The authors investigated the chronic toxicity of aqueous Pb to the amphipod Hyalella azteca (Hyalella) in 42-d tests using 2 different diets: 1) the yeastþcereal leafþtrout pellet (YCT) diet, fed at the uniform low ration used in standard methods for sediment toxicity tests; and 2) a new diet of diatomsþTetraMin flakes (DT), fed at increasing rations over time, that has been optimized for use in Hyalella water-only tests. Test endpoints included survival, weight, biomass, fecundity, and total young. Lethal effects of Pb were similar for the DT and YCT tests (20% lethal concentration [LC20]¼13 mg/L and 15mg/L, respectively, as filterable Pb). In contrast, weight and fecundity endpoints were not significantly affected in the DT test at Pb concentrations up to 63 mg/L, but these endpoints were significantly reduced by Pb in the YCT test—and in a 2005 test in the same laboratory with a diet of conditioned Rabbit Chow (RC-2005). The fecundity and total young endpoints from the YCT and RC-2005 tests were considered unreliable because fecundity in controls did not meet test acceptability criteria, but both of these tests still produced lower Pb effect concentrations (for weight or biomass) than the test with the DT diet. The lowest biotic ligand model–normalized effect concentrations for the 3 tests ranged from 3.7mg/L (weight 20% effect concentration [EC20] for the RC-2005 test) to 8.2 mg/L (total young EC20 for the DT test), values that would rank Hyalella as the second or third most sensitive of 13 genera in a species sensitivity distribution for chronic Pb toxicity. These results demonstrate that toxicity tests with Hyalella fed optimal diets can meet more stringent test acceptability criteria for control performance, but suggest that results of these tests may underestimate sublethal toxic effects of Pb to Hyalella under suboptimal feeding regimes.
","language":"English","publisher":"Setac Press","doi":"10.1002/etc.3341","usgsCitation":"Besser, J.M., Ivey, C.D., Brumbaugh, W.G., and Ingersoll, C.G., 2016, Effect of diet quality on chronic toxicity of aqueous lead to the amphipod Hyalella azteca: Environmental Toxicology and Chemistry, v. 35, p. 1825-1834, https://doi.org/10.1002/etc.3341.","productDescription":"10 p.","startPage":"1825","endPage":"1834","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063482","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":324365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","edition":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-18","publicationStatus":"PW","scienceBaseUri":"576e59aee4b07657d1a43c55","contributors":{"authors":[{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":640705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":640706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brumbaugh, William G. 0000-0003-0081-375X bbrumbaugh@usgs.gov","orcid":"https://orcid.org/0000-0003-0081-375X","contributorId":493,"corporation":false,"usgs":true,"family":"Brumbaugh","given":"William","email":"bbrumbaugh@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":640707,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":640708,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209271,"text":"70209271 - 2016 - Practical bias correction in aerial surveys of large mammals: Validation of hybrid double-observer with sightability method against known abundance of feral horse (Equus caballus) populations","interactions":[],"lastModifiedDate":"2020-03-26T11:47:16","indexId":"70209271","displayToPublicDate":"2016-05-03T11:31:24","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Practical bias correction in aerial surveys of large mammals: Validation of hybrid double-observer with sightability method against known abundance of feral horse (Equus caballus) populations","title":"Practical bias correction in aerial surveys of large mammals: Validation of hybrid double-observer with sightability method against known abundance of feral horse (Equus caballus) populations","docAbstract":"Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and <0.7 standard errors. Precision varied widely, from 6.1% CV to 25.0% CV. In contrast, the pair of surveys conducted around a known management removal produced an estimated change in population between the surveys that was significantly larger than the known reduction. Although the deviation between was only 9.1%, the precision estimate (CV = 1.6%) may have been artificially low. It was apparent that use of a helicopter in those surveys perturbed the horses, introducing detection error and heterogeneity in a manner that could not be corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0154902","usgsCitation":"Lubow, B., and Ransom, J.I., 2016, Practical bias correction in aerial surveys of large mammals: Validation of hybrid double-observer with sightability method against known abundance of feral horse (Equus caballus) populations: PLoS ONE, v. 11, no. 5, e0154902, 15 p., https://doi.org/10.1371/journal.pone.0154902.","productDescription":"e0154902, 15 p.","ipdsId":"IP-074372","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471028,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0154902","text":"Publisher Index Page"},{"id":373550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Nevada, Utah, Wyoming","otherGeospatial":"Cedar Mountain Herd Management Area, Little Owyhee Herd Management Area, McCullough Peaks Herd Management Area, Sand Wash Herd Management Area, Snowstorm Mountains Herd Management Area ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.00634765625,\n 44.308126684886126\n ],\n [\n -107.89672851562499,\n 44.308126684886126\n ],\n [\n -107.89672851562499,\n 44.98034238084973\n ],\n [\n -109.00634765625,\n 44.98034238084973\n ],\n [\n -109.00634765625,\n 44.308126684886126\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.984375,\n 40.26276066437183\n ],\n [\n -108.017578125,\n 40.26276066437183\n ],\n [\n -108.017578125,\n 40.896905775860006\n ],\n [\n -108.984375,\n 40.896905775860006\n ],\n [\n -108.984375,\n 40.26276066437183\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.48876953125,\n 39.9434364619742\n ],\n [\n -112.87353515625,\n 39.9434364619742\n ],\n [\n -112.87353515625,\n 40.6306300839918\n ],\n [\n -113.48876953125,\n 40.6306300839918\n ],\n [\n -113.48876953125,\n 39.9434364619742\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.24609374999999,\n 41.393294288784865\n ],\n [\n -116.52099609375,\n 41.393294288784865\n ],\n [\n -116.52099609375,\n 41.95131994679697\n ],\n [\n -117.24609374999999,\n 41.95131994679697\n ],\n [\n -117.24609374999999,\n 41.393294288784865\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-03","publicationStatus":"PW","contributors":{"editors":[{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":785658,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Lubow, Bruce C.","contributorId":131076,"corporation":false,"usgs":false,"family":"Lubow","given":"Bruce C.","affiliations":[{"id":7230,"text":"Natural Resource Ecology Laboratory, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":785656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Jason I.","contributorId":139841,"corporation":false,"usgs":false,"family":"Ransom","given":"Jason","email":"","middleInitial":"I.","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":785657,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170087,"text":"ofr20161030 - 2016 - Improve wildlife species tracking—Implementing an enhanced global positioning system data management system for California condors","interactions":[],"lastModifiedDate":"2016-05-03T10:16:16","indexId":"ofr20161030","displayToPublicDate":"2016-05-03T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1030","title":"Improve wildlife species tracking—Implementing an enhanced global positioning system data management system for California condors","docAbstract":"U.S. Fish and Wildlife Service (USFWS) staff in the Pacific Southwest Region and at the Hopper Mountain National Wildlife Refuge Complex requested technical assistance to improve their global positioning system (GPS) data acquisition, management, and archive in support of the California Condor Recovery Program. The USFWS deployed and maintained GPS units on individual Gymnogyps californianus (California condor) in support of long-term research and daily operational monitoring and management of California condors. The U.S. Geological Survey (USGS) obtained funding through the Science Support Program to provide coordination among project participants, provide GPS Global System for Mobile Communication (GSM) transmitters for testing, and compare GSM/GPS with existing Argos satellite GPS technology. The USFWS staff worked with private companies to design, develop, and fit condors with GSM/GPS transmitters. The Movebank organization, an online database of animal tracking data, coordinated with each of these companies to automatically stream their GPS data into Movebank servers and coordinated with USFWS