The white‐tailed deer (Odocoileus virginianus) is an ecologically important species in forests of North America. Effective management of forests requires accurate, precise estimates of deer population abundance to plan and justify management actions. Spotlight surveys in combination with distance sampling are a common method of estimating deer population abundance; however, spotlight surveys are known to have serious drawbacks such as high costs and sampling biases. Therefore, we tested the effectiveness of enumerating deer from unmanned aerial vehicle (UAV) flights, conducted 1 and 6 March 2018, to develop population and density estimates in 2 United States National Parks: Harpers Ferry National Historic Park (HAFE) and Monocacy National Battlefield (MONO). Concurrent spotlight surveys at MONO enabled us to compare estimates obtained by the 2 methods. Deer density estimates by 4 observers of UAV‐obtained thermal imagery from HAFE were 94.5 ± 3.9 deer/km2. Concurrent UAV and spotlight surveys at MONO found 19.7 ± 0.5 deer/km2 and 6.4 ± 4.9 deer/km2, respectively; suggesting that spotlight surveys may significantly underestimate deer densities. Despite the logistical challenges to UAV operation, our findings demonstrate that UAVs will become an invaluable tool for wildlife management as technology improves. © 2021 The Wildlife Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with cross-correlation in two fundamentally different ways and therefore might be associated with different accuracy and uncertainty of predicted flood quantiles. We perform a Monte Carlo experiment based on a dataset of annual maximum flood series for 20 catchments in a hydrologically homogeneous region. Based on a log-Pearson type III parent distribution, we generate 3000 realizations of the region with different degrees of cross-correlation. For each realization, GLS and TK are applied in leave-one-out cross-validation to predict at-site flood quantiles. Our study shows that (a) TK outperforms GLS when catchment area is the only catchment descriptor used for predicting “true” population (theoretical) flood quantiles, regardless of the level of cross-correlation, and (b) GLS and TK perform similarly when multiple catchment descriptors are used.
Bat fatalities at wind energy facilities in North America are predominantly comprised of migratory, tree-dependent species, but it is unclear why these bats are at higher risk. Factors influencing bat susceptibility to wind turbines might be revealed by temporal patterns in their behaviors around these dynamic landscape structures. In northern temperate zones, fatalities occur mostly from July through October, but whether this reflects seasonally variable behaviors, passage of migrants, or some combination of factors remains unknown. In this study, we examined video imagery spanning one year in the state of Colorado in the United States, to characterize patterns of seasonal and nightly variability in bat behavior at a wind turbine. We detected bats on 177 of 306 nights representing approximately 3,800 hr of video and > 2,000 discrete bat events. We observed bats approaching the turbine throughout the night across all months during which bats were observed. Two distinct seasonal peaks of bat activity occurred in July and September, representing 30% and 42% increases in discrete bat events from the preceding months June and August, respectively. Bats exhibited behaviors around the turbine that increased in both diversity and duration in July and September. The peaks in bat events were reflected in chasing and turbine approach behaviors. Many of the bat events involved multiple approaches to the turbine, including when bats were displaced through the air by moving blades. The seasonal and nightly patterns we observed were consistent with the possibility that wind turbines invoke investigative behaviors in bats in late summer and autumn coincident with migration and that bats may return and fly close to wind turbines even after experiencing potentially disruptive stimuli like moving blades. Our results point to the need for a deeper understanding of the seasonality, drivers, and characteristics of bat movement across spatial scales.
The 2018 eruption on the lower East Rift Zone of Kīlauea Volcano produced one of the largest and most destructive lava flows in Hawai’i during the past 200 years. Over the course of more than 3 months, twenty-four fissures erupted, and the rate of lava effusion varied by two orders of magnitude, with significant implications for evolving flow behavior and hazards. Syn-eruptive data were collected to quantify these changes in lava effusion rate, including video of flow through channels and digital elevation models acquired using small unoccupied aircraft systems, airborne lidar, and airborne single-pass interferometric synthetic aperture radar. Topographic data through time allowed calculation of subaerial lava flow volume and time-averaged discharge rate over the course of the eruption, which we integrated with pre- and post-eruption bathymetric surveys. Repeat videos of the near-vent channel were analyzed with particle velocimetry to extract flow velocities, and these were combined with open channel flow theory to calculate a time series of instantaneous effusion rates. Results show a general increase in dense rock equivalent (DRE) effusion rate from ~7 to ~100 m3/s from early to late May for the whole flow field and ≥ 200 m3/s by mid-June after the eruption had focused at a primary vent. By the end of the eruption in August, 0.9–1.4 km3 DRE of lava had erupted, with 0.4 km3 deposited on land and at least 0.5 km3 offshore. The trends in effusion rate through time reflect magmatic processes in the connected summit and rift zone system that controlled eruption rate, with resulting implications for lava flow dynamics and hazards.