This report summarizes the software and algorithms that are used to calibrate images returned by the High Resolution Imaging Science Experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter (MRO) spacecraft. The instrument design and data processing methods are summarized below, followed by a description of relevant calibration data and details of the calibration procedure. In this document, we describe the software that uses those coefficients and matrices to radiometrically calibrate HiRISE data. This software is included in version 3 of the Integrated Software for Imagers and Spectrometers (ISIS3), which is developed and maintained by the U.S. Geological Survey Astrogeology Science Center in Flagstaff, Ariz., for the international planetary science community via funding from the National Aeronautics and Space Administration. ISIS3 is freely available to the scientific community and can be obtained at http://isis.astrogeology.usgs.gov/index.html. Support for ISIS3 is provided at https://github.com/USGS-Astrogeology/ISIS3.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C27","usgsCitation":"Becker, K.J., Milazzo, M.P., Delamere, W.A., Herkenhoff, K.E., Eliason, E.M., Russell, P.S., Keszthelyi, L.P., and McEwen, A.S., 2021, hical—The HiRISE radiometric calibration software developed within the ISIS3 planetary image processing suite: U.S. Geological Survey Techniques and Methods, book 7, chap. C27, 23 p., https://doi.org/10.3133/tm7C27.","productDescription":"v, 23 p.","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-069330","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":394544,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c27/covrthb.jpg"},{"id":394545,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c27/tm7c27.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The Yucaipa groundwater subbasin (referred to in this report as the Yucaipa subbasin) is located about 75 miles (mi) east of of Los Angeles and about 12 mi southeast of the City of San Bernardino. In the Yucaipa subbasin, as in much of southern California, limited annual rainfall and large water demands can strain existing water supplies; therefore, understanding local surface water and groundwater conditions is essential for managing these resources. To better understand the hydrogeology and water resources in the Yucaipa subbasin, especially groundwater, the San Bernardino Valley Municipal Water District and the U.S. Geological Survey initiated a cooperative study to evaluate the hydrogeologic system of the Yucaipa subbasin and the encompassing Yucaipa Valley watershed. Previous studies of the area provided information on general geologic and hydrologic conditions, but this study provides the first comprehensive definition of the hydrogeology of the subsurface throughout the entire subbasin.
The Yucaipa subbasin is located between the northwest trending San Andreas fault zone and San Jacinto fault. Several northeast-trending dip-slip faults dissect the Yucaipa subbasin, providing the mechanism for structural relief within the sediment-filled subbasin and between the subbasin and surrounding mountains and highlands. Several of these dip-slip faults have been previously identified as potential barriers to groundwater flow. This report provides a synthesis of previous studies and a discussion of the geologic interpretations that were used as the foundation for hydrogeologic classification of the Yucaipa subbasin. Notably, this report (1) adopts the recently named and classified sedimentary deposits of Live Oak Canyon geologic formation and extends the mapped distribution of the formation into the Yucaipa subbasin, and (2) adopts the interpretation that activity along the Banning fault predates the deposition of most basin-fill sedimentary materials in the Yucaipa subbasin.
Four hydrogeologic units were classified in the Yucaipa subbasin: (1) crystalline basement, (2) consolidated sedimentary materials, (3) unconsolidated sediment, and (4) surficial materials. The crystalline basement unit forms the bottom boundary of the aquifer system, and the three other units comprise the basin-fill aquifer system. The four hydrogeologic units vary in extent, thickness, and structural relief across the subbasin, with the unconsolidated sediment unit serving as the primary aquifer unit. A three-dimensional hydrogeologic framework model was developed for the Yucaipa subbasin and surrounding area to characterize the thickness, extent, and hydrogeologic variability of the aquifer system. Geologic maps, borehole geophysical logs, drillers’ lithology logs, and depth-to-basement gravity data were used to map and interpolate the subsurface extent and structure of the hydrogeologic units within the subbasin. Faults and structures of geologic and (or) hydrogeologic importance were included in the model for future evaluation of their potential effects on groundwater flow. The resulting hydrogeologic framework is consistent with existing geologic concepts and the tectonic and structural history of the Yucaipa subbasin and surrounding area. The framework is also suitable for use in basin-scale hydrogeologic investigations.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is rapidly driving global biodiversity declines. How wetland macroinvertebrate assemblages are responding is unclear, a concern given their vital function in these ecosystems. Using a data set from 769 minimally impacted depressional wetlands across the globe (467 temporary and 302 permanent), we evaluated how temperature and precipitation (average, range, variability) affects the richness and beta diversity of 144 macroinvertebrate families. To test the effects of climatic predictors on macroinvertebrate diversity, we fitted generalized additive mixed-effects models (GAMM) for family richness and generalized dissimilarity models (GDMs) for total beta diversity. We found non-linear relationships between family richness, beta diversity, and climate. Maximum temperature was the main climatic driver of wetland macroinvertebrate richness and beta diversity, but precipitation seasonality was also important. Assemblage responses to climatic variables also depended on wetland water permanency. Permanent wetlands from warmer regions had higher family richness than temporary wetlands. Interestingly, wetlands in cooler and dry-warm regions had the lowest taxonomic richness, but both kinds of wetlands supported unique assemblages. Our study suggests that climate change will have multiple effects on wetlands and their macroinvertebrate diversity, mostly via increases in maximum temperature, but also through changes in patterns of precipitation. The most vulnerable wetlands to climate change are likely those located in warm-dry regions, where entire macroinvertebrate assemblages would be extirpated. Montane and high-latitude wetlands (i.e., cooler regions) are also vulnerable to climate change, but we do not expect entire extirpations at the family level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.153052","usgsCitation":"Epele, L., Grech, M.G., Williams-Subiza, E.A., Stenert, C., McLean, K., Greig, H., Maltchik, L., Pires, M.M., Bird, M.S., Boissezon, A., Boix, D., Demierre, E., García, P., Gascón, S., Jeffries, M., Kneitel, J.M., Loskutov, O., Manzo, L.M., Mataloni, G., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E.E., Wu, H., Wissinger, S., and Batzer, D., 2022, Perils of life on the edge: Climatic threats to global diversity patterns of wetland macroinvertebrates: Science of the Total Environment, v. 820, 153052, 10 p., https://doi.org/10.1016/j.scitotenv.2022.153052.","productDescription":"153052, 10 p.","ipdsId":"IP-127993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":449106,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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Argentina","active":true,"usgs":false}],"preferred":false,"id":835120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams-Subiza, Emilio A. 0000-0001-9480-527X","orcid":"https://orcid.org/0000-0001-9480-527X","contributorId":279553,"corporation":false,"usgs":false,"family":"Williams-Subiza","given":"Emilio","email":"","middleInitial":"A.