The timing and location of spawning events are important data for managers seeking to control invasive grass carp populations. Ichthyoplankton tows for grass carp eggs and larvae can be used to detect spawning events; however, these samples can be highly debris-laden, and are expensive and laborious to process. An alternative method, environmental DNA (eDNA) technology, has proven effective in determining the presence of aquatic species. The objectives of this project were to assess the use of eDNA collections and quantitative eDNA analysis to assess the potential spawning of grass carp in five reservoir tributaries, and to compare those results to the more traditional method of ichthyoplankton tows. Grass carp eDNA was detected in 56% of sampling occasions and was detected in all five rivers. Concentrations of grass carp eDNA were orders of magnitude higher in June, corresponding to elevated discharge and egg presence. Grass carp environmental DNA flux (copies/h) was lower when no eggs were present and was higher when velocities and discharge increased and eggs were present. There was a positive relationship between grass carp eDNA flux and egg flux. Our results support the further development of eDNA analysis as a method to detect the spawning events of grass carp or other rheophilic spawners.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes5030027","usgsCitation":"Hayer, C., Bayless, M.F., George, A.E., Thompson, N., Richter, C.A., and Chapman, D., 2020, Use of environmental DNA to detect grass carp spawning events: Fishes, v. 5, no. 3, 27, 10 p., https://doi.org/10.3390/fishes5030027.","productDescription":"27, 10 p.","ipdsId":"IP-120266","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":455503,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes5030027","text":"Publisher Index Page"},{"id":436810,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WBOLYW","text":"USGS data release","linkHelpText":"Asian carp eDNA and egg morphology data collected from Truman Reservoir tributaries, Missouri, USA, 2014"},{"id":378085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Hayer, Cari-Ann chayer@usgs.gov","contributorId":177628,"corporation":false,"usgs":false,"family":"Hayer","given":"Cari-Ann","email":"chayer@usgs.gov","affiliations":[],"preferred":false,"id":797721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bayless, Michael F.","contributorId":239697,"corporation":false,"usgs":false,"family":"Bayless","given":"Michael","email":"","middleInitial":"F.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":797722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797725,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":797726,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212537,"text":"sir20205067 - 2020 - Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys","interactions":[],"lastModifiedDate":"2020-08-28T12:29:29.790982","indexId":"sir20205067","displayToPublicDate":"2020-08-27T12:35:16","publicationYear":"2020","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":"2020-5067","displayTitle":"Bathymetric Surveys of Morse and Geist Reservoirs in Central Indiana made with a Multibeam Echosounder, 2016, and Comparison with Previous Surveys","title":"Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys","docAbstract":"The U.S. Geological Survey, in cooperation with Citizens Energy Group, conducted a bathymetric survey of Morse and Geist Reservoirs in central Indiana in April and May of 2016 with a multibeam echosounder. Both reservoirs serve as water supply, flood control, and recreational resources for the city of Indianapolis and the surrounding communities.
Morse and Geist Reservoirs were surveyed to create updated bathymetric maps, determine storage capacities (volume) at specified water-surface elevations, and compare current conditions to historical surveys. Bathymetric data were collected using a high-resolution multibeam echosounder, and supplemental data were collected in coves and other shallow areas using an acoustic Doppler current profiler. The data were processed and combined using HYPACK and ArcMap software to develop a triangulated irregular network, a 5-foot gridded bathymetric dataset, a reservoir capacity table, and a bathymetric contour map for each reservoir.
The computed volume of Morse Reservoir was 23,136 acre-feet (7.54 billion gallons) with a surface area of 1,439 acres (62.7 million square feet). The computed volume of Geist Reservoir was 21,146 acre-feet (6.89 billion gallons) with a surface area of 1,853 acres (80.7 million square feet).
Between 1996 and 2016, lake bottom elevations have increased by a mean of 0.32 feet in Morse Reservoir and 0.27 feet in Geist Reservoir. The data indicate higher sedimentation rates in the upper parts of each reservoir as compared to near the dam and higher sedimentation rates in Morse Reservoir (0.5 inch per year) than in Geist Reservoir (0.2 inch per year). The differences between the current and historical surveys may be due to sedimentation, differences in accuracy between previous surveys, or a combination of both.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205067","collaboration":"Prepared in cooperation with Citizens Energy Group","usgsCitation":"Boldt, J.A., and Martin, Z.W., 2020, Bathymetric surveys of Morse and Geist Reservoirs in central Indiana made with a multibeam echosounder, 2016, and comparison with previous surveys: U.S. Geological Survey Scientific Investigations Report 2020–5067, 39 p., https://doi.org/10.3133/sir20205067.","productDescription":"Report: viii, 39 p.; Data Release; Additional Reports","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-116783","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":377662,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067.pdf","text":"Report","size":"31.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5067"},{"id":377911,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067_Morse_Reservoir_2016.pdf","text":"Bathymetric Map of Morse Reservoir near Noblesville, Indiana, 2016","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— High resolution file"},{"id":377912,"rank":5,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2020/5067/sir20205067_Geist_Reservoir_2016.pdf","text":"Bathymetric Map of Geist Reservoir near Fishers, Indiana, 2016","size":"23.4 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— High resolution file"},{"id":377663,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A2ITC6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry of Morse and Geist Reservoirs in central Indiana, 2016"},{"id":377661,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5067/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Hamilton County, Marion County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-85.8617,40.2201],[-85.863,40.139],[-85.8624,39.9436],[-85.8625,39.9286],[-85.9369,39.9272],[-85.9379,39.87],[-85.9541,39.8696],[-85.9518,39.6969],[-85.9523,39.638],[-86.248,39.6335],[-86.3268,39.6318],[-86.3281,39.8526],[-86.328,39.8662],[-86.325,39.8662],[-86.3267,39.9238],[-86.2967,39.9246],[-86.2757,39.925],[-86.2385,39.9259],[-86.239,39.9549],[-86.2417,40.0419],[-86.242,40.1304],[-86.2424,40.1807],[-86.2435,40.2152],[-86.1285,40.2176],[-86.0135,40.2186],[-85.9015,40.2194],[-85.8617,40.2201]]]},\"properties\":{\"name\":\"Hamilton\",\"state\":\"IN\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Somatic growth exerts strong control on patterns in the abundance of animal populations via effects on maturation, fecundity, and survival rates of juveniles and adults. In this paper, we quantify abiotic and biotic drivers of rainbow trout growth in the Colorado River, AZ, and the resulting impact on spatial and temporal variation in abundance. Inferences are based on approximately 10,000 observations of individual growth grates obtained through an intensive mark‐recapture effort conducted over five years (2012‐2016) in a 130 km‐long study segment downstream of Glen Canyon Dam. Prey availability, turbidity‐driven feeding efficiency, and intra‐specific competition were the dominant drivers of rainbow trout growth. Discharge, water temperature, and solar insulation were also evaluated but had a smaller influence. Mixed‐effect models explained 79‐82% of the variability in observed growth rates, with fixed covariate effects explaining 79‐87% of the total variation in growth parameters across five reaches and 18 quarterly sampling intervals. Reductions in growth owing in part to a phosphorous‐driven decline in prey availability, led to substantive weight loss and poor fish condition. This in turn lowered survival rates and delayed maturation, which led to a rapid decline in abundance and later recruitments. Reductions in feeding efficiency, due to episodic inputs of fine sediment from tributaries, and warmer water temperatures, contributed to reduced growth in downstream reaches, which led to more severe declines in abundance. Somatic growth rates increased following the population collapse due to reduced competition, and in the absence of substantive increases in prey availability. Our study elucidates important linkages between abiotic and biotic factors, somatic growth, and vital rates, and demonstrates how variation in somatic growth influences temporal and spatial patterns in abundance.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1427","usgsCitation":"Korman, J., Yard, M.D., Dzul, M.C., Yackulic, C., Dodrill, M., Deemer, B., and Kennedy, T., 2020, Changes in prey, turbidity, and competition reduce somatic growth and cause the collapse of a fish population: Ecological Monographs, v. 91, no. 1, e01427, https://doi.org/10.1002/ecm.1427.","productDescription":"e01427","ipdsId":"IP-116364","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436811,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90ODKZ3","text":"USGS data release","linkHelpText":"Rainbow trout growth data and growth covariate data downstream of Glen Canyon Dam in the Colorado River, Arizona, 2012 - 2016"},{"id":378203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.6925048828125,\n 36.76309161490538\n ],\n [\n -111.3519287109375,\n 36.76309161490538\n ],\n [\n -111.3519287109375,\n 37.00035919622158\n ],\n [\n -111.6925048828125,\n 37.00035919622158\n ],\n [\n -111.6925048828125,\n 36.76309161490538\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":798084,"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":798068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798069,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798070,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798071,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798072,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798073,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212845,"text":"70212845 - 2020 - Spatiotemporal modeling of dengue fever risk in Puerto Rico","interactions":[],"lastModifiedDate":"2020-08-31T14:07:32.839427","indexId":"70212845","displayToPublicDate":"2020-08-27T09:06:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6475,"text":"Spatial and Spatio-temporal Epidemiology","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal modeling of dengue fever risk in Puerto Rico","docAbstract":"Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.
