The Southcentral Alaska (SCAK) sea otter (Enhydra lutris) stock is the northernmost stock of sea otters, a keystone predator known for structuring nearshore marine ecosystems. We conducted aerial surveys within the range of the SCAK sea otter stock to provide recent estimates of sea otter abundance and distribution. We defined three survey regions: (1) Eastern Cook Inlet (2017), (2) Outer Kenai Peninsula (2019), and (3) Prince William Sound (2014 and 2017). Combined, the three regional estimates yielded an overall abundance estimate of 21,617 sea otters (standard error [SE] = 2,190) with an average density of 1.96 sea otters per square kilometer (km2; SE = 0.55). Sea otters were distributed unevenly across the survey regions and densities varied from 0.52 sea otters/km2 (SE = 0.18) in the deep rock-walled glacial fjords along parts of the Outer Kenai Peninsula to nearly 20 sea otters/km2 (SE = 6.70) in shallow soft-bottom communities such as those in Orca Inlet and Kachemak Bay. These survey results represent the best available contemporary information concerning the distribution, density, and abundance of sea otters across the range of the SCAK stock. Survey data files have been standardized and formatted in data releases associated with this report so that they can be queried and displayed with standard geographic information system and database management software. In addition to providing contemporary information on sea otter populations, this report details how an observer-based aerial survey method has been applied in Alaska over 2 decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211122","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Esslinger, G.G., Robinson, B.H., Monson, D.H., Taylor, R.L., Esler, D., Weitzman, B.P., and Garlich-Miller, J., 2021, Abundance and distribution of sea otters (Enhydra lutris) in the southcentral Alaska stock, 2014, 2017, and 2019: U.S. Geological Survey Open-File Report 2021–1122, 19 p., https://doi.org/10.3133/ofr20211122.","productDescription":"Report: iv, 19 p.; 4 Data Releases","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-125417","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":394901,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TTJVBC","linkHelpText":"Sea otter aerial survey data from the outer Kenai Peninsula, Alaska, 2019"},{"id":394902,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KNKOG1","linkHelpText":"Sea otter aerial survey data from western Prince William Sound, Alaska, 2017"},{"id":394904,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9OG6SR5","linkHelpText":"Sea otter aerial survey data from northern and eastern Prince William Sound, Alaska, 2014"},{"id":394903,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q4DA3T","linkHelpText":"Sea otter aerial survey data from lower Cook Inlet, Alaska, 2017"},{"id":435988,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OG6SR5","text":"USGS data release","linkHelpText":"Sea Otter Aerial Survey Data from Northern and Eastern Prince William Sound, Alaska, 2014"},{"id":394895,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1122/covrthb.jpg"},{"id":394896,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1122/ofr20211122.pdf","text":"Report","size":"26 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3466796875,\n 57.868131763328826\n ],\n [\n -143.0419921875,\n 57.868131763328826\n ],\n [\n -143.0419921875,\n 61.80428390136847\n ],\n [\n -155.3466796875,\n 61.80428390136847\n ],\n [\n -155.3466796875,\n 57.868131763328826\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
As part of a long-term cooperative program to monitor water quality within the Scituate Reservoir drainage area, the U.S. Geological Survey in cooperation with the Providence Water Supply Board collected streamflow and water-quality data at the Scituate Reservoir and tributaries. Streamflow and concentrations of chloride and sodium estimated from records of specific conductance were used to calculate loads of chloride and sodium during water year 2018 (October 1, 2017, through September 30, 2018) for tributaries to the Scituate Reservoir, Rhode Island. Streamflow was measured or estimated by the U.S. Geological Survey following standard methods at 23 streamgages; 14 of these streamgages are equipped with instrumentation capable of continuously monitoring water level, specific conductance, and water temperature. Water-quality samples were collected by the Providence Water Supply Board at 36 sampling stations, which also include the 14 continuous-record streamgages maintained by the U.S. Geological Survey, during water year 2018 as part of a long-term sampling program; all stations are in the Scituate Reservoir drainage area. Water-quality data collected by the Providence Water Supply Board are summarized by using values of central tendency and are used, in combination with measured (or estimated) streamflows, to calculate loads and yields (loads per unit area) of selected water-quality constituents for water year 2018.
The largest tributary to the reservoir, the Ponaganset River, which was monitored by the U.S. Geological Survey, contributed a mean streamflow of 33 cubic feet per second to the reservoir during water year 2018. For the same period, annual mean streamflows measured (or estimated) for the other monitoring stations in this study ranged from about 0.34 to about 20 cubic feet per second. Together, tributaries equipped with instrumentation capable of continuously monitoring specific conductance transported about 3,100 metric tons of chloride and 1,900 metric tons of sodium to the Scituate Reservoir during water year 2018; annual chloride yields for the tributaries ranged from 18 to 140 metric tons per square mile, and annual sodium yields ranged from 12 to 80 metric tons per square mile.
At the stations where water-quality samples were collected by the Providence Water Supply Board, the medians of the median concentrations were 25.8 milligrams per liter for chloride, 0.001 milligram per liter as nitrogen for nitrite, 0.11 milligram per liter as nitrogen for nitrate, 0.04 milligram per liter as phosphate for orthophosphate, 1,200 colony forming units per 100 milliliters for total coliform bacteria, and 10 colony forming units per 100 milliliters for Escherichia coli (E. coli). The medians of the median daily loads of chloride, nitrite, nitrate, orthophosphate, total coliform, and E. coli bacteria were 220 kilograms per day, 15 grams per day, less than 890 grams per day, 360 grams per day, 93,000 million colony forming units per day, and less than 700 million colony forming units per day, respectively. The medians of the median yields of chloride, nitrite, nitrate, orthophosphate, total coliform, and E. coli bacteria were 110 kilograms per day per square mile, 5.5 grams per day per square mile, 250 grams per day per square mile, 210 grams per day per square mile, 36,000 million colony forming units per day per square mile, and 410 million colony forming units per day per square mile, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1144","collaboration":"Prepared in cooperation with the Providence Water Supply Board","usgsCitation":"Smith, K.P., 2022, Streamflow, water quality, and constituent loads and yields, Scituate Reservoir drainage area, Rhode Island, water year 2018: U.S. Geological Survey Data Report 1144, 36 p., https://doi.org/10.3133/dr1144.","productDescription":"Report: v, 36 p.; Data release; Dataset","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120140","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501199,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112156.htm","linkFileType":{"id":5,"text":"html"}},{"id":394803,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1144/images/"},{"id":394802,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1144/dr1144.XML"},{"id":394801,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":394800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WK8N0F","text":"USGS data release","linkHelpText":"Water quality data from the Providence Water Supply Board for tributary streams to the Scituate Reservoir, water year 2018–19"},{"id":394799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1144/dr1144.pdf","text":"Report","size":"1.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1144"},{"id":394798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1144/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Scituate Reservoir Drainage Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.77642822265625,\n 41.69752591075902\n ],\n [\n -71.54022216796875,\n 41.69752591075902\n ],\n [\n -71.54022216796875,\n 41.92680320648791\n ],\n [\n -71.77642822265625,\n 41.92680320648791\n ],\n [\n -71.77642822265625,\n 41.69752591075902\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Wave energy conversion technologies have recently attracted more attention as part of global efforts to replace fossil fuels with renewable energy resources. While ocean waves can provide renewable energy, they can also be destructive to coastal areas that are often densely populated and vulnerable to coastal erosion. There have been a variety of efforts to mitigate the impacts of wave- and storm-induced erosion; however, they are either temporary solutions or approaches that are not able to adapt to a changing climate. This study explores a green and sustainable approach to mitigating coastal erosion from hurricanes through wave energy conversion. A barrier island, Dauphin Island, off the coast of Alabama, is used as a test case. The potential use of wave energy converter farms to mitigate erosion due to hurricane storm surges while simultaneously generating renewable energy is explored through simulations that are forced with storm data using the XBeach model. It is shown that wave farms can impact coastal morphodynamics and have the potential to reduce dune and beach erosion, predominantly in the western portion of the island. The capacity of wave farms to influence coastal morphodynamics varies with the storm intensity.