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stenert, Cristina","contributorId":279554,"corporation":false,"usgs":false,"family":"Stenert","given":"Cristina","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greig, Hamish S.","contributorId":279555,"corporation":false,"usgs":false,"family":"Greig","given":"Hamish S.","affiliations":[{"id":57280,"text":"University of Maine, 212 Deering Hall, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":835124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maltchik, Leonardo 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de Paisaje (GESAP) INIBIOMA, Universidad Nacional del Comahue, CONICET, Quintral 1250, San Carlos de Bariloche (8400), Argentina","active":true,"usgs":false}],"preferred":false,"id":835131,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gascón, Stephanie","contributorId":279563,"corporation":false,"usgs":false,"family":"Gascón","given":"Stephanie","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835132,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Jeffries, Michael","contributorId":279564,"corporation":false,"usgs":false,"family":"Jeffries","given":"Michael","email":"","affiliations":[{"id":57285,"text":"Department of Geography & Environmental Sciences, Northumbria University, Newcastle upon Tune, NE1 8ST, UK","active":true,"usgs":false}],"preferred":false,"id":835133,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kneitel, Jamie M. 0000-0002-7841-1198","orcid":"https://orcid.org/0000-0002-7841-1198","contributorId":279565,"corporation":false,"usgs":false,"family":"Kneitel","given":"Jamie","email":"","middleInitial":"M.","affiliations":[{"id":57286,"text":"Department of Biological Sciences, California State University-Sacramento, Sacramento, CA 95819-6077, USA","active":true,"usgs":false}],"preferred":false,"id":835134,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Loskutov, Olga","contributorId":279566,"corporation":false,"usgs":false,"family":"Loskutov","given":"Olga","email":"","affiliations":[{"id":57287,"text":"Institute of Biology of Komi Scientific Centre of the Ural Branch of the Russian Academy of Sciences, 28 Kommunisticheskaya Street, 167982 Syktyvkar, Russia","active":true,"usgs":false}],"preferred":false,"id":835135,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Manzo, Luz 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C.","contributorId":279569,"corporation":false,"usgs":false,"family":"Mlambo","given":"Musa","email":"","middleInitial":"C.","affiliations":[{"id":57289,"text":"Department of Freshwater Invertebrates, Albany Museum, and Department of Zoology and Entomology, Rhodes University, Makhanda (Grahamstown) 6139, South Africa.","active":true,"usgs":false}],"preferred":false,"id":835138,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Oertli, Beat 0000-0002-8372-9045","orcid":"https://orcid.org/0000-0002-8372-9045","contributorId":279570,"corporation":false,"usgs":false,"family":"Oertli","given":"Beat","email":"","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835139,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Sala, Jordi","contributorId":279571,"corporation":false,"usgs":false,"family":"Sala","given":"Jordi","email":"","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835140,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Scheibler, Erica E. 0000-0001-6802-8702","orcid":"https://orcid.org/0000-0001-6802-8702","contributorId":279572,"corporation":false,"usgs":false,"family":"Scheibler","given":"Erica","email":"","middleInitial":"E.","affiliations":[{"id":57290,"text":"Entomology Laboratory, IADIZA CCT Mendoza CONICET, Av. Adrián Ruiz Leal s/n, Parque General San Martín, 5500, Mendoza, Argentina","active":true,"usgs":false}],"preferred":false,"id":835141,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wu, Haitao","contributorId":279573,"corporation":false,"usgs":false,"family":"Wu","given":"Haitao","email":"","affiliations":[{"id":57291,"text":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, Jilin, 130012, China","active":true,"usgs":false}],"preferred":false,"id":835142,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Wissinger, Scott A","contributorId":279574,"corporation":false,"usgs":false,"family":"Wissinger","given":"Scott A","affiliations":[{"id":57292,"text":"Biology and Environmental Science Departments, Allegheny College, Meadville, PA 16335, USA","active":true,"usgs":false}],"preferred":false,"id":835143,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Batzer, Darold P.","contributorId":279575,"corporation":false,"usgs":false,"family":"Batzer","given":"Darold P.","affiliations":[{"id":57293,"text":"Department of Entomology, University of Georgia, Athens, GA, USA","active":true,"usgs":false}],"preferred":false,"id":835144,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70228197,"text":"70228197 - 2022 - Experimental inoculation trial to determine the effects of temperature and humidity on White-nose Syndrome in hibernating bats","interactions":[],"lastModifiedDate":"2022-02-08T12:07:52.183997","indexId":"70228197","displayToPublicDate":"2022-01-19T10:25:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Experimental inoculation trial to determine the effects of temperature and humidity on White-nose Syndrome in hibernating bats","docAbstract":"Disease results from interactions among the host, pathogen, and environment. Inoculation trials can quantify interactions among these players and explain aspects of disease ecology to inform management in variable and dynamic natural environments. White-nose Syndrome, a disease caused by the fungal pathogen, Pseudogymnoascus destructans (Pd), has caused severe population declines of several bat species in North America. We conducted the first experimental infection trial on the tri-colored bat, Perimyotis subflavus, to test the effect of temperature and humidity on disease severity. We also tested the effects of temperature and humidity on fungal growth and persistence on substrates. Unexpectedly, only 37% (35/95) of bats experimentally inoculated with Pd at the start of the experiment showed any infection response or disease symptoms after 83 days of captive hibernation. There was no evidence that temperature or humidity influenced infection response. Temperature had a strong effect on fungal growth on media plates, but the influence of humidity was more variable and uncertain. Designing laboratory studies to maximize research outcomes would be beneficial given the high costs of such efforts and potential for unexpected outcomes. Understanding the influence of microclimates on host–pathogen interactions remains an important consideration for managing wildlife diseases, particularly in variable environments.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-022-04965-x","usgsCitation":"Frick, W., Johnson, E., Cheng, T., Lankton, J.S., Warne, R., Dallas, J., Parise, K.L., Foster, J.T., Boyles, J.G., and McGuire, L.P., 2022, Experimental inoculation trial to determine the effects of temperature and humidity on White-nose Syndrome in hibernating bats: Scientific Reports, v. 12, 971, 13 p., https://doi.org/10.1038/s41598-022-04965-x.","productDescription":"971, 13 p.","ipdsId":"IP-132226","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":449109,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-04965-x","text":"Publisher Index Page"},{"id":435994,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZM9JIW","text":"USGS data release","linkHelpText":"Histopathology of tri-colored bats (Perimyotis subflavus) exposed to the fungus Pseudogymnoascus destructans under varying temperature and humidity conditions"},{"id":395535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Frick, Winifred F.","contributorId":139722,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred F.","affiliations":[{"id":12892,"text":"Dept of Ecology & Evolutionary Biology, Univ of California","active":true,"usgs":false}],"preferred":false,"id":833371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Emily R.","contributorId":194346,"corporation":false,"usgs":false,"family":"Johnson","given":"Emily R.","affiliations":[],"preferred":false,"id":833372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cheng, Tina L.","contributorId":127716,"corporation":false,"usgs":false,"family":"Cheng","given":"Tina L.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":833373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":833374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warne, Robin","contributorId":274838,"corporation":false,"usgs":false,"family":"Warne","given":"Robin","email":"","affiliations":[{"id":56665,"text":"Cooperative Wildlife Research Laboratory and School of Biological Sciences, Southern Illinois University, Carbondale, IL USA 62901","active":true,"usgs":false}],"preferred":false,"id":833375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dallas, Jason","contributorId":274839,"corporation":false,"usgs":false,"family":"Dallas","given":"Jason","email":"","affiliations":[{"id":56665,"text":"Cooperative Wildlife Research Laboratory and School of Biological Sciences, Southern Illinois University, Carbondale, IL USA 62901","active":true,"usgs":false}],"preferred":false,"id":833376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parise, Katy L.","contributorId":201310,"corporation":false,"usgs":false,"family":"Parise","given":"Katy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":833377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Foster, Jeffrey T.","contributorId":177905,"corporation":false,"usgs":false,"family":"Foster","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":833378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Boyles, Justin G.","contributorId":274840,"corporation":false,"usgs":false,"family":"Boyles","given":"Justin","email":"","middleInitial":"G.","affiliations":[{"id":56665,"text":"Cooperative