to improve Movebank software for managing transmitter data, including proofing/error checking of incoming GPS data. The USGS arranged to pull raw GPS data from Movebank into the USGS California Condor Management and Analysis Portal (CCMAP) (https://my.usgs.gov/ccmap) for production and dissemination of a daily map of condor movements including various automated alerts. Further, the USGS developed an automatic archiving system for pulling raw and proofed Movebank data into USGS ScienceBase to comply with the Federal Information Security Management Act of 2002. This improved data management system requires minimal manual intervention resulting in more efficient data flow from GPS data capture to archive status. As a result of the project’s success, Pinnacles National Park and the Ventana Wildlife Society California condor programs became partners and adopted the same workflow, tracking, and data archive system. This GPS tracking data management model and workflow should be applicable and beneficial to other wildlife tracking programs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161030","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service, Region 8","usgsCitation":"Waltermire, R.G., Emmerich, C.U., Mendenhall, L.C., Bohrer, Gil, Weinzierl, R.P., McGann, A.J., Lineback, P.K., Kern, T.J., and Douglas, D.C., 2016, Improve wildlife species tracking—Implementing an enhanced global positioning system data management system for California condors: U.S. Geological Survey Open-File Report 2016–1030, 46 p., https://dx.doi.org/10.3133/ofr20161030.","productDescription":"vi, 46 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066019","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320820,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1030/ofr20161030.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1032"},{"id":320819,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1030/coverthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29980468749999,\n 37.07271048132946\n ],\n [\n -122.1240234375,\n 36.86204269508728\n ],\n [\n -121.79443359375,\n 36.87962060502676\n ],\n [\n -121.88232421875,\n 36.686041276581925\n ],\n [\n -122.05810546875,\n 36.63316209558658\n ],\n [\n -121.9482421875,\n 36.2265501474709\n ],\n [\n -121.53076171875,\n 35.871246850027966\n ],\n [\n -121.201171875,\n 35.60371874069731\n ],\n [\n -120.9814453125,\n 35.31736632923788\n ],\n [\n -120.95947265624999,\n 35.137879119634185\n ],\n [\n -120.76171875,\n 35.10193405724606\n ],\n [\n -120.65185546875,\n 34.66935854524543\n ],\n [\n -120.80566406250001,\n 34.52466147177172\n ],\n [\n -120.25634765624999,\n 34.34343606848294\n ],\n [\n -119.94873046875,\n 34.379712580462204\n ],\n [\n -119.59716796875,\n 34.34343606848294\n ],\n [\n -119.2236328125,\n 34.21634468843465\n ],\n [\n -118.91601562499999,\n 33.88865750124075\n ],\n [\n -118.5205078125,\n 33.88865750124075\n ],\n [\n -118.19091796875,\n 33.63291573870476\n ],\n [\n -117.83935546874999,\n 33.486435450999885\n ],\n [\n -117.6416015625,\n 33.87041555094183\n ],\n [\n -116.23535156249999,\n 34.03445260967645\n ],\n [\n -116.25732421875,\n 35.7286770448517\n ],\n [\n -116.89453125,\n 35.60371874069731\n ],\n [\n -117.22412109375,\n 36.38591277287651\n ],\n [\n -117.97119140625,\n 37.212831514455964\n ],\n [\n -118.47656249999999,\n 37.35269280367274\n ],\n [\n -118.95996093749999,\n 37.09023980307208\n ],\n [\n -119.77294921874999,\n 37.19533058280065\n ],\n [\n -120.34423828125,\n 37.28279464911045\n ],\n [\n -121.06933593749999,\n 37.23032838760387\n ],\n [\n -121.31103515625,\n 37.45741810262938\n ],\n [\n -121.55273437499999,\n 37.24782120155428\n ],\n [\n -121.9482421875,\n 37.125286284966805\n ],\n [\n -122.29980468749999,\n 37.07271048132946\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Dams impound the majority of rivers and provide important societal benefits, especially daily water releases that enable on-peak hydroelectricity generation. Such “hydropeaking” is common worldwide, but its downstream impacts remain unclear. We evaluated the response of aquatic insects, a cornerstone of river food webs, to hydropeaking using a life history–hydrodynamic model. Our model predicts that aquatic-insect abundance will depend on a basic life-history trait—adult egg-laying behavior—such that open-water layers will be unaffected by hydropeaking, whereas ecologically important and widespread river-edge layers, such as mayflies, will be extirpated. These predictions are supported by a more-than-2500-sample, citizen-science data set of aquatic insects from the Colorado River in the Grand Canyon and by a survey of insect diversity and hydropeaking intensity across dammed rivers of the Western United States. Our study reveals a hydropeaking-related life history bottleneck that precludes viable populations of many aquatic insects from inhabiting regulated rivers.
","language":"English","publisher":"American Institute of Biological Sciences","publisherLocation":"Washington, D.C.","doi":"10.1093/biosci/biw059","usgsCitation":"Kennedy, T.A., Muehlbauer, J.D., Yackulic, C.B., Lytle, D., Miller, S., Dibble, K.L., Kortenhoeven, E.W., Metcalfe, A.N., and Baxter, C., 2016, Flow management for hydropower extirpates aquatic insects, undermining river food webs: BioScience, v. 66, no. 7, p. 561-575, https://doi.org/10.1093/biosci/biw059.","productDescription":"15 p.","startPage":"561","endPage":"575","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069041","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471029,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biw059","text":"Publisher Index Page"},{"id":438616,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WM1BH4","text":"USGS data release","linkHelpText":"Flow management for hydropower extirpates aquatic insects, undermining river food websData"},{"id":320875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-02","publicationStatus":"PW","scienceBaseUri":"5729cbb2e4b0b13d3919a342","contributors":{"authors":[{"text":"Kennedy, Theodore A. 0000-0003-3477-3629 tkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":167537,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","email":"tkennedy@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muehlbauer, Jeffrey D. 0000-0003-1808-580X jmuehlbauer@usgs.gov","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":5045,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","email":"jmuehlbauer@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lytle, D.A.","contributorId":85422,"corporation":false,"usgs":true,"family":"Lytle","given":"D.A.","email":"","affiliations":[],"preferred":false,"id":628491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, S.A.","contributorId":66389,"corporation":false,"usgs":true,"family":"Miller","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":628492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dibble, Kimberly L. 0000-0003-0799-4477 kdibble@usgs.gov","orcid":"https://orcid.org/0000-0003-0799-4477","contributorId":5174,"corporation":false,"usgs":true,"family":"Dibble","given":"Kimberly","email":"kdibble@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kortenhoeven, Eric W. ekortenhoeven@usgs.gov","contributorId":5046,"corporation":false,"usgs":true,"family":"Kortenhoeven","given":"Eric","email":"ekortenhoeven@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628493,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Metcalfe, Anya N. 0000-0002-6286-4889 ametcalfe@usgs.gov","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":5271,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","email":"ametcalfe@usgs.gov","middleInitial":"N.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628494,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baxter, Colden V.","contributorId":47334,"corporation":false,"usgs":false,"family":"Baxter","given":"Colden V.","affiliations":[{"id":13656,"text":"Idaho State Univ.","active":true,"usgs":false}],"preferred":false,"id":628501,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70170721,"text":"70170721 - 2016 - Timing and composition of continental volcanism at Harrat Hutaymah, western Saudi Arabia","interactions":[],"lastModifiedDate":"2016-05-04T08:38:28","indexId":"70170721","displayToPublicDate":"2016-05-02T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Timing and composition of continental volcanism at Harrat Hutaymah, western Saudi Arabia","docAbstract":"Harrat Hutaymah is an alkali basalt volcanic field in north-central Saudi Arabia, at the eastern margin of a large Neogene continental, intraplate magmatic province. Lava flow, tephra and spatter cone compositions in the field include alkali olivine basalts and basanites. These compositions contrast with the predominantly tholeiitic, fissure-fed basalts found along the eastern margin of the Red Sea. The Hutaymah lava flows were erupted through Proterozoic arc-associated plutonic and meta-sedimentary rocks of the Arabian shield, and commonly contain a range of sub-continental lithospheric xenoliths, although the lavas themselves show little indication of crustal contamination. Previous radiometric dating of this volcanic field (a single published K–Ar age; 1.8 Ma) is suspiciously old given the field measurement of normal magnetic polarity only (i.e. Brunhes interval, ≤ 780 Ka). We report new age determinations on 14 lava flows by the 40Ar–39Ar laser step heating method, all younger than ~ 850 Ka, to better constrain the time frame of volcanism, and major, trace and rare earth element compositions to describe the chemical variation of volcanic activity at Harrat Hutaymah. Crystal fractionation was dominated by olivine ± clinopyroxene at a range of upper mantle and crustal pressures. Rapid ascent and eruption of magma is indicated by the array of lower crustal and lithospheric xenoliths observed in lava flows and tephra. Modeling suggests 1–7% melting of an enriched asthenospheric mantle source occurred beneath Harrat Hutaymah under a relatively thick lithospheric cap (60–80 km).