The record of mining legacy and water quality was investigated in sediments collected in 2018 from four trenches in the Aztec, New Mexico, drinking-water reservoir #1. Bulk chemical analysis of sediments with depth in the reservoir revealed variable trace-element (uranium, vanadium, arsenic, copper, sulfur, silver, lead, and zinc) concentrations, which appear to coincide with historical mining and milling operations. Cesium-137 age dating, which identified the location of the 1963 radioactive fallout maximum, combined with the known age of the bottom and top of the sediment trenches, was used to estimate a polynomial sedimentation rate (average rate = 1.7 cm/yr). The clay size fraction (< 0.004 mm) was the dominant grain-size fraction of the sediments. Abundant fine-grained phyllosilicate (clay) minerals, predominantly montmorillonite and kaolinite, may explain sorption properties of trace elements. Scanning electron microscopy evaluation of sediments from two trenches showed copper and zinc associated with sulfur, and arsenic associated with iron and aluminum oxides. Results from laboratory batch experiments indicated that uranium, vanadium, and arsenic were released when sediments were reacted with a 150 mg/L sodium bicarbonate solution whereas copper was released when sediments were reacted with 2 mMol/L acetic acid. Observed concentrations from the two leach tests were below regulatory thresholds for delivery of solids to a landfill and were below drinking-water standards. Diatom relative abundance indicates that the water quality in the reservoir was not impaired by high metal concentrations.
We examine the genetic history and population status of Hawaiian hoary bats (Lasiurus semotus), the most isolated bats on Earth, and their relationship to northern hoary bats (Lasiurus cinereus), through whole-genome analysis of single-nucleotide polymorphisms mapped to a de novo-assembled reference genome. Profiles of genomic diversity and divergence indicate that Hawaiian hoary bats are distinct from northern hoary bats, and form a monophyletic group, indicating a single ancestral colonization event 1.34 Ma, followed by substantial divergence between islands beginning 0.51 Ma. Phylogenetic analysis indicates Maui is central to the radiation across the archipelago, with the southward expansion to Hawai‘i and westward to O‘ahu and Kaua‘i. Because this endangered species is of conservation concern, a clearer understanding of the population genetic structure of this bat in the Hawaiian Islands is of timely importance.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gbe/evaa137","usgsCitation":"Pinzari, C., Kang, L., Michalak, P., Jermiin, L.S., Price, D., and Bonaccorso, F., 2020, Analysis of genomic sequence data reveals the origin and evolutionary separation of Hawaiian hoary bat populations: Genome Biology and Evolution, v. 12, no. 9, p. 1504-1514, https://doi.org/10.1093/gbe/evaa137.","productDescription":"11 p.","startPage":"1504","endPage":"1514","ipdsId":"IP-080029","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":455511,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gbe/evaa137","text":"Publisher Index Page"},{"id":379341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hawai'i, Kaua'i, Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.016845703125,\n 18.8335153964335\n ],\n [\n -154.68749999999997,\n 18.8335153964335\n ],\n [\n -154.68749999999997,\n 20.437307950568957\n ],\n [\n -156.016845703125,\n 20.437307950568957\n ],\n [\n -156.016845703125,\n 18.8335153964335\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.81884765625,\n 20.550508894195637\n ],\n [\n -155.80810546875,\n 20.550508894195637\n ],\n [\n -155.80810546875,\n 21.01272715296389\n ],\n [\n -156.81884765625,\n 21.01272715296389\n ],\n [\n -156.81884765625,\n 20.550508894195637\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.5107421875,\n 21.197216077387107\n ],\n [\n -157.576904296875,\n 21.197216077387107\n ],\n [\n -157.576904296875,\n 21.70847301324597\n ],\n [\n -158.5107421875,\n 21.70847301324597\n ],\n [\n -158.5107421875,\n 21.197216077387107\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.9169921875,\n 21.790107059807873\n ],\n [\n -159.1259765625,\n 21.790107059807873\n ],\n [\n -159.1259765625,\n 22.28909641872304\n ],\n [\n -159.9169921875,\n 22.28909641872304\n ],\n [\n -159.9169921875,\n 21.790107059807873\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Pinzari, Corinna A. 0000-0001-9794-7564","orcid":"https://orcid.org/0000-0001-9794-7564","contributorId":208455,"corporation":false,"usgs":false,"family":"Pinzari","given":"Corinna A.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":801213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kang, Lin","contributorId":192324,"corporation":false,"usgs":false,"family":"Kang","given":"Lin","email":"","affiliations":[],"preferred":false,"id":801214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michalak, Pawel","contributorId":139209,"corporation":false,"usgs":false,"family":"Michalak","given":"Pawel","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":801215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jermiin, Lars S","contributorId":241599,"corporation":false,"usgs":false,"family":"Jermiin","given":"Lars","email":"","middleInitial":"S","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":801216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Price, Donald K","contributorId":241601,"corporation":false,"usgs":false,"family":"Price","given":"Donald K","affiliations":[{"id":33776,"text":"University of Nevada, Las Vegas","active":true,"usgs":false}],"preferred":false,"id":801217,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonaccorso, Frank 0000-0002-5490-3083 fbonaccorso@usgs.gov","orcid":"https://orcid.org/0000-0002-5490-3083","contributorId":143709,"corporation":false,"usgs":true,"family":"Bonaccorso","given":"Frank","email":"fbonaccorso@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":801218,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212677,"text":"fs20203044 - 2020 - Water priorities for the Nation—U.S. Geological Survey Integrated Water Availability Assessments","interactions":[],"lastModifiedDate":"2020-08-27T15:56:10.141621","indexId":"fs20203044","displayToPublicDate":"2020-08-26T19:25:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3044","displayTitle":"Water Priorities for the Nation—U.S. Geological Survey Integrated Water Availability Assessments","title":"Water priorities for the Nation—U.S. Geological Survey Integrated Water Availability Assessments","docAbstract":"The United States faces growing challenges to its water supply, infrastructure, and aquatic ecosystems because of population growth, climate change, floods and droughts, and aging water delivery systems. To help address these challenges, the U.S. Geological Survey (USGS) Water Resources Mission Area has established new strategic priorities that capitalize on the operational and scientific strengths of the USGS to address these complex societal issues. The USGS Integrated Water Availability Assessment Program within the Water Resources Mission Area will provide nationally consistent assessments of water available for human and ecological needs and identify factors that limit water availability. Integrated Water Availability Assessments will assess current conditions, long-term trends, factors responsible for current conditions and observed trends, and provide forecasts of future changes at national and regional scales.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203044","usgsCitation":"Miller, M.P., Clark, B.R., Eberts, S.M., Lambert, P.M., and Toccalino, P., 2020, Water priorities for the Nation—U.S. Geological Survey Integrated Water Availability Assessments: U.S. Geological Survey Fact Sheet 2020–3044, 2 p., https://doi.org/10.3133/fs20203044.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-119143","costCenters":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":377858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3044/coverthb.jpg"},{"id":377897,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3044/fs20203044.pdf","text":"Report","size":"977 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3044"}],"country":"United 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This report presents an updated grade and tonnage model for tungsten skarn deposits. As a critical component of the U.S. Geological Survey’s three-part form of quantitative mineral resource assessment, robust grade and tonnage models are essential to transforming mineral resource assessments into effective tools for decision makers. Using the best data available at the time of publication, this represents the first attempt in nearly 30 years to capture current mineral inventory and cumulative production data for worldwide tungsten skarn deposits. The accuracy of modern assessments of undiscovered tungsten skarn resources is highly influenced by the use of current data on the distribution of the grades and tonnages of well-explored tungsten skarn deposits. Primary factors affecting the changes to these distributions in the model presented here compared with those of previous models are the inclusion of important deposits, especially those in China that had been omitted in previous models; expanded mineral inventories resulting from increased exploration; and changes to international reporting standards. These factors have resulted in dramatic increases in average ore tonnage and slight decreases in the average grade of tungsten skarn deposits compared with previous models. Large increases in contained metal are observed among many of the individual deposits incorporated within this model that were also included in previous tungsten skarn grade and tonnage models. This report also provides recommendations for input parameters related to grade and tonnage models to use with software tools designed to facilitate the three-part form of quantitative mineral resource assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205085","usgsCitation":"Green, C.J., Lederer, G.W., Parks, H.L., and Zientek, M.L., 2020, Grade and tonnage model for tungsten skarn deposits—2020 update: U.S. Geological Survey Scientific Investigations Report 2020–5085, 23 p., https://doi.org/10.3133/sir20205085.