{"pageNumber":"656","pageRowStart":"16375","pageSize":"25","recordCount":184617,"records":[{"id":70209182,"text":"70209182 - 2020 - Is your ad hoc model selection strategy affecting your multimodel inference?","interactions":[],"lastModifiedDate":"2020-03-23T07:06:30","indexId":"70209182","displayToPublicDate":"2020-01-16T07:05:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Is your ad hoc model selection strategy affecting your multimodel inference?","docAbstract":"(Yackulic)  1.\tEcologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated.\n2.\tWe begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models.\n3.\tWe tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies.\n4.\tSequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages.  The structure of non-target sub-models was less important. \n5.\t Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2997","usgsCitation":"Morin, D.J., Yackulic, C.B., Diffendorfer, J., Lesmeister, D.B., Nielsen, C., Reid, J., and Schauber, E.M., 2020, Is your ad hoc model selection strategy affecting your multimodel inference?: Ecosphere, v. 11, no. 1, e02997, https://doi.org/10.1002/ecs2.2997.","productDescription":"e02997","ipdsId":"IP-106290","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2997","text":"Publisher Index Page"},{"id":373428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana J.","contributorId":200306,"corporation":false,"usgs":false,"family":"Morin","given":"Dana","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":785265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":223504,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James","email":"jediffendorfer@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":785267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lesmeister, Damon B. 0000-0003-1102-0122","orcid":"https://orcid.org/0000-0003-1102-0122","contributorId":205006,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon","email":"","middleInitial":"B.","affiliations":[{"id":37019,"text":"USDA Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":785268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielsen, Clayton","contributorId":223505,"corporation":false,"usgs":false,"family":"Nielsen","given":"Clayton","email":"","affiliations":[{"id":40724,"text":"Cooperative Wildlife Research Laboratory and Department of Forestry, Southern Illinois University, 251 Life Science II, Mail Code 6504, Carbondale, Illinois 62901 USA","active":true,"usgs":false}],"preferred":false,"id":785269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reid, Janice","contributorId":89391,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":785270,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schauber, Eric M.","contributorId":223506,"corporation":false,"usgs":false,"family":"Schauber","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":40725,"text":"Illinois Natural History Survey, Prairie Research Institute, University of Illinois Urbana-Champaign, 1816 S. Oak St., Champaign, IL 61820 USA","active":true,"usgs":false}],"preferred":false,"id":785271,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70228439,"text":"70228439 - 2020 - A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears","interactions":[],"lastModifiedDate":"2022-02-10T13:12:06.682769","indexId":"70228439","displayToPublicDate":"2020-01-16T07:04:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears","docAbstract":"<div>We designed a flexible protocol for monitoring fish species richness in nonwadeable rivers. Nine sites were sampled seasonally with six gears in two physiographic regions in Missouri (USA). Using resampling procedures and mixed-effects modeling, we quantified richness and compositional overlap among gears, identified efficient gear combinations, and evaluated protocol performance across regions and seasons. We detected 25–75 species per sample and 89 185 fish. On average, no single gear detected &gt;62% of observed species, but an optimized, integrated-gear protocol with four complementary gears on average detected 90% of species while only requiring 51.9% of initial sampling effort. Neither season nor physiographic region explained low spatiotemporal variation in percent richness detected by the integrated-gear protocol. In contrast, equivalent effort with an electrofishing-only protocol was 53.5% less efficient, seasonally biased and imprecise (36.1%–82.3% of richness), and on average detected 15.9% less of observed richness. Altogether, riverine fish richness is likely underestimated with single-gear survey designs. When paired with existing wadeable-stream inventories, our customizable approach could benefit regional monitoring by comprehensively documenting riverine contributions to riverscape biodiversity.</div>","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0315","usgsCitation":"Dunn, C., and Paukert, C.P., 2020, A flexible survey design for monitoring spatiotemporal fish richness in nonwadeable rivers: optimizing efficiency by integrating gears: Canadian Journal Fisheries and Aquatic Sciences, v. 77, no. 6, p. 978-990, https://doi.org/10.1139/cjfas-2019-0315.","productDescription":"13 p.","startPage":"978","endPage":"990","ipdsId":"IP-111381","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"77","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey G.","contributorId":275809,"corporation":false,"usgs":false,"family":"Dunn","given":"Corey G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":834297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834298,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70228899,"text":"70228899 - 2020 - An agricultural water use package for MODFLOW and GSFLOW","interactions":[],"lastModifiedDate":"2022-02-23T12:45:47.212533","indexId":"70228899","displayToPublicDate":"2020-01-16T06:43:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7599,"text":"Environmental Modeling and Software","active":true,"publicationSubtype":{"id":10}},"title":"An agricultural water use package for MODFLOW and GSFLOW","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"abs0010\" class=\"abstract author\" lang=\"en\"><div id=\"abssec0010\"><p id=\"abspara0010\"><span>The Agricultural Water Use (AG) Package was developed for simulating demand-driven and supply-constrained agricultural water use in MODFLOW and GSFLOW models. The AG Package uses pre-existing hydrologic simulation provided by MODFLOW and GSFLOW. Three options are available for simulating water use for agriculture: (1) user-specified demands, (2) demands determined by a user-specified irrigation trigger value that is compared to the ratio of the simulated actual to&nbsp;potential evapotranspiration&nbsp;(ET), and (3) demands determined by minimizing the difference between potential and actual&nbsp;ET. The latter two approaches use energy and soil-water balance to determine crop-water demands. Irrigation withdrawals are diverted into canals and routed to fields using the MODFLOW&nbsp;</span>SFR<span>&nbsp;</span>Package, or irrigation water is provided/supplemented by groundwater. Combined with MODFLOW or GSFLOW, the AG Package can simulate dynamic water use by agriculture in developed basins while providing flexibility to represent a range of irrigation practices.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2019.104617","usgsCitation":"Niswonger, R.G., 2020, An agricultural water use package for MODFLOW and GSFLOW: Environmental Modeling and Software, v. 125, 104617, 16 p., https://doi.org/10.1016/j.envsoft.2019.104617.","productDescription":"104617, 16 p.","ipdsId":"IP-109425","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":458119,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2019.104617","text":"Publisher Index Page"},{"id":396332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835828,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70212873,"text":"70212873 - 2020 - The U.S. Geological Survey’s Rapid Seismic Array Deployment for the 2019 Ridgecrest Earthquake Sequence","interactions":[],"lastModifiedDate":"2020-09-02T00:53:37.858187","indexId":"70212873","displayToPublicDate":"2020-01-15T19:51:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The U.S. Geological Survey’s Rapid Seismic Array Deployment for the 2019 Ridgecrest Earthquake Sequence","docAbstract":"<p>Rapid seismic deployments following large earthquakes capture ephemeral near‐field recordings of aftershocks and ambient noise that can provide valuable data for seismological studies. The U.S. Geological Survey installed 19 temporary seismic stations following the 4 July 2019 M<sub>w</sub> 6.4 and 6 July 2019 (UTC) M<sub>w</sub> 7.1 earthquakes near the city of Ridgecrest, California. The stations record the aftershock sequence beginning two days after the mainshock and are expected to remain in the field through approximately January 2020. The deployment augments the permanent seismic network in the area to improve azimuthal coverage and provide additional near‐field observations. This article summarizes the motivation and goals of the deployment; details of station installation, instrumentation, and configurations; and initial data quality and observations from the network. We expect these data to be useful for a range of studies including detailing near‐field variability in strong ground motions, determining stress drops and rupture directivity of small events, imaging the fault zone, documenting the evolution of crustal properties within and outside of the fault zone, and others.</p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190296","usgsCitation":"Cochran, E.S., Wolin, E., McNamara, D.E., Yong, A., Wilson, D.C., Alvarez, M., van der Elst, N., McClain, A.R., and Steidl, J.H., 2020, The U.S. Geological Survey’s Rapid Seismic Array Deployment for the 2019 Ridgecrest Earthquake Sequence: Seismological Research Letters, v. 91, p. 1952-1960, https://doi.org/10.1785/0220190296.","productDescription":"9 p.","startPage":"1952","endPage":"1960","ipdsId":"IP-113314","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":378081,"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              -117.91900634765624,\n              35.302797817405796\n            ],\n            [\n              -117.32711791992186,\n              35.302797817405796\n            ],\n            [\n              -117.32711791992186,\n              35.9157474194997\n            ],\n            [\n              -117.91900634765624,\n              35.9157474194997\n            ],\n            [\n              -117.91900634765624,\n              35.302797817405796\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"91","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolin, Emily 0000-0003-1610-1191","orcid":"https://orcid.org/0000-0003-1610-1191","contributorId":221834,"corporation":false,"usgs":true,"family":"Wolin","given":"Emily","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McNamara, Daniel E. 0000-0001-6860-0350 mcnamara@usgs.gov","orcid":"https://orcid.org/0000-0001-6860-0350","contributorId":402,"corporation":false,"usgs":true,"family":"McNamara","given":"Daniel","email":"mcnamara@usgs.gov","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, David C. 0000-0001-9667-9449 dwilson@usgs.gov","orcid":"https://orcid.org/0000-0001-9667-9449","contributorId":239707,"corporation":false,"usgs":true,"family":"Wilson","given":"David","email":"dwilson@usgs.gov","middleInitial":"C.