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2016.01.010","usgsCitation":"Duncan, R.A., Kent, A.J., Thornber, C., Schliedler, T.D., and Al-Amri, A.M., 2016, Timing and composition of continental volcanism at Harrat Hutaymah, western Saudi Arabia: Journal of Volcanology and Geothermal Research, v. 313, p. 1-14, https://doi.org/10.1016/j.jvolgeores.2016.01.010.","productDescription":"14 p.","startPage":"1","endPage":"14","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070061","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":471030,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2016.01.010","text":"Publisher Index Page"},{"id":320813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Saudi Arabia","otherGeospatial":"Harrat Hutaymah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 30,\n 1\n ],\n [\n 30,\n 40\n ],\n [\n 55,\n 40\n ],\n [\n 55,\n 1\n ],\n [\n 30,\n 1\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"313","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57286c1be4b0b13d3917ce12","contributors":{"authors":[{"text":"Duncan, Robert A.","contributorId":167399,"corporation":false,"usgs":false,"family":"Duncan","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":628172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kent, Adam J R","contributorId":168855,"corporation":false,"usgs":false,"family":"Kent","given":"Adam","email":"","middleInitial":"J R","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":628173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thornber, Carl 0000-0002-6382-4408 cthornber@usgs.gov","orcid":"https://orcid.org/0000-0002-6382-4408","contributorId":167396,"corporation":false,"usgs":true,"family":"Thornber","given":"Carl","email":"cthornber@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":628171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schliedler, Tyler D","contributorId":169027,"corporation":false,"usgs":false,"family":"Schliedler","given":"Tyler","email":"","middleInitial":"D","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":628174,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Al-Amri, Abdullah M","contributorId":169028,"corporation":false,"usgs":false,"family":"Al-Amri","given":"Abdullah","email":"","middleInitial":"M","affiliations":[{"id":24707,"text":"King Saud University, Riyahd, KSA","active":true,"usgs":false}],"preferred":false,"id":628175,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70162383,"text":"sir20165008 - 2016 - Geology of tight oil and potential tight oil reservoirs in the lower part of the Green River Formation, Uinta, Piceance, and Greater Green River Basins, Utah, Colorado, and Wyoming","interactions":[],"lastModifiedDate":"2016-05-02T10:42:55","indexId":"sir20165008","displayToPublicDate":"2016-05-02T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5008","title":"Geology of tight oil and potential tight oil reservoirs in the lower part of the Green River Formation, Uinta, Piceance, and Greater Green River Basins, Utah, Colorado, and Wyoming","docAbstract":"The recent successful development of a tight oil play in the Eocene-age informal Uteland Butte member of the lacustrine Green River Formation in the Uinta Basin, Utah, using modern horizontal drilling and hydraulic fracturing techniques has spurred a renewed interest in the tight oil potential of lacustrine rocks. The Green River Formation was deposited by two large lakes, Lake Uinta in the Uinta and Piceance Basins and Lake Gosiute in the Greater Green River Basin. These three basins contain the world’s largest in-place oil shale resources with recent estimates of 1.53 trillion, 1.33 trillion, and 1.44 trillion barrels of oil in place in the Piceance, Uinta, and Greater Green River Basins, respectively. The Uteland Butte member was deposited during an early freshwater stage of the lake in the Uinta Basin prior to deposition of the assessed oil shale intervals. This report only presents information on the early freshwater interval and overlying brackish-water interval in all three basins because these intervals are most likely to have tight oil potential. Burial histories of the three basins were reconstructed to study (1) variations in subsidence and lake development, and (2) post deposition burial that led to the development of a petroleum system in only the Uinta Basin. The Uteland Butte member is a successful tight oil play because it is thermally mature for hydrocarbon generation and contains organic-rich shale, brittle carbonate, and porous dolomite. Abnormally high pressure in parts of the Uteland Butte is also important to production. Variations in organic richness of the Uteland Butte were studied using Fischer assay analysis from oil shale assessments, and pressures were studied using drill-stem tests. Freshwater lacustrine intervals in the Piceance and Greater Green River Basins are immature for hydrocarbon generation and contain much less carbonate than the Uteland Butte member. The brackish-water interval in the Uinta Basin is thermally mature for hydrocarbon generation but is clay-rich and contains little carbonate, and thus is a poor prospect for tight oil development.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165008","usgsCitation":"Johnson, R.C., Birdwell, J.E., Mercier, T.J., and Brownfield, M.E., 2016, Geology of tight oil and potential tight oil reservoirs in the lower part of the Green River Formation, Uinta, Piceance, and Greater Green River Basins, Utah, Colorado, and Wyoming: U.S. Geological Survey Scientific Investigations Report 2016–5008, 63 p., https://dx.doi.org/10.3133/sir20165008.","productDescription":"Report: vii, 63 p.; Table 1; Figure 29","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059890","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":320649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5008/coverthb.jpg"},{"id":320650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5008/sir20165008.pdf","text":"Report","size":"53.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5008"},{"id":320651,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2016/5008/sir20165008_fig29.pdf","text":"Figure 29","size":"1.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5008 Figure 29"},{"id":320672,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5008/sir20165008_Table1UtelandFischerassay.xlsx","text":"Table 1","size":"68.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5008 Table 1"}],"country":"United States","state":"Colorado, Utah, Wyoming","otherGeospatial":"Green River Basin, Piceance River Basin, Uinta River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.566162109375,\n 38.62545397209084\n ],\n [\n -111.566162109375,\n 43.30119623257966\n ],\n [\n -106.336669921875,\n 43.30119623257966\n ],\n [\n -106.336669921875,\n 38.62545397209084\n ],\n [\n -111.566162109375,\n 38.62545397209084\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver Federal Center
Denver, CO 80225-0046
Population viability analysis (PVA) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density-dependence, environmental stochasticity, and specification of uncertainties. Developing a fully specified population model from commonly available data sources – notably, mark–recapture studies – remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. We present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark–recapture dataset. Unlike standard mark–recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. Furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. We apply this approach to 9 bird species and demonstrate the feasibility of using data from the Monitoring Avian Productivity and Survivorship (MAPS) program. Bias-correction factors for raw estimates of survival and fecundity derived from mark–recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. Our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of PVA. This method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern.