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-021-01443-6","usgsCitation":"Dietterich, H., Diefenbach, A., Soule, S.A., Zoeller, M.H., Patrick, M.R., Major, J., and Lundgren, P., 2021, Lava effusion rate evolution and erupted volume during the 2018 Kīlauea lower East Rift Zone eruption: Bulletin of Volcanology, v. 83, no. 25, 18 p., https://doi.org/10.1007/s00445-021-01443-6.","productDescription":"18 p.","ipdsId":"IP-122554","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488679,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/gsofacpubs/2493","text":"External Repository"},{"id":384747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano, Hawaii volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.0507354736328,\n 19.321511226817176\n ],\n [\n -155.25054931640625,\n 19.369454073094243\n ],\n [\n -155.35011291503906,\n 19.39082944712291\n ],\n [\n -155.4242706298828,\n 19.204186382298897\n ],\n [\n -155.39749145507812,\n 19.191217165341648\n ],\n [\n -155.12832641601562,\n 19.2748506284423\n ],\n [\n -155.0507354736328,\n 19.321511226817176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","issue":"25","noUsgsAuthors":false,"publicationDate":"2021-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diefenbach, Angela K. 0000-0003-0214-7818","orcid":"https://orcid.org/0000-0003-0214-7818","contributorId":204743,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Angela K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soule, S. Adam 0000-0002-4691-6300","orcid":"https://orcid.org/0000-0002-4691-6300","contributorId":221052,"corporation":false,"usgs":false,"family":"Soule","given":"S.","email":"","middleInitial":"Adam","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":813191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zoeller, Michael H. 0000-0003-4716-8567","orcid":"https://orcid.org/0000-0003-4716-8567","contributorId":214557,"corporation":false,"usgs":true,"family":"Zoeller","given":"Michael","email":"","middleInitial":"H.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":813193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Major, J. J. 0000-0003-2449-4466","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":29461,"corporation":false,"usgs":true,"family":"Major","given":"J. J.","affiliations":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":813194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lundgren, Paul 0000-0002-6771-2876","orcid":"https://orcid.org/0000-0002-6771-2876","contributorId":215622,"corporation":false,"usgs":false,"family":"Lundgren","given":"Paul","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":813195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220125,"text":"70220125 - 2021 - Genome sequences of 26 white sucker hepatitis B virus isolates from white sucker, catostomus commersonii, inhabiting transboundary waters from Alberta, Canada, to the Great Lakes, USA","interactions":[],"lastModifiedDate":"2021-04-21T11:45:17.577839","indexId":"70220125","displayToPublicDate":"2021-03-18T06:43:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5813,"text":"Microbiology Resource Announcements","active":true,"publicationSubtype":{"id":10}},"title":"Genome sequences of 26 white sucker hepatitis B virus isolates from white sucker, catostomus commersonii, inhabiting transboundary waters from Alberta, Canada, to the Great Lakes, USA","docAbstract":"We report 26 genomes of the white sucker hepatitis B virus (WSHBV) from the white sucker, Catostomus commersonii. Genome length ranged from 3541 to 3543 bp and nucleotide identity was 96.7% or greater across genomes. This work suggests a geographical range of this virus that minimally extends from the Athabasca River, Alberta, Canada to the Great Lakes, USA.","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/MRA.01425-20","usgsCitation":"Adams, C.R., Blazer, V., Sherry, J., Cornman, R.S., and Iwanowicz, L., 2021, Genome sequences of 26 white sucker hepatitis B virus isolates from white sucker, catostomus commersonii, inhabiting transboundary waters from Alberta, Canada, to the Great Lakes, USA: Microbiology Resource Announcements, v. 10, no. 11, 3 p., https://doi.org/10.1128/MRA.01425-20.","productDescription":"3 p.","ipdsId":"IP-096274","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":453046,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1128/mra.01425-20","text":"External Repository"},{"id":385241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Cynthia R. 0000-0003-4383-530X cradams@usgs.gov","orcid":"https://orcid.org/0000-0003-4383-530X","contributorId":176965,"corporation":false,"usgs":true,"family":"Adams","given":"Cynthia","email":"cradams@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":814544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":814545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherry, Jim","contributorId":257544,"corporation":false,"usgs":false,"family":"Sherry","given":"Jim","email":"","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":814546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":814547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":814548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218820,"text":"sir20215005 - 2021 - Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator","interactions":[],"lastModifiedDate":"2021-03-18T11:47:02.407154","indexId":"sir20215005","displayToPublicDate":"2021-03-17T12:49:05","publicationYear":"2021","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":"2021-5005","displayTitle":"Supporting Data and Simulation of Hypothetical Bighead Carp Egg and Larvae Development and Transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator","title":"Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator","docAbstract":"Data collection, along with hydraulic and fluvial egg transport modeling, was completed along a 70.9-mile reach of the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam in Kentucky and Indiana. Water-quality data collected in this reach included surface measurements and vertical profiles of water temperature, specific conductance, pH, dissolved oxygen, turbidity, relative chlorophyll, and relative phycocyanin. Data were collected during two surveys: October 27–November 4, 2016, and June 26–29, 2017. Streamflow and velocity data were collected simultaneously with the water-quality data at cross sections and along longitudinal lines (corresponding to the water-quality surface measurements) and at selected stationary locations (corresponding to the water-quality vertical profiles). The data were collected to understand variability of flow and water-quality conditions relative to simulated reaches of the Ohio River and to aid in identifying parts of the reach that may provide conditions favorable to spawning and recruitment habitat for Hypophthalmichthys nobilis (bighead carp).
A copy of an existing step-backwater model of Ohio River flows was obtained from the National Weather Service and used to simulate hydraulic conditions for four different streamflows. Streamflows were selected to represent typical conditions ranging from a high-streamflow event to a seasonal dry-weather event, with two streamflows between these extremes for this reach of the Ohio River. Outputs from the hydraulic model, a range of five water temperatures observed in water-quality data, and four potential spawning locations were used as input to the Fluvial Egg Drift Simulator to simulate the extents and quantile positions of developing bighead carp, from egg hatching to the gas bladder inflation stage, under each scenario. A total of 80 simulations were run.