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":797746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alvarez, Mark 0000-0002-1361-5616","orcid":"https://orcid.org/0000-0002-1361-5616","contributorId":222021,"corporation":false,"usgs":true,"family":"Alvarez","given":"Mark","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797747,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"van der Elst, Nicholas 0000-0002-3812-1153 nvanderelst@usgs.gov","orcid":"https://orcid.org/0000-0002-3812-1153","contributorId":147858,"corporation":false,"usgs":true,"family":"van der Elst","given":"Nicholas","email":"nvanderelst@usgs.gov","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797748,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McClain, Adria Ruth 0000-0001-9371-9994","orcid":"https://orcid.org/0000-0001-9371-9994","contributorId":239708,"corporation":false,"usgs":true,"family":"McClain","given":"Adria","email":"","middleInitial":"Ruth","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797749,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steidl, Jamison Haase 0000-0003-0612-7654","orcid":"https://orcid.org/0000-0003-0612-7654","contributorId":239709,"corporation":false,"usgs":true,"family":"Steidl","given":"Jamison","email":"","middleInitial":"Haase","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797750,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70207962,"text":"70207962 - 2020 - Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016","interactions":[],"lastModifiedDate":"2023-06-23T14:25:48.582192","indexId":"70207962","displayToPublicDate":"2020-01-15T13:49:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016","docAbstract":"<div class=\"abstract toc-section\"><p>About 62,000 dead or dying common murres (<i>Uria aalge</i>), the trophically dominant fish-eating seabird of the North Pacific, washed ashore between summer 2015 and spring 2016 on beaches from California to Alaska. Most birds were severely emaciated and, so far, no evidence for anything other than starvation was found to explain this mass mortality. Three-quarters of murres were found in the Gulf of Alaska and the remainder along the West Coast. Studies show that only a fraction of birds that die at sea typically wash ashore, and we estimate that total mortality approached 1 million birds. About two-thirds of murres killed were adults, a substantial blow to breeding populations. Additionally, 22 complete reproductive failures were observed at multiple colonies region-wide during (2015) and after (2016–2017) the mass mortality event. Die-offs and breeding failures occur sporadically in murres, but the magnitude, duration and spatial extent of this die-off, associated with multi-colony and multi-year reproductive failures, is unprecedented and astonishing. These events co-occurred with the most powerful marine heatwave on record that persisted through 2014–2016 and created an enormous volume of ocean water (the “Blob”) from California to Alaska with temperatures that exceeded average by 2–3 standard deviations. Other studies indicate that this prolonged heatwave reduced phytoplankton biomass and restructured zooplankton communities in favor of lower-calorie species, while it simultaneously increased metabolically driven food demands of ectothermic forage fish. In response, forage fish quality and quantity diminished. Similarly, large ectothermic groundfish were thought to have increased their demand for forage fish, resulting in greater top-predator demands for diminished forage fish resources. We hypothesize that these bottom-up and top-down forces created an “ectothermic vise” on forage species leading to their system-wide scarcity and resulting in mass mortality of murres and many other fish, bird and mammal species in the region during 2014–2017.</p></div>","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0226087","usgsCitation":"Piatt, J.F., Parrish, J.K., Renner, H.M., Schoen, S.K., Jones, T., Arimitsu, M.L., Kuletz, K.J., Bodenstein, B., Garcia-Reyes, M., Duerr, R., Corcoran, R., Kaler, R., McChesney, G.J., Golightly, R.T., Coletti, H.A., Suryan, R., Burgess, H.K., Lindsey, J., Lindquist, K., Warzybok, P., Jahncke, J., Roletto, J., and Sydeman, W., 2020, Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016: PLoS ONE, no. 15, e0226087, 32 p.; Data release, https://doi.org/10.1371/journal.pone.0226087.","productDescription":"e0226087, 32 p.; Data 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December 31, 2016 and shipped to USGS National Wildlife Health Center for cause of death determination"}],"country":"United States, Canada","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -139.04296875,\n              60.75915950226991\n            ],\n            [\n              -146.07421875,\n              61.77312286453146\n            ],\n            [\n              -153.10546875,\n              62.59334083012024\n            ],\n            [\n              -158.02734375,\n              60.673178565817715\n            ],\n            [\n              -162.0703125,\n              61.01572481397616\n            ],\n            [\n              -165.76171875,\n              62.512317938386914\n            ],\n            [\n              -167.16796875,\n            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,{"id":70209223,"text":"70209223 - 2020 - A 100-year geoelectric hazard analysis for the U.S. high-voltage power grid","interactions":[],"lastModifiedDate":"2020-03-25T06:19:56","indexId":"70209223","displayToPublicDate":"2020-01-15T13:47:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"A 100-year geoelectric hazard analysis for the U.S. high-voltage power grid","docAbstract":"A once-per-century geoelectric hazard map is created for the United States high-voltage\n\tpower grid. A statistical extrapolation from 31 years of magnetic field measurements is\n\tmade by identifying 84 geomagnetic storms with the Kp and Dst indices. Data from 24\n\tgeomagnetic observatories, 1079 magnetotelluric survey sites, and 17,258 transmission\n\tlines are utilized to perform a geoelectric hazard analysis with the most comprehensive\n\tdata publicly available. With this data we estimate once-per-century geoelectric fields at\n\tthe magnetotelluric survey sites and calculate the theoretical voltages within transmission\n\tlines in the United States power grid. Once-per-century geoelectric field strengths span\n\tmore than three orders of magnitude from a minimum of 0.02 V/km at a site in Idaho to a\n\tmaximum of 26.8 V/km at a site in Maine, with nearly 30% of the surveyed land area ex-\n\tceeding 1 V/km. We show the influence that geoelectric field polarization has on geoelec-\n\ttric hazards when viewed on a power transmission network. The calculated transmission\n\tline voltages can exceed 1000 V in some transmission lines. Four regions in the United\n\tStates with particularly notable geoelectric hazards are identified and discussed: the East\n\tcoast, Pacific Northwest, upper Midwest, and the Denver metropolitan area.","language":"English","publisher":"Wiley","doi":"10.1029/2019SW002329","usgsCitation":"Lucas, G., Love, J.J., Kelbert, A., Bedrosian, P.A., and Rigler, E.J., 2020, A 100-year geoelectric hazard analysis for the U.S. high-voltage power grid: Space Weather, v. 18, no. 2, e2019SW002329, https://doi.org/10.1029/2019SW002329.","productDescription":"e2019SW002329","ipdsId":"IP-113155","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":458123,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019sw002329","text":"Publisher Index Page"},{"id":373484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70215322,"text":"70215322 - 2020 - RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics","interactions":[],"lastModifiedDate":"2020-10-16T14:45:24.319061","indexId":"70215322","displayToPublicDate":"2020-01-15T09:15:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics","docAbstract":"<p><span>Genomic tools are lacking for invasive and native populations of sea lamprey (</span><i>Petromyzon marinus</i><span>). Our objective was to discover single nucleotide polymorphism (SNP) loci to conduct pedigree analyses to quantify reproductive contributions of adult sea lampreys and dispersion of sibling larval sea lampreys of different ages in Great Lakes tributaries. Additional applications of data were explored using additional geographically expansive samples. We used restriction site‐associated DNA sequencing (RAD‐Seq) to discover genetic variation in Duffins Creek (DC), Ontario, Canada, and the St. Clair River (SCR), Michigan, USA. We subsequently developed RAD capture baits to genotype 3,446 RAD loci that contained 11,970 SNPs. Based on RAD capture assays, estimates of variance in SNP allele frequency among five Great Lakes tributary populations (mean&nbsp;</span><i>F</i><sub>ST</sub><span>&nbsp;0.008; range 0.00–0.018) were concordant with previous microsatellite‐based studies; however, outlier loci were identified that contributed substantially to spatial population genetic structure. At finer scales within streams, simulations indicated that accuracy in genetic pedigree reconstruction was high when 200 or 500 independent loci were used, even in situations of high spawner abundance (e.g., 1,000 adults). Based on empirical collections of larval sea lamprey genotypes, we found that age‐1 and age‐2 families of full and half‐siblings were widely but nonrandomly distributed within stream reaches sampled. Using the genomic scale set of SNP loci developed in this study, biologists can rapidly genotype sea lamprey in non‐native and native ranges to investigate questions pertaining to population structuring and reproductive ecology at previously unattainable scales.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.6001","usgsCitation":"Sard, N., Smith, S., Homola, J., Kanefsky, J., Bravener, G., Adams, J.V., Holbrook, C., Hrodey, P.J., Tallon, K., and Scribner, K.T., 2020, RAPTURE (RAD capture) panel facilitates analyses characterizing sea lamprey reproductive ecology and movement dynamics: Ecology and Evolution, v. 10, no. 3, p. 1469-1488, https://doi.org/10.1002/ece3.6001.","productDescription":"20 p.","startPage":"1469","endPage":"1488","ipdsId":"IP-111320","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":458125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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]\n}","volume":"10","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Sard, Nicholas","contributorId":243196,"corporation":false,"usgs":false,"family":"Sard","given":"Nicholas","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":801708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Seth","contributorId":189234,"corporation":false,"usgs":false,"family":"Smith","given":"Seth","email":"","affiliations":[],"preferred":false,"id":801709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homola, Jared","contributorId":243197,"corporation":false,"usgs":false,"family":"Homola","given":"Jared","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":801710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanefsky, Jeannette","contributorId":243198,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeannette","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":801711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bravener, Gale","contributorId":150995,"corporation":false,"usgs":false,"family":"Bravener","given":"Gale","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":801712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":801714,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hrodey, Peter J.","contributorId":205578,"corporation":false,"usgs":false,"family":"Hrodey","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":6599,"text":"U.S. Fish and Wildlife Service, Marquette Biological Station","active":true,"usgs":false}],"preferred":false,"id":801715,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tallon, Kevin","contributorId":173286,"corporation":false,"usgs":false,"family":"Tallon","given":"Kevin","email":"","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":801716,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Scribner, Kim T.","contributorId":95434,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":801717,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70236131,"text":"70236131 - 2020 - Peak ground motions and site response at Anza and Imperial Valley, California","interactions":[],"lastModifiedDate":"2022-08-30T14:00:58.547116","indexId":"70236131","displayToPublicDate":"2020-01-15T08:55:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Peak ground motions and site response at Anza and Imperial Valley, California","docAbstract":"<p><span>Power spectra of shear-waves for eighteen earthquakes from the Anza-Imperial Valley region were inverted for source, mid-path Q, site attenuation and site response. The motivation was whether differences in site attenuation (parameterized as&nbsp;</span><i>t*, r/cQ,</i><span>&nbsp;where&nbsp;</span><i>r</i><span>&nbsp;is distance along ray path near the site,&nbsp;</span><i>c</i><span>&nbsp;is shear velocity and&nbsp;</span><i>Q</i><span>&nbsp;is the quality factor that parameterizes attenuation) and site response could be correlated with residuals in peak values of velocity or acceleration after removing the affect of distance-dependent attenuation. We decomposed spectra of S-waves from horizontal components of 18 earthquakes from 2010 to 2018 into a common source for each event with ω</span><sup>−2</sup><span>&nbsp;spectral fall-off at high frequencies and then projected the residuals onto path and site terms following the methodology of Boatwright et al. (Bull Seismol Soc Am 81:1754–1782, 1991). The site terms were constrained to have an amplification at a particular frequency governed by V</span><sub>S30</sub><span>&nbsp;at two of the sites which had downhole shear-wave logs. The 18 events, 3 &lt; M &lt; 4, had moments between approximately 10</span><sup>20</sup><span>&nbsp;and 10</span><sup>22</sup><span>&nbsp;dyne-cm, and stress drops between 1 and 100&nbsp;bars. Average mid-crust attenuation had a Q of 844 reflecting the average path through the crystalline rock of the San Jacinto Mountains.