","language":"English","publisher":"Elsevier Science Ltd.","publisherLocation":"Kidlington, Oxford","doi":"10.1016/j.biocon.2016.02.031","collaboration":"Stony Brook University; University of Wisconsin-Madison; U.S. Fish and Wildlife Service","usgsCitation":"Ryu, H.Y., Kevin T. Shoemaker, Kneip, E., Anna Pidgeon, Heglund, P., Bateman, B., Thogmartin, W.E., and Akcakaya, R., 2016, Developing population models with data from marked individuals: Biological Conservation, v. 197, p. 190-199, https://doi.org/10.1016/j.biocon.2016.02.031.","productDescription":"10 p.","startPage":"190","endPage":"199","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066416","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":320884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"197","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5729cbafe4b0b13d3919a2ef","contributors":{"authors":[{"text":"Ryu, Hae Yeong","contributorId":169059,"corporation":false,"usgs":false,"family":"Ryu","given":"Hae","email":"","middleInitial":"Yeong","affiliations":[{"id":25401,"text":"Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":628323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kevin T. 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The primary purpose of these guidelines is to provide a summary of existing fish swimming and leaping performance data and the best available scientific information on safe, timely and effective passage for 14 diadromous fish species using Atlantic Coast rivers and streams. These guidelines apply to passage sites where complete barrier removal is not possible. This technical memorandum presents seven key physical design parameters based on the biometrics and swimming mode and performance of each target fishes for application in the design of NLFs addressing passage of a species or an assemblage of these species. The passage parameters include six dimensional guidelines recommended for minimum weir opening width and depth, minimum pool length, width and depth, and maximum channel slope, along with a maximum flow velocity guideline for each species. While\r\nthese guidelines are targeted for the design of step‐pool NLFs, the information may also have application in the design of other NLF types being considered at passage restoration sites and grade control necessary for infrastructure protection upstream of some dam removals, and in considering passage performance at sites such as natural bedrock features.","language":"English","publisher":"NOAA National Marine Fisheries Service","usgsCitation":"Turek, J., Haro, A.J., and Towler, B., 2016, Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes, iii., 48 p.","productDescription":"iii., 48 p.","ipdsId":"IP-064934","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":336275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321862,"type":{"id":15,"text":"Index Page"},"url":"https://repository.library.noaa.gov/view/noaa/28919"}],"country":"United States","otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.53543042272173,\n 25.604386278077925\n ],\n [\n -78.9738695738202,\n 26.2643242769976\n ],\n [\n -77.65563187117027,\n 27.236312736346505\n ],\n [\n -77.71124176588978,\n 30.200566890880694\n ],\n [\n -75.63764563171657,\n 31.546671317157745\n ],\n [\n -72.88778473754125,\n 34.107153920647505\n ],\n [\n -71.80667946744646,\n 38.30485849898287\n ],\n [\n -68.12544832929673,\n 40.622781401735494\n ],\n [\n -66.51825275575436,\n 43.557615229438966\n ],\n [\n -66.93056853663478,\n 44.9924864367367\n ],\n [\n -70.50837479109403,\n 44.130583189469974\n ],\n [\n -71.56583190151437,\n 42.4531846455987\n ],\n [\n -71.04268393869013,\n 41.91302433499763\n ],\n [\n -73.12852497871336,\n 41.628116403040906\n ],\n [\n -74.47837257632011,\n 40.75996139502365\n ],\n [\n -74.85717819557229,\n 39.49615977300374\n ],\n [\n -75.36275241829512,\n 40.14818872121907\n ],\n [\n -76.70262042023853,\n 39.66328529391899\n ],\n [\n -77.26910128913559,\n 38.52431672198591\n ],\n [\n -76.80124506434308,\n 36.52548541174258\n ],\n [\n -77.48886662326828,\n 34.86340904706914\n ],\n [\n -78.19352283187868,\n 34.321846961811204\n ],\n [\n -79.02627770062944,\n 34.25764313694468\n ],\n [\n -79.67952029102847,\n 33.43439936645508\n ],\n [\n -81.32614282245044,\n 32.564707556457805\n ],\n [\n -82.03773749487134,\n 30.379248385899615\n ],\n [\n -80.44494631692717,\n 26.744769560981013\n ],\n [\n -80.53543042272173,\n 25.604386278077925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b548c1e4b01ccd54fddfb8","contributors":{"authors":[{"text":"Turek, James","contributorId":169721,"corporation":false,"usgs":false,"family":"Turek","given":"James","email":"","affiliations":[{"id":25574,"text":"NOAA/NMFS Restoration Center","active":true,"usgs":false}],"preferred":false,"id":630824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haro, Alexander J. 0000-0002-7188-9172 aharo@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-9172","contributorId":2917,"corporation":false,"usgs":true,"family":"Haro","given":"Alexander","email":"aharo@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":630823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Towler, Brett","contributorId":141164,"corporation":false,"usgs":false,"family":"Towler","given":"Brett","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":630825,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70177914,"text":"70177914 - 2016 - Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","interactions":[],"lastModifiedDate":"2016-10-26T11:50:49","indexId":"70177914","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA","docAbstract":"Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2016.01.003","usgsCitation":"Groff, S.C., Loftin, C., Drummond, F., Bushmann, S., and McGill, B.J., 2016, Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA: Environmental Modelling and Software, v. 79, p. 1-9, https://doi.org/10.1016/j.envsoft.2016.01.003.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-064763","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":488536,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2016.01.003","text":"Publisher Index Page"},{"id":330404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.95294189453125,\n 43.98491011404692\n ],\n [\n -68.95294189453125,\n 44.904523389609324\n ],\n [\n -67.1978759765625,\n 44.904523389609324\n ],\n [\n -67.1978759765625,\n 43.98491011404692\n ],\n [\n -68.95294189453125,\n 43.98491011404692\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5811c0f2e4b0f497e79a5a6f","chorus":{"doi":"10.1016/j.envsoft.2016.01.003","url":"http://dx.doi.org/10.1016/j.envsoft.2016.01.003","publisher":"Elsevier BV","authors":"Groff Shannon C., Loftin Cynthia S., Drummond Frank, Bushmann Sara, McGill Brian","journalName":"Environmental Modelling & Software","publicationDate":"5/2016","auditedOn":"2/1/2017","publiclyAccessibleDate":"1/29/2017"},"contributors":{"authors":[{"text":"Groff, Shannon C.","contributorId":176308,"corporation":false,"usgs":false,"family":"Groff","given":"Shannon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":652153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":652139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drummond, Frank","contributorId":176309,"corporation":false,"usgs":false,"family":"Drummond","given":"Frank","email":"","affiliations":[],"preferred":false,"id":652154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bushmann, Sara","contributorId":176310,"corporation":false,"usgs":false,"family":"Bushmann","given":"Sara","email":"","affiliations":[],"preferred":false,"id":652155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGill, Brian J.","contributorId":146422,"corporation":false,"usgs":false,"family":"McGill","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":652156,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178062,"text":"70178062 - 2016 - Developing fish trophic interaction indicators of climate change for the Great Lakes","interactions":[],"lastModifiedDate":"2016-11-01T15:10:34","indexId":"70178062","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Developing fish trophic interaction indicators of climate change for the Great Lakes","docAbstract":"This project addressed regional climate change effects on aquatic food webs in the Great Lakes. We sought insights by examining Lake Erie as a representative system with a high level of anthropogenic impacts, strong nutrient gradients, seasonal hypoxia, and spatial overlap of cold- and cool-water fish guilds. In Lake Erie and in large embayments throughout the Great Lakes basin, this situation is a concern for fishery managers, as climate change may exacerbate hypoxia and reduce habitat volume for some species. We examined fish community composition, fine-scale distribution, prey availability, diets, and biochemical tracers for dominant fishes from study areas with medium-high nutrient levels (mesotrophic, Fairport study area), and low nutrient levels (oligotrophic, Erie study area). This multi-year database (2011-2013) provides the ability to contrast years with wide variation in rainfall, winter ice-cover, and thermal stratification. In addition, multiple indicators of dietary and distributional responses to environmental variability will allow resource managers to select the most informative approach for addressing specific climate change questions. Our results support the incorporation of some relatively simple and cost-efficient approaches into existing agency monitoring programs to track the near-term condition status of fish and fish community composition by functional groupings. Other metrics appear better suited for understanding longer-term changes, and may take more resources to implement on an ongoing basis. Although we hypothesized that dietary overlap and similarity in selected species would be sharply different during thermal stratification and hypoxic episodes, we found little evidence of this. Instead, to our surprise, this study found that fish tended to aggregate at the edges of hypoxia, highlighting potential spatial changes in catch efficiency of the fishery. This work has had several positive impacts on a wide range of resource management and stakeholder activities, most notably in Lake Erie. The results were instrumental in the development of an interim decision rule for dealing with data collected during hypoxic events to improve stock assessment of Yellow Perch. In addition, novel findings from this study regarding spatial and temporal variability in hypoxia have aided US-Environmental Protection Agency in the development of a modified sampling protocol to more accurately quantify the central basin hypoxic zone, and this directly addressed a goal of the Great Lakes Water Quality Agreement of 2012 to reduce the extent and severity of hypoxia. Finally, the study areas developed in this project formed the basis for food web sampling in the 2014 bi-national Coordinated Science and Monitoring Initiative work in Lake Erie.
