Background: Ecological barriers can shape the movement strategies of migratory animals that navigate around or across them, creating migratory divides. Wind plays a large role in facilitating aerial migrations, and can temporally or spatially change the challenge posed by an ecological barrier, with beneficial winds potentially converting a barrier to a corridor. Here, we explore the role wind plays in shaping initial southbound migration strategy between two populations departing from different locations along an ecological barrier.
Methods: Using GPS satellite transmitters, we tracked the southbound migration of two populations of Short-billed Dowitchers (Limnodromus griseus caurinus) from breeding grounds in Alaska to wintering sites in coastal Mexico. The breeding grounds were positioned in distinct regions along an ecological barrier, the Gulf of Alaska. Between the two populations, we compared migratory timing, wind availability at, and tailwind support en route across the Gulf of Alaska.
Results: Route choice and arrival timing to wintering sites differed markedly between the two populations: individuals departing from the more westerly site (King Salmon) left at the same time as those from further east (Beluga) but crossed the Gulf of Alaska farther west and arrived along the Pacific coast of Mexico an average of 19 days earlier than their counterparts. Dowitchers from both sites used a slight tailwind to cue departure, but once aloft over the Gulf of Alaska, birds from the more westerly site had up to ten times more tailwind assistance than birds from the more easterly one.
Conclusions: The distinct migration strategies, and degree of wind assistance experienced, of these two populations demonstrates how differences in wind availability along migratory routes may form the basis for intraspecific variation in migration strategies with potential carryover effects. Future changes in wind regimes may therefore interact with changes in habitat availability to influence migration patterns and migratory bird conservation.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-024-00509-2","usgsCitation":"Bathrick, R.E., Johnson, J.A., Ruthrauff, D.R., Snyder, R., Stager, M., and Senner, N.R., 2024, Migratory strategies across an ecological barrier: Is the answer blowing in the wind?: Movement Ecology, v. 12, no. 1, e70, 15 p., https://doi.org/10.1186/s40462-024-00509-2.","productDescription":"e70, 15 p.","ipdsId":"IP-160741","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466850,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-024-00509-2","text":"Publisher Index Page"},{"id":464123,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -165.95960564872016,\n 59.55615778068645\n ],\n [\n -165.95960564872016,\n 55.99945856187486\n ],\n [\n -136.85750784523026,\n 55.99945856187486\n ],\n [\n -136.85750784523026,\n 59.55615778068645\n ],\n [\n -165.95960564872016,\n 59.55615778068645\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-10-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Bathrick, Rosalyn E.","contributorId":346300,"corporation":false,"usgs":false,"family":"Bathrick","given":"Rosalyn","email":"","middleInitial":"E.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":918618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, James A. 0000-0002-2312-0633","orcid":"https://orcid.org/0000-0002-2312-0633","contributorId":299054,"corporation":false,"usgs":false,"family":"Johnson","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":918619,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruthrauff, Daniel R. 0000-0003-1355-9156 druthrauff@usgs.gov","orcid":"https://orcid.org/0000-0003-1355-9156","contributorId":4181,"corporation":false,"usgs":true,"family":"Ruthrauff","given":"Daniel","email":"druthrauff@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":918620,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snyder, Rebekah","contributorId":346301,"corporation":false,"usgs":false,"family":"Snyder","given":"Rebekah","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":918621,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stager, Maria","contributorId":346302,"corporation":false,"usgs":false,"family":"Stager","given":"Maria","email":"","affiliations":[{"id":82825,"text":"U Mass Amherst","active":true,"usgs":false}],"preferred":false,"id":918622,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Senner, Nathan R.","contributorId":140465,"corporation":false,"usgs":false,"family":"Senner","given":"Nathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":918623,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261411,"text":"70261411 - 2024 - Previous reproductive success and environmental variation influence nest-site fidelity of a subarctic-nesting goose","interactions":[],"lastModifiedDate":"2024-12-09T15:40:30.029495","indexId":"70261411","displayToPublicDate":"2024-10-11T08:32:09","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Previous reproductive success and environmental variation influence nest-site fidelity of a subarctic-nesting goose","docAbstract":"Nest-site fidelity is a common strategy in birds and is believed to be adaptive due to familiarity with local conditions. Returning to previously successful nest sites (i.e., the win-stay lose-switch strategy) may be beneficial when habitat quality is spatially variable and temporally predictable; however, changes in environmental conditions may constrain dispersal decisions despite previous reproductive success. We used long-term (2000–2017) capture-mark-reencounter data and hierarchical models to examine fine-scale nest-site fidelity of emperor geese (Anser canagicus) on the Yukon-Kuskokwim Delta in Alaska. Our objectives were to quantify nest-site dispersal distances, determine whether dispersal distance is affected by previous nest fate, spring timing, or major flooding events on the study area, and determine if nest-site fidelity is adaptive in that it leads to higher nest survival. Consistent with the win-stay lose-switch strategy, expected dispersal distance for individuals that failed their nesting attempt in the previous year (207.9 m,14 95% HPDI:150.9–271.4) was greater than expected dispersal distance for individuals who nested successfully in the previous year (125.8 m, 95% HPDI:107.1–145.9). Expected dispersal distance was slightly greater following years of major flooding events for individuals that nested successfully, although this pattern was not observed for individuals who failed their nesting attempt. We did not find evidence that expected dispersal distance was influenced by spring timing. Importantly, dispersal distance was positively related to daily survival probability of emperor goose nests for individuals who failed their previous nesting attempt, suggesting an adaptive benefit to the win-stay lose-switch strategy. Our results highlight the importance of previous experience and environmental variation for informing dispersal decisions of a long-lived goose species. However, it is unclear if dispersal decisions based on previous experience will continue to be adaptive as variability in environmental conditions increases in northern breeding areas.