&nbsp;</span><i>t*</i><span>&nbsp;for each station corresponded to the geologic environment such that stations on hard rock had low&nbsp;</span><i>t*</i><span>&nbsp;(e.g. stations KNW, PFO and RDM) a station in the San Jacinto fault zone (station SND) had a moderate&nbsp;</span><i>t*</i><span>&nbsp;of 0.035&nbsp;s and stations in the Imperial Valley usually had higher&nbsp;</span><i>t*s</i><span>. Generally&nbsp;</span><i>t*</i><span>&nbsp;correlated with average amplification suggesting that sites characterized by low surface velocities and higher attenuation also have more amplification in the 1–6&nbsp;Hz band. Residuals of peak values were determined by subtracting the prediction of Boore and Atkinson (</span>2008<span>). There is a correlation between average amplification and peak velocity, but not peak acceleration. Interestingly, there is less scatter at high values of amplification although there is also less data. Scatter in values of peak velocity and peak acceleration are higher at shorter compared to longer durations. When using a frequency-dependent form for&nbsp;</span><i>Q</i><span>, variances are higher, sometimes much higher; the dataset does not support frequency-dependent&nbsp;</span><i>Q</i><span>, which is not similar to results from the Imperial Valley and northeastern North America.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00024-019-02366-2","usgsCitation":"Fletcher, J.P., and Boatwright, J., 2020, Peak ground motions and site response at Anza and Imperial Valley, California: Pure and Applied Geophysics, v. 177, p. 2753-2769, https://doi.org/10.1007/s00024-019-02366-2.","productDescription":"17 p.","startPage":"2753","endPage":"2769","ipdsId":"IP-103872","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":458127,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00024-019-02366-2","text":"Publisher Index Page"},{"id":405902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Anza","otherGeospatial":"Imperial Valley","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117,\n              32.7\n            ],\n            [\n              -115.2,\n              32.7\n            ],\n            [\n              -115.2,\n              33.8\n            ],\n            [\n              -117,\n              33.8\n            ],\n            [\n              -117,\n              32.7\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"177","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fletcher, Jon Peter B. 0000-0001-8885-6177 jfletcher@usgs.gov","orcid":"https://orcid.org/0000-0001-8885-6177","contributorId":1216,"corporation":false,"usgs":true,"family":"Fletcher","given":"Jon","email":"jfletcher@usgs.gov","middleInitial":"Peter B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boatwright, John","contributorId":219666,"corporation":false,"usgs":false,"family":"Boatwright","given":"John","affiliations":[{"id":40044,"text":"USGS, deceased","active":true,"usgs":false}],"preferred":false,"id":850201,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70207810,"text":"sir20195151 - 2020 - Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018","interactions":[],"lastModifiedDate":"2022-04-25T20:37:40.39933","indexId":"sir20195151","displayToPublicDate":"2020-01-15T08:04:46","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":"2019-5151","displayTitle":"Storage Capacity and Sedimentation Characteristics of the San Antonio Reservoir, California, 2018","title":"Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018","docAbstract":"<p>The San Antonio Reservoir is a large water storage facility in Alameda County, California, and is a major component of the Hetch Hetchy Regional Water System (RWS). The RWS is a water-supply system owned and operated by the San Francisco Public Utilities Commission (SFPUC) and provides water for about 2.7 million people in the San Francisco, Santa Clara, Alameda, and San Mateo Counties. The San Antonio Reservoir is one of two RWS reservoirs in Alameda County and the third largest of the RWS reservoirs in the San Francisco Bay Area. The reservoir was formed by the James H. Turner Dam, which was completed in 1965. At the time of construction, the reservoir was estimated to have 50,500 acre-feet (acre-ft) of storage capacity. That early estimate was based on a 1963 pre-construction topographic map, which was drawn from aerial photographs. The capacity of the reservoir was later surveyed in 1994 and 2000. These two later surveys did not include the upper 18 feet (ft) of the reservoir, which represents roughly 30 percent of the overall storage volume. To determine the storage capacity and provide updated stage-capacity curves up to the spillway, the U.S. Geological Survey, in cooperation with the SFPUC, surveyed the bathymetry and shoreline of the reservoir in April 2018.</p><p>The bathymetric survey was performed by making depth soundings using a boat-mounted, multibeam echosounder. At the time of the survey, the water level was between 13 and 14 ft below the spillway elevation. To measure capacity between the water line up to the spillway elevation, topography along most of the shoreline was surveyed from the boat using a terrestrial Light Detection and Ranging (LiDAR) scanner and in other areas by using ground-survey techniques. Location during bathymetric and topographic data collection was determined using a Global Navigation Satellite System-Real Time Network system. Vertical profiles of sound speed were collected periodically. The sound-speed profiles were used to spatially and temporally adjust the sound-speed calculations used to determine depth from the soundings. Approximately 125 kilometers (78 miles) of transects with a total of about 560 million depth soundings and topographic LiDAR points were collected (about 160 per square meter). In addition, approximately 500 topographic survey points were collected in shallow, wadable areas and on land near the upper reservoir area using a Global Navigation Satellite System receiver attached to a fixed length survey rod. Depth soundings, terrestrial LiDAR points, topographic survey points, and a digitized shoreline were merged and interpolated to generate a digital elevation model (DEM) of the reservoir. Gridded elevation data extracted from the DEM were then tabulated to determine total reservoir capacity and create reservoir stage-surface area and stage-storage capacity tables.</p><p>Results of the reservoir capacity analysis indicated that the reservoir has 53,266 (plus or minus 140) acre-ft of storage capacity, which is an increase of 2,766 acre-ft (or 5.5 percent) greater than the original 1965 estimate; the increase is likely due to improved survey methods. Also, at the time of this 2018 survey, Intake #1 (the lowest intake) was not in operation. Intake #1 is estimated to be buried approximately 10 ft below the bed, whereas Intake #2 is about 20 ft above the bed. There are five intakes at different elevation levels; however, when consecutive lower intakes become inoperable due to sedimentation, the live storage capacity (capacity available for use) is reduced. At the time of this survey, the remaining live storage (above Intake #2) was approximately 52,363 acre-ft.</p><p>The 2018 stage-capacity curve was compared to the original 1965 stage-capacity curve. Although overall, the changes indicate an increase in storage capacity, the change in volume at 372.7 ft North American Vertical Datum of 1988 (370 ft National Geodetic Vertical Datum of 1929, NGVD 29) shows a decrease of 733 acre-ft (the elevation of 370 ft NGVD 29 was used because it is the lowest elevation available for the 1965 stage-capacity curves). This finding agrees with the observed accumulation of sediment over Intake #1. That volume was converted to an annual sediment yield of 0.35 acre-ft per square mile (or 165 cubic meters per square kilometer), which is of the same order of magnitude as that found in other watersheds for the Coast Ranges in California. A decrease of 733 acre-ft between 1965 and 2018 thus represents a loss of 1.5 percent of the overall storage capacity in the reservoir. The updated stage-surface area and stage-capacity tables provided in this report and online (<a href=\"https://doi.org/10.5066/P9KC9DU8\" data-mce-href=\"https://doi.org/10.5066/P9KC9DU8\">https://doi.org/10.5066/P9KC9DU8</a>) can be used by the SFPUC to improve reservoir operations and serve as an accurate baseline to monitor bathymetric changes in the future.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195151","collaboration":"Prepared in cooperation with the San Francisco Public Utilities Commission","usgsCitation":"Marineau, M.D., Wright, S.A, and Lopez, J.V., 2020, Storage capacity and sedimentation characteristics of the San Antonio Reservoir, California, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5151, 34 p., https://doi.org/10.3133/sir20195151.","productDescription":"Report: vi, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-105258","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":399623,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109595.htm"},{"id":371223,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KC9DU8","linkHelpText":"Bathymetry, Stage-Area, and Stage-Volume Tables for the San Antonio Reservoir, California, 2018"},{"id":371222,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5151/sir20195151.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5151"},{"id":371221,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5151/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Antonio Reservoir","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.81846618652345,\n              37.596415965954684\n            ],\n            [\n              -121.85348510742188,\n              37.57138553454929\n            ],\n            [\n              -121.841983795166,\n              37.565262680889965\n            ],\n            [\n              -121.8335723876953,\n              37.56186087804736\n            ],\n            [\n              -121.82378768920898,\n              37.5711134184077\n            ],\n            [\n              -121.81726455688477,\n              37.582541440297746\n            ],\n            [\n              -121.80301666259766,\n              37.5814531328266\n            ],\n            [\n              -121.80473327636719,\n              37.59083926161267\n            ],\n            [\n              -121.81846618652345,\n              37.596415965954684\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_ca@usgs.gov\" data-mce-href=\"mailto:dc_ca@usgs.gov\">Director</a>,<br><a href=\"https://ca.water.usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://ca.water.usgs.gov\">California Water Science Center</a><br><a href=\"https://usgs.gov/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://usgs.gov\">U.S. Geological Survey</a><br>6000 J Street, Placer Hall<br>Sacramento, California 95819</p>","tableOfContents":"<p></p><ul><li>Abstract</li><li>Introduction</li><li>Data and Sample Collection</li><li>Data Analysis</li><li>Results</li><li>Discussion of Reservoir Sedimentation</li><li>Summary</li><li>References Cited</li><li>Glossary</li></ul><p></p>","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2020-01-15","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Marineau, Mathieu D. 0000-0002-6568-0743 mmarineau@usgs.gov","orcid":"https://orcid.org/0000-0002-6568-0743","contributorId":4954,"corporation":false,"usgs":true,"family":"Marineau","given":"Mathieu","email":"mmarineau@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":779408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, Scott 0000-0002-0387-5713 sawright@usgs.gov","orcid":"https://orcid.org/0000-0002-0387-5713","contributorId":1536,"corporation":false,"usgs":true,"family":"Wright","given":"Scott","email":"sawright@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":779409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez, Joan V. 0000-0003-4477-7025 jvlopez@usgs.gov","orcid":"https://orcid.org/0000-0003-4477-7025","contributorId":221656,"corporation":false,"usgs":true,"family":"Lopez","given":"Joan","email":"jvlopez@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":779410,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70224972,"text":"70224972 - 2020 - Estimation of nonlinear water-quality trends in high-frequency monitoring data","interactions":[],"lastModifiedDate":"2021-10-11T13:02:50.792784","indexId":"70224972","displayToPublicDate":"2020-01-15T07:58:56","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}},"title":"Estimation of nonlinear water-quality trends in high-frequency monitoring data","docAbstract":"<div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0100\">Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.