Results from the Fluvial Egg Drift Simulator scenarios (which include only the hydraulic influences on survival that result from settling, irrespective of mortality from other physical or biological factors such as excess turbulence, fertilization failure, predation, or starvation) indicate that most eggs will hatch, about half will die, and a quarter of the surviving larvae will reach the gas bladder inflation stage within the model reach. The overall mean percentage of embryos surviving to the gas bladder inflation stage was 13.1 percent. Individual simulations have embryo survival percentages as high as 49.1 percent. The highest embryo survival percentages occurred for eggs spawned at a streamflow of 38,100 cubic feet per second and water temperatures of 24 to 30 degrees Celsius. Conversely, embryo survival percentages were lowest for the lowest and highest streamflows regardless of water temperature or spawn location. Under low water temperature and high-streamflow conditions, some of the eggs did not hatch nor did the larvae reach the gas bladder inflation stage until passing beyond the downstream model domain. Although the final quantile positions of the eggs and larvae beyond the downstream model domain are unknown, the outcomes still provide useful information about conditions favorable to spawning and recruitment habitat for bighead carp in the Ohio River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215005","collaboration":"Biological Threats and Invasive Species Research Program","usgsCitation":"Ostheimer, C.J., Boldt, J.A., and Buszka, P.M., 2021, Supporting data and simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator: U.S. Geological Survey Scientific Investigations Report 2021–5005, 30 p., https://doi.org/10.3133/sir20215005.","productDescription":"Report: v, 30 p.; 2 Data Releases","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-116266","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":384390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5005/coverthb.jpg"},{"id":384391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5005/sir20215005.pdf","text":"Report","size":"10.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5005"},{"id":384392,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQHEPU","text":"USGS data release","linkHelpText":"Velocity and water-quality surveys in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, October 27–November 4, 2016, and June 26–29, 2017"},{"id":384393,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JHLGZL","text":"USGS data release","linkHelpText":"Geospatial data and models for the simulation of hypothetical bighead carp egg and larvae development and transport in the Ohio River between Markland Locks and Dam and McAlpine Locks and Dam, Kentucky and Indiana, by use of the Fluvial Egg Drift Simulator"}],"country":"United States","state":"Indiana, Kentucky","otherGeospatial":"Ohio River, Markland Locks and Dam, McAlpine Locks and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.660400390625,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.40194908237822\n ],\n [\n -84.935302734375,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.85682013474361\n ],\n [\n -85.660400390625,\n 38.40194908237822\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd., Suite 100
Columbus, OH 43229–1737
Floods are one of the most costly and frequent natural disasters in Nevada. For example, the 1997 New Year’s flood has been estimated to have caused more than $1 billion in damage across northern Nevada (Truckee River Flood Management Authority, 2017). In 2014, more than 2 miles of Interstate 15 in southern Nevada was heavily damaged by the remnants of Hurricane Norbert combined with monsoonal rains (Sutko, 2015). Flooding in Nevada is highly variable in cause and the season of the year. Flooding can be caused by snowmelt, rain on snow, and flash flooding during thunderstorms. Peak streamflow estimates are critical for planning by government agencies; designation of flood zones; and design of infrastructure including culverts, bridges, and roadways. In order to provide accurate estimates of flood frequencies, long-term data collection of peak streamflows would be needed because the accuracy of estimates improves with longer datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213015","collaboration":"Prepared in cooperation with Nevada Department of Transportation","usgsCitation":"Schmidt, K., 2021, Peak streamflow determinations in Nevada: A cooperative program with the USGS and Nevada Department of Transportation: U.S. Geological Survey, Fact Sheet 2021-3015, 4 p., https://doi.org/10.3133/fs20213015.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-112970","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":384413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3015/covrthb.jpg"},{"id":384414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3015/fs20213015.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United 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\"}}]}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Using a geology-based assessment methodology, the U.S. Geological Survey estimated undiscovered, technically recoverable mean resources of 288 million barrels of oil and 2.6 trillion cubic feet of gas in the Mowry Shale in the Wind River Basin Province, Wyoming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213006","usgsCitation":"Finn, T.M., Schenk, C.J., Mercier, T.J., Tennyson, M.E., Woodall, C.A., Marra, K.R., Le, P.A., Leathers-Miller, H.M., and Ellis, G.S., 2021, Assessment of continuous oil and gas resources in the Mowry Shale, Wind River Basin Province, Wyoming, 2020: U.S. Geological Survey Fact Sheet 2021-3006, 2 p., https://doi.org/10.3133/fs20213006.","productDescription":"Report: 2 p.; Data Release","onlineOnly":"N","ipdsId":"IP-121964","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":384385,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SGAGSU","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project - Wind River Basin, Mowry Shale Formation Assessment Unit Boundaries and Assessment Input Data Forms"},{"id":384384,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3006/fs20213006.pdf","text":"Report","size":"1.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 32021-3006"},{"id":384383,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3006/coverthb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wind River Basin Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.4345703125,\n 41.50857729743935\n ],\n [\n -104.765625,\n 41.50857729743935\n ],\n [\n -104.765625,\n 43.866218006556394\n ],\n [\n -110.4345703125,\n 43.866218006556394\n ],\n [\n -110.4345703125,\n 41.50857729743935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