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.70313","usgsCitation":"Thompson, J.M., Uher-Koch, B.D., Daniels, B.L., Riecke, T., Schmutz, J.A., and Sedinger, B.S., 2024, Previous reproductive success and environmental variation influence nest-site fidelity of a subarctic-nesting goose: Ecology and Evolution, v. 14, no. 10, e70313, 10 p., https://doi.org/10.1002/ece3.70313.","productDescription":"e70313, 10 p.","ipdsId":"IP-164482","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.70313","text":"Publisher Index Page"},{"id":464921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon-Kuskokwim Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -168.9696931685749,\n 61.4150203271945\n ],\n [\n -168.9696931685749,\n 59.36637273016359\n ],\n [\n -161.49030953760507,\n 59.36637273016359\n ],\n [\n -161.49030953760507,\n 61.4150203271945\n ],\n [\n -168.9696931685749,\n 61.4150203271945\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Jordan M.","contributorId":303133,"corporation":false,"usgs":false,"family":"Thompson","given":"Jordan","email":"","middleInitial":"M.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":920511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uher-Koch, Brian D. 0000-0002-1885-0260 buher-koch@usgs.gov","orcid":"https://orcid.org/0000-0002-1885-0260","contributorId":5117,"corporation":false,"usgs":true,"family":"Uher-Koch","given":"Brian","email":"buher-koch@usgs.gov","middleInitial":"D.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":920512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daniels, Bryan L.","contributorId":304964,"corporation":false,"usgs":false,"family":"Daniels","given":"Bryan","email":"","middleInitial":"L.","affiliations":[{"id":66195,"text":"Yukon Delta National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":920513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Riecke, Thomas V.","contributorId":171482,"corporation":false,"usgs":false,"family":"Riecke","given":"Thomas V.","affiliations":[],"preferred":false,"id":920514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmutz, Joel A.","contributorId":304965,"corporation":false,"usgs":false,"family":"Schmutz","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":66196,"text":"Alaska Science Center WTEB (retired)","active":true,"usgs":false}],"preferred":false,"id":920515,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sedinger, Benjamin S.","contributorId":304966,"corporation":false,"usgs":false,"family":"Sedinger","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":920516,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259631,"text":"70259631 - 2024 - Body size and early marine conditions drive changes in Chinook salmon productivity across northern latitude ecosystems","interactions":[],"lastModifiedDate":"2024-10-18T11:59:17.555913","indexId":"70259631","displayToPublicDate":"2024-10-08T06:57:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Body size and early marine conditions drive changes in Chinook salmon productivity across northern latitude ecosystems","docAbstract":"Disentangling the influences of climate change from other stressors affecting the population dynamics of aquatic species is particularly pressing for northern latitude ecosystems, where climate-driven warming is occurring faster than the global average. Chinook salmon (Oncorhynchus tshawytscha) in the Yukon-Kuskokwim (YK) region occupy the northern extent of their species' range and are experiencing prolonged declines in abundance resulting in fisheries closures and impacts to the well-being of Indigenous people and local communities. These declines have been associated with physical (e.g., temperature, streamflow) and biological (e.g., body size, competition) conditions, but uncertainty remains about the relative influence of these drivers on productivity across populations and how salmon–environment relationships vary across watersheds. To fill these knowledge gaps, we estimated the effects of marine and freshwater environmental indicators, body size, and indices of competition, on the productivity (adult returns-per-spawner) of 26 Chinook salmon populations in the YK region using a Bayesian hierarchical stock-recruitment model. Across most populations, productivity declined with smaller spawner body size and sea surface temperatures that were colder in the winter and warmer in the summer during the first year at sea. Decreased productivity was also associated with above average fall maximum daily streamflow, increased sea ice cover prior to juvenile outmigration, and abundance of marine competitors, but the strength of these effects varied among populations. Maximum daily stream temperature during spawning migration had a nonlinear relationship with productivity, with reduced productivity in years when temperatures exceeded thresholds in main stem rivers. These results demonstrate for the first time that well-documented declines in body size of YK Chinook salmon were associated with declining population productivity, while taking climate into account.
Based on the reports of Ewert and others (2005, 2018) and Moran and others (2008), most U.S. volcanoes are currently under-monitored and are likely to remain so until the goals of the National Volcano Early Warning System are fulfilled. In addition, volcanoes determined to have low to moderate threat levels (Ewert and others 2005, 2018) could awaken suddenly and, as a result, may need to have instrumentation installed rapidly. For these reasons, equipment caches would ideally be readily available for rapid response in the event of unrest at under-monitored volcanoes or during a volcanic crisis. Given that volcanoes in Alaska and Hawai‘i are frequently active, it is likely that several U.S. volcanoes could experience unrest simultaneously, as happened in 2018, 2019, and 2020, when unrest or eruptions occurred at Great Sitkin Volcano, Alaska; Mauna Loa, Hawai‘i; Mount Cleveland, Alaska; Semisopochnoi Island, Alaska; Shishaldin Volcano, Alaska; Mount Veniaminof, Alaska, as well as the most destructive documented eruption of Kīlauea, Hawai‘i. Therefore, we recommend that sufficient numbers of seismometers, infrasound sensors, Global Navigation Satellite System (GNSS) receivers, remote cameras, gas-monitoring instruments, and airborne and ground-based remote-sensing systems be made available and placed in a state of readiness at each observatory with the capability of bringing a level-2 monitoring network to near level-4 readiness. These rapid-response caches would ideally include sufficient equipment to provide real-time data telemetry, including satellite telemetry, where available, applicable, and appropriate. Rapid-response caches would be maintained in a state of readiness so that instruments can be deployed within several hours to days. Although the primary focus of the caches would be to enable rapid increases to a volcano observatory’s real-time monitoring capabilities, not all scenarios of volcanic unrest are conducive to rapid deployment of real-time data telemetry. Non-telemetered, campaign instruments, particularly seismometers and GNSS stations, can also be deployed to aid in detection of early signs of volcanic unrest given the data can be recovered in a timely fashion.