Wildlife Research Laboratory and School of Biological Sciences, Southern Illinois University, Carbondale, IL USA 62901","active":true,"usgs":false}],"preferred":false,"id":833379,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McGuire, Liam P.","contributorId":274841,"corporation":false,"usgs":false,"family":"McGuire","given":"Liam","email":"","middleInitial":"P.","affiliations":[{"id":56667,"text":"Department of Biological Sciences, Texas Tech University, Lubbock, TX USA 79401","active":true,"usgs":false}],"preferred":false,"id":833380,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236588,"text":"70236588 - 2022 - Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms","interactions":[],"lastModifiedDate":"2022-09-12T14:35:34.563214","indexId":"70236588","displayToPublicDate":"2022-01-19T09:29:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms","docAbstract":"Predicting how species respond to changes in climate is critical to conserving biodiversity. Modeling efforts to date have largely centered on predicting the effects of warming temperatures on temperate species phenology. In and near the tropics, the effects of a warming planet on species phenology are more likely to be driven by changes in the seasonal precipitation cycle rather than temperature. To demonstrate the importance of considering precipitation-driven phenology in ecological studies, we present a case study wherein we construct a mechanistic population model for a rare subtropical butterfly (Miami blue butterfly, Cyclargus thomasi bethunebakeri) and use a suite of global climate models to project butterfly populations into the future. Across all iterations of the model, the trajectory of Miami blue populations is uncertain. We identify both biological uncertainty (unknown diapause survival rate) and climate uncertainty (ambiguity in the sign of precipitation change across climate models), and their interaction as key factors that determine persistence vs. extinction. Despite uncertainty, the most optimistic iteration of the model predicts that Miami blue butterfly populations will decline under the higher emissions scenario (RCP 8.5). The lack of climate model agreement across the projection ensemble suggests that investigations into the effect of climate change on precipitation-driven phenology require a higher level of rigor in the uncertainty analysis compared to analogous studies of temperature. For tropical species, a mechanistic approach that incorporates both biological and climate uncertainty is the best path forward to understand the effect shifting precipitation regimes have on phenology and population dynamics.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecochg.2022.100051","usgsCitation":"Henry, E.H., Terando, A., Morris, W., Daniels, J.C., and Haddad, N.M., 2022, Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms: Climate Change Ecology, v. 3, 100051, 10 p., https://doi.org/10.1016/j.ecochg.2022.100051.","productDescription":"100051, 10 p.","ipdsId":"IP-115502","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":449111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecochg.2022.100051","text":"Publisher Index Page"},{"id":406534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.18597412109375,\n 24.505893706264033\n ],\n [\n -81.86325073242188,\n 24.505893706264033\n ],\n [\n -81.86325073242188,\n 24.605820556242126\n ],\n [\n -82.18597412109375,\n 24.605820556242126\n ],\n [\n -82.18597412109375,\n 24.505893706264033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henry, Erica H","contributorId":296418,"corporation":false,"usgs":false,"family":"Henry","given":"Erica","email":"","middleInitial":"H","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":851456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morris, William F.","contributorId":296419,"corporation":false,"usgs":false,"family":"Morris","given":"William F.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":851458,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniels, Jaret C.","contributorId":223585,"corporation":false,"usgs":false,"family":"Daniels","given":"Jaret","email":"","middleInitial":"C.","affiliations":[{"id":40743,"text":"Florida Museum of Natural History and University of Florida","active":true,"usgs":false}],"preferred":false,"id":851459,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haddad, Nick M.","contributorId":229345,"corporation":false,"usgs":false,"family":"Haddad","given":"Nick","email":"","middleInitial":"M.","affiliations":[{"id":41625,"text":"Kellogg Biological Station and Department of Integrative Biology, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":851460,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237287,"text":"70237287 - 2022 - Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams","interactions":[],"lastModifiedDate":"2022-10-06T13:37:24.906327","indexId":"70237287","displayToPublicDate":"2022-01-19T08:26:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams","docAbstract":"Metals and polycyclic aromatic hydrocarbons (PAHs) are common pollutants in urban streambed sediment, yet their occurrence is highly variable and difficult to predict. To investigate sources of PAHs and metals to streambed sediment, we sampled pavement dust, soil, and streambed sediment in 10 urban watersheds in three regions of the United States and applied a fallout-radionuclide-based sediment-source analysis to quantify the pavement dust contribution to stream sediment (%dust). We also mapped the area of sealcoated pavement in each watershed (%sealed) to investigate the role of coal-tar pavement sealant (CTS) as a PAH source. Median total and carbon-normalized total PAH concentrations were significantly higher in streambed sediment in the Northeast (54.3 mg/kg and 2.71 mg/gOC) and Southeast (5.37 mg/kg and 1.36 mg/gOC), where CTS is commonly used, than in the Northwest (2.11 mg/kg and 0.071 mg/gOC), where CTS is rarely used. Generalized additive models indicated that %sealed and in some cases %dust significantly affected total PAH concentrations in streambed sediments. The %dust was a significant variable for common urban metals: Cu, Pb, and Zn. These findings advance our quantitative understanding of the role of pavement dust as a source and a vector of contaminants to urban streams.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c00414","usgsCitation":"Van Metre, P.C., Mahler, B., Qi, S.L., Gellis, A.C., Fuller, C.C., and Schmidt, T., 2022, Sediment sources and sealed-pavement area drive polycyclic aromatic hydrocarbon and metal occurrence in urban streams: Environmental Science and Technology, v. 56, no. 3, p. 1615-1626, https://doi.org/10.1021/acs.est.1c00414.","productDescription":"12 p.","startPage":"1615","endPage":"1626","ipdsId":"IP-121967","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9729ZGL","text":"USGS data release","linkHelpText":"Mapped sealed and unsealed pavement and concentrations of metals, polycyclic aromatic hydrocarbons, and radionuclides for soils, pavement dust, and stream sediment for 10 urban watersheds"},{"id":408026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, New York, North Carolina, Oregon, Virginia, Washington","city":"Albany, Atlanta, Charlotte, Greensboro, Portland, Rochester, Salem, Seattle, Syracuse","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.44262695312501,\n 47.27922900257082\n ],\n [\n -122.244873046875,\n 47.27922900257082\n ],\n [\n -122.244873046875,\n 47.82053186746053\n ],\n [\n -122.44262695312501,\n 47.82053186746053\n ],\n [\n -122.44262695312501,\n 47.27922900257082\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n 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35.97800618085566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Metre, Peter C. 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":211144,"corporation":false,"usgs":true,"family":"Van Metre","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":853983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mahler, Barbara 0000-0002-9150-9552 bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":853987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":853988,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230062,"text":"70230062 - 2022 - Soil moisture response to seasonal drought conditions and post-thinning forest structure","interactions":[],"lastModifiedDate":"2022-08-01T16:55:02.409081","indexId":"70230062","displayToPublicDate":"2022-01-19T06:17:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Soil moisture response to seasonal drought conditions and post-thinning forest structure","docAbstract":"Prolonged drought conditions in semi-arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below-average total precipitation. We used the data to assess the effects of fore-summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine-scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand-level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought.
Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear (Ursus arctos) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km2 during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22185","usgsCitation":"Schmidt, J., Wilson, T.L., Thompson, W., and Mangipane, B., 2022, Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration: Journal of Wildlife Management, v. 86, no. 3, e22185, 15 p., https://doi.org/10.1002/jwmg.22185.","productDescription":"e22185, 15 p.","ipdsId":"IP-130320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.33642551397307,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 61.82002560280111\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Joshua H.","contributorId":349537,"corporation":false,"usgs":false,"family":"Schmidt","given":"Joshua H.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, William L.","contributorId":349538,"corporation":false,"usgs":false,"family":"Thompson","given":"William L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924369,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck A.","contributorId":349540,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924370,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227434,"text":"dr1148 - 2022 - Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary","interactions":[],"lastModifiedDate":"2022-01-19T12:22:31.582508","indexId":"dr1148","displayToPublicDate":"2022-01-18T14:45:43","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1148","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data Summary","title":"Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary","docAbstract":"We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the San Antonio Dam near Upland, California, in 2021. Four vireo surveys were conducted between April 16 and July 15, 2021, and three flycatcher surveys were conducted between May 27 and July 15, 2021.
We detected one transient vireo and one transient flycatcher. No territorial vireos or flycatchers were observed. The vireo was found in riparian scrub habitat dominated by native mule fat (Baccharis salicifolia), whereas the flycatcher was using habitat dominated by non-native tamarisk (Tamarix ramosissima).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1148","programNote":"Ecosystems Mission Area-Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2022, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary: U.S. Geological Survey Data Report 1148, 8 p., https://doi.org/10.3133/dr1148.","productDescription":"vii, 8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-135056","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":394401,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1148/images"},{"id":394400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1148/dr1148.xml"},{"id":394398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1148/covrthb.jpg"},{"id":394399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1148/dr1148.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Los Angeles County, San Bernardino County","otherGeospatial":"San Antonio Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.76039123535155,\n 34.12942636161218\n ],\n [\n -117.56744384765625,\n 34.12942636161218\n ],\n [\n -117.56744384765625,\n 34.231673921638475\n ],\n [\n -117.76039123535155,\n 34.231673921638475\n ],\n [\n -117.76039123535155,\n 34.12942636161218\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Lake St. Clair Metropark Beach (LSCMB) in Michigan is a public beach near the mouth of the Clinton River that has a history of beach closures for public health concerns. The Clinton River is designated as a Great Lakes Area of Concern, and the park has a Beneficial Use Impairment for beach closings because of elevated Escherichia coli (E. coli) concentrations. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and in collaboration with the Michigan Department of the Environment, Great Lakes, and Energy, Macomb County Health Department, and Huron-Clinton Metroparks, completed a 2-year study to determine sources of E. coli in LSCMB. Samples were collected during dry and wet weather periods to observe the sampling sites under different conditions. Nearshore surface water samples were collected biweekly July through October in 2018 and May through September in 2019. There were 20 sampling sites along the shoreline of the park and in the channel north of the park. In addition to collecting nearshore surface-water samples, samples were collected from shallow groundwater, lake-bottom material, standing water on the beach and surrounding the recreational beach area, solids (beach sands and detritus), and offshore surface-water sites. In 2019, additional samples for microbial source tracking (MST) were collected on three dates in midsummer and were analyzed for human (HF183) and bird/waterfowl (GFD) MST markers. The concentrations of E. coli at LSCMB (in order of highest to lowest E. coli concentrations) were as follows: shallow groundwater nearest to the water’s edge, surface sands and organic matter (detritus), standing water on the beach, nearshore surface water in and surrounding the recreational beach area, lake-bottom material, and offshore surface water. The combination of low E. coli concentrations offshore and higher concentrations nearshore indicate nearshore sources, possibly from beach sands or groundwater, rather than sources coming from offshore Lake St. Clair waters. The subset of samples for MST analysis did not have enough positive results to illustrate MST trends, but this study demonstrated that both human and waterfowl sources can affect the water quality at LCSMB.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215089","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Fogarty, L.R., Maurer, J.A., Hyslop, I.M., Totten, A.R., Kephart, C.M., and Brennan, A.K., 2021, Understanding sources and distribution of Escherichia coli at Lake St. Clair Metropark Beach, Macomb County, Michigan: U.S. Geological Survey Scientific Investigations Report 2021–5089, 34 p., https://doi.org/10.3133/sir20215089.","productDescription":"Report: ix, 34 p.; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-125120","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502112,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112127.htm","linkFileType":{"id":5,"text":"html"}},{"id":394369,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5089/images"},{"id":394366,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5089/coverthb.jpg"},{"id":394367,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5089/sir20215089.pdf","text":"Report","size":"8.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5089"},{"id":394368,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Michigan","county":"Macomb County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-83.1025,42.8884],[-82.9839,42.8939],[-82.8674,42.8958],[-82.7384,42.8967],[-82.7276,42.6807],[-82.7366,42.6755],[-82.7474,42.6731],[-82.7578,42.6656],[-82.7593,42.6611],[-82.7593,42.6598],[-82.7594,42.6589],[-82.7765,42.6544],[-82.7901,42.6552],[-82.8002,42.6541],[-82.8093,42.6466],[-82.8168,42.635],[-82.8176,42.6323],[-82.82,42.6215],[-82.8178,42.616],[-82.8119,42.6103],[-82.7989,42.6081],[-82.7941,42.6053],[-82.7906,42.5997],[-82.7901,42.5983],[-82.7765,42.5957],[-82.7741,42.5933],[-82.7774,42.5912],[-82.7837,42.5891],[-82.7853,42.5823],[-82.7822,42.5708],[-82.7843,42.5672],[-82.785,42.5654],[-82.7874,42.5664],[-82.7904,42.5692],[-82.7984,42.5717],[-82.8139,42.5717],[-82.8252,42.5702],[-82.8348,42.5659],[-82.8458,42.559],[-82.849,42.5563],[-82.848,42.5518],[-82.8525,42.5487],[-82.8551,42.547],[-82.8623,42.5408],[-82.871,42.5288],[-82.873,42.5261],[-82.8771,42.5194],[-82.8829,42.5027],[-82.8824,42.4886],[-82.8831,42.4873],[-82.8832,42.485],[-82.884,42.4823],[-82.8836,42.4786],[-82.8831,42.4759],[-82.8827,42.4713],[-82.8727,42.4611],[-82.8687,42.4546],[-82.8687,42.4537],[-82.9691,42.4492],[-83.0843,42.4463],[-83.0867,42.5355],[-83.0905,42.6238],[-83.0986,42.801],[-83.1025,42.8884]]]},\"properties\":{\"name\":\"Macomb\",\"state\":\"MI\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the water-rich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min–1. Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions—initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20–30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49490.1","usgsCitation":"Van Eaton, A.R., Smith, C.M., Pavolonis, M.J., and Said, R., 2022, Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines: Geology, v. 50, no. 4, p. 491-495, https://doi.org/10.1130/G49490.1.","productDescription":"5 p.","startPage":"491","endPage":"495","ipdsId":"IP-128543","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467203,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/geol.s.17265287","text":"External Repository"},{"id":466649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Taal volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 120.90268362290232,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 14.102133334852596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra M","contributorId":257012,"corporation":false,"usgs":false,"family":"Smith","given":"Cassandra","email":"","middleInitial":"M","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":924037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":924038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Said, Ryan 0000-0002-8095-4204","orcid":"https://orcid.org/0000-0002-8095-4204","contributorId":257003,"corporation":false,"usgs":false,"family":"Said","given":"Ryan","email":"","affiliations":[{"id":51953,"text":"Vaisala, Inc.","active":true,"usgs":false}],"preferred":false,"id":924039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229710,"text":"70229710 - 2022 - No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","interactions":[],"lastModifiedDate":"2022-03-16T14:50:49.888833","indexId":"70229710","displayToPublicDate":"2022-01-18T09:44:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","docAbstract":"Volcanic fallout in polar ice sheets provides important opportunities to date and correlate ice-core records as well as to investigate the environmental impacts of eruptions. Only the geochemical characterization of volcanic ash (tephra) embedded in the ice strata can confirm the source of the eruption, however, and is a requisite if historical eruption ages are to be used as valid chronological checks on annual ice layer counting. Here we report the investigation of ash particles in a Greenland ice core that are associated with a volcanic sulfuric acid layer previously attributed to the 79 CE eruption of Vesuvius. Major and trace element composition of the particles indicates that the tephra does not derive from Vesuvius but most likely originates from an unidentified eruption in the Aleutian arc. Using ash dispersal modeling, we find that only an eruption large enough to include stratospheric injection is likely to account for the sizable (24–85 µm) ash particles observed in the Greenland ice at this time. Despite its likely explosivity, this event does not appear to have triggered significant climate perturbations, unlike some other large extratropical eruptions. In light of a recent re-evaluation of the Greenland ice-core chronologies, our findings further challenge the previous assignation of this volcanic event to 79 CE. We highlight the need for the revised Common Era ice-core chronology to be formally accepted by the wider ice-core and climate modeling communities in order to ensure robust age linkages to precisely dated historical and paleoclimate proxy records.