","language":"English","publisher":"MDPI AG","doi":"10.3390/jmse10020143","usgsCitation":"Ozkan, C., Mayo, T., and Passeri, D., 2022, The potential of wave energy conversion to mitigate coastal erosion from hurricanes: Journal of Marine Science and Engineering, v. 10, no. 2, p. 1-26, https://doi.org/10.3390/jmse10020143.","productDescription":"143, 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-126398","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":449028,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10020143","text":"Publisher Index Page"},{"id":394882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35411071777344,\n 30.22317846163011\n ],\n [\n -88.06777954101562,\n 30.22317846163011\n ],\n [\n -88.06777954101562,\n 30.355397662121728\n ],\n [\n -88.35411071777344,\n 30.355397662121728\n ],\n [\n -88.35411071777344,\n 30.22317846163011\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Morales, Rafael","contributorId":272228,"corporation":false,"usgs":false,"family":"Morales","given":"Rafael","email":"","affiliations":[],"preferred":false,"id":831787,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Ozkan, Cigdem","contributorId":272200,"corporation":false,"usgs":false,"family":"Ozkan","given":"Cigdem","email":"","affiliations":[{"id":18879,"text":"University of Central Florida","active":true,"usgs":false}],"preferred":false,"id":831708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mayo, Talea","contributorId":272201,"corporation":false,"usgs":false,"family":"Mayo","given":"Talea","email":"","affiliations":[{"id":40432,"text":"Emory University","active":true,"usgs":false}],"preferred":false,"id":831709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":831710,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227682,"text":"70227682 - 2022 - Guidelines for volcano-observatory operations during crises: Recommendations from the 2019 Volcano Observatory Best Practices meeting","interactions":[],"lastModifiedDate":"2022-01-26T17:02:08.978401","indexId":"70227682","displayToPublicDate":"2022-01-26T10:22:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3841,"text":"Journal of Applied Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Guidelines for volcano-observatory operations during crises: Recommendations from the 2019 Volcano Observatory Best Practices meeting","docAbstract":"In November 2019, the fourth meeting on Volcano Observatory Best Practices workshop was held in Mexico City as a series of talks, discussions, and panels. Volcanologists from around the world offered suggestions for ways to optimize volcano-observatory crisis operations. By crisis, we mean unrest that may or may not lead to eruption, the eruption itself, or its aftermath, all of which require analysis and communications by the observatory. During a crisis, the priority of the observatory should be to acquire, process, analyze, and interpret data in a timely manner. A primary goal is to communicate effectively with the authorities in charge of civil protection. Crisis operations should rely upon exhaustive planning in the years prior to any actual unrest or eruptions. Ideally, nearly everything that observatories do during a crisis should be envisioned, prepared, and practiced prior to the actual event. Pre-existing agreements and exercises with academic and government collaborators will minimize confusion about roles and responsibilities. In the situation where planning is unfinished, observatories should prioritize close ties and communications with the land and civil-defense authorities near the most threatening volcanoes. \nTo a large extent, volcanic crises become social crises, and any volcano observatory should have a communication strategy, a lead communicator, regular status updates, and a network of colleagues outside the observatory who can provide similar messaging to a public that desires consistent and authoritative information. Checklists permit tired observatory staff to fulfill their duties without forgetting key communications, data streams, or protocols that need regular fulfilment (Bretton et al. 2018; Newhall et al. 2020). Observatory leaders need to manage staff workload to prevent exhaustion and ensure that expertise is available as needed. Event trees and regular group discussions encourage multi-disciplinary thinking, consideration of disparate viewpoints, and documentation of all group decisions and consensus. Though regulations, roles and responsibilities differ around the world, scientists can justify their actions in the wake of an eruption if they document their work, are thoughtful and conscientious in their deliberations, and carry out protocols and procedures developed prior to volcanic unrest. This paper also contains six case studies of volcanic eruptions or observatory actions that illustrate some of the topics discussed herein. Specifically, we discuss Ambae (Vanuatu) in 2017–2018, Kīlauea (USA) in 2018, Etna (Italy) in 2018, Bárðarbunga (Iceland) in 2014, Cotopaxi (Ecuador) in 2015, and global data sharing to prepare for eruptions at Nyiragongo (Democratic Republic of Congo).","language":"English","publisher":"BioMed Central","doi":"10.1186/s13617-021-00112-9","usgsCitation":"Lowenstern, J.B., Wallace, K.L., Barsotti, S., Sandri, L., Stovall, W., Bernard, B., Privitera, E., Komorowski, J., Fournier, N., Baligizi, C., and Gareabiti, E., 2022, Guidelines for volcano-observatory operations during crises: Recommendations from the 2019 Volcano Observatory Best Practices meeting: Journal of Applied Volcanology, v. 11, p. 1-24, https://doi.org/10.1186/s13617-021-00112-9.","productDescription":"3, 24 p.","startPage":"1","endPage":"24","ipdsId":"IP-123190","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449031,"rank":0,"type":{"id":40,"text":"Open Access Publisher 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Jean-Christophe","contributorId":272204,"corporation":false,"usgs":false,"family":"Komorowski","given":"Jean-Christophe","email":"","affiliations":[{"id":47633,"text":"IPGP","active":true,"usgs":false}],"preferred":false,"id":831718,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fournier, Nico","contributorId":272205,"corporation":false,"usgs":false,"family":"Fournier","given":"Nico","email":"","affiliations":[{"id":47796,"text":"GNS, New Zealand","active":true,"usgs":false}],"preferred":false,"id":831719,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baligizi, Charles","contributorId":272206,"corporation":false,"usgs":false,"family":"Baligizi","given":"Charles","email":"","affiliations":[{"id":36925,"text":"Goma Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":831720,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gareabiti, 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waves of emerging vegetation coinciding with rising daily mean temperatures initiating snowmelt across the landscape. Snowmelt also causes rivers and streams draining these regions to swell, a process referred to as to as the ‘spring pulse.’ Networks of streamgages measuring streamflow in these regions often have long-term and continuous periods of record available in real-time and at the daily time step, and thus produce data with potential to predict temporal migration patterns for species exploiting green waves. We tested the potential of models informed by streamflow data to predict timing of spring migration of mule deer (Odocoileus hemionus) herds in a headwater basin of the Colorado River. Models using streamflow data were compared with those informed by traditional temperature-derived measures of the onset of spring. Non-parametric linear-regression techniques were used to test for temporal stationarity in each variable, and logistic-regression models were used to produce probabilities of migration initiation. Our analysis indicates that models using daily streamflow data can perform as well as those using temperature-derived data to predict past-migration patterns, and nearly as well in potential to forecast future migrations. The best performing model was used to generate probabilities of onset of migration for mule deer herds over the 69-year period-of-record from a streamgage. That model indicated spring migration has been trending toward earlier initiations, with modeled median initiations shifting from a Julian day of 123 in the mid 20th century to Julian day 115 over the most recent two decades. The period of 1960 to 1979 had the latest modeled median initiations with Julian day of 128. The analyses demonstrate promise for merging existing hydrologic and biological data collection platforms in these regions to explore timing of past migration patterns and predict migration onsets in real-time.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0262078","usgsCitation":"Alexander, J.S., Murr, M.L., and Eddy-Miller, C.A., 2022, Testing the potential of streamflow data to predict spring migration of an ungulate herds: PLoS ONE, v. 17, no. 1, p. 1-18, https://doi.org/10.1371/journal.pone.0262078.","productDescription":"e0262078, 18 p.","startPage":"1","endPage":"18","ipdsId":"IP-125176","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":449034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0262078","text":"Publisher Index Page"},{"id":394871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Little Snake River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.45703125,\n 40.45321727150385\n ],\n [\n -108.00933837890625,\n 40.70562793820589\n ],\n [\n -107.46826171874999,\n 40.84913799774759\n ],\n [\n -107.0892333984375,\n 40.86991083161536\n ],\n [\n -107.05078125,\n 41.00477542222947\n ],\n [\n -107.490234375,\n 41.539421883822854\n ],\n [\n -108.446044921875,\n 41.54764462357737\n ],\n [\n -108.8031005859375,\n 41.20552261955812\n ],\n [\n -108.45703125,\n 40.45321727150385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Grignolio, Stefano","contributorId":272227,"corporation":false,"usgs":false,"family":"Grignolio","given":"Stefano","email":"","affiliations":[{"id":35987,"text":"Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy","active":true,"usgs":false}],"preferred":false,"id":831783,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Alexander, Jason S. 0000-0002-1602-482X jalexand@usgs.gov","orcid":"https://orcid.org/0000-0002-1602-482X","contributorId":261330,"corporation":false,"usgs":true,"family":"Alexander","given":"Jason","email":"jalexand@usgs.gov","middleInitial":"S.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murr, Marissa L.","contributorId":252938,"corporation":false,"usgs":false,"family":"Murr","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":50476,"text":"Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming","active":true,"usgs":false}],"preferred":false,"id":831740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eddy-Miller, Cheryl A. 0000-0002-4082-750X","orcid":"https://orcid.org/0000-0002-4082-750X","contributorId":195780,"corporation":false,"usgs":true,"family":"Eddy-Miller","given":"Cheryl","email":"","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":false,"id":831741,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227687,"text":"70227687 - 2022 - Earthquake early warning for estimating floor shaking levels of tall buildings","interactions":[],"lastModifiedDate":"2022-03-28T16:45:42.640525","indexId":"70227687","displayToPublicDate":"2022-01-26T09:28:11","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":"Earthquake early warning for estimating floor shaking levels of tall buildings","docAbstract":"This article investigates methods to improve earthquake early warning (EEW) predictions of shaking levels for residents of tall buildings. In the current U.S. Geological Survey ShakeAlert EEW system, regions far from an epicenter will not receive alerts due to low predicted ground‐shaking intensities. However, residents of tall buildings in those areas may still experience significant shaking due to the acceleration amplification caused by tall buildings’ dynamic behavior, as recently experienced by residents of the 52‐story building in downtown Los Angeles (DTLA) during the 2019 M 7.1 Ridgecrest earthquake. Using more than 400 recorded response data acquired from 77 instrumented buildings in California, here we compare the Federal Emergency Management Agency (FEMA) P‐58 and American Society of Civil Engineers (ASCE) 7‐16 simplified equations for peak floor acceleration (PFA), finding that the ASCE estimation is close to the median of data recorded in large and long‐distance events, whereas the current FEMA estimation is not suitable. In the second part of this article, four instrumented tall buildings in DTLA are extensively studied, and the performance of the simplified and response spectrum (RS) methods giving both an estimation of the free‐field horizontal peak ground acceleration (PGA) and pseudospectral acceleration is evaluated. The results show that the RS method is as accurate as the response history analysis as long as the ground‐motion RS is accurate, whereas the ASCE 7‐16 prediction is conservative. However, when ground‐motion RS or PGA is estimated for DTLA using a ground‐motion model (GMM), the performance of the RS method significantly degrades due to underestimation by the GMM at long periods. The results of this study imply that a nonergodic GMM, which may give more accurate prediction in Los Angeles, could improve the results for PFA when the building’s behavior is dominated by a few long‐period fundamental modes, as is the case for the 52‐story building in DTLA.