Given the geographic separation of the U.S. Geological Survey Volcano Science Center’s (VSC) four volcano observatory offices, the logistical difficulties in shipping equipment rapidly between them in response to unrest, the possible scenario that a volcano could reawaken with just hours or days of precursory unrest, and the difference in operating environments (for example, tropical Hawai‘i compared to subarctic Alaska), we recommend three rapid-response instrument caches—for Hawai‘i, Alaska, and the lower 48 States. For the lower 48 States, a single cache shared among the Cascades Volcano Observatory, Yellowstone Volcano Observatory, and the California Volcano Observatory could be warehoused in California or Washington. Although these rapid-response caches would be located at one of the observatories, they would ideally be owned and maintained by VSC, and together form a flexible VSC-wide instrument pool. To maintain continuity of monitoring capabilities, this rapid-response cache could also serve to replace instruments destroyed during an on-going eruption. However, to retain eruption-response readiness, we recommend instruments in the rapid-response cache not be permanently reallocated to an observatory’s monitoring network unless they are replaced.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062M","usgsCitation":"Flinders, A.F., 2024, Special topic—Rapid-response instrumentation, chap. M of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–M, 4 p., https://doi.org/10.3133/sir20245062M.","productDescription":"iii, 4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-153111","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462409,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/m/covrthbm.jpg"},{"id":462410,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/m/sir20245062m.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Explosive eruptions create plumes of volcanic ash and gas that can rise more than 30,000 feet (9.1 kilometers [km]) above sea level within minutes of eruption onset. The resulting clouds disperse under prevailing winds and may cause hazardous conditions hundreds to thousands of kilometers from the volcano, including in international airspace. Rapid detection and characterization of explosive activity is vital to mitigate the wide-ranging effects of volcanic ash. Ashfall thicknesses as small as a millimeter or so on the ground can affect infrastructure, agriculture, and air quality, requiring extensive clean-up procedures (Schuster, 1981; Warrick and others, 1981, U.S. Geological Survey, 2022). Volcanic clouds also pose substantial threats to aircraft. Since 1953, 88 encounters between airplanes and ash clouds have been documented worldwide (International Civil Aviation Organization, 2015, appendix F), resulting in aircraft damage and, in 9 cases, engine failure (Guffanti and others, 2010). In 1982, two large passenger planes suffered complete engine failure owing to eruptions in Indonesia (Global Volcanism Program, 1982) and a similar incident occurred over Alaska in 1989 (Casadevall, 1994). In all three cases, they were able to restart some engine capability and land safely once they emerged from the ash clouds, although with substantial damage (Guffanti and others, 2010).
The clear threat to aviation has led to establishment of nine Volcanic Ash Advisory Centers (VAAC) around the world to monitor and rapidly disseminate information about volcanic eruptions to the aviation community. U.S. Geological Survey (USGS) volcano observatories issue the Volcano Observatory Notice for Aviation that informs of preeruptive unrest or eruptive activity. When ash-producing eruptions do occur, volcano observatories work closely with their regional VAAC to ensure consistency and accuracy in eruption onset time, cloud altitude, ash production, and duration as reported in Volcanic Ash Advisories. Explosive volcanism in the United States and Commonwealth of the Northern Mariana Islands prompts 50–100 such advisories in any given year (table J1). This collaborative effort is greatly aided by USGS detection and monitoring of eruption clouds to ensure a timely and coordinated response.
To support these efforts to provide guidance on ash transport and fallout, the USGS developed the Ash3d volcanic ash dispersion model (https://vsc-ash.wr.usgs.gov/ash3d-gui) (Schwaiger and others, 2012). Automated simulations are run daily by the USGS for volcanoes that are in elevated states of unrest, and in response mode when eruptions occur. During eruptions, the model output is provided to local National Weather Service Weather Forecast Offices to guide them in the issuance of their information products (such as special weather statements, ashfall advisories, or ashfall warnings), as well as to State and local governments and the public. Characterization of the eruption source is needed to estimate the parameters used to initialize the Ash3d model, and by the Anchorage and Washington VAACs to initialize other dispersion models that inform forecasts for the airborne volcanic cloud. The source parameters that can be provided by observation during an eruption include eruption start time, eruption cloud height over time, and eruption duration. Other, nonobservable source parameters, such as mass eruption rate and grain-size distribution, are based on empirical correlations and study of historical deposits. The goal is to provide a time series of cloud heights, mass eruption rates, and particle-size distributions that accurately reflects current conditions. When feasible, the USGS also provides guidance on the nature of ongoing eruptions and forecasts future activity using petrologic monitoring of collected tephra samples.