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117570","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":377895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5085/sir20205085.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5085"},{"id":377894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5085/coverthb.jpg"}],"contact":"Eastern Mineral and Environmental Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Drone use in wildlife biology has greatly increased as they become cheaper and easier to deploy in the field. In this paper we describe a less invasive method of using drones and exploring their limitations for studying colonial nesting waterbirds. Western Grebes, like most colonial nesting waterbirds, can be very sensitive to human interaction. Using a 3DR Solo quad copter equipped with a high-resolution digital camera we were able to effectively map and monitor a Western Grebe breeding colony throughout the nesting period with a series of 6 flights. We were able to use drone collected aerial imagery to model nest survival while minimizing disturbance to the birds. However, we were not able to deploy the drone at all of our study sites. Our ability to effectively deploy the drone was hindered by the environmental and vegetation characteristics of a site. Drone technology can be a useful tool, especially when studying a species sensitive to human interaction. However, there researchers should carefully consider their species and study site to evaluate if a drone is the proper tool to meet their objectives.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-020-09743-y","usgsCitation":"Lachman, D., Conway, C.J., Vierling, K., and Matthews, T., 2020, Drones provide a better method to find nests and estimate nest survival for colonial waterbirds: A demonstration with Western Grebes: Wetlands Ecology and Management, v. 28, p. 837-845, https://doi.org/10.1007/s11273-020-09743-y.","productDescription":"9 p.","startPage":"837","endPage":"845","ipdsId":"IP-119243","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Valley County","otherGeospatial":"Cascade Reservoir, Deer Flat National Wildlife Refuge, Lake 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Ty","contributorId":280032,"corporation":false,"usgs":false,"family":"Matthews","given":"Ty","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":835916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212769,"text":"70212769 - 2020 - Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States","interactions":[],"lastModifiedDate":"2020-09-10T20:47:33.696034","indexId":"70212769","displayToPublicDate":"2020-08-26T11:10:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States","title":"Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States","docAbstract":"The growing use of engineered particles (e.g., nanosized and pigment sized particles, 1 to 100 nm and 100 to 300 nm, respectively) in a variety of consumer products increases the likelihood of their release into the environment. Wastewater treatment plants (WWTPs) are an important pathways of introduction of engineered particles to the aquatic systems. This study reports the concentrations, removal efficiencies, and particle size distributions of Ag and TiO2 engineered particles in five WWTPs in three states in the United States. The concentration of Ag engineered particles was quantified as the total Ag concentration, whereas the concentration of TiO2 engineered particles was quantified using mass-balance calculations and shifts in the elemental ratio of Ti/Nb above their natural background elemental ratio. Ratios of Ti/Nb in all WWTP influents, activated sludges, and effluents were 2–12 times higher (e.g., 519 to 3243) than the natural background Ti/Nb ratio (e.g., 267 ± 9), indicating that 49–92% of Ti originates from anthropogenic sources. The concentration of TiO2 engineered particles (in μg TiO2 L−1) in the influent, activated sludge, and effluent varied within the ranges of 70–670, 3570–6700, and 7–30, respectively. The concentration of Ag engineered particles (in μg Ag L−1) in the influent, activated sludge, and effluent varied within the ranges of 0.11–0.33, 1.45–1.65, and 0.01–0.04, respectively. The overall removal efficiency (e.g., effluent/influent concentrations) of TiO2 engineered particles (e.g., 90 to 96%) was higher than that for Ag engineered particles (e.g., 82 to 95%). Particles entering WWTPs are in the nanosized range for Ag (e.g., >99%) and a mixture of nanosized (e.g., 15 to 90%) and pigment sized particles (e.g., 10 to 85%) for TiO2. Nearly all Ag (>99%) and 55 to 100% of TiO2 particles discharged to surface water with WWTP effluent are within the nanosize range. This study provides evidence that TiO2 and Ag engineered nanomaterials enter aquatic systems with WWTP effluents, and that their concentrations are expected to increase with the increased applications of TiO2 and Ag engineered nanomaterials in consumer products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.142017","usgsCitation":"Md. Mahmudun Nabi, Wang, J., Meyer, M., Croteau, M.N., Ismail, N., and Baalousha, M., 2020, Concentrations and size distribution of TiO2 and Ag engineered particles in five wastewater treatment plants in the United States: Science of the Total Environment, v. 753, 142017, 11 p., https://doi.org/10.1016/j.scitotenv.2020.142017.","productDescription":"142017, 11 p.","ipdsId":"IP-120434","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455516,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.142017","text":"Publisher Index Page"},{"id":377935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Massachusetts, South Carolina","city":"Amherst, Columbia, Mt. Pleasant, Palo Alto","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23251342773438,\n 37.38161597475995\n ],\n [\n -122.10067749023438,\n 37.38161597475995\n ],\n [\n -122.10067749023438,\n 37.52551993630741\n ],\n [\n -122.23251342773438,\n 37.52551993630741\n ],\n [\n -122.23251342773438,\n 37.38161597475995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.55130767822266,\n 42.3643786536149\n ],\n [\n -72.49500274658203,\n 42.3643786536149\n ],\n [\n -72.49500274658203,\n 42.40317854182803\n ],\n [\n -72.55130767822266,\n 42.40317854182803\n ],\n [\n -72.55130767822266,\n 42.3643786536149\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.0897445678711,\n 33.951049661182104\n ],\n [\n -80.97610473632811,\n 33.951049661182104\n ],\n [\n -80.97610473632811,\n 34.0236404659703\n ],\n [\n -81.0897445678711,\n 34.0236404659703\n ],\n [\n -81.0897445678711,\n 33.951049661182104\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.90596771240234,\n 32.75306566002286\n ],\n [\n -79.80966567993164,\n 32.75306566002286\n ],\n [\n -79.80966567993164,\n 32.82738462221177\n ],\n [\n -79.90596771240234,\n 32.82738462221177\n ],\n [\n -79.90596771240234,\n 32.75306566002286\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"753","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Md. Mahmudun Nabi","contributorId":239632,"corporation":false,"usgs":false,"family":"Md. Mahmudun Nabi","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Jingjing","contributorId":239635,"corporation":false,"usgs":false,"family":"Wang","given":"Jingjing","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Madeleine","contributorId":239638,"corporation":false,"usgs":false,"family":"Meyer","given":"Madeleine","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":797439,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ismail, Niveen","contributorId":239641,"corporation":false,"usgs":false,"family":"Ismail","given":"Niveen","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":797440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baalousha, Mohammed","contributorId":239642,"corporation":false,"usgs":false,"family":"Baalousha","given":"Mohammed","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":797441,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212678,"text":"ofr20201078 - 2020 - Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17","interactions":[],"lastModifiedDate":"2020-08-26T15:51:06.049297","indexId":"ofr20201078","displayToPublicDate":"2020-08-26T10:30:00","publicationYear":"2020","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":"2020-1078","displayTitle":"Assessment of Dissolved-Selenium Concentrations and Loads in the Lower Gunnison River Basin, Colorado, as Part of the Selenium Management Program, 2011–17","title":"Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17","docAbstract":"The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 to measure concentrations of selenium in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains. Selenium is essential for life, but elevated amounts can cause reproductive failure, deformities, and other harmful effects. The primary goal of the Selenium Management Program is to meet the State of Colorado water-quality standard of 4.6 micrograms per liter (µg/L) for dissolved selenium at the U.S. Geological Survey (USGS) streamflow-gaging station number 09152500—Gunnison River near Grand Junction, Colorado—herein referred to as “Whitewater.” The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, has completed a review of dissolved-selenium data collected from the Selenium Management Program network during Water Year (WY) 2017 (October 1, 2016 through September 30, 2017) to further the understanding of the status and trends of selenium in the basin. This report presents the percentile values for selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency’s Clean Water Act section 303(d), which uses percentile values for concentrations. Also presented are dissolved-selenium loads at 14 sites in the lower Gunnison River Basin for WYs 2011–17. Annual dissolved-selenium loads were calculated for six sites with continuous U.S. Geological Survey streamflow-gaging stations. These six sites are referred to as “core” sites in this report. The remaining sites, which do not have streamflow-gaging stations, are referred to as “ancillary” sites in this report. During WY 2017, the loads calculated at the six core sites ranged from 306 pounds (lb) at Uncompahgre River at Colona to 12,600 lb at Whitewater, respectively.