Habitat degradation combined with climate change increases the threat of extinction for stream fishes. In response to these threats, efforts to reestablish species within formerly occupied streams or translocation to suitable areas may be effective conservation strategies. In the absence of historic species presence data, identifying locations where suitable habitat exists across many fluvial habitats may limit the effectiveness of reestablishments. We present an approach that ranks habitat for stream fish reestablishment over large areas using best available information. Using the locally extirpated Arctic grayling (Thymallus arcticus) in Michigan, USA as an example, we integrate information on species preferences and relationships between species with similar habitat requirements and landscape predictors of habitat to rank stream suitability. We find that unfragmented streams throughout the historical range of Arctic grayling and areas previously unoccupied by the species are potential locations for conservation action. However, we note that projected increases in summer water temperatures may reduce the amount of thermally suitable habitat in some top-ranked locations by up to 30%. Given its inherent flexibility in data requirements, our landscape-level approach may be a valuable tool that supports planning for species reestablishment.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-021-04791-8","usgsCitation":"Tingley, R.W., Infante, D.M., Dean, E., Schemske, D.W., Cooper, A.R., Ross, J., and Daniel, W., 2022, A landscape approach for identifying potential reestablishment sites for extirpated stream fishes: an example with Arctic grayling (Thymallus arcticus) in Michigan: Hydrobiologia, v. 849, p. 1397-1415, https://doi.org/10.1007/s10750-021-04791-8.","productDescription":"19 p.","startPage":"1397","endPage":"1415","ipdsId":"IP-125422","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"849","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Tingley, Ralph William 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":258043,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","email":"","middleInitial":"William","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":840119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Infante, Dana M.","contributorId":146114,"corporation":false,"usgs":false,"family":"Infante","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":16583,"text":"Department of Fisheries and Wildlife, 480 Wilson Rd. 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":840120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dean, Emily M.","contributorId":289990,"corporation":false,"usgs":false,"family":"Dean","given":"Emily M.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":840121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schemske, Douglas W.","contributorId":171953,"corporation":false,"usgs":false,"family":"Schemske","given":"Douglas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":840122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, Arthur R. 0000-0002-0557-8560","orcid":"https://orcid.org/0000-0002-0557-8560","contributorId":220307,"corporation":false,"usgs":false,"family":"Cooper","given":"Arthur","email":"","middleInitial":"R.","affiliations":[{"id":7266,"text":"Michigan State University, Department of Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":840123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ross, Jared 0000-0002-0582-3589","orcid":"https://orcid.org/0000-0002-0582-3589","contributorId":289993,"corporation":false,"usgs":false,"family":"Ross","given":"Jared","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":840124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":840125,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230327,"text":"70230327 - 2022 - Ready for real time: Performance of Global Navigation Satellite System in 2019 Mw 7.1 Ridgecrest, California, rapid response products","interactions":[],"lastModifiedDate":"2022-04-07T12:15:07.764939","indexId":"70230327","displayToPublicDate":"2022-01-26T07:03:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Ready for real time: Performance of Global Navigation Satellite System in 2019 Mw 7.1 Ridgecrest, California, rapid response products","docAbstract":"Global Navigation Satellite Systems (GNSSs) have undergone notable advancement in the last few decades, leading to the availability of a dataset with capabilities well beyond its original intended purpose. The proliferation of high‐rate (1 Hz or greater) GNSS receivers in areas of seismological interest now allows for routine consideration of dynamic earthquake ground motions, with centimeter‐level displacement accuracy via precise point positioning methods. Real‐time (RT) GNSS observations, from stations that are both telemetered and processed to displacement with minimal latency, have lower accuracy compared to post‐processed (PP) GNSS displacements due to imprecise knowledge of atmospheric conditions, satellite clocks, and satellite orbits in RT. Whether the quality of RT high‐rate GNSS is sufficient for use in rapid response products remains to be thoroughly examined. Here, we highlight RT GNSS displacement time series processed during the 2019
Wildlife managers tasked with understanding habitat and resource selection at the population level attempt to characterize patterns in nature that aid and inform conservation. Resource selection functions (RSFs), such as discrete choice analyses, are the standard convention to characterize the effects of habitat attributes on resource selection patterns. These tools are invaluable for wildlife management and conservation and have proven successful in numerous studies. However, the analysis of small datasets using RSF becomes problematic when attempting to account for complex sources of variation, and the inclusion of factors such as weather or intrinsic variation on target species' response may produce models with poor predictive ability. We compared the application of generalized linear mixed-effects modeling (GLMM) and redundancy analysis (RDA) on Appalachian spotted skunk (Spilogale putorius putorius) den selection data at four study sites within the George Washington, Jefferson, and Monongahela National Forests, and surrounding private lands in the Appalachian Mountains of western Virginia and northeastern West Virginia. We assessed the need for the inclusion of alternative sources of variation (i.e., weather conditions and individual intrinsic variation) in addition to standard habitat attributes to better identify sources of variation in den selection. The RDA elucidated complex and opposing relationships, whereby den type use was based on reproductive status or weather condition, which were not evident in the GLMM model that relied solely on habitat measures. Our results demonstrated the importance of examining resource selection data using multivariate techniques in addition to conventional discrete choice analyses to better understand intricate habitat–species relationships, especially for small datasets. Furthermore, from our analyses, we proposed that spotted skunks are neither a true generalist nor specialist species. We introduced and define the term “conditional specialist” to represent a species that is conditionally selective of a given resource in response to one or more current environmental or intrinsic conditions.
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Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924521,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227465,"text":"sir20215135 - 2022 - Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09","interactions":[],"lastModifiedDate":"2026-04-08T16:26:04.962799","indexId":"sir20215135","displayToPublicDate":"2022-01-25T13:59:52","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":"2021-5135","displayTitle":"Groundwater Hydrology in the Area of Savannah and Gunstocker Creeks in Northeastern Hamilton, Southern Meigs, and Northwestern Bradley Counties, Tennessee, 2007–09","title":"Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09","docAbstract":"The U.S. Geological Survey, in cooperation with the Savannah Valley Utility District, evaluated the groundwater hydrology of the Valley and Ridge carbonate rock aquifer in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, from 2007 through 2009. The evaluation included, and built on, the results of test drilling conducted in the area in 1974 to determine the potential for groundwater as a source of public supply for the utility and the results of an investigation conducted to define recharge areas for wells used by groundwater-source public-supply water systems throughout Hamilton County in the early 1990s.
Groundwater-level data collected from wells open to the aquifer in the study area were used to prepare potentiometric-surface maps for fall 1992, spring and fall 1993, summer 2008, and spring 2009 conditions. Two primary groundwater basins were delineated from the maps—the larger of which coincides with the watershed of Savannah Creek in the southern part of the study area and the smaller of which coincides with the watershed of Gunstocker Creek in the northern part of the study area. Both basins are characterized by potentiometric surfaces that contain a central area of low-altitude groundwater levels and low gradients relative to the basin margins that reflect the orientation of enhanced permeability along dissolution-enlarged features that have developed parallel to strike in the aquifer. The recharge area of the Savannah Creek groundwater basin is estimated to be about 31 square miles, and the recharge area of the Gunstocker Creek groundwater basin is estimated to be about 17 square miles.
Recharge to the aquifer in the Savannah Creek and Gunstocker Creek groundwater basins primarily occurs in the uplands area along White Oak Mountain in the eastern part of the study area and along the western boundaries of the basins. Groundwater flows toward the potentiometric lows in each basin, discharging as base flow to the streams and to springs locally. Groundwater withdrawals for public supply by the utility influence the potentiometric low in the north-central part of the Savannah Creek groundwater basin and disrupt flow in the creek and nearby Anderson Spring, particularly during the summer and fall seasons. No large groundwater withdrawals currently occur in the Gunstocker Creek basin, but there is potential for groundwater supply development in the basin.