</p></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.136686","usgsCitation":"Yang, G., and Moyer, D.L., 2020, Estimation of nonlinear water-quality trends in high-frequency monitoring data: Science of the Total Environment, v. 715, 136686, 12 p., https://doi.org/10.1016/j.scitotenv.2020.136686.","productDescription":"136686, 12 p.","ipdsId":"IP-113815","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":467305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.136686","text":"Publisher Index Page"},{"id":390382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390371,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.136686"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -75.1904296875,\n              38.41916639395372\n            ],\n            [\n              -75.223388671875,\n              38.64261790634527\n            ],\n            [\n              -75.35522460937499,\n              38.79690830348427\n            ],\n            [\n              -75.498046875,\n              38.87392853923629\n            ],\n            [\n              -75.5419921875,\n              39.0533181067413\n    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gyang@usgs.gov","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":197859,"corporation":false,"usgs":true,"family":"Yang","given":"Guoxiang","email":"gyang@usgs.gov","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyer, Douglas L. 0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824951,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70217271,"text":"70217271 - 2020 - A new sampler for the collection and retrieval of dry dust deposition","interactions":[],"lastModifiedDate":"2021-01-14T17:18:48.48971","indexId":"70217271","displayToPublicDate":"2020-01-14T11:18:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"A new sampler for the collection and retrieval of dry dust deposition","docAbstract":"<p><span>Atmospheric dust can influence biogeochemical cycles, accelerate snowmelt, and affect air, water quality, and human health. Yet, the bulk of atmospherically transported material remains poorly quantified in terms of total mass fluxes and composition. This lack of information stems in part from the challenges associated with measuring dust deposition. Here we report on the design and efficacy of a new dry deposition sampler (Dry Deposition Sampling Unit (DSU)) and method that quantifies the gravitational flux of dust particles. The sampler can be used alone or within existing networks such as those employed by the National Atmospheric Deposition Program (NADP). Because the samplers are deployed sterile and the use of water to remove trapped dust is not required, this method allows for the recovery of unaltered dry material suitable for subsequent chemical and microbiological analyses. The samplers were tested in the laboratory and at 15 field sites in the western United States. With respect to material retention, sampler performance far exceeded commonly used methods. Retrieval efficiency was &gt;97% in all trials and the sampler effectively preserved grain size distributions during wind exposure experiments. Field tests indicated favorable comparisons to dust-on-snow measurement across sites (</span><i>r</i><sup>2</sup><span>&nbsp;0.70,&nbsp;</span><i>p</i><span>&nbsp;&lt;&nbsp;0.05) and within sites to co-located aerosol data (</span><i>r</i><sup>2</sup><span>&nbsp;0.57–0.99,&nbsp;</span><i>p</i><span>&nbsp;&lt;&nbsp;0.05). The inclusion of dust deposition and composition monitoring into existing networks increases spatial and temporal understanding of the atmospheric transport on materials and substantively furthers knowledge of the effects of dust on terrestrial ecosystems and human exposure to dust and associated deleterious compounds.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.aeolia.2020.100600","usgsCitation":"Brahney, J., Wetherbee, G.A., Sexstone, G.A., Youngbull, C., Strong, P., and Heindel, R.C., 2020, A new sampler for the collection and retrieval of dry dust deposition: Aeolian Research, v. 45, 100600, 10 p., https://doi.org/10.1016/j.aeolia.2020.100600.","productDescription":"100600, 10 p.","ipdsId":"IP-111272","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":458132,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.aeolia.2020.100600","text":"Publisher Index Page"},{"id":382170,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Nevada, Utah, Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -115.927734375,\n              33.7243396617476\n            ],\n            [\n              -110.1708984375,\n              35.92464453144099\n            ],\n            [\n              -107.5341796875,\n              38.09998264736481\n            ],\n            [\n              -103.4033203125,\n              38.92522904714054\n            ],\n            [\n              -102.568359375,\n              40.1452892956766\n            ],\n            [\n              -104.853515625,\n              41.57436130598913\n            ],\n            [\n              -109.248046875,\n              44.11914151643737\n            ],\n            [\n              -112.67578124999999,\n              44.653024159812\n            ],\n            [\n              -115.75195312499999,\n              44.402391829093915\n            ],\n            [\n              -116.76269531249999,\n              39.095962936305476\n            ],\n            [\n              -116.89453125,\n              34.19817309627726\n            ],\n            [\n              -115.927734375,\n              33.7243396617476\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brahney, J.","contributorId":247745,"corporation":false,"usgs":false,"family":"Brahney","given":"J.","affiliations":[],"preferred":false,"id":808220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294 wetherbe@usgs.gov","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":1044,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"wetherbe@usgs.gov","middleInitial":"A.","affiliations":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"preferred":true,"id":808221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 0000-0001-8913-0546 sexstone@usgs.gov","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":5159,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham","email":"sexstone@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Youngbull, C.","contributorId":247746,"corporation":false,"usgs":false,"family":"Youngbull","given":"C.","email":"","affiliations":[],"preferred":false,"id":808223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strong, P.","contributorId":102292,"corporation":false,"usgs":true,"family":"Strong","given":"P.","email":"","affiliations":[],"preferred":false,"id":808224,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heindel, Ruth C. 0000-0001-6292-2076","orcid":"https://orcid.org/0000-0001-6292-2076","contributorId":225133,"corporation":false,"usgs":false,"family":"Heindel","given":"Ruth","email":"","middleInitial":"C.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":808225,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208835,"text":"70208835 - 2020 - Integrating ecosystem resilience and resistance into decision support tools for multi-scale population management of a sagebrush indicator species","interactions":[],"lastModifiedDate":"2020-03-03T08:07:45","indexId":"70208835","displayToPublicDate":"2020-01-14T08:05:15","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":"Integrating ecosystem resilience and resistance into decision support tools for multi-scale population management of a sagebrush indicator species","docAbstract":"Imperiled sagebrush (Artemisia spp.) ecosystems of western North America are experiencing unprecedented conservation planning efforts. Advances in decision-support tools operationalize concepts of ecosystem resilience by quantitatively linking spatially explicit variation in soil and plant processes to outcomes of biotic and abiotic disturbances. However, failure to consider higher trophic-level fauna of conservation concern in these tools can hinder efforts to operationalize resilience owing to spatiotemporal lags between slower reorganization of plant and soil processes following disturbance, and faster behavioral and demographic responses of fauna to disturbance. Here, we provide multi-scale examples of decision-support tools for management and restoration actions that evaluate general resilience mapped to variation in soil moisture and temperature regimes through new lenses of habitat selection and population performance responses for an at-risk obligate species to sagebrush ecosystems, the greater sage-grouse (Centrocercus urophasianus). We then briefly describe general pathways going forward for more explicit integration of sage-grouse fitness with factors influencing variation in sagebrush resilience to disturbance and resistance to invasive species (e.g., annual grasses). The intended product of these efforts is a more targeted operational definition of resilience for managers by using quantifiable metrics that help limit chances of spatiotemporal mismatches among restoration responses owing to differences in engineering resilience between sagebrush ecosystem processes and sage-grouse population dynamics. Moreover, spatial resilience can be promoted though explicit consideration of sage-grouse and sagebrush predicted responses to active and passive management treatments across space and time. We describe tools that include multi-scale geospatial overlays and simulation analyses of post-disturbance land cover recovery aimed at prioritizing primary threats to sagebrush ecosystems in the Great Basin in the western portion of sage-grouse range (i.e., grass-fire cycles and conifer expansion), but underlying concepts have broader application to a range of ecosystems.","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2019.00493","usgsCitation":"Ricca, M.A., and Coates, P.S., 2020, Integrating ecosystem resilience and resistance into decision support tools for multi-scale population management of a sagebrush indicator species: Frontiers in Ecology and Evolution, v. 7, 493, 22 p., https://doi.org/10.3389/fevo.2019.00493.","productDescription":"493, 22 p.","ipdsId":"IP-111417","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458134,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00493","text":"Publisher Index Page"},{"id":437161,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P960W8MD","text":"USGS data release","linkHelpText":"Additional mapping tools for Great Basin wildfire and conifer management to increase operational resilience: integrating sagebrush ecosystem and sage-grouse response"},{"id":372831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783566,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209626,"text":"70209626 - 2020 - The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses","interactions":[],"lastModifiedDate":"2020-04-16T12:03:44.002862","indexId":"70209626","displayToPublicDate":"2020-01-14T06:58:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses","docAbstract":"Hydrometeorologic monitoring networks are ubiquitous in contemporary earth-system science. Network stakeholders often inquire about the importance of sites and their locations when discussing funding and monitoring design. Support vector machines (SVMs) can be useful by their assigning each monitoring site as either a support or nonsupport vector. A potentiometric surface was created from synthetic data and 800 random observation locations (sites) as an analog to a groundwater-level network. Using generalized additive models for potentiometric surface prediction, simulations show that a subsample of support vectors from the 800 sites will out perform random samples of sample size equaling the support vector count. Support vector percentages from simulation quantify the recurrence that SVMs assign each site as a support vector, and these percentages in turn measure site importance. An example application of support vector percentages identifies important monitoring sites needed to regionalize the 0.1 annual exceedance probability peak streamflow. The results indicate that 152 of 283 streamgages with support vector percentages equalling 100 percent have not operated since about 2000 and generally have much smaller drainage areas than the greater streamgage network in Texas. The drainage area disparity is an indication of historical imbalance in peak streamflow data acquisition from various stream sizes in Texas.