","language":"English","publisher":"Northeast Climate Science Center","usgsCitation":"Kraus, R.T., Knight, C.T., Gorman, A.M., Kocovsky, P.M., Weidel, B., and Rogers, M.W., 2016, Developing fish trophic interaction indicators of climate change for the Great Lakes, 70 p.","productDescription":"70 p.","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":330639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330638,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necsc.umass.edu/biblio/developing-fish-trophic-interaction-indicators-climate-change-great-lakes"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5819a9c4e4b0bb36a4c91029","contributors":{"authors":[{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Carey T.","contributorId":56529,"corporation":false,"usgs":true,"family":"Knight","given":"Carey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":652684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Ann Marie","contributorId":145525,"corporation":false,"usgs":false,"family":"Gorman","given":"Ann","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":652685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":652686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":652688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176812,"text":"70176812 - 2016 - To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors","interactions":[],"lastModifiedDate":"2016-10-11T15:27:47","indexId":"70176812","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors","docAbstract":"Roads are significant barriers to landscape-scale movements of individuals or populations of many wildlife taxa. The decision by an animal near a road to either cross or not cross may be influenced by characteristics of the road, environmental conditions, traits of the individual animal, and other aspects of the context within which the decision is made. We considered such factors in a mixed-effects logistic regression model describing the nightly road crossing probabilities of invasive nocturnal Brown Treesnakes (Boiga irregularis) through short-term radiotracking of 691 snakes within close proximity to 50 road segments across the island of Guam. All measures of road magnitude (traffic volume, gap width, surface type, etc.) were significantly negatively correlated with crossing probabilities. Snake body size was the only intrinsic factor associated with crossing rates, with larger snakes crossing roads more frequently. Humidity was the only environmental variable affecting crossing rate. The distance of the snake from the road at the start of nightly movement trials was the most significant predictor of crossings. The presence of snake traps with live mouse lures during a portion of the trials indicated that localized prey cues reduced the probability of a snake crossing the road away from the traps, suggesting that a snake's decision to cross roads is influenced by local foraging opportunities. Per capita road crossing rates of Brown Treesnakes were very low, and comparisons to historical records suggest that crossing rates have declined in the 60+ yr since introduction to Guam. We report a simplified model that will allow managers to predict road crossing rates based on snake, road, and contextual characteristics. Road crossing simulations based on actual snake size distributions demonstrate that populations with size distributions skewed toward larger snakes will result in a higher number of road crossings. Our method of modeling per capita road crossing probabilities as a binary response variable, influenced by contextual factors, may be useful for describing or predicting road crossings by individuals of other taxa provided that appropriate spatial and temporal resolution can be achieved and that potentially influential covariate data can be obtained.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1292","usgsCitation":"Siers, S.R., Reed, R., and Savidge, J.A., 2016, To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors: Ecosphere, v. 7, no. 5, e01292; 19 p., https://doi.org/10.1002/ecs2.1292.","productDescription":"e01292; 19 p.","ipdsId":"IP-069215","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1292","text":"Publisher Index Page"},{"id":329465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-31","publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d143713","contributors":{"authors":[{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":650394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70161743,"text":"70161743 - 2016 - Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","interactions":[],"lastModifiedDate":"2016-06-28T16:09:00","indexId":"70161743","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","docAbstract":"We present a revised ground‐motion prediction equation (GMPE) for computing medians and standard deviations of peak ground acceleration (PGA) and 5% damped pseudospectral acceleration (PSA) response ordinates of the horizontal component of randomly oriented ground motions to be used for seismic‐hazard analyses and engineering applications. This GMPE is derived from the expanded Next Generation Attenuation (NGA)‐West 1 database (see Data and Resources; Chiou et al., 2008). The revised model includes an anelastic attenuation term as a function of quality factor (Q0) to capture regional differences in far‐source (beyond 150 km) attenuation, and a new frequency‐dependent sedimentary‐basin scaling term as a function of depth to the 1.5 km/s shear‐wave velocity isosurface to improve ground‐motion predictions at sites located on deep sedimentary basins. The new Graizer–Kalkan 2015 (GK15) model, developed to be simple, is applicable for the western United States and other similar shallow crustal continental regions in active tectonic environments for earthquakes with moment magnitudes (M) 5.0–8.0, distances 0–250 km, average shear‐wave velocities in the upper 30 m (VS30) 200–1300 m/s, and spectral periods (T) 0.01–5 s. Our aleatory variability model captures interevent (between‐event) variability, which decreases with magnitude and increases with distance. The mixed‐effect residuals analysis reveals that the GK15 has no trend with respect to the independent predictor parameters. Compared to our 2007–2009 GMPE, the PGA values are very similar, whereas spectral ordinates predicted are larger at T<0.2 s and they are smaller at longer periods.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"El Cerito, CA","doi":"10.1785/0120150194","usgsCitation":"Graizer, V., and Kalkan, E., 2016, Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 687-707, https://doi.org/10.1785/0120150194.","productDescription":"21 p.","startPage":"687","endPage":"707","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065326","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":324563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Armenia, Georgia, Italy, Taiwan, Turkey, United States, Uzbekistan","state":"Alaska, California, Nevada","volume":"106","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-06","publicationStatus":"PW","scienceBaseUri":"57739fb7e4b07657d1a90d78","contributors":{"authors":[{"text":"Graizer, Vladimir;","contributorId":152040,"corporation":false,"usgs":false,"family":"Graizer","given":"Vladimir;","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":587625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":587624,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159049,"text":"70159049 - 2016 - Estimating forest and woodland aboveground biomass using active and passive remote sensing","interactions":[],"lastModifiedDate":"2016-06-01T13:43:53","indexId":"70159049","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Estimating forest and woodland aboveground biomass using active and passive remote sensing","docAbstract":"Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.