A conceptual model of the groundwater hydrology of the area developed from the evaluation indicates that Chickamauga Lake is the base-level control on groundwater discharge from the Savannah Creek and Gunstocker Creek basins and that lake stage affects the potentiometric surfaces and groundwater discharge in the most downgradient parts of the basins as a result of inferred hydraulic connection between the aquifer and the lake. The model also infers that captured surface water from sections of Savannah Creek and the Hiwassee River that are embayed by the lake could recharge the aquifer and serve as a source of water withdrawn by wells in each basin if the potentiometric surfaces were lowered to altitudes less than the stage of the lake, particularly under potential future groundwater-development scenarios in the Gunstocker Creek basin.
Geochemical analysis of samples collected from six wells for the study indicate that groundwater in the Valley and Ridge aquifer in the area generally is a calcium-magnesium-bicarbonate type, and although the water generally is hard, it is suitable for most uses. Trace-element concentrations were less than primary drinking-water criteria in all the samples.
Results of the investigation indicate that options are available for additional groundwater withdrawal in the study area. Water-level data collected since 1975 at the Savannah Valley Utility District Smith Road well site indicate that some additional amount of groundwater is available for withdrawal from the aquifer in the Savannah Creek groundwater basin. The potentiometric low within the Gunstocker Creek groundwater basin indicates that an area with enhanced permeability is present as a northeastern counterpart to the potentiometric low within the Savannah Creek basin. Because the Gunstocker Creek basin is about one-half the total area of the Savannah Creek basin, a commensurate decrease in available groundwater storage is likely. Furthermore, groundwater withdrawal locations in the Gunstocker Creek basin would be closer to—and possibly connected hydraulically to—the Hiwassee River, thus increasing the potential for induced surface-water recharge in the basin if sustained drawdown from pumping lowered groundwater levels to altitudes less than the stage of the river.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215135","isbn":"978-1-4113-4435-8","collaboration":"Prepared in cooperation with the Savannah Valley Utility District","programNote":"Water Availability and Use Science Program","usgsCitation":"Carmichael, J.K., 2022, Groundwater hydrology in the area of Savannah and Gunstocker Creeks in northeastern Hamilton, southern Meigs, and northwestern Bradley Counties, Tennessee, 2007–09: U.S. Geological Survey Scientific Investigations Report 2021–5135, 31 p., 5 pls., https://doi.org/10.3133/sir20215135.","productDescription":"Report: vii, 31 p.; Data Release; 5 Plates: 20.00 x 30.00 inches or smaller","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-104265","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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640 Grassmere Park, Suite 100
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The advent of continuous digital recording of seismograph stations in Alaska did not occur until the fall of 2002. Continuous records of seismic waveforms prior to 2002 were recorded only in analog form. The Alaska Volcano Observatory (AVO) has a substantial archive of continuous analog records made on helicorders in a collection maintained by the University of Alaska Fairbanks Geophysical Institute. As part of the response to the 2006 Augustine Volcano eruption, the AVO scanned analog drum records of the 1986 Augustine eruption to aid in comparing the progression of volcanic seismicity in 2006 with the seismic record of the 1986 eruption. The scanned records proved useful, prompting subsequent efforts to preserve records from other notable episodes of volcanic unrest as readily available scanned images. The data archive accompanying this report contains scanned images of select drum records for the eruptions at Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), as well as for the 1996 earthquake swarm at Akutan Peak.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1146","usgsCitation":"Dixon, J.P., and Power, J.A., 2022, Alaska Volcano Observatory archive of seismic drum records of eruptions of Augustine Volcano (1986), Redoubt Volcano (1989–90), Mount Spurr (1992), and Pavlof Volcano (1996), and the 1996 earthquake swarm at Akutan Peak: U.S. Geological Survey Data Report 1146, 10 p., https://doi.org/10.3133/dr1146.","productDescription":"Report: v, 10 p.; 5 Databases","numberOfPages":"10","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-117992","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":501202,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112152.htm","linkFileType":{"id":5,"text":"html"}},{"id":394675,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_PavlofVolcano.zip","text":"Scanned images of select drum records for the 1996 eruption at Pavlof Volcano","size":"1.35 GB","linkFileType":{"id":6,"text":"zip"}},{"id":394672,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_AkutanVolcano.zip","text":"Scanned images of select drum records for the 1996 earthquake swarm at Akutan Volcano","size":"300 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394674,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_MountSpurr.zip","text":"Scanned images of select drum records for the 1992 eruption at Mount Spurr","size":"620 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394673,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_AugustineVolcano.zip","text":"Scanned images of select drum records for the 1986 eruption at Augustine Volcano","size":"150 MB","linkFileType":{"id":6,"text":"zip"}},{"id":394676,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/dr/1146/dr1146_RedoubtVolcano.zip","text":"Scanned images of select drum records for the 1989–90 eruption at Redoubt Volcano","size":"2 GB","linkFileType":{"id":6,"text":"zip"}},{"id":394671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1146/dr1146.pdf","text":"Report","size":"12 MB"},{"id":394670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1146/covrthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano, Redoubt Volcano, Mount Spurr, and Pavlof Volcano, Akutan Peak","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.6163330078125,\n 59.30726326510286\n ],\n [\n -153.31008911132812,\n 59.30726326510286\n ],\n [\n -153.31008911132812,\n 59.438791328713094\n ],\n [\n -153.6163330078125,\n 59.438791328713094\n ],\n [\n -153.6163330078125,\n 59.30726326510286\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.0999755859375,\n 60.212533353918424\n ],\n [\n -152.083740234375,\n 60.212533353918424\n ],\n [\n -152.083740234375,\n 60.53026587299222\n ],\n [\n -153.0999755859375,\n 60.53026587299222\n ],\n [\n -153.0999755859375,\n 60.212533353918424\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.8143310546875,\n 61.0649302984405\n ],\n [\n -151.907958984375,\n 61.0649302984405\n ],\n [\n -151.907958984375,\n 61.37435749785111\n ],\n [\n -152.8143310546875,\n 61.37435749785111\n ],\n [\n -152.8143310546875,\n 61.0649302984405\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -166.2286376953125,\n 53.98839506479995\n ],\n [\n -165.465087890625,\n 53.98839506479995\n ],\n [\n -165.465087890625,\n 54.271639968447985\n ],\n [\n -166.2286376953125,\n 54.271639968447985\n ],\n [\n -166.2286376953125,\n 53.98839506479995\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.11013793945312,\n 55.20865504825601\n ],\n [\n -161.57318115234375,\n 55.20865504825601\n ],\n [\n -161.57318115234375,\n 55.531739499542304\n ],\n [\n -162.11013793945312,\n 55.531739499542304\n ],\n [\n -162.11013793945312,\n 55.20865504825601\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
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Anchorage, AK 99508