The aims of providing accurate observable parameters are achieved through analysis of (1) near-real-time meteorological satellite data, (2) ground-based cameras (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–G, 11 p., https://doi.org/10.3133/sir20245062g.\">chapter G, this volume; Orr and others, 2024), (3) weather radar, (4) volcanic lightning detection, and (5) ground-based ash sensors and sampling. Explosive eruptions can be detected by a variety of geophysical monitoring, including infrasound (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–C, 11 p., https://doi.org/10.3133/sir20245062c.\">chapter C, this volume; Lyons and others, 2024) and seismicity (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–B, 9 p., https://doi.org/10.3133/sir20245062b.\">chapter B, this volume; Thelen and others, 2024). However, those methods cannot quantify the altitude, ash content, and dispersal dynamics of resulting volcanic clouds. Ideally, all available sources of monitoring data are synthesized to develop a coherent understanding of eruptive activity. The guidance summarized here provides a framework for characterizing volcanic clouds in the atmosphere and tracking the evolution of explosive eruption dynamics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062J","usgsCitation":"Schneider, D.J., and Van Eaton, A.R., 2024, Special topic—Eruption plumes and clouds, chap. J of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–J, 12 p., https://doi.org/10.3133/sir20245062J.","productDescription":"iii, 12 p.","numberOfPages":"12","onlineOnly":"N","ipdsId":"IP-154938","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462415,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/j/covrthbj.jpg"},{"id":462416,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/j/sir20245062j.pdf","text":"Report","size":"14 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Lahars, or debris flows that originate from a volcano (Pierson and Scott, 1985; Pierson, 1995), are among the most destructive, far-reaching, and persistent hazards on stratovolcanoes. Lahars may be triggered by syneruptive rapid melting of snow and ice, lake breakouts, or heavy rains in conjunction with large eruptive columns. Alternatively, lahars can follow eruptions, when clastic deposits are mobilized by heavy rainfall or lake breakouts, occurring sporadically for years to decades after large eruptions. Some lahars can travel many tens of kilometers in river drainages stemming from volcanoes, as during the 1980 eruption of Mount St. Helens (Washington) (for example, Janda and others, 1981), recent eruptions of Redoubt Volcano (Alaska) (fig. H1; Dorava and Meyer, 1994; Waythomas and others, 2013), and the 1991 eruption of Mount Pinatubo (Philippines) (Major and others, 1996; Pierson and others, 1996). Large lahars are less likely in the absence of eruptive activity, but still possible. The Electron Mudflow at Mount Rainier (approximately A.D. 1500), Wash., is an example of a potential noneruptive lahar, likely initiated by a spontaneous collapse of weak rock, that reached the Puget Lowland after it flowed dozens of kilometers without a recognized eruptive trigger (Sisson and Vallance, 2009).
The extreme hazard posed by lahars was demonstrated tragically by the 1985 Nevado del Ruiz (Colombia) catastrophe that claimed the lives of more than 20,000 people (Naranjo and others, 1986). The potential to provide warnings of minutes to hours in advance of lahar arrival in a populated area (for example, Voight, 1990) is a strong reason to provide special monitoring attention to the hazard. Populated river valleys are located downstream from many very high threat and high threat volcanoes, and these areas could be affected by lahars (for example, Hoblitt and others, 1998). The volume and mobility of lahars are two characteristics that can influence the extent of downstream effects (for example, George and others, 2022). The flows that reach the farthest downstream are mobile and voluminous. Additionally, entrainment of material as a lahar travels downstream may increase the volume, and a lahar that starts small may grow to a destructive size under certain conditions.
Increasingly, stratovolcanoes host recreational enthusiasts who could be affected by relatively localized geologic hazards, such as rainfall-induced debris flows, glacial outburst floods, rockfalls, and avalanches. These types of events can be common on many volcanoes, occurring seasonally in the case of debris flows and several times per year in the case of avalanches and rockfalls (for example, Allstadt and others, 2018). Many very high threat stratovolcanoes, especially within the contiguous United States, have low eruption frequencies (less than once per century), such that monitoring networks could be used more often for detection and characterization of small surface flows than for identification of volcanic unrest. Such information can be used to validate avalanche forecasts, inform rescue efforts, or notify other agencies of potentially damaged infrastructure (for example, roads, powerlines, or trails). Note that although many of these smaller surface flows create seismic and infrasound waves, the signals are typically highly distorted by the complex volcanic topography and geology. In general, the smaller the flow, the weaker the geophysical signals that it generates, and thus a denser geophysical network is required to study smaller flows (for example, Allstadt and others, 2018).
Lahar detection may not be an appropriate or necessary monitoring capability for all volcanoes. Some very high threat volcanoes, like Kīlauea and Mauna Loa, have no lahar hazards currently, and thus no detection, tracking, and characterization capabilities for lahars are needed. At other very high threat volcanoes, such as Pavlof Volcano, Alaska, lahars might be common but pose minimal threat because the volcano is so remote. Ideally, the local observatory would understand the combination of hazard and risk associated with surface flows and assign monitoring and detection capabilities appropriately. Several volcano monitoring techniques (for example, Real-Time Seismic Amplitude Measurement [RSAM], amplitude-based locations, and infrasound array processing) can be adapted to also detect, characterize, and track debris flows, lahars, and other surface flows, so instrumentation installed for detecting volcanic unrest and eruptions can have multiple purposes. The utility of instrumentation for the purpose of monitoring unrest and lahars further justifies the importance and utility of a dense network of monitoring stations, even if the volcano remains quiescent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062H","usgsCitation":"Thelen, W.A., Lyons, J.J., Iezzi, A.M., and Moran, S.C., 2024, Monitoring lahars, chap. H of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062H.","productDescription":"iii, 6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-152734","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462445,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/h/covrthbh.jpg"},{"id":462446,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/h/sir20245062h.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Volcanic unrest can trigger appreciable change to surface waters such as streams, springs, and volcanic lakes. Magma degassing produces gases and soluble salts that are absorbed into groundwater that feeds streams and lakes. As magma ascends, the amount of heat and degassing will increase, and so will any related geochemical and thermal signal. Subsurface magma movement can cause pressurization that alters hydrostatic head and may induce groundwater discharge. Fluid-pressure changes have been linked to distal volcano-tectonic earthquakes (White and McCausland, 2016; Coulon and others, 2017) and phreatic eruptions (for example, Yamaoka and others, 2016). Clearly, changes in groundwater and surface waters are both indicators of unrest and clues to how and where magma is rising toward the surface. Where possible, it is prudent to incorporate real-time hydrologic data into multiparameter monitoring of restless volcanoes. Hydrologic dynamics can also be tracked by changes in groundwater levels that are commonly measured in shallow boreholes (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume, on boreholes; Hurwitz and Lowenstern, 2024).