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-18-45-2022","usgsCitation":"Plunkett, G., Sigl, M., Schwaiger, H., Tomlinson, E., Toohey, M., McConnell, J.R., Pilcher, J.R., Hasegawa, T., and Siebe, C., 2022, No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology: Climate of the Past, v. 18, no. 1, p. 45-65, https://doi.org/10.5194/cp-18-45-2022.","productDescription":"21 p.","startPage":"45","endPage":"65","ipdsId":"IP-129942","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-18-45-2022","text":"Publisher Index Page"},{"id":397152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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0000-0001-9051-5240","orcid":"https://orcid.org/0000-0001-9051-5240","contributorId":288526,"corporation":false,"usgs":false,"family":"McConnell","given":"Joseph","email":"","middleInitial":"R.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":838052,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pilcher, Jonathan R.","contributorId":288527,"corporation":false,"usgs":false,"family":"Pilcher","given":"Jonathan","email":"","middleInitial":"R.","affiliations":[{"id":61787,"text":"Queen’s University Belfast","active":true,"usgs":false}],"preferred":false,"id":838053,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hasegawa, Takeshi","contributorId":288528,"corporation":false,"usgs":false,"family":"Hasegawa","given":"Takeshi","email":"","affiliations":[{"id":61789,"text":"Ibaraki University","active":true,"usgs":false}],"preferred":false,"id":838054,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Siebe, Claus 0000-0002-3959-9028","orcid":"https://orcid.org/0000-0002-3959-9028","contributorId":288529,"corporation":false,"usgs":false,"family":"Siebe","given":"Claus","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":838055,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70227367,"text":"sir20215119 - 2022 - Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19","interactions":[],"lastModifiedDate":"2026-04-02T19:50:24.033174","indexId":"sir20215119","displayToPublicDate":"2022-01-18T09:40:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5119","displayTitle":"Characterization of Ambient Groundwater Quality Within a Statewide, Fixed-Station Monitoring Network in Pennsylvania, 2015–19","title":"Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19","docAbstract":"Pennsylvania leads the Nation in the number of individuals that use groundwater for private domestic water supply; more than 3 million rural and suburban Pennsylvania residents rely on private domestic supplies for drinking water. These supplies are not regulated nor routinely monitored; thus relevant groundwater-quality information is not widely available. The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PaDEP) Safe Drinking Water Bureau, established a statewide, fixed-station ambient groundwater quality network in 2015. The goals for the Pennsylvania Groundwater Monitoring Network (GWMN) include characterizing ambient groundwater quality conditions in rural areas of the State and documenting potential changes in conditions over time. Seventeen wells were selected for monitoring at 6-month intervals beginning in 2015. Since then, several wells have been added to the GWMN, bringing the total number of wells sampled in the fall of 2019 to 28. Routinely monitored constituents included physical characteristics and chemical concentrations in filtered and unfiltered samples (major and trace elements, nutrients, and organic compounds). Samples for volatile organic compounds (VOCs), radionuclides, and dissolved hydrocarbon gases were collected during the first sampling event at each well.
To offer insights on the quality of groundwater used for domestic supply in Pennsylvania, summary statistics for the 221 GWMN samples collected during 2015–19 are compared to U.S. Environmental Protection Agency (EPA) drinking-water standards, which are applicable to public water supplies. Results show that samples across the GWMN generally meet drinking-water standards for inorganic and organic constituents; however, a percentage of samples had concentrations that exceeded maximum contaminant level (MCL) thresholds for nitrate (3 percent) and secondary maximum contaminant level (SMCL) thresholds for iron (32 percent), manganese (36 percent), and aluminum (5 percent). Radon-222 activities, which were sampled only during the initial visit to a well, exceeded the lower proposed drinking water standard of 300 picocuries per liter (pCi/L) in 64 percent of wells in the GWMN; additionally, 7 percent of wells exceeded the higher proposed standard of 4,000 pCi/L. There were no exceedances for VOCs, but one well had a tribromomethane detection. Three wells had detectable concentrations of methane, with one sample exceeding the Pennsylvania action level of 7 milligrams per liter (mg/L).
The pH and dissolved oxygen concentrations varied widely across the GWMN and were correlated with dissolved metal concentrations and other chemical characteristics of groundwater samples. Considering all samples collected for the study, the pH ranged from 4.2 to 8.3; 42 percent of pH values were either above or below the SMCL range of 6.5–8.5. The highest pH values resulted from contamination of loose grout used in the construction of one well and decreased to levels consistent with other wells in the vicinity after repeated sampling rounds. Dissolved oxygen (DO), which ranged from 0 to 13.9 mg/L, influences the mobility and prevalence of constituents with variable oxidation state, including iron, manganese, and nitrogen species. Samples with acidic pH (less than 6.5) and (or) low DO had the highest concentrations of manganese and iron, whereas those with neutral to alkaline pH values had the highest concentrations of calcium, magnesium, sodium, and other major ions. Analysis of major ions indicates that calcium/bicarbonate water types are the most common, with a few characterized as calcium/chloride or sodium/chloride, and most others as mixed water types including calcium-magnesium/bicarbonate, sodium-magnesium/bicarbonate, and sodium/bicarbonate-chloride.
Nonparametric statistical methods were used to evaluate the data for spatial and temporal trends. A principal components analysis (PCA) model developed with ranked data values for the entire network resulted in three components, (1) dissolved solids, (2) redox, and (3) sodium-chloride, which explained 74.5 percent of variance in the dataset. On the basis of individual contributions to the PCA, certain wells were identified through hierarchical cluster analysis that shared relevant water-quality characteristics. The spatial distribution of sampling locations and the temporal trends of constituent concentrations indicate that hydrogeologic setting and topographic position as defined in the PCA model are important factors affecting the spatial and temporal patterns of groundwater quality in the GWMN.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215119","collaboration":"Prepared in cooperation with Pennsylvania Department of Environmental Protection","usgsCitation":"Conlon, M.D., and Duris, J.W., 2022, Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19: U.S. Geological Survey Scientific Investigations Report 2021–5119, 118 p., https://doi.org/10.3133/sir20215119.","productDescription":"Report: x, 118 p.; Data Release","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120798","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":394185,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NWQW7C","text":"USGS data release","linkHelpText":"Data for characterization of ambient groundwater quality within a state-wide, fixed station 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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Underground infiltration basins (UIBs) mimic the natural hydrologic cycle by allowing stormwater to recharge local groundwater aquifers. However, little is known about the potential transport of organic contaminants to receiving groundwater. We conducted a pilot study in which we collected paired grab samples of stormwater runoff flowing into two UIBs (inflow) and shallow groundwater adjacent to the UIBs. Samples were collected coincident with three rain events and analyzed for volatile organic compounds, semi-volatile organic compounds, pharmaceuticals, and pesticides. Few contaminants were detected in groundwater, compared with inflow, and groundwater concentrations were typically an order of magnitude less. With one exception (trichloroethene), all groundwater concentrations were at least two orders of magnitude below available guidance or screening values. This short communication highlights information gaps in understanding the hydrologic connectivity between UIBs and receiving groundwater and potential consequent contaminant transport to the subsurface from varying climatic conditions.