Improved understanding of angling behavior in recreational fisheries can help managers account for partial controllability in systems in which angling effort is not directly regulated. Relevant aspects of angling behavior include fishing participation, site choice, and catch-and-release actions and can be considered at scales of both aggregate fishing effort and individual-decision-making. Using creel survey data collected across eight fishery seasons, we predicted aggregate angling effort (e.g., daily number of boating trips engaged in fishing) and characterized influences of individual trip-based site choice for recreational fisheries acting on fall-run Chinook salmon and coho salmon near the mouth of the Columbia River, United States of America. We applied an overdispersed Poisson likelihood-based generalized linear model with an autoregressive structure to aggregate boating effort and a multinomial logit model to trip-based site choice decisions among separate ocean and estuary fishing zones. Predictive models explained 71% and 79% of out-of-sample and in-sample variability in aggregate effort, respectively, and included the covariates weekend status, Chinook salmon catch rate, tidal range, and pre-season expectations of fish abundance. In addition to reinforcing the importance of Chinook salmon catch rate and tidal range, site choice model selection revealed influences of weather, fishery restrictions, expected fishery season lengths, and the individual-specific characteristics of boat length and guide status on decision-making. Model results provide predictive models for short-term fishery planning, highlight heterogeneity in individual decision-making, and illustrate the value of standardized creel data for evaluating angling behavior.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2022.106235","usgsCitation":"Jensen, A.J., Dundas, S., and Peterson, J., 2022, Phenomenological and mechanistic modeling of recreational angling behavior using creel data: Fisheries Research, v. 249, 106235, 15 p., https://doi.org/10.1016/j.fishres.2022.106235.","productDescription":"106235, 15 p.","ipdsId":"IP-125527","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Buoy 10 Fishery, Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.02658508278576,\n 46.242723068563095\n ],\n [\n -124.02658508278576,\n 46.14556352523286\n ],\n [\n -123.71717267683536,\n 46.14556352523286\n ],\n [\n -123.71717267683536,\n 46.242723068563095\n ],\n [\n -124.02658508278576,\n 46.242723068563095\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"249","noUsgsAuthors":false,"publicationDate":"2022-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Jensen, Alexander J.","contributorId":272039,"corporation":false,"usgs":false,"family":"Jensen","given":"Alexander","email":"","middleInitial":"J.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":934953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dundas, Steven J.","contributorId":354015,"corporation":false,"usgs":false,"family":"Dundas","given":"Steven J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":934954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934952,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227633,"text":"dr1149 - 2022 - Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, California—2021 Data summary","interactions":[],"lastModifiedDate":"2022-01-24T12:27:59.618799","indexId":"dr1149","displayToPublicDate":"2022-01-21T11:28:13","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":"1149","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the Mojave River Dam, San Bernardino County, 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 Mojave River Dam, San Bernardino County, 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 Mojave River Dam study area near Hesperia, California, in 2021. Four vireo surveys were conducted between April 16 and July 16, 2021, and three flycatcher surveys were conducted between May 27 and July 16, 2021.
We detected four territorial male vireos, including two that were paired and two with undetermined breeding status. No juveniles were observed during surveys. Vireo territories were found in three habitat types: (1) riparian scrub, (2) willow-cottonwood, and (3) willow-sycamore, with willow-cottonwood being the most commonly recorded habitat type. Red or arroyo willow (Salix laevigata or lasiolepis) was the dominant plant species in most vireo territories.
No territorial or transient flycatchers were observed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1149","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 Mojave River Dam, San Bernardino County, California—2021 Data Summary: U.S. Geological Survey Data Report 1149, 7 p., https://doi.org/10.3133/dr1149.","productDescription":"vi, 7 p.","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-135057","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":394666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1149/covrthb.jpg"},{"id":394667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1149/dr1149.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":394668,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1149/dr1149.xml"},{"id":394669,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1149/images"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Mojave River Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.28866577148438,\n 34.32132236979802\n ],\n [\n -117.19802856445311,\n 34.32132236979802\n ],\n [\n -117.19802856445311,\n 34.38821261603411\n ],\n [\n -117.28866577148438,\n 34.38821261603411\n ],\n [\n -117.28866577148438,\n 34.32132236979802\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
Understanding the thermal tolerances of stream fishes, including sport fishes, is important for assessing thermal stressors that are common across the landscape. Our study objectives were to determine the thermal tolerances of 17 stream fishes (15 species and 2 genetically distinct populations of juvenile Smallmouth Bass Micropterus dolomieu: the Neosho subspecies M. dolomieu velox and the Ouachita strain M. sp. cf. dolomieu velox). Fish were collected from the field and acclimated to laboratory conditions at 20°C or 25°C, with dissolved oxygen maintained above 6 mg/L. We determined the critical thermal maximum (CTM) using an incomplete block design with 9–11 replications for each species. During trials, we increased the water temperature at a rate of 2°C per hour until fish experienced loss of equilibrium. The estimated CTM ranged from 32.43°C to 38.26°C among species. The CTM values differed significantly between taxonomic groups and species, including the genetically distinct populations of Smallmouth Bass. The Neosho subspecies of Smallmouth Bass had a significantly lower thermal tolerance than the Ouachita strain at both acclimation temperatures; however, the magnitude of the difference was about 0.5°C greater at the higher acclimation temperature. Closely related species, including the Bigeye Shiner Notropis boops and Kiamichi Shiner N. ortenburgeri, had significantly different thermal tolerances despite occupying similar riverine locations. Our results suggest that our perceptions of a species’ thermal tolerance based on that of closely related species or that of species using similar habitat may be incorrect. Moreover, the differences in thermal tolerances among populations may be an important consideration for conservation and management actions, such as stocking decisions. Laboratory data such as those provided in this study can be integrated with field data to better assess thermal responses of fishes in a changing environment.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10749","usgsCitation":"Brewer, S.K., Mollenhauer, R., Alexander, J., and Moore, D., 2022, Critical thermal maximum of stream fishes including distinct populations of Smallmouth Bass: North American Journal of Fisheries Management, v. 42, no. 2, p. 352-360, https://doi.org/10.1002/nafm.10749.","productDescription":"9 p.","startPage":"352","endPage":"360","ipdsId":"IP-128880","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Ouachita Mountain ecoregion, Ozark Highlands ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.8664012156679,\n 36.99681119417036\n ],\n [\n -95.5111277927191,\n 36.20967738751284\n ],\n [\n -95.222344013415,\n 35.57857852939986\n ],\n [\n -94.48359481054393,\n 35.687747319317126\n ],\n [\n -94.62462874927394,\n 36.45854467258876\n ],\n [\n -94.62462874927394,\n 37.01290083943225\n ],\n [\n -94.8664012156679,\n 36.99681119417036\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.43940916621646,\n 34.76329149945565\n ],\n [\n -96.02893897983913,\n 34.719164102780056\n ],\n [\n -96.6840830246433,\n 34.27659649424358\n ],\n [\n -96.35114096908727,\n 33.96540469177903\n ],\n [\n -94.48236943144916,\n 33.992123122916226\n ],\n [\n -94.43940916621646,\n 34.76329149945565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":908839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mollenhauer, R.","contributorId":276144,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"R.