Although inferred to be common, relatively few volcano-hydrology anomalies are well documented, and many are essentially anecdotal (Newhall and others, 2001), reflecting the fact that high-resolution time series remain rare. Extreme examples include the 2008 eruption of Nevado del Huila, Colombia, where relatively minor phreatomagmatic eruptions were accompanied by expulsion of as much as 300 million cubic meters of groundwater from fissures high on the volcano (Worni and others, 2011), generating large lahars. Substantial decreases in flow rate from springs about 8 kilometers from the summit of Mayon Volcano, Philippines, have been noted before most eruptions in the 20th century (Newhall and others, 2001). Stream monitoring at Redoubt Volcano in 2009 allowed Werner and others (2012) to recognize that groundwater was unable to absorb (or scrub) the high flux of volcanic gas and that a high CO2/SO2 precursor signal had been evident for 5 months prior to the eruption. A key to better interpreting hydrologic anomalies—or even identifying them—is therefore obtaining adequate baseline data.
Most hydrologic monitoring at U.S. volcanoes has been accomplished by intermittent sampling surveys with annual or less frequent sampling (for example, https://hotspringchem.wr.usgs.gov/index.php). More frequent sampling, however, generally is needed to establish reliable baselines. A recent hydrologic and hydrothermal monitoring experiment at 25 sites and 10 of the 12 level 4 (very high threat) volcanoes in the U.S. portion of the Cascade Range demonstrated that there is sufficient temporal variability in hydrothermal fluxes, even during quiescent periods, that one-time measurements will commonly have limited interpretive value (Crankshaw and others, 2018). Thus, surveys are best augmented with data from streamgages (for example, Evans and others, 2004; Bergfeld and others, 2008). Streamflow (water discharge) data allow measured temperature and specific conductance to be converted to heat and solute mass fluxes, which could be insightful parameters for detecting anomalous activity (McCleskey and others, 2012). At the Yellowstone Caldera, long-term monitoring of river solutes has allowed calculation of the chloride flux, a proxy for heat discharge (Hurwitz and others, 2007; McCleskey and others, 2016) from the subsurface magma. This is readily accomplished because data from streamgages are continuously recorded and archived by the U.S. Geological Survey (USGS) National Water Information System (NWIS) (USGS, 2024).
Similar studies on stratovolcanoes or shield volcanoes would be scientifically useful, and yet are logistically challenging, requiring streamgages on numerous radial drainages complemented by either frequent manual sampling or numerous deployments of equipment to measure water temperature and specific conductance as a proxy for water chemistry. Another challenge is that some volcanic areas, especially shield volcanoes, are characterized by near-surface porous rocks and soils, such that surface streams are rare and replaced by distant, dilute large-volume springs with only a trace of any original volcanically sourced water (Manga, 2001; Hurwitz and others, 2021).
Volcanic lakes are worthy of special attention for monitoring efforts, as their temperature and composition can provide evidence of increased flux of volatile-rich fluids from below. Quantifying changes in volatile and heat release from magma can be simpler in lakes than for volcanoes with radial drainages and no major lakes. Moreover, volcanic lakes pose a range of hazards themselves, including phreatomagmatic eruptions, debris flows, flank collapse, tsunamis, and toxic gas release (Mastin and Witter, 2000; Delmelle and others, 2015; Manville, 2015; Rouwet and others, 2015)—hazards that have historically been responsible for substantial loss of life at many volcanoes worldwide (Manville, 2015). Catastrophic CO2 release at Lake Nyos, Cameroon, in 1986 suffocated about 1,750 people and about 3,500 livestock and was probably triggered by a large landslide into the gas-saturated lake (Kling and others, 1987; Evans and others, 1993). Gas-charged springs in Soda Bay within Clear Lake (California) have caused almost a dozen deaths to bathers in the past hundred years (ABC News, 2000). A 2005 example of lake overturn and abundant gas release was documented at Mount Chiginagak in Alaska (Schaefer and others, 2008) but did not result in any human casualties. Although thermally stratified lakes, which promote trapping of exsolved magmatic gas, tend to develop in tropical regions, the phenomenon can also arise where salinity creates meromixis (a condition in which a lake does not mix completely), as occurs in Mono Lake, California (Jellison and Melack, 1993; Jellison and others, 1998).
If magma erupts or flows into a lake, the interaction between hot magma and cold water can be explosive (Mastin and others, 2004; Zimanowski and others, 2015) and substantially expand the area affected by the eruption. Another hazard is the breaching of crater rims by landslides triggered by volcanic and (or) seismic activity. Under some circumstances, substantial volumes of water can be displaced, leading to large floods and lahars. Late Holocene lake flooding from Aniakchak Crater in the Alaska Peninsula (Waythomas, 2022) and from Paulina Lake in Newberry Crater, Oregon (Chitwood and Jensen, 2000), caused by the failure of outlet sills, testify to the substantial hazards at lake-filled calderas.