By using discrete water-quality samples and the associated discharge measurements, instantaneous loads were calculated for 14 sites in WYs 2011–17 where discrete water-quality sampling took place. Median instantaneous loads ranged from 0.52 pounds per day (lb/d) at Uncompahgre River at Colona to 35.7 lb/d at Whitewater. Mean instantaneous loads ranged from 0.63 lb/d at Cummings Gulch at mouth to 35.5 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/d. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado’s water-quality standard for dissolved selenium of 4.6 µg/L was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated by using estimated dissolved-selenium concentrations from linear regression models for the six core sites with U.S. Geological Survey streamflow-gaging stations. The 85th-percentile concentrations for WY 2017 based on this method ranged from 0.68 µg/L at Uncompahgre River at Colona to 140 µg/L at Loutzenhizer Arroyo at North River Road. The 85th percentiles for concentrations of dissolved selenium also were calculated from water-quality samples collected during WY 2017 from sites with sufficient data. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.75 µg/L at Uncompahgre River at Colona to 106 µg/L at Loutzenhizer Arroyo at North River Road.
An analysis was completed for Whitewater to determine if an upward or downward trend exists for dissolved-selenium loads during two time periods. The first time period included all data at Whitewater, whereas the second time period focused on more recent data. The trend analysis indicates a decrease from 22,200 to 12,600 lb, which is a 43.1 percent (9,600 lb) reduction during the time period WY 1986 through WY 2017. The trend analysis for the annual dissolved-selenium load for WY 1995 through WY 2017 indicates a decrease of 6,600 lb per year, or 35.5 percent. An evaluation of laboratory bias was completed for selenium data which was used in the trend analysis. Findings indicated a potential positive bias of approximately 12 percent may exist in the data from October 2005 through August 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201078","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2020, Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17: U.S. Geological Survey Open-File Report 2020–1078, 21 p., https://doi.org/10.3133/ofr20201078","productDescription":"v, 21 p.","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1078/ofr20201078.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1078"},{"id":377860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
The immune system of ectotherms, particularly non-avian reptiles, remains poorly characterized regarding the genes involved in immune function, and their function in wild populations. We used RNA-Seq to explore the systemic response of Mojave desert tortoise (Gopherus agassizii) gene expression to three levels of Mycoplasma infection to better understand the host response to this bacterial pathogen. We found over an order of magnitude more genes differentially expressed between male and female tortoises (1,037 genes) than differentially expressed among immune groups (40 genes). There were 8 genes differentially expressed among both variables that can be considered sex-biased immune genes in this tortoise. Among experimental immune groups we find enriched GO biological processes for cysteine catabolism, regulation of type 1 interferon production, and regulation of cytokine production involved in immune response. Sex-biased transcription involves iron ion transport, iron ion homeostasis, and regulation of interferon-beta production to be enriched. More detailed work is needed to assess the seasonal response of the candidate genes found here. How seasonal fluctuation of testosterone and corticosterone modulate the immunosuppression of males and their susceptibility to Mycoplasma infection also warrants further investigation, as well as the importance of iron in the immune function and sex-biased differences of this species. Finally, future transcriptional studies should avoid drawing blood from tortoises via subcarapacial venipuncture as the variable aspiration of lymphatic fluid will confound the differential expression of genes.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0238202","usgsCitation":"Xu, C., Dolby, G.A., Drake, K.K., Esque, T., and Kusumi, K., 2020, Immune and sex-biased gene expression in the threatened Mojave desert tortoise, Gopherus agassizii: PLoS ONE, v. 15, no. 8, e0238202, 26 p., https://doi.org/10.1371/journal.pone.0238202.","productDescription":"e0238202, 26 p.","ipdsId":"IP-120652","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455519,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0238202","text":"Publisher Index Page"},{"id":414542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Cindy","contributorId":303295,"corporation":false,"usgs":false,"family":"Xu","given":"Cindy","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":867047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dolby, Greer A. 0000-0002-5923-0690","orcid":"https://orcid.org/0000-0002-5923-0690","contributorId":222726,"corporation":false,"usgs":false,"family":"Dolby","given":"Greer","email":"","middleInitial":"A.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":867048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, K. Kristina 0000-0003-0711-7634 kdrake@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-7634","contributorId":3799,"corporation":false,"usgs":true,"family":"Drake","given":"K.","email":"kdrake@usgs.gov","middleInitial":"Kristina","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kusumi, Kenro","contributorId":167536,"corporation":false,"usgs":false,"family":"Kusumi","given":"Kenro","email":"","affiliations":[],"preferred":false,"id":867051,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212798,"text":"70212798 - 2020 - Distribution and transport of Olympia oyster, Ostrea lurida, larvae in northern Puget Sound, Washington, USA","interactions":[],"lastModifiedDate":"2020-08-28T13:12:46.044405","indexId":"70212798","displayToPublicDate":"2020-08-26T08:05:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2455,"text":"Journal of Shellfish Research","active":true,"publicationSubtype":{"id":10}},"title":"Distribution and transport of Olympia oyster, Ostrea lurida, larvae in northern Puget Sound, Washington, USA","docAbstract":"As efforts for restoring Olympia oyster (Ostrea lurida) populations have expanded, there is an increased need to understand local factors that could influence the long-term success of these projects. To address concerns over potential limitations to recruitment at a restoration site in northern Puget Sound, Washington, USA, a study was developed to characterize physical processes governing larval transport in conjunction with larval abundance and environmental factors. Larval presence was not associated with tide cycle, season, or a combination of tide cycle and season. In terms of location, larvae were more likely to be present at offshore and intertidal sites versus the estuarine lagoon where the adult population resides. Larval density was higher during late summer ebbs versus early summer floods. Across sampling dates and locations, larval sizes ranged from 184 to 263 µm, indicating that larvae were released into the water column throughout the reproductive season and retained in the embayment for at least ~16 days. Throughout different tidal cycles in Skagit Bay, acoustic Doppler current profilers were used to measure current direction and velocities, concurrent with plankton sampling. Surface currents in the study area alternated between a clockwise and counterclockwise gyre during initial ebb and flood tides, respectively. Larvae exported from the source population during initial to mid-ebbs are swept into a northward gyre, and potentially retained at intertidal sites alongshore. These results will provide resource managers attempting to restore native bivalves with the ability to expand populations by identifying optimal areas for habitat enhancement through natural recruitment.","language":"English","publisher":"BioOne","doi":"10.2983/035.039.0204","usgsCitation":"Grossman, S., Grossman, E.E., Barber, J.S., Gamblewood, S., and Crosby, S.C., 2020, Distribution and transport of Olympia oyster, Ostrea lurida, larvae in northern Puget Sound, Washington, USA: Journal of Shellfish Research, v. 39, no. 2, p. 215-233, https://doi.org/10.2983/035.039.0204.","productDescription":"19 p.","startPage":"215","endPage":"233","ipdsId":"IP-117290","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":377979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Northern Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.002197265625,\n 46.9502622421856\n ],\n [\n -121.97021484374999,\n 46.9502622421856\n ],\n [\n -121.97021484374999,\n 49.224772722794825\n ],\n [\n -126.002197265625,\n 49.224772722794825\n ],\n [\n -126.002197265625,\n 46.9502622421856\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grossman, S.K.","contributorId":239652,"corporation":false,"usgs":false,"family":"Grossman","given":"S.K.","email":"","affiliations":[{"id":47954,"text":"Swinomish