We investigated seismic velocity changes (dv/v) associated with the 2019 Ridgecrest earthquake sequence with high‐frequency autocorrelations of ambient seismic noise data. Daily autocorrelation functions were computed for the entirety of 2019 and the first quarter of 2020 for broadband stations within the region, including the temporary broadband stations installed during the aftershock deployment. Travel time shifts in the daily autocorrelation functions, relative to the mean autocorrelation waveform, were computed to produce dv/v time series, which are sensitive to the evolving material properties of the shallow crust surrounding the Ridgecrest fault zone (RFZ). A short‐term velocity drop follows the Mw 7.1 earthquake at stations in the vicinity of the rupture surface, while those greater than 50 km away showed no such drop. The maximum, absolute changes in seismic velocity are proportional to the logarithm of distance from the fault rupture and to the peak dynamic strain experienced during the earthquake. Near the areas of the highest coseismic slip within the RFZ, seismic velocities recovered over 3 months. However, in the vicinity of the nearby Garlock fault, where triggered slip manifested, and north of the RFZ, seismic velocities recovered within a month. We interpret the seismic velocity changes and their recovery to be largely due to changes in the physical properties of the shallow crust, such as fault zone damage recovery caused by the earthquake rupture process and in response to the large dynamic stresses of passing seismic waves from the mainshock.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB021465","usgsCitation":"Boschelli, J.D., Moschetti, M.P., and Sens-Schonfelder, C., 2021, Temporal seismic velocity variations: Recovery following from the 2019 Mw 7.1 Ridgecrest, California earthquake: Journal of Geophysical Research-Solid Earth, v. 126, no. 4, e2020JB021465, 12 p., https://doi.org/10.1029/2020JB021465.","productDescription":"e2020JB021465, 12 p.","ipdsId":"IP-119443","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453047,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb021465","text":"Publisher Index Page"},{"id":385462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.50927734374999,\n 34.31168124115256\n ],\n [\n -116.5045166015625,\n 34.31168124115256\n ],\n [\n -116.5045166015625,\n 36.500805317604794\n ],\n [\n -119.50927734374999,\n 36.500805317604794\n ],\n [\n -119.50927734374999,\n 34.31168124115256\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Boschelli, Joshua Dakota 0000-0002-0046-5655","orcid":"https://orcid.org/0000-0002-0046-5655","contributorId":257864,"corporation":false,"usgs":true,"family":"Boschelli","given":"Joshua","email":"","middleInitial":"Dakota","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sens-Schonfelder, C","contributorId":257865,"corporation":false,"usgs":false,"family":"Sens-Schonfelder","given":"C","email":"","affiliations":[{"id":52144,"text":"GFZ, Germany","active":true,"usgs":false}],"preferred":false,"id":815184,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218723,"text":"70218723 - 2021 - Corticosteroid control of Na+/K+-ATPase in the intestine of the sea lamprey (Petromyzon marinus)","interactions":[],"lastModifiedDate":"2021-04-14T14:22:30.953192","indexId":"70218723","displayToPublicDate":"2021-03-17T09:20:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1738,"text":"General and Comparative Endocrinology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Corticosteroid control of Na+/K+-ATPase in the intestine of the sea lamprey (Petromyzon marinus)","title":"Corticosteroid control of Na+/K+-ATPase in the intestine of the sea lamprey (Petromyzon marinus)","docAbstract":"Anadromous sea lamprey (Petromyzon marinus) larvae undergo a months-long true metamorphosis during which they develop seawater (SW) tolerance prior to downstream migration and SW entry. We have previously shown that intestinal Na+/K+-ATPase (NKA) activity increases during metamorphosis and is critical to the osmoregulatory function of the intestine in SW. The present study investigated the role of 11-deoxycortisol (S) in controlling NKA in the anterior (AI) and posterior (PI) intestine during sea lamprey metamorphosis. In a tissue profile, nka mRNA and protein were most abundant in the gill, kidney, and AI. During metamorphosis, AI nka mRNA increased 10-fold, whereas PI nka mRNA did not change. Specific corticosteroid receptors were found in the AI, which had a higher binding affinity for S compared to 11-deoxycorticosterone (DOC). In vivo administration of S in mid-metamorphic lamprey upregulated NKA activity 3-fold in the AI and PI, whereas administration of DOC did not affect intestinal NKA activity. During a 24 h SW challenge test, dehydration of white muscle moisture was rescued by prior treatment with S, which was associated with increased intestinal nka mRNA and NKA activity. These results indicate that intestinal osmoregulation in sea lamprey is a target for control by S during metamorphosis and the development of SW tolerance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ygcen.2021.113756","usgsCitation":"Barany-Ruiz, A., Shaughnessy, C.A., and McCormick, S.D., 2021, Corticosteroid control of Na+/K+-ATPase in the intestine of the sea lamprey (Petromyzon marinus): General and Comparative Endocrinology, v. 307, 113756, 8 p., https://doi.org/10.1016/j.ygcen.2021.113756.","productDescription":"113756, 8 p.","ipdsId":"IP-123588","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":385092,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"307","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barany-Ruiz, Andre","contributorId":229635,"corporation":false,"usgs":false,"family":"Barany-Ruiz","given":"Andre","email":"","affiliations":[{"id":41532,"text":"Univ of Cadiz","active":true,"usgs":false}],"preferred":false,"id":811530,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaughnessy, Ciaran A. 0000-0003-2146-9126","orcid":"https://orcid.org/0000-0003-2146-9126","contributorId":229634,"corporation":false,"usgs":false,"family":"Shaughnessy","given":"Ciaran","email":"","middleInitial":"A.","affiliations":[{"id":37062,"text":"UMASS","active":true,"usgs":false}],"preferred":false,"id":811531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":811532,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219045,"text":"70219045 - 2021 - Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies","interactions":[],"lastModifiedDate":"2021-04-22T18:25:04.371556","indexId":"70219045","displayToPublicDate":"2021-03-17T08:29:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning models of arsenic in private wells throughout the conterminous United States as a tool for exposure assessment in human health studies","docAbstract":"Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.