Several volcanic systems in the United States host lakes known to receive heat and gas from underlying magma. These lakes vary widely in area, depth, and chemical composition. Lakes are present at level 4 volcanoes, including Crater Lake and Newberry Volcano in Oregon; Yellowstone Caldera in Wyoming; Long Valley Caldera, Clear Lake volcanic field, Medicine Lake, and Salton Buttes in California; and Aniakchak Crater, Mount Katmai, Fisher Caldera, Mount Okmok, and Kaguyak Crater, among others, in Alaska. A water lake was present in Halemaʻumaʻu, the crater of Kīlauea, Hawai‘i (fig. F1), from October 2019 to December 2020. Level 3 volcanoes with lakes include Mono Lake volcanic field (Calif.), Mount Bachelor (Ore.), Ukinrek Maars and Mount Chiginagak (Alaska), and Soda Lake (Nevada). In addition, there are lakes at many levels 1 and 2 volcanoes. In the United States, there are no strongly acidic lakes that receive abundant input of magmatic gas, such as those found at Mount Ruapehu (New Zealand), Ijen and Kelud (Indonesia), and Poás (Costa Rica). Nevertheless, many contain fluids that provide clues to magmatic processes below.
Since publication of a previous report on recommended instrumentation for volcano monitoring (Moran and others, 2008), continuous hydrologic monitoring has become increasingly feasible. However, changes in water pressure, temperature, and chemistry remain, in general, poorly studied phenomena at volcanoes (Sparks, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Recent efforts by the USGS have included the temporary study of Cascade Range volcanoes, which included frequent (15 minute to hourly) temporal sampling of temperature, depth, and conductivity (Crankshaw and others, 2018; Ingebritsen and Evans, 2019). At Yellowstone Caldera, many streamgages have now added thermistors and specific conductance sensors, allowing estimation of time-dependent chloride flux as a proxy for variations in subsurface heat flux (McCleskey and others, 2012, 2016). Efforts to better understand lakes have also accelerated, with bathymetric mapping and sampling carried out at several locations in the United States. Especially thorough work was done at Yellowstone Lake thanks to the Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE, https://hdylake.org) project, funded primarily by the National Science Foundation. In addition to geophysical surveys and recovery of cores and other samples, HD-YLAKE investigations included remotely operated vehicle (ROV) investigations of hydrothermal vents on the lake floor (fig. F2). Data collected by the ROV provided a better understanding of the thermal and chemical influx from lake-bottom hydrothermal systems (Sohn and others, 2017).
In this chapter, we focus on detecting changes in the chemistry, temperature, discharge, or water levels of streams, springs, and lakes that can be caused by seismicity, volumetric strains, or increases in gas flux associated with ascending magma. There is unavoidable overlap with other chapters of this report. Samples of water and gas can also be obtained in boreholes (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. \">chapter K, this volume; Hurwitz and Lowenstern, 2024), both shallow and deep. Gas monitoring (of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062e.\">chapter E, this volume; Lewicki and others, 2024) relies in part on samples from springs and wells, particularly where measurable gas plumes are absent. Water acts as a trigger and lubricant for landslides and sediment-rich floods, and so hydrology has obvious relevance for lahar monitoring, as discussed in of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062h. \">chapter H (this volume; Thelen and others, 2024). Shared situational awareness among scientists engaged in geophysical, gas, and hydrologic monitoring will improve overall understanding of the volcanic hazard.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062F","usgsCitation":"Ingebritsen, S.E., and Hurwitz, S., 2024, Streams, springs, and volcanic lakes for volcano monitoring, chap. F of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–F, 9 p., https://doi.org/10.3133/sir20245062F.","productDescription":"iii, 9 p.","numberOfPages":"9","onlineOnly":"N","ipdsId":"IP-149695","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/covrthbf.jpg"},{"id":462450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/f/sir20245062f.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
As magma rises through the crust, decreasing pressure conditions allow volatiles to exsolve from the magma. These volatiles then migrate upward through the crust, where they can be stored at shallower levels or escape to the atmosphere. Rising magma also heats rock masses beneath volcanic centers, causing water in shallow aquifers and hydrothermal systems to boil and release additional gases and steam (see chapter F, this volume; Ingebritsen and Hurwitz, 2024). The chemistry and quantity of gases that reach the surface during periods of quiescence or volcanic unrest can reveal that gas-rich magma is ascending, crystallizing, or alternatively stalling, with important implications for volcanic hazard (for example, Sutton and others, 1992; Aiuppa and others, 2007, 2021; Werner and others, 2009, 2011, 2012; Moretti and others, 2013; de Moor and others, 2016; Lewicki and others, 2019; Edmonds and others, 2022; Kern and others, 2022; Kunrat and others, 2022).
Most volcanoes in Alaska and the western United States are characterized by weak degassing, with one or more low-temperature fumaroles (typically near the local boiling temperature of water) and connect to a deeper and sometimes extensive hydrothermal system (for example, McGee and others, 2001; Symonds and others, 2003a, b). Hydrothermal systems will affect the chemistry of rising gases exsolved from deeper magma (Symonds and others, 2001), including sulfur dioxide (SO2), hydrogen chloride (HCl), and water vapor (for example, Doukas and Gerlach, 1995; Gerlach and others, 1998, 2008; Symonds and others, 2001; Werner and others, 2013). As an example, depending on factors such as temperature, pressure, and oxidation state, rising SO2 will react with groundwater to form hydrogen sulfide (H2S) gas, dissolved sulfate (SO42−), or elemental sulfur (Christenson, 2000; Symonds and others, 2001; Werner and others, 2008). The reaction and dissolution of SO2 into shallow groundwater is commonly referred to as scrubbing, and can reduce the likelihood that ascending, degassing magma can be detected. Carbon dioxide, however, in addition to exsolving from magma early in the ascent process, is not easily removed by hydrothermal fluids (Lowenstern, 2001). As scrubbing and other processes take place, the SO2/H2S, CO2/SO2, and CO2/H2S ratios may change. High rates of SO2 emission indicate that magma has moved to relatively shallow levels in the volcano and that the system has heated up enough to establish dry pathways from depth to the surface. Monitoring multiple gas species and the total output of those species is thereby useful for volcano monitoring during both periods of quiescence, to establish background degassing conditions, and during unrest, when gas geochemistry and emission rates can provide information on changing conditions, such as magma ascent.