","language":"English","publisher":"Ingenta","doi":"10.14358/PERS.82.4.271","usgsCitation":"Wu, Z., Dye, D.G., Vogel, J.M., and Middleton, B.R., 2016, Estimating forest and woodland aboveground biomass using active and passive remote sensing: Photogrammetric Engineering and Remote Sensing, v. 82, no. 4, p. 271-281, https://doi.org/10.14358/PERS.82.4.271.","productDescription":"11 p.","startPage":"271","endPage":"281","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-058621","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":471037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.82.4.271","text":"Publisher Index Page"},{"id":322022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57500761e4b0ee97d51bb5ca","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dye, Dennis G. 0000-0002-7100-272X ddye@usgs.gov","orcid":"https://orcid.org/0000-0002-7100-272X","contributorId":4233,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","email":"ddye@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vogel, John M. 0000-0002-8226-1188 jvogel@usgs.gov","orcid":"https://orcid.org/0000-0002-8226-1188","contributorId":3167,"corporation":false,"usgs":true,"family":"Vogel","given":"John","email":"jvogel@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577542,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":577543,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70187263,"text":"70187263 - 2016 - Do transmitters affect survival and body condition of American beavers Castor canadensis?","interactions":[],"lastModifiedDate":"2017-04-27T10:43:33","indexId":"70187263","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Do transmitters affect survival and body condition of American beavers Castor canadensis?","docAbstract":"One key assumption often inferred with using radio-equipped individuals is that the transmitter has no effect on the metric of interest. To evaluate this assumption, we used a known fate model to assess the effect of transmitter type (i.e. tail-mounted or peritoneal implant) on short-term (one year) survival and a joint live—dead recovery model and results from a mark—recapture study to compare long-term (eight years) survival and body condition of ear-tagged only American beavers Castor canadensis to those equipped with radio transmitters in Voyageurs National Park, Minnesota, USA. Short-term (1-year) survival was not influenced by transmitter type (wi = 0.64). Over the 8-year study period, annual survival was similar between transmitter-equipped beavers (tail-mounted and implant transmitters combined; 0.76; 95% CI = 0.45–0.91) versus ear-tagged only (0.78; 95% CI = 0.45–0.93). Additionally, we found no difference in weight gain (t9 = 0.25, p = 0.80) or tail area (t11 = 1.25, p = 0.24) from spring to summer between the two groups. In contrast, winter weight loss (t22 = - 2.03, p = 0.05) and tail area decrease (t30 = - 3.04, p = 0.01) was greater for transmitterequipped (weight = - 3.09 kg, SE = 0.55; tail area = - 33.71 cm2, SE = 4.80) than ear-tagged only (weight = - 1.80 kg, SE = 0.33; tail area = - 12.38 cm2, SE = 5.13) beavers. Our results generally support the continued use of transmitters on beavers for estimating demographic parameters, although we recommend additional assessments of transmitter effects under different environmental conditions.
","language":"English","publisher":"Nordic Board for Wildlife Research","doi":"10.2981/wlb.00160","usgsCitation":"Smith, J.B., Windels, S.K., Wolf, T., Klaver, R.W., and Belant, J.L., 2016, Do transmitters affect survival and body condition of American beavers Castor canadensis?: Wildlife Biology, v. 22, no. 3, p. 117-123, https://doi.org/10.2981/wlb.00160.","productDescription":"7 p.","startPage":"117","endPage":"123","ipdsId":"IP-069209","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":471036,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00160","text":"Publisher Index Page"},{"id":340493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59030325e4b0e862d230f725","contributors":{"authors":[{"text":"Smith, Joshua B.","contributorId":71883,"corporation":false,"usgs":true,"family":"Smith","given":"Joshua","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":693143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windels, Steve K.","contributorId":182422,"corporation":false,"usgs":false,"family":"Windels","given":"Steve","email":"","middleInitial":"K.","affiliations":[{"id":18939,"text":"Voyageurs National Park","active":true,"usgs":false}],"preferred":false,"id":693144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wolf, Tiffany","contributorId":191470,"corporation":false,"usgs":false,"family":"Wolf","given":"Tiffany","affiliations":[],"preferred":false,"id":693145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":693121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belant, Jerrold L.","contributorId":108394,"corporation":false,"usgs":false,"family":"Belant","given":"Jerrold","email":"","middleInitial":"L.","affiliations":[{"id":35599,"text":"Carnivore Ecology Laboratory, Mississippi State University, Mississippi State, MS","active":true,"usgs":false}],"preferred":false,"id":693146,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191143,"text":"70191143 - 2016 - Economic impacts of a California tsunami","interactions":[],"lastModifiedDate":"2017-09-27T16:56:47","indexId":"70191143","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2823,"text":"Natural Hazards Review","active":true,"publicationSubtype":{"id":10}},"title":"Economic impacts of a California tsunami","docAbstract":"The economic consequences of a tsunami scenario for Southern California are estimated using computable general equilibrium analysis. The economy is modeled as a set of interconnected supply chains interacting through markets but with explicit constraints stemming from property damage and business downtime. Economic impacts are measured by the reduction of Gross Domestic Product for Southern California, Rest of California, and U.S. economies. For California, total economic impacts represent the general equilibrium (essentially quantity and price multiplier) effects of lost production in industries upstream and downstream in the supply-chain of sectors that are directly impacted by port cargo disruptions at Port of Los Angeles and Port of Long Beach (POLA/POLB), property damage along the coast, and evacuation of potentially inundated areas. These impacts are estimated to be $2.2 billion from port disruptions, $0.9 billion from property damages, and $2.8 billion from evacuations. Various economic-resilience tactics can potentially reduce the direct and total impacts by 80–85%.
BACKGROUND
Genetically modified (GM) varieties of soybean, corn and cotton have largely replaced conventional varieties in the United States. The most widely used applications of GM technology have been the development of crops that are resistant to a specific broad-spectrum herbicide (primarily glyphosate) or that produce insecticidal compounds within the plant itself. With the widespread adoption of GM crops, a decline in the use of conventional pesticides was expected.
RESULTS
There has been a reduction in the annual herbicide application rate to corn since the advent of GM crops, but the herbicide application rate is mostly unchanged for cotton. Herbicide use on soybean has increased. There has been a substantial reduction in the amount of insecticides used on both corn and cotton since the introduction of GM crops.
CONCLUSIONS
The observed changes in pesticide use are likely to be the result of many factors, including the introduction of GM crops, regulatory restrictions on some conventional pesticides, introduction of new pesticide technologies and changes in farming practices. In order to help protect human and environmental health and to help agriculture plan for the future, more detailed and complete documentation on pesticide use is needed on a frequent and ongoing basis.
","language":"English","publisher":"Wiley","doi":"10.1002/ps.4082","usgsCitation":"Coupe, R.H., and Capel, P.D., 2016, Trends in pesticide use on soybean, corn and cotton since the introduction of major genetically modified crops in the United States: Pest Management Science, v. 72, no. 5, p. 1013-1022, https://doi.org/10.1002/ps.4082.","productDescription":"10 p.","startPage":"1013","endPage":"1022","ipdsId":"IP-066541","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":347436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"72","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-08-10","publicationStatus":"PW","scienceBaseUri":"5a07ea42e4b09af898c8cc70","contributors":{"authors":[{"text":"Coupe, Richard H. 0000-0001-8679-1015 rhcoupe@usgs.gov","orcid":"https://orcid.org/0000-0001-8679-1015","contributorId":551,"corporation":false,"usgs":true,"family":"Coupe","given":"Richard","email":"rhcoupe@usgs.gov","middleInitial":"H.","affiliations":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true}],"preferred":true,"id":716096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":716095,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192021,"text":"70192021 - 2016 - Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure","interactions":[],"lastModifiedDate":"2017-10-25T15:51:49","indexId":"70192021","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure","docAbstract":"Wind energy is a rapidly expanding industry with potential indirect effects to wildlife populations that are largely unexplored. In 2011 and 2012, we monitored 211 nests of 2 grassland songbirds, Horned Lark (Eremophila alpestris) and McCown's Longspur (Rhynchophanes mccownii), at 3 wind farms and 2 undeveloped reference sites in Wyoming, USA. We evaluated several indices of reproductive investment and success: clutch size, size-adjusted nestling mass, daily nest survival rate, and number of fledglings. We compared reproductive success between wind farms and undeveloped sites and modeled reproductive success within wind farms as a function of wind energy infrastructure and habitat. Size-adjusted nestling mass of Horned Lark was weakly negatively related to turbine density. In 2011, nest survival of Horned Lark decreased 55% as turbine density increased from 10 to 39 within 2 km of the nest. In 2012, however, nest survival of Horned Lark was best predicted by the combination of vegetation height, distance to shrub edge, and turbine density, with survival increasing weakly with increasing vegetation height. McCown's Longspur nest survival was weakly positively related to vegetation density at the nest site when considered with the amount of grassland habitat in the neighborhood and turbine density within 1 km of the nest. Habitat and distance to infrastructure did not explain clutch size or number of fledglings for either species, or size-adjusted nestling mass for McCown's Longspur. Our results suggest that the influence of wind energy infrastructure varies temporally and by species, even among species using similar habitats. Turbine density was repeatedly the most informative measure of wind energy development. Turbine density could influence wildlife responses to wind energy production and may become increasingly important to consider as development continues in areas with high-quality wind resources.