Barrier coasts, including barrier islands, beach-ridge plains, and associated landforms, can assume a broad spectrum of morphologies over multi-decadal scales that reflect conditions of sediment availability, accommodation, and relative sea-level rise. However, the quantitative thresholds of these controls on barrier-system behavior remain largely unexplored, even as modern sea-level rise and anthropogenic modification of sediment availability increasingly reshape the world's sandy coastlines. In this study, we conceptualize barrier coasts as sediment-partitioning frameworks, distributing sand delivered from the shoreface to the subaqueous and subaerial components of the coastal system. Using an idealized morphodynamic model, we explore thresholds of behavioral and morphologic change over decadal to centennial timescales, simulating barrier evolution within quasi-stratigraphic morphological cross sections. Our results indicate a wide diversity of barrier behaviors can be explained by the balance of fluxes delivered to the beach vs. the dune or backbarrier, including previously understudied forms of transgression that allow the subaerial system to continue accumulating sediment during landward migration. Most importantly, our results show that barrier state transitions between progradation, cross-shore amalgamation, aggradation, and transgression are controlled largely through balances within a narrow range of relative sea-level rise and sediment flux. This suggests that, in the face of rising sea levels, subtle changes in sediment fluxes could result in significant changes in barrier morphology. We also demonstrate that modeled barriers with reduced vertical sediment accommodation are highly sensitive to the magnitude and direction of shoreface fluxes. Therefore, natural barriers with limited sediment accommodation could allow for exploration of the future effects of sea-level rise and changing flux magnitudes over a period of years as opposed to the decades required for similar responses in sediment-rich barrier systems. Finally, because our model creates stratigraphy generated under different input parameters, we propose that it could be used in combination with stratigraphic data to hindcast the sensitivity of existing barriers and infer changes in prehistoric morphology, which we anticipate will provide a baseline to assess the reliability of forward modeling predictions.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/esurf-9-183-2021","usgsCitation":"Ciarletta, D.J., Miselis, J.L., Shawler, J.L., and Hein, C.J., 2021, Quantifying thresholds of barrier geomorphic change in a cross-shore sediment-partitioning model: Earth Surface Dynamics, v. 9, p. 183-203, https://doi.org/10.5194/esurf-9-183-2021.","productDescription":"21 p.","startPage":"183","endPage":"203","ipdsId":"IP-122455","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453052,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/esurf-9-183-2021","text":"Publisher Index Page"},{"id":436457,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3D29V","text":"USGS data release","linkHelpText":"Python-based Subaerial Barrier Sediment Partitioning (pySBSP) model (ver. 1.0, February 2024)"},{"id":436456,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DE6QCL","text":"USGS data release","linkHelpText":"Subaerial Barrier Sediment Partitioning (SBSP) Model Version 1.0"},{"id":384718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ciarletta, Daniel J. 0000-0002-8555-2239","orcid":"https://orcid.org/0000-0002-8555-2239","contributorId":256700,"corporation":false,"usgs":true,"family":"Ciarletta","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":813078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":813079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shawler, Justin L.","contributorId":256701,"corporation":false,"usgs":false,"family":"Shawler","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":813080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hein, Christopher J.","contributorId":256702,"corporation":false,"usgs":false,"family":"Hein","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":813081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223217,"text":"70223217 - 2021 - Development of a simulated lung fluid leaching method to assess the release of potentially toxic elements from volcanic ash","interactions":[],"lastModifiedDate":"2021-08-18T12:49:40.865699","indexId":"70223217","displayToPublicDate":"2021-03-17T07:48:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1226,"text":"Chemosphere","active":true,"publicationSubtype":{"id":10}},"title":"Development of a simulated lung fluid leaching method to assess the release of potentially toxic elements from volcanic ash","docAbstract":"Freshly erupted volcanic ash contains a range of soluble elements, some of which can generate harmful effects in living cells and are considered potentially toxic elements (PTEs). This work investigates the leaching dynamics of ash-associated PTEs in order to optimize a method for volcanic ash respiratory hazard assessment. Using three pristine (unaffected by precipitation) ash samples, we quantify the release of PTEs (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V, Zn) and major cations typical of ash leachates (Mg, Na, Ca, K) in multiple simulated lung fluid (SLF) preparations and under varying experimental parameters (contact time and solid to liquid ratio). Data are compared to a standard water leach (WL) to ascertain whether the WL can be used as a simple proxy for SLF leaching. The main findings are: PTE concentrations reach steady-state dissolution by 24 h, and a relatively short contact time (10 min) approximates maximum dissolution; PTE dissolution is comparatively stable at low solid to liquid ratios (1:100 to 1:1000); inclusion of commonly used macromolecules has element-specific effects, and addition of a lung surfactant has little impact on extraction efficiency. These observations indicate that a WL can be used to approximate lung bioaccessible PTEs in an eruption response situation. This is a useful step towards standardizing in vitro methods to determine the soluble-element hazard from inhaled ash.
Sexual segregation has been intensely studied across diverse ecosystems and taxa, but studies are often limited to periods when animals occupy distinct seasonal ranges. Some avian and marine studies have revealed that habitat segregation, when sexes differ spatially or temporally in use of the physical landscape, is common during the migratory period and characterized by sex-specific differences in migratory behaviors. Recent research highlights the importance of understanding movement patterns in the context of the full annual life cycle and highlights the need to extend relevant theories of sexual segregation to the migratory period. We tested predictions from two leading hypotheses of sexual segregation, the forage-selection hypothesis (FSH) and the reproductive strategy hypothesis (RSH) as applied to the migratory period. We collected global positioning system (GPS) location data for male and female mule deer (Odocoileus hemionus) in south-central Wyoming and northwest Colorado and tested the main predictions of the FSH and RSH. Both sexes showed high fidelity to their migratory routes, but route fidelity was more variable in males. Males also started spring migrations earlier, ended spring and autumn migrations later, and spent 22% more time on stopover sites during spring migrations. Consequently, males took twice as long in spring and 44% longer in autumn to complete migration. Our results revealed clear sex-specific migratory behaviors and supported predictions of the RSH that male foraging behaviors optimize body condition for the autumn rut, and females prioritize foraging while balancing reproductive constraints. Specifically, males timed their movements with spring green-up as optimally as females, and the timing of male migrations and use of stopovers suggested that males prioritized time in areas of high-quality forage. This refutes predictions of the FSH during the migratory period that males should consistently choose habitats with abundant, low-quality forage. Our findings provide an important contribution to sexual segregation theory by extending relevant theories to understand male and female movements during the migratory period.
Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring that practitioners have theoretical and practical understanding of several advanced topics. Worse, implementing these important analyses can take substantial time and effort. This additional effort is often cited as justification for postponing, or even forgoing, these analyses.
Herein, we present scripting tools to facilitate the efficient and repeatable construction of high-dimensional, geostatistical-based PEST interfaces, including uncertainty analyses. As demonstrated, these tools can be applied with minimal effort to a model with varied temporal and spatial discretization. Ultimately, these tools can enable low-cost access to valuable decision-support analyses earlier and more frequently during the modeling workflow.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105022","usgsCitation":"White, J., Hemmings, B., Fienen, M., and Knowling, M., 2021, Towards improved environmental modeling outcomes: Enabling low-cost access to high-dimensional, geostatistical-based decision-support analyses: Environmental Modelling & Software, v. 139, 105022, 9 p., https://doi.org/10.1016/j.envsoft.2021.105022.","productDescription":"105022, 9 p.","ipdsId":"IP-127193","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy","contributorId":260166,"corporation":false,"usgs":false,"family":"White","given":"Jeremy","affiliations":[{"id":52529,"text":"Interra","active":true,"usgs":false}],"preferred":false,"id":817388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hemmings, Brioch","contributorId":260167,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":817389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew","contributorId":260168,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","affiliations":[{"id":36897,"text":"University of Adelaide","active":true,"usgs":false}],"preferred":false,"id":817391,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219216,"text":"70219216 - 2021 - Climate change impacts and strategies for adaptation for water resource management in Indiana","interactions":[],"lastModifiedDate":"2021-04-01T11:23:45.878005","indexId":"70219216","displayToPublicDate":"2021-03-17T06:50:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate change impacts and strategies for adaptation for water resource management in Indiana","docAbstract":"Changes to water resources are critical to all sectors of the economy. Climate change will affect the timing and quantity of water available in the environment as well as have an adverse effect on the quality of that water. Floods, droughts, and changing patterns of water scarcity—when water is not available in sufficient enough quantities or of a suitable quality at the right time to fulfill demand—are all critical factors when considering how and where Indiana will be able to economically develop in the future. Management of water resources will become even more important as different sectors try to minimize the risk of water scarcity in the face of increasing climate variability. This paper focuses on observed changes to Indiana’s water resources and how the availability and quality of those resources are likely to change in the face of future climate. Generally, Indiana is becoming wetter but with the projected increase coming primarily in the winter and spring. Summer water use will increase the likelihood of water shortages and the need for improved water management. In particular, Indiana may benefit from investment in methods to increase short-term storage of water—retaining more of the overabundance from winter and spring to relieve summer shortages.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-021-02979-4","usgsCitation":"Cherkauer, K.A., Bowling, L., Byun, K., Chaubey, I., Chin, N., Ficklin, D., Hamlet, A., Kines, S., Lee, C., Neupane, R., Pignotti, G., Rahman, S., Singh, S., Valappil-Femeena, P., and Williamson, T.N., 2021, Climate change impacts and strategies for adaptation for water resource management in Indiana: Climatic Change, v. 163, 21, 20 p., https://doi.org/10.1007/s10584-021-02979-4.","productDescription":"21, 20 p.","ipdsId":"IP-108809","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":490070,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Climate_change_impacts_and_strategies_for_adaptation_for_water_resource_management_in_Indiana/24780879","text":"External 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The USGS national mission is to monitor, analyze, and predict current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable information at scales and timeframes relevant to decision-makers. Consistent with the national mission, the USGS in Alaska provides timely and objective scientific information to help address issues and inform management decisions across five inter-connected themes:
The USGS in Alaska consists of approximately 350 scientists and support staff working in three Alaska-based science centers, a Cooperative Research Unit, and USGS centers outside Alaska, with a combined annual science budget of about $60 million. In the last 5 years, USGS research in Alaska has produced many scientific benefits resulting from more than 1,100 publications. Publications relevant to Alaska can be conveniently searched by keyword through the USGS Publications Warehouse at https://pubs.er.usgs.gov/search?q=Alaska.
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Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
","tableOfContents":"
Western Yellow-billed Cuckoo (Cuckoo; Coccyzus americanus) populations continue to decline in the western United States despite efforts to increase availability of riparian forest. Cuckoos have unique breeding habitat requirements such as large contiguous tracts of riparian forest (>80 ha), large estimated home ranges (20–90 ha), and dense vertical structure around the nest. However, local habitat-scale features may be missing in landscapes of predominantly mature riparian forest that may need to be specifically managed for nesting. We used historical nest data (n = 95) from the South Fork Kern River Valley, California, from 1985 to 1996 to identify important nest site features that may be missing in current riparian forests. We found that increased canopy cover and vertical structure at all levels in the canopy greatly increased the probability of Cuckoo nesting. With smaller estimated effect sizes, the probability of Cuckoo nesting increased with increasing willows and forbs and smaller mean tree dbh. Cuckoos selected plots with disproportionately high percent willow cover relative to availability plots regardless of whether sites had low or high percent willow available. Counts of fledged young were positively related to willow percentage. No vegetation variable influenced daily survival rate which was 0.991 (LCI = 0.980, UCI = 0.996). Overall 17-day nest success was likely high (0.86, LCI = 0.71, UCI = 0.93). In the absence of natural processes that create early successional stage forest, specific management for early successional stage forest is needed to increase the probability of Cuckoo nesting and nest productivity.