To provide context for multidisciplinary volcano forecasts, we focus on the following two key required capabilities: (1) characterizing baseline geochemistry and gas discharge from volcanoes and volcanic regions and (2) monitoring changes in gas geochemistry and discharge to inform forecasts of volcanic eruptions and their effects. Sufficient baseline data must be collected to identify and interpret anomalous degassing associated with volcanic unrest (for example, Sorey and others, 1998; Rouwet and others, 2014). Differences in volcano type, baseline degassing rates, local hydrology, and geography (for example, high versus low latitude) will result in a different baseline for each volcano. Volcanoes of any threat level that exhibit one or more degassing phenomena would ideally be monitored by techniques needed to establish baseline degassing data, with the sampling frequency of baseline data dictated by the threat level (table E1). Additional monitoring techniques become necessary during periods of unrest.
In general, three of the most important techniques for gas monitoring are (1) direct sampling of fumarole, spring, and soil gases for laboratory geochemical measurements, (2) measurements of the chemical composition of the volcanic plume and emission rates of major gas species (for example, H2O, CO2, SO2, and H2S) by satellite, airborne, or ground-based techniques, and (3) measurements of diffuse emissions of CO2 and other gases through soils. Various methods and instruments may be useful both for baseline studies and during unrest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245062E","usgsCitation":"Lewicki, J.L., Kern, C., Kelly, P.J., Nadeau, P.A., Elias, T., and Clor, L.E., 2024, Volcanic gas monitoring, chap. E of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062E.","productDescription":"iv, 11 p.","numberOfPages":"11","onlineOnly":"N","ipdsId":"IP-150252","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5062/e/sir20245062e.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":462451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5062/e/covrthbe.jpg"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
4230 University Drive
Anchorage, AK 99508
Climate change alters the sources and age of carbon in Arctic food webs by fostering the release of older carbon from degrading permafrost. Radiocarbon (14C) traces carbon sources and age, but data before rapid warming are rare and limit assessments over time. We capitalized on 14C data collected ~ 40 years ago that used fish as natural samplers by resampling the same species today. Among resampled fish, those using freshwater food webs had the oldest 14C ages (> 1000 yr BP), while those using marine food webs had the youngest 14C ages (near modern). One migratory species encompassed the entire range of 14C ages because juveniles fed in freshwater streams and adults fed in offshore marine habitats. Over ~ 40 yr, average 14C ages of freshwater and marine feeding fish shifted closer to atmospheric values, suggesting a potential influence from “greening of the Arctic.”
Well integrity monitoring has always been a critical component of subsurface oil and gas operations. Distributed fiber-optic sensing is an emerging technology that shows great promise for monitoring processes, both in boreholes and in other settings. In this study, we present a case study of using distributed temperature sensing (DTS) technology to monitor a cemented and plugged well in the Alaska North Slope (ANS). The well was drilled as part of a long-term gas hydrate study, and the downhole DTS data were recorded over a period of approximately 2 years. By applying a temporal gradient and removing instrument instability noise, we reveal subtle (<0.001°C/h) thermal anomalies, which are characterized by brief warming periods followed by longer cooling periods at discrete depths along the borehole. The observed coherent events show an upward trajectory from deeper formations into the overlying permafrost interval, with the thermal anomalies concentrated in relatively coarse-grained sandstone layers. We also observe that the upward migration rate of the DTS anomalies varies with formation lithology and that there is a spatial and temporal correlation between the subsurface events and measured wellhead annular pressures. We interpret that the observed warming events represent the exothermic process of gas hydrate formation that is occurring in association with the upward migration of gas outside the well casing, and this interpretation is confirmed by numerical simulations. These observations demonstrate the ability of suitably processed DTS data to detect subtle processes and highlight the value of DTS technologies for wellbore integrity monitoring.