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124522","collaboration":"","usgsCitation":"Asquith, W.H., 2020, The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses: Journal of Hydrology, v. 583, 124522, 10 p., https://doi.org/10.1016/j.jhydrol.2019.124522.","productDescription":"124522, 10 p.","ipdsId":"IP-104552","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":374045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70208149,"text":"70208149 - 2020 - Soil surface elevation dynamics in a mangrove-to-marsh ecotone characterized by vegetation shifts","interactions":[],"lastModifiedDate":"2020-02-25T08:17:01","indexId":"70208149","displayToPublicDate":"2020-01-13T17:56:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Soil surface elevation dynamics in a mangrove-to-marsh ecotone characterized by vegetation shifts","docAbstract":"Mangrove forest encroachment into coastal marsh habitats has been described in subtropical regions worldwide in recent decades. To better understand how soil processes may influence vegetation change, we studied soil surface elevation change, accretion rates, and soil subsurface change across a coastal salinity gradient in Florida, USA, an area with documented mangrove encroachment into saline marshes. Our aim was to identify if variations in the soil variables studied exist and to document any associated vegetation shifts. We used surface elevation tables and marker horizons to document the soil variables over 5 years in a mangrove-to-marsh transition zone or ecotone. Study sites were located in three marsh types (brackish, salt, and transition) and in riverine mangrove forests. Mangrove forest sites had significantly higher accretion rates than marsh sites and were the only locations where elevation gain occurred. Significant loss in surface elevation occurred at transition and salt marsh sites. Transition marshes, which had a significantly higher rate of shallow subsidence compared to other wetland types, appear to be most vulnerable to submergence or to a shift to mangrove forest. Submergence can result in herbaceous vegetation mortality and conversion to open water, with severe implications to the quantity and quality of wetland services provided.","language":"English","publisher":"Springer","doi":"10.1007/s10750-019-04170-4","usgsCitation":"Howard, R.J., From, A., Krauss, K.W., Andres, K.D., Cormier, N., Allain, L.K., and Savarese, M., 2020, Soil surface elevation dynamics in a mangrove-to-marsh ecotone characterized by vegetation shifts: Hydrobiologia, v. 847, p. 1087-1106, https://doi.org/10.1007/s10750-019-04170-4.","productDescription":"20 p.","startPage":"1087","endPage":"1106","ipdsId":"IP-098005","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437162,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XZYJ2X","text":"USGS data release","linkHelpText":"Soil surface elevation dynamics in a mangrove-to-marsh ecotone characterized by vegetation 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,{"id":70211824,"text":"70211824 - 2020 - Pulsed flow-through auto-feeding beaker systems for the laboratory culture of juvenile freshwater mussels","interactions":[],"lastModifiedDate":"2020-08-10T12:37:20.374625","indexId":"70211824","displayToPublicDate":"2020-01-13T17:06:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":853,"text":"Aquaculture","active":true,"publicationSubtype":{"id":10}},"title":"Pulsed flow-through auto-feeding beaker systems for the laboratory culture of juvenile freshwater mussels","docAbstract":"<p><span>Newly metamorphosed freshwater mussels are small and delicate, so that captive laboratory culture presents challenges for handling; for maintenance of suitable microhabitat, water quality, and food; and for avoidance of competitors and predators. To address these challenges, a new pulsed flow-through auto-feeding beaker system was developed for culturing juvenile mussels. In this system, groups of mussels were maintained in 300- to 1000-mL beakers with a thin layer of sand substrate. The water in the beakers was static except for pulses that were delivered every 1 or 2&nbsp;h and that displaced about half of the water in each beaker per water cycle. A peristaltic pump delivered food to multiple mixing cells where the water was automatically mixed with food just before the water delivery. In testing this approach, newly metamorphosed mussels of 4 species were cultured in the system for 84 to 357 d. The sand and beakers were replaced weekly. Survival was high (&gt;85% at day 84) for&nbsp;</span><i>Lampsilis siliquoidea</i><span>&nbsp;and&nbsp;</span><i>Villosa iris</i><span>, but relatively lower for&nbsp;</span><i>Anodonta californiensis</i><span>&nbsp;(29% at day 155) and&nbsp;</span><i>Margaritifera falcata</i><span>&nbsp;(23% at day 357). Growth rate ranged among the 4 species from 27 to 60&nbsp;μm/d, with the slowest rate for&nbsp;</span><i>M. falcata</i><span>&nbsp;and fastest for&nbsp;</span><i>A. californiensis.</i><span>&nbsp;Overall, the new pulsed flow-through auto-feeding beaker system improved survival and growth of juvenile mussels versus other methods previously tested. Additionally, a simplified system for the water and food delivery was developed with a single mixing cell. The use of both systems indicate that they are suitable for laboratory experiments and for captive culture of juvenile mussels.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.aquaculture.2020.734959","usgsCitation":"Kunz, J.L., Brunson, E., Barnhart, M., Glidewell, E.A., Wang, N., and Ingersoll, C.G., 2020, Pulsed flow-through auto-feeding beaker systems for the laboratory culture of juvenile freshwater mussels: Aquaculture, v. 520, 734959, 8 p., https://doi.org/10.1016/j.aquaculture.2020.734959.","productDescription":"734959, 8 p.","ipdsId":"IP-113839","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":437163,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P989BFTB","text":"USGS data release","linkHelpText":"Survival and growth of juvenile freshwater mussels in a flow-through auto-feeding system"},{"id":377215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kunz, James L. 0000-0002-1027-158X jkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-1027-158X","contributorId":3309,"corporation":false,"usgs":true,"family":"Kunz","given":"James","email":"jkunz@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":795248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brunson, Eric 0000-0001-6624-0902","orcid":"https://orcid.org/0000-0001-6624-0902","contributorId":201761,"corporation":false,"usgs":true,"family":"Brunson","given":"Eric","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":795251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, M. 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Christopher","affiliations":[],"preferred":false,"id":795250,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Glidewell, Elizabeth A.","contributorId":189302,"corporation":false,"usgs":false,"family":"Glidewell","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":795252,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":795249,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":795253,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208025,"text":"70208025 - 2020 - A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels","interactions":[],"lastModifiedDate":"2020-10-28T15:09:08.954274","indexId":"70208025","displayToPublicDate":"2020-01-13T16:41:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5840,"text":"Environmental DNA","active":true,"publicationSubtype":{"id":10}},"title":"A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels","docAbstract":"<p><span>Resource managers may be hesitant to make decisions based on environmental (e)DNA results alone since eDNA is an indirect method of species detection. One way to reduce the uncertainty of eDNA is to identify laboratory‐based protocols that ensure repeatable and reproducible results. We conducted a double‐blind round‐robin analysis of probe‐based assays for DNA of dreissenid (</span><i>Dreissena</i><span>&nbsp;spp.) mussels, which are prolific aquatic invaders that can cause significant economic and ecological impacts. DNA extract from water samples spiked with known amounts of dreissenid DNA and from water samples collected from waters with and without dreissenids were analyzed by four independent research laboratories. We used results to calculate detection repeatability within laboratories and assays, detection reproducibility among laboratories and assays, and estimated dreissenid DNA copy number precision and accuracy. Laboratory and assay repeatability and reproducibility of detection results were high, 91% and 92%, respectively. The estimated copy numbers were neither precise nor accurate for samples spiked with &lt;773 gene copies. These results suggest that eDNA surveillance of dreissenid mussels, using the protocols evaluated herein, can generate reliable detection data for decision‐making. However, managers should be cautious about using the quantitative information often associated with eDNA detections, especially when DNA is at lower abundance. Our results provide strong support that eDNA has the potential to provide repeatable and reproducible evidence under varying laboratory conditions and for different sample water chemistries. This is reassuring since the demand for eDNA surveillance is widespread and number of laboratories that process eDNA samples is growing steadily.