","language":"English","publisher":"American Ornithological Society","doi":"10.1650/CONDOR-15-25.1","usgsCitation":"Mahoney, A., and Chalfoun, A.D., 2016, Reproductive success of Horned Lark and McCown's Longspur in relation to wind energy infrastructure: The Condor, v. 118, no. 2, p. 360-375, https://doi.org/10.1650/CONDOR-15-25.1.","productDescription":"16 p.","startPage":"360","endPage":"375","ipdsId":"IP-064787","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-25.1","text":"Publisher Index Page"},{"id":347413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"118","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f1a2a7e4b0220bbd9d9f7c","contributors":{"authors":[{"text":"Mahoney, Anika","contributorId":198389,"corporation":false,"usgs":false,"family":"Mahoney","given":"Anika","email":"","affiliations":[],"preferred":false,"id":715909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chalfoun, Anna D. achalfoun@usgs.gov","contributorId":3735,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":713852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70186182,"text":"70186182 - 2016 - Demographic outcomes of diverse migration strategies assessed in a metapopulation of tundra swans","interactions":[],"lastModifiedDate":"2017-03-31T10:15:09","indexId":"70186182","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Demographic outcomes of diverse migration strategies assessed in a metapopulation of tundra swans","docAbstract":"Background\nMigration is a prominent aspect of the life history of many avian species, but the demographic consequences of variable migration strategies have only infrequently been investigated, and rarely when using modern technological and analytical methods for assessing survival, movement patterns, and long-term productivity in the context of life history theory. We monitored the fates of 50 satellite-implanted tundra swans (Cygnus columbianus) over 4 years from five disparate breeding areas in Alaska, and used known-fate analyses to estimate monthly survival probability relative to migration distance, breeding area, migratory flyway, breeding status, and age. We specifically tested whether migratory birds face a trade-off, whereby long-distance migrants realize higher survival rates at the cost of lower productivity because of reduced time on breeding areas relative to birds that migrate shorter distances and spend more time on breeding areas.\nResults\nAnnual migration distances varied significantly among breeding areas (1020 to 12720 km), and were strongly negatively correlated with time spent on breeding areas (r = −0.986). Estimates of annual survival probability varied by wintering area (Pacific coast, Alaska Peninsula, and Eastern seaboard) and ranged from 0.79 (95%CI: 0.70–0.88) to 1.0, depending on criteria used to discern mortalities from radio failures. We did not find evidence for a linear relationship between migration distance and survival as swans from the breeding areas with the shortest and longest migration distances had the highest survival probabilities. Survival was lower in the first year post-marking than in subsequent years, but there was not support for seasonal differences in survival. Productivity varied among breeding populations and was generally inversely correlated to survival, but not migration distance or time spent on breeding areas.\nConclusions\nTundra swans conformed to a major tenet of life history theory, as populations with the highest survival generally had the lowest productivity. The lack of a uniform relationship between time spent on breeding areas and productivity, or time spent on wintering areas and survival, indicates that factors other than temporal investment dictate demographic outcomes in this species. The tremendous diversity of migration strategies we identify in Alaskan tundra swans, without clear impacts on survival, underscores the ability of this species to adapt to different environments and climatic regimes.\nKeywords: Cygnus columbianus, Known fate, Life history, Metapopulation, Migration distance, Productivity, Satellite telemetry, Survival, Transmitter effects, Tundra swan","language":"English","publisher":"BioMed Central","doi":"10.1186/s40462-016-0075-8","usgsCitation":"Ely, C.R., and Meixell, B.W., 2016, Demographic outcomes of diverse migration strategies assessed in a metapopulation of tundra swans: Movement Ecology, v. 4, no. 10, HTML document , https://doi.org/10.1186/s40462-016-0075-8.","productDescription":"HTML document ","ipdsId":"IP-065806","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":471317,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-016-0075-8","text":"Publisher Index Page"},{"id":338915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":338873,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1186/s40462-016-0075-8"}],"volume":"4","issue":"10","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-01","publicationStatus":"PW","scienceBaseUri":"58df6ac1e4b02ff32c6aea39","chorus":{"doi":"10.1186/s40462-016-0075-8","url":"http://dx.doi.org/10.1186/s40462-016-0075-8","publisher":"Springer Nature","authors":"Ely Craig R., Meixell Brandt W.","journalName":"Movement Ecology","publicationDate":"5/1/2016"},"contributors":{"authors":[{"text":"Ely, Craig R. 0000-0003-4262-0892 cely@usgs.gov","orcid":"https://orcid.org/0000-0003-4262-0892","contributorId":3214,"corporation":false,"usgs":true,"family":"Ely","given":"Craig","email":"cely@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":687776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":687777,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170937,"text":"70170937 - 2016 - Assessing predation risks for small fish in a large river ecosystem between contrasting habitats and turbidity conditions","interactions":[],"lastModifiedDate":"2016-05-11T14:06:47","indexId":"70170937","displayToPublicDate":"2016-04-30T15:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":737,"text":"American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Assessing predation risks for small fish in a large river ecosystem between contrasting habitats and turbidity conditions","docAbstract":"This study examined predation risk for juvenile native fish between two riverine shoreline habitats, backwater and debris fan, across three discrete turbidity levels (low, intermediate, high) to understand environmental risks associated with habitat use in a section of the Colorado River in Grand Canyon, AZ. Inferences are particularly important to juvenile native fish, including the federally endangered humpback chub Gila cypha. This species uses a variety of habitats including backwaters which are often considered important rearing areas. Densities of two likely predators, adult rainbow trout Oncorhynchus mykiss and adult humpback chub, were estimated between habitats using binomial mixture models to examine whether higher predator density was associated with patterns of predation risk. Tethering experiments were used to quantify relative predation risk between habitats and turbidity conditions. Under low and intermediate turbidity conditions, debris fan habitat showed higher relative predation risk compared to backwaters. In both habitats the highest predation risk was observed during intermediate turbidity conditions. Density of likely predators did not significantly differ between these habitats. This information can help managers in Grand Canyon weigh flow policy options designed to increase backwater availability or extant turbidity conditions.