","language":"English","publisher":"Wiley","doi":"10.1111/rec.13376 ","usgsCitation":"Wohner, P., Laymon, S., Stanek, J., King, S.L., and Cooper, R., 2021, Early successional riparian vegetation is important for western Yellow-billed Cuckoo nesting habitat: Restoration Ecology, v. 29, no. 5, e13376, 13 p., https://doi.org/10.1111/rec.13376 .","productDescription":"e13376, 13 p.","ipdsId":"IP-123208","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":396670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South Fork Kern River 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P.J.","contributorId":287172,"corporation":false,"usgs":false,"family":"Wohner","given":"P.J.","affiliations":[{"id":61497,"text":"Cuckoo Conservation Initiative","active":true,"usgs":false}],"preferred":false,"id":836846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laymon, S.A.","contributorId":287173,"corporation":false,"usgs":false,"family":"Laymon","given":"S.A.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":836847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanek, J.E.","contributorId":287174,"corporation":false,"usgs":false,"family":"Stanek","given":"J.E.","email":"","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":836848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, R.J.","contributorId":287175,"corporation":false,"usgs":false,"family":"Cooper","given":"R.J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":836850,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218819,"text":"fs20213008 - 2021 - Trolley Operated Automatic Discharge System (TOADS)—An automated system for horizontal profiling of water velocity and river discharge measurements","interactions":[],"lastModifiedDate":"2021-03-22T20:51:07.926472","indexId":"fs20213008","displayToPublicDate":"2021-03-16T14:06:35","publicationYear":"2021","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":"2021-3008","displayTitle":"Trolley Operated Automatic Discharge System (TOADS)—An Automated System for Horizontal Profiling of Water Velocity and River Discharge Measurements","title":"Trolley Operated Automatic Discharge System (TOADS)—An automated system for horizontal profiling of water velocity and river discharge measurements","docAbstract":"Hydroacoustics have revolutionized how the U.S. Geological Survey (USGS) measures streamflow by increasing the efficiency and quality of the measurement. However, the ability to determine the full range of streamflow at a streamflow-gaging station remains limited because in-person flow measurements still must be made by qualified personnel. As a result, streamflow during flood events typically is measured infrequently in comparison to the duration of the event, usually after the peak flow has occurred. To overcome these difficulties, the USGS has developed the Trolley Operated Automatic Discharge System (TOADS), an automated system for measuring streamflow without the need for onsite personnel. Investment by USGS in TOADS and other innovative technologies and methods provides substantial improvements to flood assessment and watershed management, making the USGS the continued world leader in surface-water hydrology.
Streamflow measurements made with TOADS are analogous to a moving-boat measurement, which measures the flow at a point in a river by moving from bank to bank and measuring water velocities at various depths below the boat. The TOADS uses hydroacoustic technology to profile water velocity across a river while moving vertically through the water column to measure flow at multiple depths. Use of TOADS to measure streamflow can save substantial time and money, provide improved flow ratings by taking numerous targeted automated measurements over a range of conditions, and provide a safe alternative to standard boat measurements when river conditions are hazardous. The TOADS can be programmed to measure flow based on a variety of triggers (including river stage, amount of flow, time of day) and can take repeated measurements at user-specified intervals during floods, droughts, and other events of interest.
Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The Green Sturgeon Acipenser medirostris is an anadromous, long-lived species that is distributed along the Pacific coast of North America. Green Sturgeon is vulnerable to global change because of its sensitive life history (e.g., delayed maturation) and few spawning locations. The persistence of Green Sturgeon is threatened by habitat modification, altered flows, and rising river temperatures. In 2006, because of persistent stressors, the U.S. Endangered Species Act listed the southern distinct population segment as threatened. Despite increased research efforts on this species after the listing, substantial gaps in basic population information for Green Sturgeon remain. We present the only published information on age structure and growth of a threatened population of Green Sturgeon. By analyzing archived fin rays collected from 1984 to 2016, we revealed highly variable growth among individuals. We detected several age classes from 0 to 26 y and found similar growth rates of southern distinct population segment Green Sturgeon compared with northern population Green Sturgeon. Although limited, this analysis is an important first step to understanding Green Sturgeon population dynamics and highlights critical research needs.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-073","usgsCitation":"Ulaski, M., and Quist, M.C., 2021, Filling knowledge gaps for a threatened species: Age and growth of Green Sturgeon of the southern distinct population segment: Journal of Fish and Wildlife Management, v. 12, no. 1, p. 234-240, https://doi.org/10.3996/JFWM-20-073.","productDescription":"7 p.","startPage":"234","endPage":"240","ipdsId":"IP-123529","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-073","text":"Publisher Index Page"},{"id":396498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.826171875,\n 33.063924198120645\n ],\n [\n -115.48828125000001,\n 33.063924198120645\n ],\n [\n -115.48828125000001,\n 48.8936153614802\n ],\n [\n -126.826171875,\n 48.8936153614802\n ],\n [\n -126.826171875,\n 33.063924198120645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Ulaski, Marta","contributorId":280108,"corporation":false,"usgs":false,"family":"Ulaski","given":"Marta","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":836042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836043,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227714,"text":"70227714 - 2021 - Determinants of gray wolf (Canis lupus) sightings in Denali National Park","interactions":[],"lastModifiedDate":"2022-01-27T16:40:15.231474","indexId":"70227714","displayToPublicDate":"2021-03-16T10:18:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":894,"text":"Arctic","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Determinants of gray wolf (Canis lupus) sightings in Denali National Park","title":"Determinants of gray wolf (Canis lupus) sightings in Denali National Park","docAbstract":"Wildlife viewing within protected areas is an increasingly popular recreational activity. Management agencies are often tasked with providing these opportunities, yet quantitative analyses of factors influencing wildlife sightings are lacking. We analyzed locations of GPS-collared wolves and wolf sightings from 2945 trips in Denali National Park and Preserve, Alaska, USA, to provide a mechanistic understanding of how viewing opportunities are influenced by attributes of wolves and physical, biological, and harvest characteristics. We found that the presence of masking vegetation, den site proximity to the road, pack size, and presence of a wolf harvest closure adjacent to the park affected wolf sightings, and the influence of den proximity on sightings depended on harvest management. Wolf sightings increased with den site proximity to the road in years with a harvest closure adjacent to the park but not in the absence of the closure. The effect of the harvest closure on sightings was similar in magnitude to an increase in pack size by two wolves or a more than a two-fold decrease in masking vegetation. These findings were consistent across a 10-fold change in spatial resolution. Quantitative analysis of the factors influencing wildlife sightings provides valuable insight for agencies tasked with managing viewing opportunities.