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/edn3.68","usgsCitation":"Sepulveda, A.J., Hutchins, P.R., Jackson, C., Ostberg, C.O., Laramie, M., Amberg, J., Counihan, T., Hoegh, A.B., and Pilliod, D.S., 2020, A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels: Environmental DNA, v. 2, no. 4, p. 446-459, https://doi.org/10.1002/edn3.68.","productDescription":"14 p.","startPage":"446","endPage":"459","ipdsId":"IP-111602","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":458141,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.68","text":"Publisher Index Page"},{"id":437164,"rank":0,"type":{"id":30,"text":"Data 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B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":780271,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":780187,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70228284,"text":"70228284 - 2020 - Walleye growth declines following zebra mussel and Bythotrephes invasion","interactions":[],"lastModifiedDate":"2022-02-08T21:52:56.045081","indexId":"70228284","displayToPublicDate":"2020-01-13T15:38:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Walleye growth declines following zebra mussel and <i>Bythotrephes </i> invasion","title":"Walleye growth declines following zebra mussel and Bythotrephes invasion","docAbstract":"<p><span>Invasive species represent a threat to aquatic ecosystems globally; however, impacts can be heterogenous across systems. Documented impacts of invasive zebra mussels (</span><i>Dreissena polymorpha</i><span>) and spiny water fleas (</span><i>Bythotrephes&nbsp;cederströmii</i><span>; hereafter&nbsp;</span><i>Bythotrephes</i><span>) on native fishes are variable and context dependent across locations and time periods. Here, we use a hierarchical Bayesian analysis of a 35-year dataset on two fish species from 9 lakes to demonstrate that early life growth of ecologically important fishes are influenced by these aquatic invasive species. Walleye (</span><i>Sander vitreus</i><span>) in their first year of life&nbsp;grew more slowly&nbsp;in the presence of either invader after correcting for temperature (measured by degree days), and were on average 12 or 14% smaller at the end of their first summer following invasion by&nbsp;</span><i>Bythotrephes</i><span>&nbsp;or zebra mussels, respectively. Yellow perch (</span><i>Perca flavescens</i><span>) growth was less affected by invasion. Yellow perch on average grew more slowly in their first year of life following invasion by zebra mussels, although this effect was not statistically distinguishable from zero. Early life growth of both walleye and yellow perch was less tightly coupled to degree days in invaded systems, as demonstrated by increased variance surrounding the degree day-length relationship. Smaller first-year size is related to walleye survival and recruitment to later life stages and has important implications for lake food webs and fisheries management. Future research quantifying effects of zebra mussels and&nbsp;</span><i>Bythotrephes</i><span>&nbsp;on other population-level processes and across a wider gradient of lake types is needed to understand the mechanisms driving observed changes in walleye growth.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10530-020-02198-5","usgsCitation":"Ahrenstorff, T.D., Hansen, G., Bethke, B.J., Dumke, J., Hirsch, J., Kovalenko, K., LeDuc, J., Maki, R.P., Rantala, H., and Wagner, T., 2020, Walleye growth declines following zebra mussel and Bythotrephes invasion: Biological Invasions, v. 22, p. 1481-1495, https://doi.org/10.1007/s10530-020-02198-5.","productDescription":"15 p.","startPage":"1481","endPage":"1495","ipdsId":"IP-110055","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":458143,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-020-02198-5","text":"Publisher Index Page"},{"id":395656,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -97.91015624999999,\n              45.398449976304086\n            ],\n            [\n              -89.736328125,\n              45.398449976304086\n            ],\n            [\n              -89.736328125,\n              49.03786794532644\n            ],\n            [\n              -97.91015624999999,\n              49.03786794532644\n            ],\n            [\n              -97.91015624999999,\n              45.398449976304086\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"22","noUsgsAuthors":false,"publicationDate":"2020-01-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahrenstorff, Tyler D.","contributorId":275045,"corporation":false,"usgs":false,"family":"Ahrenstorff","given":"Tyler","email":"","middleInitial":"D.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":833604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Gretchen J. A.","contributorId":275043,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":833603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bethke, Bethany J.","contributorId":275047,"corporation":false,"usgs":false,"family":"Bethke","given":"Bethany","email":"","middleInitial":"J.","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":833605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dumke, Josh","contributorId":275049,"corporation":false,"usgs":false,"family":"Dumke","given":"Josh","email":"","affiliations":[{"id":18006,"text":"University of Minnesota Duluth","active":true,"usgs":false}],"preferred":false,"id":833606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hirsch, Jodie","contributorId":275051,"corporation":false,"usgs":false,"family":"Hirsch","given":"Jodie","email":"","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":833607,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kovalenko, Katya E.","contributorId":275052,"corporation":false,"usgs":false,"family":"Kovalenko","given":"Katya E.","affiliations":[{"id":18006,"text":"University of Minnesota Duluth","active":true,"usgs":false}],"preferred":false,"id":833608,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LeDuc, Jaime F.","contributorId":275056,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jaime F.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":833609,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Maki, Ryan P","contributorId":275061,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan","email":"","middleInitial":"P","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":833610,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rantala, Heidi","contributorId":275065,"corporation":false,"usgs":false,"family":"Rantala","given":"Heidi","affiliations":[{"id":34923,"text":"Minnesota DNR","active":true,"usgs":false}],"preferred":false,"id":833611,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833602,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70211201,"text":"70211201 - 2020 - A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States","interactions":[],"lastModifiedDate":"2020-08-06T19:35:20.478446","indexId":"70211201","displayToPublicDate":"2020-01-13T12:07:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States","docAbstract":"Nearly 60,000 utility-scale wind turbines are installed in the United States as of July, 2019, representing over 97 gigawatts of electric power capacity; US wind turbine installations continue to grow at a rapid pace. Yet, until April 2018, no publicly-available, regularly updated data source existed to describe those turbines and their locations. Under a cooperative research and development agreement, analysts from three organizations collaborated to develop and release the United States Wind Turbine Database (USWTDB) - a publicly available, continuously updated, spatially rectified data source of locations and attributes of utility-scale wind turbines in the United States. Technical specifications and wind facility data, incorporated from five sources, undergo rigorous quality control. The location of each turbine is visually verified using high-resolution aerial imagery. The quarterly-updated data are available in a variety of formats, including an interactive web application, comma-separated values (CSV), shapefile, and application programming interface (API). The data are used widely by academic researchers, engineers and developers from wind energy companies, government agencies, planners, educators, and the general public.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-020-0353-6","usgsCitation":"Rand, J., Kramer, L., Garrity, C.P., Hoen, B., Diffendorfer, J., Hunt, H., and Spears, M., 2020, A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States: Scientific Data, v. 7, 15, 12 p., https://doi.org/10.1038/s41597-020-0353-6.","productDescription":"15, 12 p.","ipdsId":"IP-112062","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":458145,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-020-0353-6","text":"Publisher Index Page"},{"id":437165,"rank":0,"type":{"id":30,"text":"Data 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,{"id":70215186,"text":"70215186 - 2020 - Longitudinal distribution of uncommon fishes in a species-rich basin","interactions":[],"lastModifiedDate":"2020-10-09T14:12:44.050435","indexId":"70215186","displayToPublicDate":"2020-01-13T09:09:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Longitudinal distribution of uncommon fishes in a species-rich basin","docAbstract":"<ol class=\"\"><li>The spatial organization of fishes in a river system was investigated to evaluate the longitudinal distribution of uncommon species. It was anticipated that overall richness of the fish community would increase in a downstream direction together with habitat extent, but that more uncommon species would occur upstream owing to greater heterogeneity among sites.</li><li>Fish were collected between 1995 and 2014 at 85 sites distributed throughout the Duck River Basin, Tennessee, USA. A site usually consisted of four habitat types: riffles, runs, pools and shoreline. Each habitat type was sampled with a multipass electrofishing protocol.</li><li>In all, 136 native fish species were collected. Of these, 71% were classified as uncommon but represented only 16% of the total count of fish collected. As expected, overall species richness increased downstream, but contrary to expectation, uncommon species did too. Some uncommon species were restricted exclusively to tributaries and headwaters, some to tributaries and mainstem, many to mainstem only, but the largest fraction of uncommon species occurred throughout the basin, but even this last group increased in richness downstream.</li><li>Conservation often focuses on uncommon species. This study suggests that a greater number of uncommon species can be conserved with an emphasis on large downstream reaches, which not only include more aquatic habitat to support larger concentrations of fish, but also shelter the uncomm</li></ol>","language":"English","publisher":"Wiley","doi":"10.1002/aqc.3262","usgsCitation":"Miranda, L.E., and Killgore, K., 2020, Longitudinal distribution of uncommon fishes in a species-rich basin: Aquatic Conservation: Marine and Freshwater Ecosystems, v. 30, no. 3, p. 577-585, https://doi.org/10.1002/aqc.3262.","productDescription":"9 p.","startPage":"577","endPage":"585","ipdsId":"IP-106148","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":458147,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/aqc.3262","text":"Publisher Index Page"},{"id":379275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Duck River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.61547851562499,\n              34.92197103616377\n            ],\n            [\n              -86.87988281249999,\n              34.92197103616377\n            ],\n            [\n              -86.87988281249999,\n              36.5184659896759\n            ],\n            [\n              -89.61547851562499,\n              36.5184659896759\n            ],\n            [\n              -89.61547851562499,\n              34.92197103616377\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"30","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":801094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Killgore, K.J.","contributorId":200191,"corporation":false,"usgs":false,"family":"Killgore","given":"K.J.","email":"","affiliations":[{"id":33009,"text":"Engineer Research and Development Center, U. S. Army Corps of Engineers, Vicksburg, Mississippi","active":true,"usgs":false}],"preferred":false,"id":801095,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70225829,"text":"70225829 - 2020 - Using the Lomb-Scargle method for wave statistics from gappy time series","interactions":[],"lastModifiedDate":"2021-11-10T14:50:24.002282","indexId":"70225829","displayToPublicDate":"2020-01-13T08:43:38","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using the Lomb-Scargle method for wave statistics from gappy time series","docAbstract":"<p><span>Sandwich Town Neck Beach in Sandwich, MA, has experienced substantial erosion and has been the subject of efforts by the town and private landowners to limit the sand loss. Erosion has been particularly dramatic in the past five years with the loss of dwellings. Sandwich's nourishment efforts presented a unique opportunity for scientists at the U.S. Geological Survey Woods Hole Coastal and Marine Science Center to monitor beach morphology and to test new technologies and techniques such as geo-referenced drone imaging. Two bottom lander deployments were performed in Cape Cod Bay at a location that was key to model the fate of waves at Sandwich Town Neck Beach and to support