","language":"English","publisher":"Water Environment Federation","doi":"10.1002/wer.10690","usgsCitation":"Elliott, S.M., Kiesling, R.L., Berg, A.M., and Schoenfuss, H.L., 2022, A pilot study to assess the influence of infiltrated stormwater on groundwater: Hydrology and trace organic contaminants: Water Environment Research, v. 94, no. 2, e10690, 9 p., https://doi.org/10.1002/wer.10690.","productDescription":"e10690, 9 p.","ipdsId":"IP-131245","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449122,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/wer.10690","text":"External Repository"},{"id":395614,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","city":"Minneapolis-St. Paul","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.59115600585936,\n 44.80814739879984\n ],\n [\n -93.13522338867188,\n 44.80814739879984\n ],\n [\n -93.13522338867188,\n 45.30773430004869\n ],\n [\n -93.59115600585936,\n 45.30773430004869\n ],\n [\n -93.59115600585936,\n 44.80814739879984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"94","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott, Sarah M. 0000-0002-1414-3024 selliott@usgs.gov","orcid":"https://orcid.org/0000-0002-1414-3024","contributorId":1472,"corporation":false,"usgs":true,"family":"Elliott","given":"Sarah","email":"selliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833540,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiesling, Richard L. 0000-0002-3017-1826 kiesling@usgs.gov","orcid":"https://orcid.org/0000-0002-3017-1826","contributorId":1837,"corporation":false,"usgs":true,"family":"Kiesling","given":"Richard","email":"kiesling@usgs.gov","middleInitial":"L.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berg, Andrew M. 0000-0001-9312-240X aberg@usgs.gov","orcid":"https://orcid.org/0000-0001-9312-240X","contributorId":5642,"corporation":false,"usgs":true,"family":"Berg","given":"Andrew","email":"aberg@usgs.gov","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":833744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schoenfuss, Heiko L.","contributorId":76409,"corporation":false,"usgs":false,"family":"Schoenfuss","given":"Heiko","email":"","middleInitial":"L.","affiliations":[{"id":13317,"text":"Saint Cloud State University","active":true,"usgs":false}],"preferred":false,"id":833745,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231252,"text":"70231252 - 2022 - Seismic background noise levels across the continental United States from USArray Transportable Array: The influence of geology and geography","interactions":[],"lastModifiedDate":"2022-07-07T16:53:37.67699","indexId":"70231252","displayToPublicDate":"2022-01-18T08:32:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismic background noise levels across the continental United States from USArray Transportable Array: The influence of geology and geography","docAbstract":"Since 2004, the most complete estimate of background noise levels across the continental U.S. was attained using 61 broadband seismic stations to calculate power spectral density (PSD) probability density functions. To improve seismic noise estimates across the U.S., we examine vertical component seismic data from the EarthScope USArray Transportable Array seismic network that rolled across the U.S. and southeastern Canada between 2004 and 2015 and form a large (10 TB) PSD database from 1679 stations that contains no smoothing or binning of the spectral estimates. Including station outages, our database has a mean of 98.9% data completeness, and we present maps showing the spatial and temporal variability of seismic noise in six bands of interest between 0.2- and 75-s period. At 0.2 s period, seismic noise across the eastern U.S. is predominantly anthropogenically generated and may be subsequently amplified more than 20 decibels in the sandy and water-saturated sediments of the southeastern U.S. Coastal Plain and Mississippi Embayment. In these sediments, 1 s noise shows similar amplification and is generated through a variety of mechanisms including cultural activity throughout Kentucky and the southeastern Appalachian Mountains, lake waves around the Great Lakes, and ocean waves throughout New England, the Pacific Northwest, and Florida. Both 0.2 and 1 s noise levels are the lowest in the Intermountain West portion of the U.S. We attribute this to a combination of installations on crystalline rocks and reduced population density. Finally, we find that sensors emplaced in sandy, water-saturated sediments observe median, diurnal variations in vertical component power at 18 to 75 s period, which we infer arise through local deformation driven by pressure variations. Ultimately, our results underscore that for shallow (<5 m depth) sensor installation, bedrock provides superior broadband noise performance compared to unconsolidated sediments.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210176","usgsCitation":"Anthony, R.E., Ringler, A.T., and Wilson, D.C., 2022, Seismic background noise levels across the continental United States from USArray Transportable Array: The influence of geology and geography: Bulletin of the Seismological Society of America, v. 112, no. 2, p. 646-668, https://doi.org/10.1785/0120210176.","productDescription":"23 p.","startPage":"646","endPage":"668","ipdsId":"IP-131111","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":400126,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": 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,{"id":70227515,"text":"70227515 - 2022 - The Coastal Imaging Research Network (CIRN)","interactions":[],"lastModifiedDate":"2022-01-20T13:26:52.29354","indexId":"70227515","displayToPublicDate":"2022-01-18T07:25:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"The Coastal Imaging Research Network (CIRN)","docAbstract":"This paper analyzes the relationship between actual reservoir conditions and predicted measures of performance of carbon dioxide enhanced oil recovery (CO2–EOR) programs. It then shows how CO2–EOR operators might maximize the value of their projects by approaching implementation using a “flexible selective” pattern development strategy, where the CO2–EOR program patterns are selectively developed based on site-specific reservoir properties. It also analyzes performance measures and economic consequences of utilizing a continuous CO2 injection strategy intended to maximize CO2 retention for a defined time period. “Net CO2 utilization,” calculated as difference between the volumes of CO2 injected and CO2 recovered in the production stream divided by the oil produced, is a standard measure of CO2–EOR carbon utilization, but it can be a misleading predictor of the actual CO2 retained in the reservoir. Asset value can be added to a CO2–EOR project by recognizing effects of variations in reservoir parameter values and basing incremental development decisions on those data. For policy analysts, the consequences of ignoring geologic variability within a reservoir that is a candidate for CO2–EOR will likely be to substantially overestimate predicted adoption of CO2–EOR in response to economic incentives. This result holds true whether the CO2–EOR program objective is to maximize net value by maximizing oil production or maximize CO2 storage with oil recovery.