","language":"English","publisher":"Arctic","doi":"10.14430/arctic72208","usgsCitation":"Borg, B.L., Arthur, S., Falke, J.A., and Prugh, L.R., 2021, Determinants of gray wolf (Canis lupus) sightings in Denali National Park: Arctic, v. 74, no. 1, p. 51-66, https://doi.org/10.14430/arctic72208.","productDescription":"16 p.","startPage":"51","endPage":"66","ipdsId":"IP-081600","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14430/arctic72208","text":"Publisher Index Page"},{"id":394977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Denali National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.006591796875,\n 62.5579204573332\n ],\n [\n -148.69995117187497,\n 62.5579204573332\n ],\n [\n -148.69995117187497,\n 64.1297836764257\n ],\n [\n -153.006591796875,\n 64.1297836764257\n ],\n [\n -153.006591796875,\n 62.5579204573332\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"74","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Borg, Bridget L.","contributorId":272257,"corporation":false,"usgs":false,"family":"Borg","given":"Bridget","email":"","middleInitial":"L.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":831871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arthur, Stephen M.","contributorId":272258,"corporation":false,"usgs":false,"family":"Arthur","given":"Stephen M.","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":831872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prugh, Laura R.","contributorId":272259,"corporation":false,"usgs":false,"family":"Prugh","given":"Laura","email":"","middleInitial":"R.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":831873,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236348,"text":"70236348 - 2021 - A probabilistic framework to model distributions of VS30","interactions":[],"lastModifiedDate":"2022-09-02T15:02:57.58682","indexId":"70236348","displayToPublicDate":"2021-03-16T09:56:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A probabilistic framework to model distributions of VS30","title":"A probabilistic framework to model distributions of VS30","docAbstract":"The time‐averaged shear‐wave velocity in the upper 30 m depth from the ground surface, or
Analysis of the water balance of the southwestern United States (SWUS) during 1900 through 2018 was used to evaluate the magnitude of the turn-of-the-century (TOC) drought in the SWUS. Results indicate that the warm season (April through September) soil moisture and runoff during the TOC drought were among the lowest values of the 1900 through 2018 period. Additionally, increases in temperature were identified as a significant driver of low soil moisture and runoff conditions during the warm season. In contrast, during the cool seasons (October through March) and the water year (October 1 through September 30) during the TOC drought, soil moisture and runoff did not indicate extremely dry conditions even though temperatures were the highest of the 1900 through 2018 period.
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","language":"English","publisher":"Elsevier","doi":"10.1016/j.fsi.2021.03.006","usgsCitation":"Ottinger, C., Smith, C.R., Blazer, V., Iwanowicz, L., Vogelbein, M.A., and Kaattari, S., 2021, Production and characterization of a mouse monoclonal antibody against smallmouth bass (Micropterus dolomieu) IgM: Fish & Shellfish Immunology, v. 113, p. 20-23, https://doi.org/10.1016/j.fsi.2021.03.006.","productDescription":"4 p.","startPage":"20","endPage":"23","ipdsId":"IP-117083","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":385079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ottinger, Christopher 0000-0003-2551-1985","orcid":"https://orcid.org/0000-0003-2551-1985","contributorId":205874,"corporation":false,"usgs":true,"family":"Ottinger","given":"Christopher","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":812427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cheyenne R. 0000-0002-7226-1774","orcid":"https://orcid.org/0000-0002-7226-1774","contributorId":219236,"corporation":false,"usgs":true,"family":"Smith","given":"Cheyenne","email":"","middleInitial":"R.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":812428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":812430,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vogelbein, Mary Ann","contributorId":255478,"corporation":false,"usgs":false,"family":"Vogelbein","given":"Mary","email":"","middleInitial":"Ann","affiliations":[{"id":51549,"text":"Virginia Institute of Marine Science, William and Mary","active":true,"usgs":false}],"preferred":false,"id":812431,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kaattari, Stephen","contributorId":255479,"corporation":false,"usgs":false,"family":"Kaattari","given":"Stephen","affiliations":[{"id":51549,"text":"Virginia Institute of Marine Science, William and Mary","active":true,"usgs":false}],"preferred":false,"id":812432,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}