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, J.","contributorId":305320,"corporation":false,"usgs":false,"family":"Alexander","given":"J.","email":"","affiliations":[],"preferred":false,"id":908841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, D.E.","contributorId":205713,"corporation":false,"usgs":false,"family":"Moore","given":"D.E.","email":"","affiliations":[],"preferred":false,"id":908842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229821,"text":"70229821 - 2022 - Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","interactions":[],"lastModifiedDate":"2022-05-13T14:55:00.650722","indexId":"70229821","displayToPublicDate":"2022-01-21T09:13:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","title":"Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie","docAbstract":"Diet analysis integrates a wide variety of visual, chemical, and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, for example, highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package mvtweedie that extends capabilities of widely used packages in R (mgcv and ggplot2) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted Puffins (Fratercula cirrhata) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980–2000) and then Pacific herring and capelin (2000–2020), while wolves (Canis lupus ligoni) in southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3637","usgsCitation":"Thorson, J.T., Arimitsu, M.L., Levi, T., and Roffler, G., 2022, Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie: Ecology, v. 103, no. 5, e3637, 9 p., https://doi.org/10.1002/ecy.3637.","productDescription":"e3637, 9 p.","ipdsId":"IP-128116","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":449065,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.3637","text":"External Repository"},{"id":397303,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorson, James T.","contributorId":146580,"corporation":false,"usgs":false,"family":"Thorson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":838473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":838474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Levi, Taal","contributorId":191295,"corporation":false,"usgs":false,"family":"Levi","given":"Taal","email":"","affiliations":[],"preferred":false,"id":838475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roffler, Gretchen","contributorId":288945,"corporation":false,"usgs":false,"family":"Roffler","given":"Gretchen","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":838476,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227620,"text":"70227620 - 2022 - Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2022-02-15T16:27:30.782829","indexId":"70227620","displayToPublicDate":"2022-01-21T09:10:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using surrogate taxa to inform response methods for invasive Grass Carp in the Laurentian Great Lakes","docAbstract":"Sampling method decisions are critical for the effective monitoring and management of fisheries. Deploying the most effective sampling methodologies is particularly important when responding to new invasive species, where early response efforts have the best chances for eradication. In the Laurentian Great Lakes, the invasive Grass Carp Ctenopharyngodon idella is sampled using boat electrofishing and the combination method of boat electrofishing within and around a trammel net enclosure. We conducted a field study to compare the effectiveness of the two methods. We used capture data for surrogate taxa (i.e., Common Carp Cyprinus carpio and buffalo Ictiobus spp.) to compare the two methods because few Grass Carp were collected during the study. The sampling methods were compared within an occupancy modeling framework using an information-criteria model selection approach to evaluate seven alternative models. The base model included sampling method, year, water temperature, and sampling effort as covariates in the detection submodel and assumed that occupancy probability was constant across sites. The other six models built on the base model by including site, water body type (i.e., lentic vs. lotic), and interaction covariates in the detection submodel. The top-performing model, built on the base model, accounted for the influence of water body type and assumed the exchangeability of site effects in the detection submodel. The results indicated that the detection probabilities for both taxa were higher for the combination method than for boat electrofishing, with a median estimated difference in detection probability between the two methods of 0.11 (95% CI: 0.04–0.22) for Common Carp and 0.18 (95% CI: 0.08–0.28) for buffalo. Given that the combination method was more effective for detecting the surrogate taxa, we expect the combination method may be preferable to only boat electrofishing for Grass Carp removal.
Golden eagles (Aquila chrysaetos) are of increasing conservation concern in western North America. Effective conservation measures for this wide-ranging, federally protected raptor species require monitoring frameworks that accommodate strong inference on the status of breeding populations across vast landscapes. We used a broad-scale sampling design to identify relationships between landscape conditions, detection rates, and site occupancy by territorial pairs of golden eagles in coastal southern California, United States. In 2016 and 2017, we surveyed 175 territory-sized sample sites (13.9-km2 randomly selected grid cells) up to four times each year and detected a pair of eagles at least once in 22 (12.6%) sites. The probability of detecting pairs of eagles varied substantially between years and declined with increasing amounts of forest cover at survey sites, which obscured observations of eagles during ground-based surveys. After accounting for variable detection, the mean estimate of expected site occupancy by eagle pairs was 0.156 (SE = 0.081). Site-level estimates of occupancy were greatest (>0.30) at sample sites with more rugged terrain conditions, <20% human development, and lower amounts of scrubland vegetation cover. The proportion of a sample site with open grassland or forest cover was not strongly correlated with occupancy. We estimated that approximately 16% of the 5,338-km2 sampling frame was used by resident pairs of golden eagles, corresponding to a sparsely distributed population of about 60 pairs (95% CI = 19 – 151 pairs). Our study provided baseline data for future surveys of golden eagles along with a widely applicable monitoring framework for identifying spatial conservation priorities in urbanizing landscapes.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2021.665792","usgsCitation":"Wiens, D., Bloom, P., Madden, M., Kolar, P., Tracey, J.A., and Fisher, R.N., 2022, Golden eagle occupancy surveys and monitoring strategy in coastal southern California, United States: Frontiers in Ecology and Environment, v. 9, 665792, 11 p., https://doi.org/10.3389/fevo.2021.665792.","productDescription":"665792, 11 p.","ipdsId":"IP-126757","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":449068,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.665792","text":"Publisher Index Page"},{"id":435993,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OIPLHH","text":"USGS data release","linkHelpText":"Detection/non-detection data on territorial pairs of golden eagles in coastal southern California, 2016-2017"},{"id":397934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.45458984375,\n 32.602361666817515\n ],\n [\n -115.97167968750001,\n 32.602361666817515\n ],\n [\n -115.97167968750001,\n 34.21634468843463\n ],\n [\n -118.45458984375,\n 34.21634468843463\n ],\n [\n -118.45458984375,\n 32.602361666817515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, David 0000-0002-2020-038X","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":267230,"corporation":false,"usgs":true,"family":"Wiens","given":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":839325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bloom, Peter H.","contributorId":289557,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter H.","affiliations":[{"id":38830,"text":"Bloom Research Inc.","active":true,"usgs":false}],"preferred":false,"id":839326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madden, Melanie C.","contributorId":289559,"corporation":false,"usgs":false,"family":"Madden","given":"Melanie C.","affiliations":[{"id":36522,"text":"U.S. Navy","active":true,"usgs":false}],"preferred":false,"id":839327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolar, Patrick 0000-0002-0076-7565 pkolar@usgs.gov","orcid":"https://orcid.org/0000-0002-0076-7565","contributorId":189512,"corporation":false,"usgs":true,"family":"Kolar","given":"Patrick","email":"pkolar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839328,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tracey, Jeff A. 0000-0002-1619-1054 jatracey@usgs.gov","orcid":"https://orcid.org/0000-0002-1619-1054","contributorId":5780,"corporation":false,"usgs":true,"family":"Tracey","given":"Jeff","email":"jatracey@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839329,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisher, Robert N. 0000-0003-2842-422X rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0003-2842-422X","contributorId":289561,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839330,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227621,"text":"70227621 - 2022 - Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product","interactions":[],"lastModifiedDate":"2022-01-21T15:10:11.763046","indexId":"70227621","displayToPublicDate":"2022-01-21T08:57:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product","docAbstract":"The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~ 25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5%. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-14-143-2022","usgsCitation":"Xian, G.Z., Smith, K., Wellington, D., Horton, J., Zhou, Q., Li, C., Auch, R.F., Brown, J.F., Zhu, Z., and Reker, R.R., 2022, Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product: Earth System Science Data, v. 14, p. 143-162, https://doi.org/10.5194/essd-14-143-2022.","productDescription":"20 p.","startPage":"143","endPage":"162","ipdsId":"IP-130588","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449071,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-143-2022","text":"Publisher Index Page"},{"id":394657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"14","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":831379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":831380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":831381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":191430,"corporation":false,"usgs":false,"family":"Horton","given":"Josephine","affiliations":[],"preferred":false,"id":831382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":831383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":831384,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":831385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":831386,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zhu, Zhe 0000-0003-4716-2309","orcid":"https://orcid.org/0000-0003-4716-2309","contributorId":272038,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":831387,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reker, Ryan R. 0000-0001-7524-0082 rreker@usgs.gov","orcid":"https://orcid.org/0000-0001-7524-0082","contributorId":174136,"corporation":false,"usgs":true,"family":"Reker","given":"Ryan","email":"rreker@usgs.gov","middleInitial":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":831388,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70230146,"text":"70230146 - 2022 - Predator–prey interactions of terrestrial invertebrates are determined by predator body size and species identity","interactions":[],"lastModifiedDate":"2022-03-30T12:15:32.216537","indexId":"70230146","displayToPublicDate":"2022-01-21T07:10:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Predator–prey interactions of terrestrial invertebrates are determined by predator body size and species identity","docAbstract":"Predator–prey interactions shape ecosystems and can help maintain biodiversity. However, for many of the earth's most biodiverse and abundant organisms, including terrestrial arthropods, these interactions are difficult or impossible to observe directly with traditional approaches. Based on previous theory, it is likely that predator–prey interactions for these organisms are shaped by a combination of predator traits, including body size and species-specific hunting strategies. In this study, we combined diet DNA metabarcoding data of 173 individual invertebrate predators from nine species (a total of 305 individual predator–prey interactions) with an extensive community body size data set of a well-described invertebrate community to explore how predator traits and identity shape interactions. We found that (1) mean size of prey families in the field usually scaled with predator size, with species-specific variation to a general size-scaling relationship (exceptions likely indicating scavenging or feeding on smaller life stages). We also found that (2) although predator hunting traits, including web and venom use, are thought to shape predator–prey interaction outcomes, predator identity more strongly influenced our indirect measure of the relative size of predators and prey (predator:prey size ratios) than either of these hunting traits. Our findings indicate that predator body size and species identity are important in shaping trophic interactions in invertebrate food webs and could help predict how anthropogenic biodiversity change will influence terrestrial invertebrates, the earth's most diverse animal taxonomic group.
The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064 nm laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy (LIBS) by analyzing the light from the resulting plasma. SuperCam LIBS spectra are preprocessed to remove ambient light, noise, and the continuum signal present in LIBS observations. Prior to quantification, spectra are masked to remove noisier spectrometer regions and spectra are normalized to minimize signal fluctuations and effects of target distance. In some cases, the spectra are also standardized or binned prior to quantification. To determine quantitative elemental compositions of diverse geologic materials at Jezero crater, Mars, we use a suite of 1198 laboratory spectra of 334 well-characterized reference samples. The samples were selected to span a wide range of compositions and include typical silicate rocks, pure minerals (e.g., silicates, sulfates, carbonates, oxides), more unusual compositions (e.g., Mn ore and sodalite), and replicates of the sintered SuperCam calibration targets (SCCTs) onboard the rover. For each major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O), the database was subdivided into five “folds” with similar distributions of the element of interest. One fold was held out as an independent test set, and the remaining four folds were used to optimize multivariate regression models relating the spectrum to the composition. We considered a variety of models, and selected several for further investigation for each element, based primarily on the root mean squared error of prediction (RMSEP) on the test set, when analyzed at 3 m. In cases with several models of comparable performance at 3 m, we incorporated the SCCT performance at different distances to choose the preferred model. Shortly after landing on Mars and collecting initial spectra of geologic targets, we selected one model per element. Subsequently, with additional data from geologic targets, some models were revised to ensure results that are more consistent with geochemical constraints. The calibration discussed here is a snapshot of an ongoing effort to deliver the most accurate chemical compositions with SuperCam LIBS.
The details and mechanisms for Neogene river reorganization in the U.S. Pacific Northwest and northern Rocky Mountains have been debated for over a century with key implications for how tectonic and volcanic systems modulate topographic development. To evaluate paleo-drainage networks, we produced an expansive data set and provenance analysis of detrital zircon U-Pb ages from Miocene to Pleistocene fluvial strata along proposed proto-Snake and Columbia River pathways. Statistical comparisons of Miocene-Pliocene detrital zircon spectra do not support previously hypothesized drainage routes of the Snake River. We use detrital zircon unmixing models to test prior Snake River routes against a newly hypothesized route, in which the Snake River circumnavigated the northern Rocky Mountains and entered the Columbia Basin from the northeast prior to incision of Hells Canyon. Our proposed ancestral Snake River route best matches detrital zircon age spectra throughout the region. Furthermore, this northerly Snake River route satisfies and provides context for shifts in the sedimentology and fish faunal assemblages of the western Snake River Plain and Columbia Basin through Miocene−Pliocene time. We posit that eastward migration of the Yellowstone Hotspot and its effect on thermally induced buoyancy and topographic uplift, coupled with volcanic densification of the eastern Snake River Plain lithosphere, are the primary mechanisms for drainage reorganization and that the eastern and western Snake River Plain were isolated from one another until the early Pliocene. Following this basin integration, the substantial increase in drainage area to the western Snake River Plain likely overtopped a bedrock threshold that previously contained Lake Idaho, which led to incision of Hells Canyon and establishment of the modern Snake and Columbia River drainage network.
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