Effectively managing take of wildlife resulting from human activities poses a major challenge for applied conservation. Demographic data essential to decisions regarding take are often expensive to collect and are either not available or based on limited studies for many species. Therefore, modeling approaches that efficiently integrate available information are important to improving the scientific basis for sustainable take thresholds. We used the prescribed take level (PTL) framework to estimate allowable take for bald eagles (Haliaeetus leucocephalus) in the conterminous United States. We developed an integrated population model (IPM) that incorporates multiple sources of information and then use the model output as the scientific basis for components of the PTL framework. Our IPM is structured to identify key parameters needed for the PTL and to quantify uncertainties in those parameters at the scale at which the United States Fish and Wildlife Service manages take. Our IPM indicated that mean survival of birds >1 year old was high and precise (0.91, 95% CI = 0.90–0.92), whereas mean survival of first-year eagles was lower and more variable (0.69, 95% CI = 0.62–0.78). We assumed that density dependence influenced recruitment by affecting the probability of breeding, which was highly imprecise and estimated to have declined from approximately 0.988 (95% CI = 0.985–0.993) to 0.66 (95% CI = 0.34–0.99) between 1994 and 2018. We sampled values from the posterior distributions of the IPM for use in the PTL and estimated that allowable take (e.g., permitted take for energy development, incidental collisions with human made structures, or removal of nests for development) ranged from approximately 12,000 to 20,000 individual eagles depending on risk tolerance and form of density dependence at the scale of the conterminous United States excluding the Southwest. Model-based thresholds for allowable take can be inaccurate if the assumptions of the underlying framework are not met, if the influence of permitted take is under-estimated, or if undetected population declines occur from other sources. Continued monitoring and use of the IPM and PTL frameworks to identify key uncertainties in bald eagle population dynamics and management of allowable take can mitigate this potential bias, especially where improved information could reduce the risk of permitting non-sustainable take.
Of all terrestrial biomes, grasslands are losing the most biodiversity the most rapidly, so there is a critical need to document and learn from large-scale restoration successes.
In the Loess Canyons ecoregion of the Great Plains, USA, an association of private ranchers and natural resource agencies has led a multi-decadal, ecoregion-scale initiative to combat the loss of grasslands to woody plant encroachment by restoring large-scale fire regimes. Here, we use 14 years of fire treatment history with 6 years of grassland bird monitoring and remotely sensed tree cover data across 136,767 ha of privately owned grassland to quantify outcomes of large-scale grassland restoration efforts.
Grassland bird richness increased across 65% (90,032 ha) of the Loess Canyons, and woody plant cover decreased up to 55% across 25% (7408 ha) of all fire-treated areas.
This was accomplished with extreme fire treatments that killed mature trees, were large (mean annual area burned was 3100 ha), spatially clustered and straddled boundaries between invasive woodlands and remaining grasslands – not heavily infested woodlands.
Findings from this study provide the first evidence of human management reversing the impacts of woody encroachment on grassland birds at an ecoregion scale.
Radio telemetry, one of the most widely used techniques for tracking wildlife and fisheries populations, has a false-positive problem. Bias from false-positive detections can affect many important derived metrics, such as home range estimation, site occupation, survival, and migration timing. False-positive removal processes have relied upon simple filters and personal opinion. To overcome these shortcomings, we have developed BIOTAS (BIOTelemetry Analysis Software) to assist with false-positive identification, removal, and data management for large-scale radio telemetry projects.
BIOTAS uses a naïve Bayes classifier to identify and remove false-positive detections from radio telemetry data. The semi-supervised classifier uses spurious detections from unknown tags and study tags as training data. We tested BIOTAS on four scenarios: wide-band receiver with a single Yagi antenna, wide-band receiver that switched between two Yagi antennas, wide-band receiver with a single dipole antenna, and single-band receiver that switched between five frequencies. BIOTAS has a built in a k-fold cross-validation and assesses model quality with sensitivity, specificity, positive and negative predictive value, false-positive rate, and precision-recall area under the curve. BIOTAS also assesses concordance with a traditional consecutive detection filter using Cohen’s
Overall BIOTAS performed equally well in all scenarios and was able to discriminate between known false-positive detections and valid study tag detections with low false-positive rates (< 0.001) as determined through cross-validation, even as receivers switched between antennas and frequencies. BIOTAS classified between 94 and 99% of study tag detections as valid.
As part of a robust data management plan, BIOTAS is able to discriminate between detections from study tags and known false positives. BIOTAS works with multiple manufacturers and accounts for receivers that switch between antennas and frequencies. BIOTAS provides the framework for transparent, objective, and repeatable telemetry projects for wildlife conservation surveys, and increases the efficiency of data processing.
","language":"English","publisher":"BioMed Central Ltd.","doi":"10.1186/s40317-022-00273-3","usgsCitation":"Nebiolo, K., and Castro-Santos, T.R., 2022, BIOTAS: BIOTelemetry Analysis Software, for the semi-automated removal of false positives from radio telemetry data: Animal Biotelemetry, v. 10, p. 1-16, https://doi.org/10.1186/s40317-022-00273-3.","productDescription":"2, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-122256","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-022-00273-3","text":"Publisher Index Page"},{"id":394441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nebiolo, Kevin","contributorId":271123,"corporation":false,"usgs":false,"family":"Nebiolo","given":"Kevin","email":"","affiliations":[{"id":56294,"text":"Kleinschmidt Associates, Essex, CT","active":true,"usgs":false}],"preferred":false,"id":830917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":830918,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227444,"text":"70227444 - 2022 - Primary deposition and early diagenetic effects on the high saturation accumulation of gas hydrate in a silt dominated reservoir in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2022-01-17T16:59:17.812984","indexId":"70227444","displayToPublicDate":"2022-01-17T10:46:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Primary deposition and early diagenetic effects on the high saturation accumulation of gas hydrate in a silt dominated reservoir in the Gulf of Mexico","docAbstract":"On continental margins, high saturation gas hydrate systems (>60% pore volume) are common in canyon and channel environments within the gas hydrate stability zone, where reservoirs are dominated by coarse-grained, high porosity sand deposits. Recent studies, including the results presented here, suggest that rapidly deposited, silt-dominated channel-levee environments can also host high saturation gas hydrate accumulations. Here we present several sedimentological data sets, including sediment composition, biostratigraphic age from calcareous nannofossils, grain size, total organic carbon (TOC), C/N elemental ratio, δ13C-TOC, CaCO3, total sulfur (TS), and δ34S-TS from sediments collected with pressure cores from a gas hydrate rich, turbidite channel-levee system in the Gulf of Mexico during the 2017 UT-GOM2-1 Hydrate Pressure Coring Expedition. Our results indicate the reservoir is composed of three main lithofacies, which have distinct sediment grain size distributions (type A-silty clay to clayey silt, type B-clayey silt, and type C-sandy silt to silty sand) that are characteristic of variable turbidity current energy regimes within a Pleistocene (< 0.91 Ma) channel-levee environment. We document that the TOC in the sediments of the reservoir is terrestrial in origin and contained within the fine fraction of each lithofacies, while the CaCO3 fraction is composed of primarily reworked grains, including Cretaceous calcareous nannofossils, and part of the detrital load. The lack of biogenic grains within the finest grained sediment intervals throughout the reservoir suggests interevent hemipelagic sediments are not preserved, resulting in a reservoir sequence of silt dominated, stacked turbidites. We observe two zones of enhanced TS at the top and bottom of the reservoir that correspond with enriched bulk sediment δ34S, indicating stalled or slowly advancing paleo-sulfate-methane transition zone (SMTZ) positions likely driven by relative decreases in sedimentation rate. Despite these two diagenetic zones, the low abundance of diagenetic precipitates throughout the reservoir allowed the primary porosity to remain largely intact, thus better preserving primary porosity for subsequent pore-filling gas hydrate. In canyon, channel, and levee environments, early diagenesis may be regulated via sedimentation rates, where high rates result in rapid progression through the SMTZ and minimal diagenetic mineralization and low rates result in the stalling of the SMTZ, enhancing diagenetic mineralization. Here, we observed some enhanced pyritization to implicate potential sedimentation rate changes, but not enough to consume primary porosity, resulting in a high saturation gas hydrate reservoir. These results emphasize the important implications of sedimentary processes, sedimentation rates, and early diagenesis on the distribution of gas hydrate in marine sediments along continental margins.