","language":"English","publisher":"University of Notre Dame","publisherLocation":"Notre Dame, IN","doi":"10.1674/0003-0031-175.2.206","usgsCitation":"Dodrill, M.J., Yard, M.D., and Pine, W.E., 2016, Assessing predation risks for small fish in a large river ecosystem between contrasting habitats and turbidity conditions: American Midland Naturalist, v. 175, no. 2, p. 206-221, https://doi.org/10.1674/0003-0031-175.2.206.","productDescription":"16 p.","startPage":"206","endPage":"221","numberOfPages":"16","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044798","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":321128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.545635932499415\n ],\n [\n -114.04907226562499,\n 36.99377838872517\n ],\n [\n -111.2860107421875,\n 36.99377838872517\n ],\n [\n -111.2860107421875,\n 35.545635932499415\n ],\n [\n -114.04907226562499,\n 35.545635932499415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"573457abe4b0dae0d5ddd2e4","contributors":{"authors":[{"text":"Dodrill, Michael J. 0000-0002-7038-7170 mdodrill@usgs.gov","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":5468,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","email":"mdodrill@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":629170,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":629169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pine, William E. III","contributorId":139959,"corporation":false,"usgs":false,"family":"Pine","given":"William","suffix":"III","email":"","middleInitial":"E.","affiliations":[{"id":13332,"text":"Uni. of Florida Department of Wildlife Ecology and Conservation","active":true,"usgs":false}],"preferred":false,"id":629171,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170957,"text":"70170957 - 2016 - Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","interactions":[],"lastModifiedDate":"2018-08-09T12:12:53","indexId":"70170957","displayToPublicDate":"2016-04-30T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria","docAbstract":"Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.
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With the goal of assisting resource managers and stewards of reefs in setting and measuring progress toward realistic goals for coral-reef conservation and restoration, we examined reef degradation in this region from a geological perspective. The importance of ecosystem services provided by coral reefs—as breakwaters that dissipate wave energy and protect shorelines and as providers of habitat for innumerable species—cannot be overstated. However, the few coral species responsible for reef building in the western Atlantic during the last approximately 1.5 million years are not thriving in the 21st century. These species are highly sensitive to abrupt temperature extremes, prone to disease infection, and have low sexual reproductive potential. Their vulnerability and the low functional redundancy of branching corals have led to the low resilience of western Atlantic reef ecosystems. The decrease in live coral cover over the last 50 years highlights the need for study of relict (senescent) reefs, which, from the perspective of coastline protection and habitat structure, may be just as important to conserve as the living coral veneer. Research is needed to characterize the geological processes of bioerosion, reef cementation, and sediment transport as they relate to modern-day changes in reef elevation. For example, although parrotfish remove nuisance macroalgae, possibly promoting coral recruitment, they will not save Atlantic reefs from geological degradation. In fact, these fish are quickly nibbling away significant quantities of Holocene reef framework. The question of how different biota covering dead reefs affect framework resistance to biological and physical erosion needs to be addressed. Monitoring and managing reefs with respect to physical resilience, in addition to ecological resilience, could optimize the expenditure of resources in conserving Atlantic reefs and the services they provide.
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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163023","collaboration":"Northeast Region Urban Landscape Capabilities Team ","usgsCitation":"U.S. Geological Survey, 2016, Urban hydrology—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3023, 2 p., https://dx.doi.org/10.3133/fs20163023.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074174","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":320426,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025","description":"FS 2016-3023"},{"id":320425,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024","description":"FS 2016-3023"},{"id":320423,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3023/coverthb.jpg"},{"id":320427,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026","description":"FS 2016-3023"},{"id":320424,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3023/fs20163023.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3023"}],"contact":"U.S. Geological Survey
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Managing the urban-water cycle has increasingly become a challenge for water-resources planners and regulators faced with the problem of providing clean drinking water to urban residents. Sanitary and combined sanitary and storm sewer networks convey wastewater to centralized treatment plants. Impervious surfaces, which include roads, parking lots, and buildings, increase stormwater runoff and the efficiency by which runoff is conveyed to nearby stream channels; therefore, impervious surfaces increase the risk of urban flooding and alteration of natural ecosystems. These challenges will increase with the expansion of urban centers and the probable effects of climate change on precipitation patterns. Understanding the urban-water cycle is critical to effectively manage water resources and to protect people, infrastructure, and urban-stream ecosystems. As a leader in water-supply, wastewater, and stormwater assessments, the U.S. Geological Survey has the expertise and resources needed to monitor, model, and interpret data related to the urban-water cycle and thereby enable water-resources managers to make informed decisions.
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As pollinators, bees are cornerstones for terrestrial ecosystem stability and key components in agricultural productivity. All animals, including bees, are associated with a diverse community of microbes, commonly referred to as the microbiome. The bee microbiome is likely to be a crucial factor affecting host health. However, with the exception of a few pathogens, the impacts of most members of the bee microbiome on host health are poorly understood. Further, the evolutionary and ecological forces that shape and change the microbiome are unclear. Here, we discuss recent progress in our understanding of the bee microbiome, and we present challenges associated with its investigation. We conclude that global coordination of research efforts is needed to fully understand the complex and highly dynamic nature of the interplay between the bee microbiome, its host, and the environment. High-throughput sequencing technologies are ideal for exploring complex biological systems, including host-microbe interactions. To maximize their value and to improve assessment of the factors affecting bee health, sequence data should be archived, curated, and analyzed in ways that promote the synthesis of different studies. To this end, the BeeBiome consortium aims to develop an online database which would provide reference sequences, archive metadata, and host analytical resources. The goal would be to support applied and fundamental research on bees and their associated microbes and to provide a collaborative framework for sharing primary data from different research programs, thus furthering our understanding of the bee microbiome and its impact on pollinator health.
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along a suture zone that now lies beneath the Sørsdal Glacier. Exact location and age of this suture zone are unknown, as is its relationship to regional deformation associated with the amalgamation of East Gondwana. To image the suture zone, magnetotelluric (MT) data were collected in Prydz Bay, East Antarctica, mainly along a profile crossing the Sørsdal Glacier and regions inland of the Vestfold Hills and Rauer Group islands. Time-frequency analysis of the MT time series yielded three important observations: (1) Wind speeds in excess of ∼8 m/s reduce coherence between electric and magnetic fields due to charged wind-blown particles of ice and snow. (2) Estimation of the MT transfer function is best between 1000 and 1400 UT when ionospheric Hall currents enhance the magnetic source field. (3) Nonplanar source field effects were minimal but detectable and removed from estimation of the MT transfer function. Inversions of MT data in 2-D and 3-D produce similar resistivity models, where structures in the preferred 3-D resistivity model correlate strongly with regional magnetic data. The electrically conductive Rauer Group is separated from the less conductive Vestfold Hills by a resistive zone under the Sørsdal Glacier, which is interpreted to be caused by oxidation during suturing. Though a suture zone has been imaged, no time constrains on suturing can be made from the MT data.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2015JB012677","usgsCitation":"Peacock, J.R., and Selway, K., 2016, Magnetotelluric investigation of the Vestfold Hills and Rauer Group, East Antarctica: Journal of Geophysical Research B: Solid Earth, v. 121, no. 4, p. 2258-2273, https://doi.org/10.1002/2015JB012677.","productDescription":"16 p.","startPage":"2258","endPage":"2273","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071990","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":471045,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015jb012677","text":"Publisher Index Page"},{"id":320586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"121","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-26","publicationStatus":"PW","scienceBaseUri":"5721d4a4e4b0b13d39129147","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224 jpeacock@usgs.gov","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":4996,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared","email":"jpeacock@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":627647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Selway, Katherine","contributorId":168903,"corporation":false,"usgs":false,"family":"Selway","given":"Katherine","email":"","affiliations":[{"id":13368,"text":"University of Adelaide, Australia","active":true,"usgs":false}],"preferred":false,"id":627648,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}