the study of beach morphological evolution. The study period was after the town nourished the beach and during a time when several intense winter storms reshaped the beach and removed much of the nourished sand. A TRDI Workhorse Sentinel V ADCP was used for both deployments. For wave bursts, the instruments collected 2048 samples at 2 Hz every hour. The first deployment during the winter of 2016 returned good quality data. The second deployment during the following winter had gaps throughout the time series from a wiring problem in the external battery pack. The timing of the gaps was random, the duration approximately 100 s. While most of the bursts started at the top of each hour, many had 1-3 gaps within. Time series data with random gaps are problematic for computing spectral density, and thus, wave statistics. This kind of situation is familiar in other scientific disciplines such as astrophysics [1], where techniques exist to find stationary signals in sparse data. One of these methods is the Lomb-Scargle technique for computing periodograms. The most useful feature of the Lomb-Scargle (LS) method is that it allows the spectral analysis of incomplete records, without having to manipulate the record to extrapolate from or replace missing data. We compared the effectiveness of LS against common methods of averaging Fourier transforms such as a simple un-windowed Fast Fourier transform (FFT), Welch's method, and TRDI's Wavesmon software; methods that are commonly used in oceanography for non-gappy data. Synthetic data series that have been artificially modified to introduce gaps were used to evaluate the performance of each method. The LS approach was able to recover spectral density even with several 100-s gaps present. The method was applied here to the gappy and non-gappy data from both Sandwich deployments, and wave statistics were obtained and compared to the wave-buoy data. LS was used to process data that contains gaps that was rejected by Wavesmon, which was approximately 39% of the dataset. Significant wave height and peak period from LS compared well with buoy data. Mean period computed on gappy data using LS produced values biased low, compared with other methods when gaps were filled with the mean value. The LS technique has potential to uncover low-frequency signals such as infragravity waves from gappy records where the non-gappy segments are not long enough to resolve them. It has potential to unlock new information from older data sets.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IEEE Oceanic Engineering Society - Current, Waves, Turbulence and Measurement Applications Workshop","conferenceDate":"Mar 10-13, 2019","language":"English","publisher":"IEEE","doi":"10.1109/CWTM43797.2019.8955285","usgsCitation":"Martini, M.A., Aretxabaleta, A., and Sherwood, C.R., 2020, Using the Lomb-Scargle method for wave statistics from gappy time series, <i>in</i> 2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM), Mar 10-13, 2019, 9 p., https://doi.org/10.1109/CWTM43797.2019.8955285.","productDescription":"9 p.","ipdsId":"IP-105269","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":391573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Sandwich","otherGeospatial":"Sandwich Town Neck Beach","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.48888206481934,\n              41.762413206292656\n            ],\n            [\n              -70.47154426574707,\n              41.762413206292656\n            ],\n            [\n              -70.47154426574707,\n              41.77297600540535\n            ],\n            [\n              -70.48888206481934,\n              41.77297600540535\n            ],\n            [\n              -70.48888206481934,\n              41.762413206292656\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martini, Marinna A. 0000-0002-7757-5158 mmartini@usgs.gov","orcid":"https://orcid.org/0000-0002-7757-5158","contributorId":2456,"corporation":false,"usgs":true,"family":"Martini","given":"Marinna","email":"mmartini@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826576,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70208001,"text":"70208001 - 2020 - Mercury and selenium concentrations in fishes of the Upper Colorado River Basin, southwestern United States: A retrospective assessment","interactions":[],"lastModifiedDate":"2020-01-23T06:22:49","indexId":"70208001","displayToPublicDate":"2020-01-13T06:18:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Mercury and selenium concentrations in fishes of the Upper Colorado River Basin, southwestern United States: A retrospective assessment","docAbstract":"Mercury (Hg) and selenium (Se) are contaminants of concern for fish in the Upper Colorado River Basin (UCRB). We explored Hg and Se in fish tissues (2,324 individuals) collected over 50 years (1962–2011) from the UCRB. Samples include native and non-native fish collected from lotic waterbodies spanning 7 major tributaries to the Colorado River. There was little variation of total mercury (THg) in fish assemblages basin-wide and only 13% (272/1959) of individual fish samples exceeded the fish health benchmark (0.27 μg THg/g ww). Most THg exceedances were observed in the White-Yampa tributary whereas the San Juan had the lowest mean THg concentration. Risks associated with THg are species specific with exceedances dominated by Colorado Pikeminnow (mean = 0.38 and standard error ± 0.08 μg THg/g ww) and Roundtail Chub (0.24 ± 0.06 μg THg/g ww). For Se, 48% (827/1720) of all individuals exceeded the fish health benchmark (5.1 μg Se/g dw). The Gunnison river had the most individual exceedances of the Se benchmark (74%) whereas the Dirty Devil had the fewest. We identified that species of management concern accumulate THg and Se to levels above risk thresholds and that fishes of the White-Yampa (THg) and Gunnison (Se) rivers are at the greatest risk in the UCRB.","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0226824","usgsCitation":"Day, N.K., Schmidt, T.S., Roberts, J., Osmundson, B., Willacker, J., and Eagles-Smith, C., 2020, Mercury and selenium concentrations in fishes of the Upper Colorado River Basin, southwestern United States: A retrospective assessment: PLoS ONE, v. 15, no. 1, e0226824, https://doi.org/10.1371/journal.pone.0226824.","productDescription":"e0226824","ipdsId":"IP-100771","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458151,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0226824","text":"Publisher Index Page"},{"id":437167,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71N802D","text":"USGS data release","linkHelpText":"Fish tissue mercury and selenium concentrations in Upper Colorado River Basin: 1962-2011"},{"id":371485,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -117.0703125,\n              36.56260003738545\n            ],\n            [\n              -114.521484375,\n              34.77771580360469\n            ],\n            [\n              -107.05078125,\n              34.59704151614417\n            ],\n            [\n              -105.2490234375,\n              36.24427318493909\n            ],\n            [\n 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Natalie K. 0000-0002-8768-5705","orcid":"https://orcid.org/0000-0002-8768-5705","contributorId":207302,"corporation":false,"usgs":true,"family":"Day","given":"Natalie","middleInitial":"K.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":221742,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis","email":"tschmidt@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":780097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":780098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osmundson, Barbara C.","contributorId":221743,"corporation":false,"usgs":false,"family":"Osmundson","given":"Barbara C.","affiliations":[{"id":40411,"text":"(Emeritus) U.S. Fish and Wildlife Service, Grand Junction, CO","active":true,"usgs":false}],"preferred":false,"id":780099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":221744,"corporation":false,"usgs":true,"family":"Willacker","given":"James","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":780100,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":780101,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70208449,"text":"70208449 - 2020 - Effects of montane watershed development on vulnerability of domestic groundwater supply during drought","interactions":[],"lastModifiedDate":"2020-02-10T18:22:15","indexId":"70208449","displayToPublicDate":"2020-01-11T18:13:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of montane watershed development on vulnerability of domestic groundwater supply during drought","docAbstract":"Climate change is expected to reduce recharge to montane aquifers in the western United States, but it is unclear how this will impact groundwater resources in watersheds where intensive surface-water development has disrupted the natural hydrologic regime. To better understand sources of recharge and associated vulnerabilities of groundwater supply in this setting, we made a detailed geochemical survey of domestic wells finished in fractured bedrock throughout the Yuba and Bear River watersheds (Sierra Nevada foothills, northern California)during historic drought (2015–2016). Stable isotopes of water and noble gas recharge temperatures closely tracked atmospheric lapse rates across a broad elevation gradient (100–2000 m), indicating groundwater inputs are dominated by local precipitation that rapidly recharges fractured bedrock during the winter wet-season. However, nearly one-quarter of wells had water isotopes that were fractionated by evaporation and warm recharge temperatures, indicative of mixing with dry-season recharge by surface water. Monte Carlo mixing models suggest evaporation-impacted groundwater samples are mixtures of local rain with an average of 28% ± 13% from diverted surface water that can recharge bedrock aquifers during the dry-season by either irrigation return flow or seepage from extensive distribution infrastructure. Wells that received recharge subsidies from diverted surface water had elevated levels of nitrate and coliform bacteria compared to those replenished exclusively by local precipitation,\nwhich are more vulnerable to supply shortage during drought. It is important to consider the impacts of increased surface-water development on the quantity and quality of groundwater recharge in rapidly developing montane watersheds.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2020.124567","usgsCitation":"Levy, Z., Fram, M.S., Faulkner, K., Alpers, C.N., Soltero, E.M., and Taylor, K.A., 2020, Effects of montane watershed development on vulnerability of domestic groundwater supply during drought: Journal of Hydrology, v. 583, 124567, 18 p., https://doi.org/10.1016/j.jhydrol.2020.124567.","productDescription":"124567, 18 p.","ipdsId":"IP-107517","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":458154,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2020.124567","text":"Publisher Index Page"},{"id":437168,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YETK9P","text":"USGS data release","linkHelpText":"Dissolved Noble Gas Concentrations and Modeled Recharge Temperatures for Groundwater from Northern Sierra Nevada Foothills Shallow Aquifer Assessment Study Units, 2015-2017: Results from the California GAMA Priority Basin Project"},{"id":372204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bear River watershed, Yuba River watershed","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -121.2451171875,\n              38.86323626888358\n            ],\n            [\n              -120.13275146484374,\n              38.86323626888358\n            ],\n            [\n              -120.13275146484374,\n              39.85282948915942\n            ],\n            [\n              -121.2451171875,\n              39.85282948915942\n            ],\n            [\n              -121.2451171875,\n              38.86323626888358\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"583","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faulkner, Kirsten 0000-0003-1628-2877","orcid":"https://orcid.org/0000-0003-1628-2877","contributorId":222341,"corporation":false,"usgs":true,"family":"Faulkner","given":"Kirsten","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soltero, Evelyn