881\n ],\n [\n -97.36083984375,\n 25.780107118422244\n ],\n [\n -97.05322265625,\n 25.958044673317843\n ],\n [\n -97.294921875,\n 27.01998400798257\n ],\n [\n -96.78955078125,\n 27.955591004642553\n ],\n [\n -94.85595703125,\n 29.19053283229458\n ],\n [\n -93.8232421875,\n 29.554345125748267\n ],\n [\n -93.2080078125,\n 29.649868677972304\n ],\n [\n -92.28515625,\n 29.439597566602902\n ],\n [\n -92.0654296875,\n 29.439597566602902\n ],\n [\n -91.7578125,\n 29.286398892934763\n ],\n [\n -91.0986328125,\n 28.97931203672246\n ],\n [\n -89.71435546875,\n 28.998531814051795\n ],\n [\n -88.92333984375,\n 28.9023972285585\n ],\n [\n -88.330078125,\n 30.391830328088137\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-01","publicationStatus":"PW","scienceBaseUri":"56c6f93fe4b0946c6524072d","contributors":{"authors":[{"text":"Adams, Kimberly","contributorId":167057,"corporation":false,"usgs":false,"family":"Adams","given":"Kimberly","email":"","affiliations":[{"id":24605,"text":"Biology Department, Texas Christian University, Fort Worth, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":620875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drenner, Ray W.","contributorId":46407,"corporation":false,"usgs":true,"family":"Drenner","given":"Ray","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":620876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chumchal, Matthew M.","contributorId":84659,"corporation":false,"usgs":true,"family":"Chumchal","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":620877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donato, David I. 0000-0002-5412-0249 didonato@usgs.gov","orcid":"https://orcid.org/0000-0002-5412-0249","contributorId":2234,"corporation":false,"usgs":true,"family":"Donato","given":"David","email":"didonato@usgs.gov","middleInitial":"I.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":620874,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159804,"text":"70159804 - 2015 - Changes in seasonality and timing of peak streamflow in snow and semi-arid climates of the north-central United States, 1910–2012","interactions":[],"lastModifiedDate":"2017-10-12T20:00:47","indexId":"70159804","displayToPublicDate":"2015-11-24T16:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Changes in seasonality and timing of peak streamflow in snow and semi-arid climates of the north-central United States, 1910–2012","docAbstract":"Changes in the seasonality and timing of annual peak streamflow in the north-central USA are likely because of changes in precipitation and temperature regimes. A source of long-term information about flood events across the study area is the U.S. Geological Survey peak streamflow database. However, one challenge of answering climate-related questions with this dataset is that even in snowmelt-dominated areas, it is a mixed population of snowmelt/spring rain generated peaks and summer/fall rain generated peaks. Therefore, a process was developed to divide the annual peaks into two populations, or seasons, snowmelt/spring, and summer/fall. The two series were then tested for the hypotheses that because of changes in precipitation regimes, the odds of summer/fall peaks have increased and, because of temperature changes, snowmelt/spring peaks happen earlier. Over climatologically and geographically similar regions in the north-central USA, logistic regression was used to model the odds of getting a summer/fall peak. When controlling for antecedent wet and dry conditions and geographical differences, the odds of summer/fall peaks occurring have increased across the study area. With respect to timing within the seasons, trend analysis showed that in northern portions of the study region, snowmelt/spring peaks are occurring earlier. The timing of snowmelt/spring peaks in three regions in the northern part of the study area is earlier by 8.7– 14.3 days. These changes have implications for water interests, such as potential changes in lead-time for flood forecasting or changes in the operation of flood-control dams.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/hyp.10693","usgsCitation":"Ryberg, K.R., Akyuz, F.A., Wiche, G.J., and Lin, W., 2015, Changes in seasonality and timing of peak streamflow in snow and semi-arid climates of the north-central United States, 1910–2012: Hydrological Processes, v. 30, no. 8, p. 1208-1218, https://doi.org/10.1002/hyp.10693.","productDescription":"11 p.","startPage":"1208","endPage":"1218","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1910-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-059283","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":311699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, North Dakota, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.7353515625,\n 48.574789910928864\n ],\n [\n -91.23046875,\n 