Indian Tribal Community Fisheries Department","active":true,"usgs":false}],"preferred":false,"id":797487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grossman, Eric E. 0000-0003-0269-6307 egrossman@usgs.gov","orcid":"https://orcid.org/0000-0003-0269-6307","contributorId":196610,"corporation":false,"usgs":true,"family":"Grossman","given":"Eric","email":"egrossman@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":797488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barber, Julie S.","contributorId":239666,"corporation":false,"usgs":false,"family":"Barber","given":"Julie","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":797538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gamblewood, S.K.","contributorId":239654,"corporation":false,"usgs":false,"family":"Gamblewood","given":"S.K.","email":"","affiliations":[{"id":47954,"text":"Swinomish Indian Tribal Community Fisheries Department","active":true,"usgs":false}],"preferred":false,"id":797539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crosby, Sean C. 0000-0002-1499-6836","orcid":"https://orcid.org/0000-0002-1499-6836","contributorId":219466,"corporation":false,"usgs":false,"family":"Crosby","given":"Sean","email":"","middleInitial":"C.","affiliations":[{"id":40000,"text":"Contractor, USGS","active":true,"usgs":false}],"preferred":false,"id":797540,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240189,"text":"70240189 - 2020 - Mineral deposits of the Mesoproterozoic Midcontinent Rift system in the Lake Superior region – A space and time classification","interactions":[],"lastModifiedDate":"2023-02-01T13:16:41.854536","indexId":"70240189","displayToPublicDate":"2020-08-26T07:13:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Mineral deposits of the Mesoproterozoic Midcontinent Rift system in the Lake Superior region – A space and time classification","docAbstract":"The Mesoproterozoic Midcontinent Rift System (MRS) of North America hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. Historically, hydrothermal deposits, such as Michigan’s native copper deposits and the White Pine sediment-hosted stratiform copper deposit, were major MRS metal producers. On-going exploration for and potential development of copper-nickel sulfide deposits hosted by the Duluth Complex of Minnesota and the opening of the Eagle nickel mine in Michigan indicate an expanding interest in MRS magmatic deposits. MRS hydrothermal and magmatic mineral deposits, many of which are significant past, present, and likely future providers of critical minerals, here are placed into a space and time metallogenic framework. To construct this framework, regional MRS mineral deposits extracted from the U.S. Geological Survey Mineral Resources Data System (MRDS) and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI) were supplemented by other known and recently recognized mineral deposits described in the literature. All mineral deposits were classified by deposit type, host rock age and type, and estimated timing of mineralization. Deposits were then put into a tectonic evolutionary framework (stages) for the MRS, which shows that deposits formed within discrete spatial and temporal stages of rift evolution.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2020.103716","usgsCitation":"Woodruff, L.G., Schulz, K.J., Nicholson, S.W., and Dicken, C.L., 2020, Mineral deposits of the Mesoproterozoic Midcontinent Rift system in the Lake Superior region – A space and time classification: Ore Geology Reviews, v. 126, 103716, 21 p., https://doi.org/10.1016/j.oregeorev.2020.103716.","productDescription":"103716, 21 p.","ipdsId":"IP-113870","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":412532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Superior region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.77134272564261,\n 38.8483191350162\n ],\n [\n -83.3091592101386,\n 38.8483191350162\n ],\n [\n -83.3091592101386,\n 49.57101080820971\n ],\n [\n -98.77134272564261,\n 49.57101080820971\n ],\n [\n -98.77134272564261,\n 38.8483191350162\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodruff, Laurel G. 0000-0002-2514-9923 woodruff@usgs.gov","orcid":"https://orcid.org/0000-0002-2514-9923","contributorId":2224,"corporation":false,"usgs":true,"family":"Woodruff","given":"Laurel","email":"woodruff@usgs.gov","middleInitial":"G.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":862907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulz, Klaus J. 0000-0003-2967-4765 kschulz@usgs.gov","orcid":"https://orcid.org/0000-0003-2967-4765","contributorId":2438,"corporation":false,"usgs":true,"family":"Schulz","given":"Klaus","email":"kschulz@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":862908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nicholson, Suzanne W. 0000-0002-9365-1894 swnich@usgs.gov","orcid":"https://orcid.org/0000-0002-9365-1894","contributorId":880,"corporation":false,"usgs":true,"family":"Nicholson","given":"Suzanne","email":"swnich@usgs.gov","middleInitial":"W.","affiliations":[],"preferred":true,"id":862910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dicken, Connie L. 0000-0002-1617-8132 cdicken@usgs.gov","orcid":"https://orcid.org/0000-0002-1617-8132","contributorId":57098,"corporation":false,"usgs":true,"family":"Dicken","given":"Connie","email":"cdicken@usgs.gov","middleInitial":"L.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":862909,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212700,"text":"70212700 - 2020 - Developing post-alert messaging for ShakeAlert, the earthquake early warning system for the West Coast of the United States of America","interactions":[],"lastModifiedDate":"2020-08-26T12:10:02.436919","indexId":"70212700","displayToPublicDate":"2020-08-26T07:04:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Developing post-alert messaging for ShakeAlert, the earthquake early warning system for the West Coast of the United States of America","docAbstract":"As ShakeAlert, the earthquake early warning system for the West Coast of the U.S., begins its transition to operational public alerting, we explore how post-alert messaging might represent system performance. Planned post-alert messaging can provide timely, crucial information to both emergency managers and ShakeAlert operators as well as calibrate expectations among various publics or public user groups and inform their responses to future alerts. There is a concern among the scientists and emergency managers that false alerts may negatively impact trust in the system, so quickly disseminated post-alert messages are necessary. For a new early warning system, such as ShakeAlert, this is particularly relevant given that the potentially affected population is likely to be unfamiliar with this system. We address this concern in six steps: (1) assessment of ShakeAlert performance to date, (2) characterization of human behavior and response to earthquake alerts, (3) presentation of a decision tree for issuing post-alert messages, (4) design of a critical set of post-alert messaging scenarios, (5) elaboration of these scenarios with message templates for a variety of communication channels, and (6) development of a typology of earthquake alerts. We further explore methods for monitoring and evaluating ShakeAlert post-alert messaging, for continuous improvement to the system.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2020.101713","usgsCitation":"McBride, S., Bostrom, A., Sutton, J., deGroot, R.M., Baltay Sundstrom, A.S., Terbush, B., Bodin, P., Dixon, M., Holland, E., Arba, R., Laustsen, P.C., Liu, S., and Vinci, M.J., 2020, Developing post-alert messaging for ShakeAlert, the earthquake early warning system for the West Coast of the United States of America: International Journal of Disaster Risk Reduction, v. 50, 101713, 11 p., https://doi.org/10.1016/j.ijdrr.2020.101713.","productDescription":"101713, 11 p.","ipdsId":"IP-110997","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455524,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2020.101713","text":"Publisher Index Page"},{"id":377874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington, Idaho, Nevada, Utah, Arizona","otherGeospatial":"West Coast of United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.87109375,\n 48.980216985374994\n ],\n [\n -126.12304687500001,\n 48.3416461723746\n ],\n [\n -124.8046875,\n 38.13455657705411\n ],\n [\n -118.47656249999999,\n 32.69486597787505\n ],\n [\n -110.12695312499999,\n 31.50362930577303\n ],\n [\n -108.6328125,\n 31.50362930577303\n ],\n [\n -108.896484375,\n 41.244772343082076\n ],\n [\n -110.91796875,\n 40.78054143186033\n ],\n [\n -111.4453125,\n 44.715513732021336\n ],\n [\n -114.60937499999999,\n 45.706179285330855\n ],\n [\n -114.169921875,\n 46.31658418182218\n ],\n [\n -116.27929687499999,\n 49.095452162534826\n ],\n [\n -122.87109375,\n 48.980216985374994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bostrom, Ann 