M","contributorId":222342,"corporation":false,"usgs":false,"family":"Soltero","given":"Evelyn","email":"","middleInitial":"M","affiliations":[{"id":40530,"text":"All About Wells","active":true,"usgs":false}],"preferred":false,"id":781924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Taylor, Kimberly A. 0000-0002-0095-6403 ktaylor@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-6403","contributorId":1601,"corporation":false,"usgs":true,"family":"Taylor","given":"Kimberly","email":"ktaylor@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781925,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70260133,"text":"70260133 - 2020 - Mechanisms for ballistic block ejection during the 2016–2017 shallow submarine eruption of Bogoslof volcano, Alaska","interactions":[],"lastModifiedDate":"2024-10-29T16:44:36.747968","indexId":"70260133","displayToPublicDate":"2020-01-11T11:38:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms for ballistic block ejection during the 2016–2017 shallow submarine eruption of Bogoslof volcano, Alaska","docAbstract":"<p><span>Ejection of ballistic blocks was a characteristic feature of the 2016–2017 Bogoslof eruption. High-resolution satellite images acquired throughout the duration of the 9-month long eruptive period permitted the recognition and mapping of ballistic blocks on the surface of Bogoslof Island. Many of the satellite images recorded the accumulation of ballistic material over several individual eruptive events, but a few images recorded the effects of a single event. The nonuniform spatial distribution of blocks suggests that some of the eruption columns were inclined. Ballistic trajectories were estimated using the Eject! model and indicate that accumulation of blocks on Bogoslof Island required launch angles of 45–80° and initial velocities of 50–100&nbsp;ms</span><sup>−1</sup><span>&nbsp;to reproduce observed travel distances. The amount of ballistic fallout observed in satellite data indicates that there must have been a shallow submarine source of rock within the conduit/upper edifice system. Dense, accidental cryptodome trachyandesite, and juvenile basalt to trachybasalt scoria make up the bulk of the surface ejecta. Abundant accidental fragments and inclined eruption columns point to periodic vent-wall collapse and jetting around edges of temporarily blocked vents as the likely cause of ballistic ejection.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00445-019-1351-4","usgsCitation":"Waythomas, C.F., and Mastin, L.G., 2020, Mechanisms for ballistic block ejection during the 2016–2017 shallow submarine eruption of Bogoslof volcano, Alaska: Bulletin of Volcanology, v. 82, 13, 20 p., https://doi.org/10.1007/s00445-019-1351-4.","productDescription":"13, 20 p.","ipdsId":"IP-113632","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463356,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -168.04572245314344,\n              53.93741980478987\n            ],\n            [\n              -168.04572245314344,\n              53.92361535465113\n            ],\n            [\n              -168.0255939265047,\n              53.92361535465113\n            ],\n            [\n              -168.0255939265047,\n              53.93741980478987\n            ],\n            [\n              -168.04572245314344,\n              53.93741980478987\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917131,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70237952,"text":"70237952 - 2020 - Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers","interactions":[],"lastModifiedDate":"2022-11-01T14:06:22.857819","indexId":"70237952","displayToPublicDate":"2020-01-11T08:57:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers","docAbstract":"<p><span>Hydraulic head and groundwater age data are effective in building understanding of groundwater systems. Yet their joint role in detecting and characterising low-permeability geological structures, i.e. hydrogeologic barriers such as faults and dykes, has not been widely studied. Here, numerical flow and transport models, using MODFLOW-NWT and MT3D-USGS, were developed with different hydrogeologic barrier configurations in a hypothetical aquifer. Computed hydraulic head and groundwater age distributions were compared to those without a barrier. The conjoint use of these datasets helps in detecting vertically-oriented barriers. Two forms of recharge were compared: (1) applied across the entire aquifer surface (uniform), and (2) applied to the upstream part of the aquifer (upgradient). The hydraulic head distribution is significantly impacted by a barrier that penetrates the aquifer’s full vertical thickness. This barrier also perturbs the groundwater age distribution when upgradient recharge prevails; however, with uniform recharge, groundwater age is not successful in detecting the barrier. When a barrier is buried, such as by younger sediment, hydraulic head data also do not clearly identify the barrier. Groundwater age data could, on the other hand, prove to be useful if sampled at depth-specific intervals. These results are important for the detection and characterisation of hydrogeologic barriers, which may play a significant role in the compartmentalisation of groundwater flow, spring dynamics, and drawdown and recovery associated with groundwater extraction.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10040-019-02095-9","usgsCitation":"Marshall, S.K., Cook, P., Konikow, L.F., Simmons, C., and Dogramaci, S., 2020, Conjoint use of hydraulic head and groundwater age data to detect hydrogeologic barriers: Hydrogeology Journal, v. 28, p. 1003-1019, https://doi.org/10.1007/s10040-019-02095-9.","productDescription":"17 p.","startPage":"1003","endPage":"1019","ipdsId":"IP-109151","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":408987,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","noUsgsAuthors":false,"publicationDate":"2020-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Sarah K.","contributorId":298728,"corporation":false,"usgs":false,"family":"Marshall","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, Peter G.","contributorId":298729,"corporation":false,"usgs":false,"family":"Cook","given":"Peter G.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konikow, Leonard F. 0000-0002-0940-3856 lkonikow@usgs.gov","orcid":"https://orcid.org/0000-0002-0940-3856","contributorId":158,"corporation":false,"usgs":true,"family":"Konikow","given":"Leonard","email":"lkonikow@usgs.gov","middleInitial":"F.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Simmons, Craig T.","contributorId":298730,"corporation":false,"usgs":false,"family":"Simmons","given":"Craig T.","affiliations":[{"id":40595,"text":"Flinders University","active":true,"usgs":false}],"preferred":false,"id":856340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dogramaci, Shawan","contributorId":298731,"corporation":false,"usgs":false,"family":"Dogramaci","given":"Shawan","email":"","affiliations":[{"id":64684,"text":"Rio Tinto Iron Ore Co.","active":true,"usgs":false}],"preferred":false,"id":856341,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70209325,"text":"70209325 - 2020 - Heterogeneity in migration strategies of the whooping crane","interactions":[],"lastModifiedDate":"2020-04-01T08:27:50","indexId":"70209325","displayToPublicDate":"2020-01-11T08:21:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Heterogeneity in migration strategies of the whooping crane","docAbstract":"Migratory birds use numerous strategies to successfully complete twice-annual movements between breeding and wintering sites. Context for conservation and management can be provided by characterizing these strategies. Variations in strategy among and within individuals support population persistence in response to changes in land use and climate. We used location data from 58 marked Whooping Cranes (Grus americana) from 2010–2016 to characterize migration strategies in the U.S. Great Plains and Canadian Prairies and southern boreal region, and to explore sources of heterogeneity in their migration strategy, including space use, timing, and performance. Whooping Cranes completed approximately 3,900-km migrations that averaged 29 days during spring and 45 days during autumn, while making 11–12 nighttime stops. At the scale of our analysis, individual Whooping Cranes showed little consistency in stopover sites used among migration seasons (i.e., low site fidelity). In contrast, individuals expressed a measure of consistency in timing, especially migration initiation date. Whooping Cranes migrated at different times based on age and reproductive status, where adults with young initiated autumn migration after other birds, and adults with and without young initiated spring migration before subadult birds. Time spent at stopover sites was associated with migration bout length and time spent at previous stopover sites, suggesting Whooping Cranes acquired energy resources at some stopover sites that they used to fuel migration. Whooping Cranes were faithful to a defined migration corridor but showed less fidelity in their selection of nighttime stopover sites; hence, spatial targeting of conservation actions may be better informed by associations with landscape and habitat features rather than documented past use at specific locations. The preservation of variation in migration strategies existing within this species that experienced a severe population bottleneck suggests that Whooping Cranes have maintained a capacity to adjust strategies when confronted with future changes in land use and climate.","language":"English","publisher":"American Ornithological Society","doi":"10.1093/condor/duz056","usgsCitation":"Pearse, A.T., Metzger, K.L., Brandt, D.A., Bidwell, M.T., Harner, M.J., Baasch, D.M., and Harrell, W.C., 2020, Heterogeneity in migration strategies of the whooping crane: The Condor, v. 122, no. 1, duz056, https://doi.org/10.1093/condor/duz056.","productDescription":"duz056","ipdsId":"IP-099078","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":437169,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NRAY6F","text":"USGS data release","linkHelpText":"Characterization of whooping crane migrations and stopover sites used in the Central Flyway, 2010-2016"},{"id":373699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Great Plains, Canadian Prairies ","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -95.09765625,\n              29.38217507514529\n            ],\n            [\n              -95.97656249999999,\n              33.797408767572485\n            ],\n            [\n              -97.119140625,\n              39.027718840211605\n            ],\n            [\n              -98.349609375,\n              45.1510532655634\n            ],\n            [\n              -100.107421875,\n              48.922499263758255\n            ],\n            [\n              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NM","active":true,"usgs":false}],"preferred":false,"id":786080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":786078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bidwell, Mark T.","contributorId":202007,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark","email":"","middleInitial":"T.","affiliations":[{"id":36318,"text":"CWS","active":true,"usgs":false}],"preferred":false,"id":786079,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harner, Mary J.","contributorId":177584,"corporation":false,"usgs":false,"family":"Harner","given":"Mary","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":786081,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baasch, David M.","contributorId":147145,"corporation":false,"usgs":false,"family":"Baasch","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":16795,"text":"Headwaters Corp, Kearney, NE","active":true,"usgs":false}],"preferred":false,"id":786082,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harrell, Wade C.","contributorId":147143,"corporation":false,"usgs":false,"family":"Harrell","given":"Wade","email":"","middleInitial":"C.","affiliations":[{"id":16793,"text":"USFWS, Ecological Services, Austwell, TX","active":true,"usgs":false}],"preferred":false,"id":786083,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
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