43.45291889355465\n ],\n [\n -90.263671875,\n 42.00032514831621\n ],\n [\n -91.4501953125,\n 40.38002840251183\n ],\n [\n -90.8349609375,\n 39.13006024213511\n ],\n [\n -102.1728515625,\n 38.61687046392973\n ],\n [\n -105.77636718749999,\n 38.92522904714054\n ],\n [\n -109.4677734375,\n 45.01141864227728\n ],\n [\n -113.09326171875,\n 48.99463598353408\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -93.7353515625,\n 48.574789910928864\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-11","publicationStatus":"PW","scienceBaseUri":"56558a32e4b071e7ea53dedf","chorus":{"doi":"10.1002/hyp.10693","url":"http://dx.doi.org/10.1002/hyp.10693","publisher":"Wiley-Blackwell","authors":"Ryberg Karen R., Akyüz F. Adnan, Wiche Gregg J., Lin Wei","journalName":"Hydrological Processes","publicationDate":"11/11/2015","auditedOn":"4/11/2016"},"contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akyuz, F. Adnan","contributorId":140760,"corporation":false,"usgs":false,"family":"Akyuz","given":"F.","email":"","middleInitial":"Adnan","affiliations":[{"id":13555,"text":"North Dakota Climate Office","active":true,"usgs":false}],"preferred":false,"id":580538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiche, Gregg J. gjwiche@usgs.gov","contributorId":1675,"corporation":false,"usgs":true,"family":"Wiche","given":"Gregg","email":"gjwiche@usgs.gov","middleInitial":"J.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lin, Wei","contributorId":93805,"corporation":false,"usgs":true,"family":"Lin","given":"Wei","email":"","affiliations":[],"preferred":false,"id":580537,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70158680,"text":"sir20155131 - 2015 - Aquifer geometry, lithology, and water levels in the Anza–Terwilliger area—2013, Riverside and San Diego Counties, California","interactions":[],"lastModifiedDate":"2015-11-25T08:14:40","indexId":"sir20155131","displayToPublicDate":"2015-11-24T16:00:00","publicationYear":"2015","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":"2015-5131","title":"Aquifer geometry, lithology, and water levels in the Anza–Terwilliger area—2013, Riverside and San Diego Counties, California","docAbstract":"The population of the Anza–Terwilliger area relies solely on groundwater pumped from the alluvial deposits and surrounding bedrock formations for water supply. The size, characteristics, and current conditions of the aquifer system in the Anza–Terwilliger area are poorly understood, however. In response to these concerns, the U.S. Geological Survey, in cooperation with the High Country Conservancy and Rancho California Water District, undertook a study to (1) improve mapping of groundwater basin geometry and lithology and (2) to resume groundwater-level monitoring last done during 2004–07 in the Anza–Terwilliger area.
\nInversion of gravity data, including new data collected for this study, was done to estimate the thickness of the alluvial deposits that form the Cahuilla and Terwilliger groundwater basins and to understand the geometry of the underlying basement complex. After processing of the gravity data, the thickness of the alluvial aquifer materials was modeled by using all available lithology, density, and geophysical data.
\nThe thickest alluvial deposits (greater than 500 feet) are in the northern part of the study area along the south side of the San Jacinto fault zone, in the southern part of the Cahuilla groundwater basin, and in the western part of the Terwilliger groundwater basin. Through most of the area of alluvial materials, the thickness of the alluvium estimated from gravity data is less than 400 feet.
\nAnalysis of more than 900 drillers’ logs indicated that in areas having relatively thick alluvium, particularly along the San Jacinto fault zone and in the Terwilliger Valley, the alluvium is predominantly composed of sands and gravels. Fine-textured sediments appeared to be discontinuous rather than forming laterally extensive, low-permeability layers. More than 500 drillers’ logs indicated only bedrock is present, indicating that the fractured bedrock is an important source of groundwater, primarily for domestic use, in the study area. The depths of the holes drilled into the bedrock indicated that fractures potentially supplying water to wells persist in the upper few hundred feet and that the permeable zone of the fractured bedrock extends to depths greater than weathered zones in the upper part of the basement complex.
\nWater-level data were collected from 59 wells during fall 2013. These data indicated that hydraulic head did not vary substantially with well depth and that the measured water levels in bedrock and alluvium were similar. Large offsets in groundwater altitude across the San Jacinto fault zone indicated that the fault zone is a barrier to groundwater flow in the northeastern part of the Anza Valley.