0000-0002-6399-3404","orcid":"https://orcid.org/0000-0002-6399-3404","contributorId":239575,"corporation":false,"usgs":false,"family":"Bostrom","given":"Ann","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":797296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutton, Jeannette","contributorId":239576,"corporation":false,"usgs":false,"family":"Sutton","given":"Jeannette","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":797297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"deGroot, Robert Michael 0000-0001-9995-4207","orcid":"https://orcid.org/0000-0001-9995-4207","contributorId":239577,"corporation":false,"usgs":true,"family":"deGroot","given":"Robert","email":"","middleInitial":"Michael","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terbush, Brian","contributorId":239578,"corporation":false,"usgs":false,"family":"Terbush","given":"Brian","email":"","affiliations":[{"id":47925,"text":"Washington Emergency Management Department","active":true,"usgs":false}],"preferred":false,"id":797300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bodin, Paul","contributorId":206932,"corporation":false,"usgs":false,"family":"Bodin","given":"Paul","email":"","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":797301,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dixon, Maximilian","contributorId":239579,"corporation":false,"usgs":false,"family":"Dixon","given":"Maximilian","email":"","affiliations":[{"id":47925,"text":"Washington Emergency Management Department","active":true,"usgs":false}],"preferred":false,"id":797302,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Holland, Emily","contributorId":239580,"corporation":false,"usgs":false,"family":"Holland","given":"Emily","email":"","affiliations":[{"id":28116,"text":"California Office of Emergency Services","active":true,"usgs":false}],"preferred":false,"id":797303,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Arba, Ryan","contributorId":239581,"corporation":false,"usgs":false,"family":"Arba","given":"Ryan","email":"","affiliations":[{"id":28116,"text":"California Office of Emergency Services","active":true,"usgs":false}],"preferred":false,"id":797304,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Laustsen, Paul C.","contributorId":239582,"corporation":false,"usgs":false,"family":"Laustsen","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":false,"id":797305,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Liu, Sophia 0000-0002-8340-4945","orcid":"https://orcid.org/0000-0002-8340-4945","contributorId":239585,"corporation":false,"usgs":true,"family":"Liu","given":"Sophia","email":"","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":797306,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Vinci, Margaret J.","contributorId":239589,"corporation":false,"usgs":false,"family":"Vinci","given":"Margaret","middleInitial":"J.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":797307,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70240330,"text":"70240330 - 2020 - Genetic and environmental indicators of climate change vulnerability for desert bighorn sheep","interactions":[],"lastModifiedDate":"2023-02-06T12:59:43.623009","indexId":"70240330","displayToPublicDate":"2020-08-26T06:53:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Genetic and environmental indicators of climate change vulnerability for desert bighorn sheep","docAbstract":"Assessments of organisms’ vulnerability to potential climatic shifts are increasingly common. Such assessments are often conducted at the species level and focused primarily on the magnitude of anticipated climate change (i.e., climate exposure). However, wildlife management would benefit from population-level assessments that also incorporate measures of local or regional potential for organismal adaptation to change. Estimates of genetic diversity, gene flow, and landscape connectivity can address this need and complement climate exposure estimates to establish management priorities at broad to local scales. We provide an example of this holistic approach for desert bighorn sheep (Ovis canadensis nelsoni) within and surrounding lands administered by the U.S. National Park Service. We used genetic and environmental data from 62 populations across the southwestern U.S. to delineate genetic structure, evaluate relationships between genetic diversity and isolation, and estimate relative climate vulnerability for populations as a function of five variables associated with species’ responses to climate change: genetic diversity, genetic isolation, geographic isolation, forward climate velocity within a population’s habitat patch (a measure of geographic movement rate required for an organism to maintain constant climate conditions), and maximum elevation within the habitat patch (a measure of current climate stress, as lower maximum elevation is associated with higher temperature, lower precipitation, and lower population persistence). Genetic structure analyses revealed a high-level division between populations in southeastern Utah and populations in the remainder of the study area, which were further differentiated into four lower-level genetic clusters. Genetic diversity decreased with population isolation, whereas genetic differentiation increased, but these patterns were stronger for native populations than for translocated populations. Populations exhibited large variation in predicted vulnerability across the study area with respect to all variables, but native populations occupying relatively intact landscapes, such as Death Valley and Grand Canyon national parks, had the lowest overall vulnerability. These results provide local and regional context for conservation and management decisions regarding bighorn populations in a changing climate. Our study further demonstrates how assessments combining multiple factors could allow a more integrated response, such as increasing efforts to maintain connectivity and thus potential for adaptation in areas experiencing rapid climate change.
Streamflow estimates for floods with an annual exceedance probability of 0.001 or lower are needed to accurately portray risks to critical infrastructure, such as nuclear powerplants and large dams. However, extrapolating flood-frequency curves developed from at-site systematic streamflow records to very low annual exceedance probabilities (less than 0.001) results in large uncertainties in the streamflow estimates. Traditionally, methods for statistically estimating flood frequency have relied on the systematic streamflow record, which provides a time series of annual maximum flood peaks, often including some historical peaks. However, most peak-flow records are less than 100 years, and uncertainties are large when trying to extrapolate magnitudes of very low annual exceedance probability events.
Other data may be available that extend the record beyond the systematic dataset. Historical data are defined as data from outside the period of systematic records but within the period of human records. Examples of historical information include flood estimates from other agencies and newspaper accounts that can be translated to flood magnitude point estimates, interval estimates, or perception thresholds (such as a statement that an 1880 flood was the largest since 1869). Paleoflood data, which may also extend the dataset, include a broad range of information about flood occurrence or magnitude from sources like sediment deposits or tree rings.
Several assumptions are made in flood-frequency analysis, and an understanding of whether the data conform to these assumptions is desired. A particularly difficult assumption to evaluate for flood-frequency analysis is the underlying assumption that the flood series is stationary—the assumption that a time series of peak flow varies around a constant mean within a particular range of values (constant variance). As the hydrologic community’s understanding of natural systems and anthropogenic effects on streamflows has evolved, the community has come to understand that many surface-water systems exhibit one or more forms of nonstationarity, and thus the stationarity assumption is often violated to some degree. However, there is currently (2020) no consensus among hydrologists regarding the most appropriate flood-frequency-analysis methods for nonstationary systems, and this topic remains an active area of research.
A literature review was completed to summarize the state of the science of flood frequency. The literature review highlights tools available to detect nonstationarities and identifies approaches that include external information to inform flood-frequency analysis. To demonstrate methods for initial data analysis and for incorporating historical and paleoflood information in flood-frequency analysis, five sites were selected: the Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada; lower reach, Rapid Creek, South Dakota; Spring Creek, South Dakota; Cherry Creek near Melvin, Colorado; and Escalante River near Escalante, Utah. The sites were chosen for the availability of published historical and paleoflood data and for their geographic diversity and unique characteristics, which highlighted issues such as autocorrelation, change points, trends, outlier peaks, or short periods of record.