This article reviews extensive geophysical survey data, ocean drilling results and long-term seafloor monitoring that constrain the distribution and concentration of gas hydrates within the accretionary prism of the northern Cascadia subduction margin, located offshore Vancouver Island in Canada. Seismic surveys and geologic studies conducted since the 1980s have mapped the bottom simulating reflector (BSR), detected gas hydrate occurrence and estimated gas hydrate and free gas concentrations. Additional constraints were obtained from seafloor-towed, controlled-source electromagnetic surveying. A component of these studies has been the examination of low-temperature seafloor vents and seeps that emit gas and fluids into the ocean. These features are identified seismically as chimney-like zones of reduced acoustic reflectivity within the sediment stratigraphy, functioning as conduits for gas and fluid migration from below the BSR to the seafloor. Gas hydrates have been recovered from the seafloor and from sediment cores at vent sites, mostly in massive (nodular) form and as a vein-like fracture filling. The Ocean Networks Canada cabled NEPTUNE observatory has gathered extensive continuous, long-term observations on gas hydrate dynamics at the seafloor and in boreholes at two nodes on the continental slope featuring high gas hydrate concentrations. Measurements taken at the observatory include a time-series of gas bubble emission rates, changes in the near-seafloor electromagnetic structure and seafloor compliance linked to gas hydrate formation and dissociation. Two Integrated Ocean Drilling Program (IODP) expeditions collected cores, measured downhole properties and deployed downhole instruments within the central accretionary prism. At IODP Site U1364, pore pressures are being monitored above and below the base of the gas hydrate stability zone at a slope setting using an “Advanced Circulation Obviation Retrofit Kit” (A-CORK). Downhole pore pressures, temperatures and electrical resistivities also are being monitored at IODP Site U1416 using the “Simple Cabled Instrument for Measuring Parameters In Situ” (SCIMPI) tool at a vent site from near-seafloor to just above the base of the gas hydrate stability zone.
The eyes of teleostean fishes typically exhibit two ossifications, the anterior and posterior sclerotics, both associated with the scleral cartilage. The West African Denticle herring Denticeps clupeoides has three scleral ossifications, including the typical two associated with the scleral cartilage (anterior and posterior sclerotic) and a third ossification (Di Dario's ossicle), spatially separated from the scleral cartilage and located within the anteromedial wall of the sclera. The medial rectus muscle inserts on the medial surface of Di Dario's ossicle, suggesting that this third sclerotic may play a role in forward rotation of the eye in this surface feeding fish.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.14996","usgsCitation":"Kubicek, K.M., Britz, R., Pinion, A.K., Bower, L.M., and Conway, K.W., 2022, Three scleral ossicles in the West African Denticle herring Denticeps clupeoides (Clupeiformes: Denticipitidae): Journal of Fish Biology, v. 100, no. 3, p. 852-855, https://doi.org/10.1111/jfb.14996.","productDescription":"4 p.","startPage":"852","endPage":"855","ipdsId":"IP-133161","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kubicek, Kole M.","contributorId":341649,"corporation":false,"usgs":false,"family":"Kubicek","given":"Kole","email":"","middleInitial":"M.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Britz, Ralf","contributorId":341651,"corporation":false,"usgs":false,"family":"Britz","given":"Ralf","email":"","affiliations":[{"id":81769,"text":"Senckenberg Natural History Collections Dresden","active":true,"usgs":false}],"preferred":false,"id":908743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pinion, Amanda K.","contributorId":341652,"corporation":false,"usgs":false,"family":"Pinion","given":"Amanda","email":"","middleInitial":"K.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bower, Luke Max 0000-0002-0739-858X","orcid":"https://orcid.org/0000-0002-0739-858X","contributorId":341034,"corporation":false,"usgs":true,"family":"Bower","given":"Luke","email":"","middleInitial":"Max","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908741,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Conway, Kevin W.","contributorId":341653,"corporation":false,"usgs":false,"family":"Conway","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908745,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227446,"text":"70227446 - 2022 - Alaska North Slope terrestrial gas hydrate systems: Insights from scientific drilling","interactions":[],"lastModifiedDate":"2022-01-17T16:30:58.678965","indexId":"70227446","displayToPublicDate":"2022-01-17T10:17:29","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Alaska North Slope terrestrial gas hydrate systems: Insights from scientific drilling","docAbstract":"A wealth of information has been accumulated regarding the occurrence of gas hydrates in nature, leading to significant advancements in our understanding of the geologic controls on their occurrence in both the terrestrial and marine settings of the Arctic. Gas hydrate accumulations discovered in the Alaska North Slope have been the focus of several important geoscience and production testing research programs. The Mount Elbert Gas Hydrate Stratigraphic Test Well of 2007 yielded one of the most complete geologic datasets on Arctic gas hydrate systems and important reservoir engineering data. The 2011/2012 field test of the Iġnik Sikumi gas hydrate production test well provided important insight into gas hydrate production technologies, yielding additional information on the petrophysical properties of gas hydrate reservoir systems. The Hydrate-01 Stratigraphic Test Well, drilled late in 2018, confirmed the geologic conditions at an Alaska North Slope drill site that was selected for an extended gas hydrate production test. In 2018, the US Geological Survey used information derived from previous scientific drilling programs to assess the volume of undiscovered, technically recoverable gas resources at a mean estimate of about 54 trillion cubic feet (~1.5 trillion cubic meters) within the gas hydrates in the North Slope of Alaska. This assessment has shown that the amount of gas stored as gas hydrates in this area is equal to about half of the known volume of conventional natural gas resources in the region.
No abstract available.
","language":"English","publisher":"Minnesota Geological Survey","usgsCitation":"McClaughry, J.D., Madin, I.P., Bennett, S.E., and Conrey, R.M., 2022, Volcano-tectonic history of the Hood River graben: A late Pliocene-Holocene intra-arc graben at the crest of the northern Oregon Cascade Range, USA: Open-File Report 22-2, 2 p.","productDescription":"2 p.","startPage":"35","endPage":"36","ipdsId":"IP-138155","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":462921,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://conservancy.umn.edu/server/api/core/bitstreams/f0230ff9-4dc6-4865-8b40-a72b2d2be87c/content"},{"id":462955,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.86121809846372,\n 44.65404950748291\n ],\n [\n -120.60902083283865,\n 44.65404950748291\n ],\n [\n -120.60902083283865,\n 45.86041592045436\n ],\n [\n -122.86121809846372,\n 45.86041592045436\n ],\n [\n -122.86121809846372,\n 44.65404950748291\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McClaughry, Jason D.","contributorId":194544,"corporation":false,"usgs":false,"family":"McClaughry","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":915960,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madin, Ian P. 0000-0003-2008-8815","orcid":"https://orcid.org/0000-0003-2008-8815","contributorId":345199,"corporation":false,"usgs":false,"family":"Madin","given":"Ian","email":"","middleInitial":"P.","affiliations":[{"id":32397,"text":"Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":915961,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, Scott E.K. 0000-0002-9772-4122 sekbennett@usgs.gov","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":5340,"corporation":false,"usgs":true,"family":"Bennett","given":"Scott","email":"sekbennett@usgs.gov","middleInitial":"E.K.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":915962,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrey, Richard M.","contributorId":194345,"corporation":false,"usgs":false,"family":"Conrey","given":"Richard","email":"","middleInitial":"M.","affiliations":[{"id":13203,"text":"School of the Environment, Washington State University","active":true,"usgs":false}],"preferred":false,"id":915963,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}