\nOn the basis of data from 33 wells, water levels mostly declined between the fall of 2006 and the fall of 2013; the median decline was 5.1 feet during this period, for a median rate of decline of about 0.7 feet/year. Based on data from 40 wells, water-level changes between fall 2004 and fall 2013 were variable in magnitude and trend, but had a median decline of 2.4 feet and a median rate of decline of about 0.3 feet/ year. These differences in apparent rates of groundwater-level change highlight the value of ongoing water-level measurements to distinguish decadal, or longer term, trends in groundwater storage often associated with climatic variability and trends. Fifty-four long-term hydrographs indicated the sensitivity of groundwater levels to climatic conditions; they also showed a general decline in water levels across the study area since 1986 and, in some cases, dating back to the 1950s.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155131","collaboration":"Prepared in cooperation with the High Country Conservancy and Rancho California Water District","usgsCitation":"Landon, M.K., Morita, A.Y., Nawikas, J.M., Christensen, A.H., Faunt, C.C., and Langenheim, V.E., 2015, Aquifer geometry, lithology, and water levels in the Anza–Terwilliger Area—2013, Riverside and San Diego Counties, California: U.S. Geological Survey Scientific Investigations Report 2015–5131, 30 p.\nhttps://dx.doi.org/10.3133/sir20155131.","productDescription":"Report: iv, 30 p.; Appendixes: 1-4","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2013-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-057158","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":311697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5131/sir20155131.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5131"},{"id":311696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5131/coverthb.jpg"},{"id":311698,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5131/sir20155131_appendixes.xlsx","text":"Appendixes 1–4","size":"422 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5131 Appendixes 1-4"}],"country":"United States","state":"California","county":"Riverside County, San Diego County","otherGeospatial":"Anza–Terwilliger Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.82174682617188,\n 33.426856918285004\n ],\n [\n -116.82174682617188,\n 33.59803478218408\n ],\n [\n -116.51481628417967,\n 33.59803478218408\n ],\n [\n -116.51481628417967,\n 33.426856918285004\n ],\n [\n -116.82174682617188,\n 33.426856918285004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
Wildlife managers need reliable methods to estimate large carnivore densities and population trends; yet large carnivores are elusive, difficult to detect, and occur at low densities making traditional approaches intractable. Recent advances in spatial capture-recapture (SCR) models have provided new approaches for monitoring trends in wildlife abundance and these methods are particularly applicable to large carnivores. We applied SCR models in a Bayesian framework to estimate mountain lion densities in the Bitterroot Mountains of west central Montana. We incorporate an existing resource selection function (RSF) as a density covariate to account for heterogeneity in habitat use across the study area and include data collected from harvested lions. We identify individuals through DNA samples collected by (1) biopsy darting mountain lions detected in systematic surveys of the study area, (2) opportunistically collecting hair and scat samples, and (3) sampling all harvested mountain lions. We included 80 DNA samples collected from 62 individuals in the analysis. Including information on predicted habitat use as a covariate on the distribution of activity centers reduced the median estimated density by 44%, the standard deviation by 7%, and the width of 95% credible intervals by 10% as compared to standard SCR models. Within the two management units of interest, we estimated a median mountain lion density of 4.5 mountain lions/100 km2 (95% CI = 2.9, 7.7) and 5.2 mountain lions/100 km2 (95% CI = 3.4, 9.1). Including harvested individuals (dead recovery) did not create a significant bias in the detection process by introducing individuals that could not be detected after removal. However, the dead recovery component of the model did have a substantial effect on results by increasing sample size. The ability to account for heterogeneity in habitat use provides a useful extension to SCR models, and will enhance the ability of wildlife managers to reliably and economically estimate density of wildlife populations, particularly large carnivores.
It is thought that grass carp (Ctenopharyngodon idella) eggs must remain suspended in the water column in order to hatch successfully. Using sand, the effects of varying sediment levels on grass carp eggs were tested at different developmental states and temperatures. Survival was high (15–35%, depending on temperature and trial) in the unburied treatment where eggs rested on a sand bed but were not covered by sediment. Survival was lower in the partial burial (5–10%) and very low (0–4%) in the full burial treatment. In all treatments, delayed hatching (organisms remaining in membranes past the stage of hatching competence) was noted. Deformities such as missing heads and pericardial edema occurred at high rates in the partial and full burials. Eggs that come in contact with the benthos and are resuspended in the water column should be considered in embryonic drift models.
","language":"English","publisher":"Wiley","doi":"10.1111/jai.12918","usgsCitation":"George, A.E., Chapman, D., Deters, J.E., Erwin, S.O., and Hayer, C., 2015, Effects of sediment burial on grass carp, Ctenopharyngodon idella (Valenciennes,1844), eggs: Journal of Applied Ichthyology, v. 31, no. 6, p. 1120-1126, https://doi.org/10.1111/jai.12918.","productDescription":"7 p.","startPage":"1120","endPage":"1126","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060743","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":471628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.12918","text":"Publisher Index Page"},{"id":311679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-12","publicationStatus":"PW","scienceBaseUri":"56558a32e4b071e7ea53dee1","contributors":{"authors":[{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":580503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":580504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deters, Joseph E. jdeters@usgs.gov","contributorId":3240,"corporation":false,"usgs":true,"family":"Deters","given":"Joseph","email":"jdeters@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":580505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erwin, Susannah O. 0000-0002-2799-0118 serwin@usgs.gov","orcid":"https://orcid.org/0000-0002-2799-0118","contributorId":5183,"corporation":false,"usgs":true,"family":"Erwin","given":"Susannah","email":"serwin@usgs.gov","middleInitial":"O.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":580506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayer, Cari-Ann chayer@usgs.gov","contributorId":150040,"corporation":false,"usgs":true,"family":"Hayer","given":"Cari-Ann","email":"chayer@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":580507,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}