An initial data analysis that involved examining records for autocorrelation, change points, and trends was completed for all sites. The flood-frequency analysis completed for this study used version 7.2 of the U.S. Geological Survey PeakFQ program. Multiple analyses were done on each site documenting the change in the flood-frequency curve when additional historical or paleoflood data were added. When other flood-frequency studies were available, their results were compared to the results here. The comparisons in some cases simply show the effect of additional years of data, whereas other comparisons show results from probability distributions or fitting methods other than those used in PeakFQ.
For the Red River of the North, flood-frequency analysis shows that paleoflood data appear necessary to reasonably estimate very low annual exceedance probabilities. For the analysis of the lower reach of Rapid Creek and Spring Creek, paleoflood information helped put a high outlier from the systematic period in context; however, very low annual exceedance probabilities at these sites still had extraordinarily large confidence bounds. These sites also showed that paleoflood information might be transferred from one site to another, with the caveat that this is a case where we had existing paleoflood data to test the transfer of paleoflood information—this is not the case at many sites, and transferring paleoflood information requires assumptions about the comparability of floods at the sites. The Cherry Creek analysis affirmed the result of an earlier study that showed that the generalized Pareto distribution was not a good distribution for estimating very low annual exceedance probabilities. The Escalante River analysis showed that adding paleoflood information might increase uncertainty for very low annual exceedance probabilities, compared to analysis with the systematic period of record information only, when the paleoflood peaks are of much larger magnitudes than the systematic record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205065","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Ryberg, K.R., Kolars, K.A., Kiang, J.E., and Carr, M.L., 2020, Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity: U.S. Geological Survey Scientific Investigations Report 2020–5065, 89 p., https://doi.org/10.3133/sir20205065.","productDescription":"Report: xii, 89 p.; 5 Tables; Appendix; Dataset","numberOfPages":"105","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088812","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":377559,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_appendix.zip","text":"Appendix 1. Data, Settings, and Output for Each Site and Scenario","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5065 Appendix 1","linkHelpText":"— Each zipped file represents the analysis for a particular site and scenario"},{"id":377557,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_7.pdf","text":"Table 7","size":"114 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 7","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under two different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 06712500 Cherry Creek near Melvin, Colorado, with comparisons to other distributions and fitting methods."},{"id":377553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065"},{"id":377554,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_4.pdf","text":"Table 4","size":"139 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 4","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under 10 different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 05OJ015 Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada, as well as results from flood-frequency studies by Burn and Goel (2001) and Harden (1999)."},{"id":377697,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":377555,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_5.pdf","text":"Table 5","size":"122 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 5","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for the lower reach of Rapid Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377556,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_6.pdf","text":"Table 6","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 6","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for Spring Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5065/coverthb.jpg"},{"id":377558,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_8.pdf","text":"Table 8","size":"116 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 8","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 09337500 Escalante River near Escalante, Utah, with comparisons to Webb and others (1988), Webb and Rathburn (1988), and Kenney and others (2008)."}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Flood-frequency (hereinafter frequency) estimates provide information used to design, operate, and maintain hydraulic structures such as bridges and dams. Failures of these structures could cause catastrophic loss of property, life, or both. In addition to frequency estimates that use annual peak streamflow, frequency estimates of flood durations are required to safely and effectively operate the numerous dams in the Columbia River Basin of the northwestern United States, and British Columbia, Canada. Frequency studies rely on U.S. Geological Survey Guidelines for Determining Flood Flow Frequency (Bulletin 17C, published in 2018). A major consideration in estimating frequencies is the use of skew coefficients, which measure the asymmetry of flood flow distributions. Large uncertainties are associated with estimating the at-site skew coefficients directly from streamflow records, which are limited in length. Skew also is sensitive to extreme events for limited record lengths. Bulletin 17C recommends using regional skew coefficients to weight with the at-site skew estimate for more reliable frequency estimates. In this study, streamflow records from 313 unregulated U.S. Geological Survey streamgage sites and 97 regulated sites with naturalized streamflow records provided by the U.S. Army Corps of Engineers were used to develop regional skew models for the Columbia River Basin. The naturalized streamflow records were synthesized by removing regulatory components such as withdrawals and reservoir storage. Skew models were developed for 1-, 3-, 7-, 10-, 15-, 30-, and 60-day flood durations and used to estimate regional skew coefficients for the Columbia River Basin.
This report used Bayesian statistical regression methods to develop and analyze regional skew models based on hydrologically important basin characteristics. After examining a suite of available basin characteristics, mean annual precipitation had the strongest correlation to skew across the flood durations. Regional skew regression models were fit using mean annual precipitation for selected subbasins in the Columbia River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205073","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Lind, G.D., Lamontagne, J.R., and Stonewall, A.J., 2020, Development of regional skew coefficients for selected flood durations in the Columbia River Basin, northwestern United States and British Columbia, Canada (ver. 1.1, October 2020): U.S. Geological Survey Scientific Investigations Report 2020–5073, 48 p., https://doi.org/10.3133/sir20205073.","productDescription":"Report: viii, 48 p.; 8 Tables; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109443","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":377840,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5073/sir20205073_table1.csv","text":"Table 1","size":"26 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2130 SW 5th Avenue
Portland, Oregon 97201
Aquatic insects are poorly represented in water quality criteria, and previous studies have suggested a lack of sensitivity in acute toxicity tests despite observational studies demonstrating the contrary. Our objectives were to determine the toxicity of nickel (Ni) and zinc (Zn) to the mayfly Neocloeon triangulifer in fed acute (96-h) and chronic exposures to estimate aqueous effect concentrations while acknowledging the importance of dietary exposure for these insects. For the chronic tests, we conducted preliminary full–life cycle (~25–30 d) and subchronic (14 d) exposures to compare the relative sensitivity of the 2 test durations under similar conditions (i.e., feeding rates). Observing similar sensitivity, we settled on 14 d as the definitive test duration. Furthermore, we conducted experiments to determine how much food could be added to a given volume of water while minimally impacting dissolved metal recovery; a ratio of food dry mass to water volume (<0.005) achieved this. In the 14-d tests, we obtained a median lethal concentration and most sensitive chronic endpoint of 147 and 23 µg/L dissolved Ni (acute to chronic ratio [ACR] = 6.4), respectively, and 81 (mean value) and 10 µg/L dissolved Zn (ACR = 8.1), respectively. The acute values are orders of magnitude lower than previously published values for mayflies, probably most importantly due to the presence of dietary exposure but also potentially with some influence of organism age and test temperature.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4683","usgsCitation":"Soucek, D.J., Dickinson, A., Schlekat, C.E., Van Genderen, E., and Hammer, E.J., 2020, Acute and chronic toxicity of nickel and zinc to a laboratory cultured mayfly (Neocloeon triangulifer) in aqueous but fed exposures: Environmental Toxicology and Chemistry, v. 39, no. 6, p. 1196-1206, https://doi.org/10.1002/etc.4683.","productDescription":"11 p.","startPage":"1196","endPage":"1206","ipdsId":"IP-132676","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":436813,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T75RNV","text":"USGS data release","linkHelpText":"Survival, reproduction, and weight of Neocloeon triangulifer after short and long-term exposures to nickel and zinc"},{"id":388543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"6","noUsgsAuthors":true,"publicationDate":"2020-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Soucek, David J. 0000-0002-7741-0193","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":224591,"corporation":false,"usgs":false,"family":"Soucek","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":821609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dickinson, Amy","contributorId":224592,"corporation":false,"usgs":false,"family":"Dickinson","given":"Amy","email":"","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":821610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schlekat, Christan E.","contributorId":139228,"corporation":false,"usgs":false,"family":"Schlekat","given":"Christan","email":"","middleInitial":"E.","affiliations":[{"id":12705,"text":"Nickel Producers Environmental Research Association, Durham, Nor","active":true,"usgs":false}],"preferred":false,"id":821611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Genderen, Eric","contributorId":242622,"corporation":false,"usgs":false,"family":"Van Genderen","given":"Eric","affiliations":[{"id":48485,"text":"International Zinc Association, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":821612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":821613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212768,"text":"70212768 - 2020 - Reducing water scarcity by improving water productivity in the United States","interactions":[],"lastModifiedDate":"2020-08-27T16:59:15.03136","indexId":"70212768","displayToPublicDate":"2020-08-25T11:55:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Reducing water scarcity by improving water productivity in the United States","docAbstract":"Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2–23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.
","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab9d39","usgsCitation":"Marston, L., Lamsal, G., Ancona, Z.H., Caldwell, P.V., Richter, B., Ruddell, B., Rushforth, R., and Davis, K.F., 2020, Reducing water scarcity by improving water productivity in the United States: Environmental Research Letters, v. 15, no. 9, 094033, 13 p., https://doi.org/10.1088/1748-9326/ab9d39.","productDescription":"094033, 13 p.","ipdsId":"IP-114542","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455531,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab9d39","text":"Publisher Index Page"},{"id":377942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": 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University","active":true,"usgs":false}],"preferred":false,"id":797433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":797434,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Kyle F. 0000-0003-4504-1407","orcid":"https://orcid.org/0000-0003-4504-1407","contributorId":239631,"corporation":false,"usgs":false,"family":"Davis","given":"Kyle","email":"","middleInitial":"F.","affiliations":[{"id":47945,"text":"Department of Geography and Spatial Sciences & Department of Plant and Soil Sciences, University of Delaware","active":true,"usgs":false}],"preferred":false,"id":797435,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228386,"text":"70228386 - 2020 - Groundwater upwelling regulates thermal hydrodynamics and salmonid movements during high-temperature events at a montane tributary confluence","interactions":[],"lastModifiedDate":"2022-02-10T17:53:31.41775","indexId":"70228386","displayToPublicDate":"2020-08-25T11:41:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater upwelling regulates thermal hydrodynamics and salmonid movements during high-temperature events at a montane tributary confluence","docAbstract":"The Smith River is a popular recreational sport fishery in western Montana, but salmonid abundances there are thought to be artificially limited by riparian land-use alterations, irrigation water withdrawals, and high summer water temperatures. We used integrated networks of temperature loggers, PIT tag antenna stations, and in situ temperature mapping to investigate the thermal hydrodynamics and associated movements of PIT-tagged salmonids at the confluence of Tenderfoot Creek, a major, unaltered coldwater tributary of the Smith River. Contrary to expectations, Tenderfoot Creek itself was not used as a thermal refuge by salmonids during periods of high water temperatures in Smith River; rather, its cool outflow plume into the main stem was used instead. Mean daily outflow water temperatures averaged 2.9°C lower than those of the Smith River during summer and ranged from 0.5°C to 6.1°C lower. Moreover, measured and estimated temperatures in the outflow were cooler (by up to 2.8°C) than in Tenderfoot Creek itself at times as a result of groundwater upwelling at the confluence. Detections of PIT-tagged fish in the thermal plume increased, especially at night, when daily mean water temperatures exceeded 20°C in the main-stem Smith River; more than four times as many PIT-tagged fish were detected in the plume (N = 52) than along the opposite bank (N = 12), which ostensibly afforded better cover. Coldwater tributary confluences may provide superior thermal refuges for salmonids—cooler than the tributaries themselves—when water temperatures in river main stems are stressful.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10259","usgsCitation":"Ritter, T.D., Zale, A.V., Grisak, G., and Lance, M.J., 2020, Groundwater upwelling regulates thermal hydrodynamics and salmonid movements during high-temperature events at a montane tributary confluence: Transactions of the American Fisheries Society, v. 149, no. 5, p. 600-619, https://doi.org/10.1002/tafs.10259.","productDescription":"20 p.","startPage":"600","endPage":"619","ipdsId":"IP-115228","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10259","text":"Publisher Index Page"},{"id":395786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Smith River, Tenderfoot Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.2964391708374,\n 46.98804000472103\n ],\n [\n -111.25751495361327,\n 46.98804000472103\n ],\n [\n -111.25751495361327,\n 46.9993095934231\n ],\n [\n -111.2964391708374,\n 46.9993095934231\n ],\n [\n -111.2964391708374,\n 46.98804000472103\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ritter, Thomas David","contributorId":275611,"corporation":false,"usgs":false,"family":"Ritter","given":"Thomas","email":"","middleInitial":"David","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834174,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zale, Alexander V. 0000-0003-1703-885X","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":244099,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":834173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grisak, Grant","contributorId":275612,"corporation":false,"usgs":false,"family":"Grisak","given":"Grant","email":"","affiliations":[{"id":48627,"text":"mtfwp","active":true,"usgs":false}],"preferred":false,"id":834175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lance, Michael J.","contributorId":275613,"corporation":false,"usgs":false,"family":"Lance","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":834176,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212621,"text":"sim3459 - 2020 - Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys","interactions":[],"lastModifiedDate":"2020-08-26T13:05:12.542236","indexId":"sim3459","displayToPublicDate":"2020-08-25T11:11:39","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3459","displayTitle":"Stratigraphic Units of Shallow Unconsolidated Deposits in Deadwood, South Dakota, Delineated by Real-Time Kinematic Surveys","title":"Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys","docAbstract":"The City of Deadwood, South Dakota, has been working on a new archeological investigation in preparation for economic growth and expansion within the city limits, through the Deadwood Historic Preservation Office. During the excavation process, buried artifacts and historical features from the late 1800s have been uncovered. The stratigraphy of shallow unconsolidated deposits in the city of Deadwood, S. Dak., was surveyed on January 29, 2020, using real-time kinematic survey methods and described to identify variations in geologic material, thickness, and depth from the land surface in support of archeological studies by the city. The findings of the study will provide city managers and the public with reliable and impartial information for their use by advancing field or analytical methodology and understanding of hydrologic processes in the study area. The primary excavation site was surveyed, and stratigraphic units were delineated from changes in material properties or depositional environment. The primary excavation site consisted of nine stratigraphic units; however, some units were not consistent along the length of the excavation and pinched out along the cross section. Survey data points also were collected for artifacts and other sites of interest. The shallow surficial geology in the study area was affected by human construction, fires, and flooding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3459","collaboration":"Prepared in cooperation with the City of Deadwood, South Dakota","usgsCitation":"Tatge, W.S., Medler, C.J., Eldridge, W.G., and Valder, J.F., 2020, Stratigraphic units of shallow unconsolidated deposits in Deadwood, South Dakota, delineated by real-time kinematic surveys: U.S. Geological Survey Scientific Investigations Map 3459, pamphlet 7 p., 1 sheet, https://dx.doi.org/10.3133/sim3459.","productDescription":"Pamphlet: vi, 7 p.; 1 Sheet: 42.75 x 35.40 inches; 1 Table","numberOfPages":"16","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119064","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":377805,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sim/3459/sim3459_table1.csv","text":"Table 1","size":"32.7 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIM 3459 Table 1","linkHelpText":"— Survey points collected for delineation of selected stratigraphic units."},{"id":377804,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3459/sim3459_pamphlet.pdf","text":"Pamphlet","size":"2.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3459 Pamphlet"},{"id":377803,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3459/sim3459.pdf","text":"Sheet 1","size":"5.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3459","linkHelpText":"— Stratigraphic Units of Shallow Unconsolidated Deposits in Deadwood, South Dakota, Delineated by Real-Time Kinematic Surveys"},{"id":377802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3459/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Deadwood","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.74698638916016,\n 44.364237624976326\n ],\n [\n -103.71814727783202,\n 44.364237624976326\n ],\n [\n -103.71814727783202,\n 44.38558741441454\n ],\n [\n -103.74698638916016,\n 44.38558741441454\n ],\n [\n -103.74698638916016,\n 44.364237624976326\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702