","language":"English","publisher":"Society of Petroleum Engineers","doi":"10.2118/223111-PA","usgsCitation":"Garcia-Ceballos, A., Jin, G., Collett, T., Merey, S., and Haines, S.S., 2024, Long-term distributed temperature sensing monitoring for near-wellbore gas migration and gas hydrate formation: SPE Journal, v. 29, no. 11, p. 5804-5819, https://doi.org/10.2118/223111-PA.","productDescription":"16 p.","startPage":"5804","endPage":"5819","ipdsId":"IP-162957","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":498024,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2118/223111-pa","text":"Publisher Index Page"},{"id":464441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151,\n 71\n ],\n [\n -151,\n 70\n ],\n [\n -148,\n 70\n ],\n [\n -148,\n 71\n ],\n [\n -151,\n 71\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia-Ceballos, Ana","contributorId":333715,"corporation":false,"usgs":false,"family":"Garcia-Ceballos","given":"Ana","email":"","affiliations":[],"preferred":false,"id":914769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Ge","contributorId":333716,"corporation":false,"usgs":false,"family":"Jin","given":"Ge","email":"","affiliations":[],"preferred":false,"id":914770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collett, Timothy 0000-0002-7598-4708","orcid":"https://orcid.org/0000-0002-7598-4708","contributorId":220812,"corporation":false,"usgs":true,"family":"Collett","given":"Timothy","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":914771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merey, Sukru","contributorId":344807,"corporation":false,"usgs":false,"family":"Merey","given":"Sukru","email":"","affiliations":[{"id":82412,"text":"Batman University","active":true,"usgs":false}],"preferred":false,"id":914772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914773,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70258655,"text":"pp1814H - 2024 - Facies variation within outcrops of the Triassic Shublik Formation, northeastern Alaska","interactions":[{"subject":{"id":70258655,"text":"pp1814H - 2024 - Facies variation within outcrops of the Triassic Shublik Formation, northeastern Alaska","indexId":"pp1814H","publicationYear":"2024","noYear":false,"chapter":"H","displayTitle":"Facies Variation within Outcrops of the Triassic Shublik Formation, Northeastern Alaska","title":"Facies variation within outcrops of the Triassic Shublik Formation, northeastern Alaska"},"predicate":"IS_PART_OF","object":{"id":70158938,"text":"pp1814 - 2015 - Studies by the U.S. Geological Survey in Alaska, Volume 15","indexId":"pp1814","publicationYear":"2015","noYear":false,"title":"Studies by the U.S. Geological Survey in Alaska, Volume 15"},"id":1}],"isPartOf":{"id":70158938,"text":"pp1814 - 2015 - Studies by the U.S. Geological Survey in Alaska, Volume 15","indexId":"pp1814","publicationYear":"2015","noYear":false,"title":"Studies by the U.S. Geological Survey in Alaska, Volume 15"},"lastModifiedDate":"2025-12-23T22:24:33.940107","indexId":"pp1814H","displayToPublicDate":"2024-09-19T13:09:32","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1814","chapter":"H","displayTitle":"Facies Variation within Outcrops of the Triassic Shublik Formation, Northeastern Alaska","title":"Facies variation within outcrops of the Triassic Shublik Formation, northeastern Alaska","docAbstract":"The Shublik Formation (Middle to Upper Triassic) is a heterogeneous unit that is a major hydrocarbon source rock in northern Alaska and the largest known Triassic phosphate accumulation in the world. This formation, which occurs in the subsurface and crops out within the Arctic Alaska basin, was deposited on a gently sloping ramp along the northwestern Laurentian margin. In this study, we document spatial and temporal facies variations within the Shublik and the overlying Karen Creek Sandstone (Upper Triassic) through 19 outcrop localities in the northeastern Brooks Range. New organic and inorganic geochemical data from these localities support and extend our sedimentologic and petrographic findings. The petrographic data come from an analysis of more than 900 thin sections, whereas age constraints come mainly from species of the pelecypod genera Daonella, Halobia, Eomonotis, and Monotis. Thirteen sites make up a main outcrop belt within which facies of the Shublik are spatially consistent but show marked vertical changes. Six additional outcrops located south of the main belt contain more distal facies of the Shublik that accumulated farther from land.
Exposures of the Shublik Formation in its main outcrop belt are divisible into five informal lithologic units, each of which formed during a distinct transgressive to regressive sequence. The basal unit of the Shublik (Anisian? to Ladinian; Middle Triassic) is quartz siltstone to very fine grained sandstone that is locally phosphatic. The three middle units (Ladinian to middle Norian; Middle to Upper Triassic) contain various proportions of siliciclastic, carbonate, phosphatic, and organic material. The uppermost unit (middle to upper Norian; Upper Triassic) is mainly shale and mudstone. Highly phosphatic strata (10–34 percent phosphorus pentoxide) are chiefly Ladinian and lower to middle Norian. Highly phosphatic Ladinian strata have been transported, are granular, and formed during transgression and early regression. In contrast, highly phosphatic Norian strata include event beds and hardgrounds, contain displaced and in situ phosphate peloids and nodules, and cap regressive parasequences. The total organic content reaches 4.97 weight percent in the main belt and is highest in muddy beds, which are found mainly in the lower parts of the three middle units. Distal sections of the Shublik are typically finer grained, more organic-rich (total organic content as high as 6.33 weight percent), and less fossiliferous than those in the main belt.
Both temporal and spatial factors shaped facies in the Shublik Formation. Shublik strata accumulated at a time of reduced tectonic activity in Arctic Alaska, resulting in diminished siliciclastic input. Marine upwelling along the northwestern Laurentian margin facilitated the development of heterozoan carbonates, as well as local concentrations of phosphatic and organic matter. Large- and small-scale eustatic cycles also affected this margin and provided further controls on Shublik facies distribution.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1814H","programNote":"Studies by the U.S. Geological Survey in Alaska, Volume 15","usgsCitation":"Dumoulin, J.A., Whidden, K.J., Rouse, W.A., and DeVera, C., 2024, Facies variation within outcrops of the Triassic Shublik Formation, northeastern Alaska, in Dumoulin, J.A., ed., Studies by the U.S. Geological Survey in Alaska, v. 15: U.S. Geological Survey Professional Paper 1814-H, 49 p., https://doi.org/10.3133/pp1814H.","productDescription":"Report: vii, 49 p.; Data Release","numberOfPages":"49","onlineOnly":"Y","ipdsId":"IP-145417","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":448141,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FAGE8O","text":"USGS Data Release","description":"Dumoulin, J.A., Whidden, K.J., Rouse, W.A., and DeVera, C., 2023, Geochemical data from selected Triassic rock samples in northeastern Alaska: U.S. Geological Survey data release, https://doi.org/10.5066/P9FAGE8O.","linkHelpText":"Geochemical data from selected Triassic rock samples in northeastern Alaska"},{"id":448791,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1814/h/pp1814h.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":448790,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1814/h/covrthb.jpg"},{"id":497965,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117493.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.77052015372473,\n 68.2773239828885\n ],\n [\n -140.1396607787249,\n 68.2773239828885\n ],\n [\n -140.1396607787249,\n 71.6150463599952\n ],\n [\n -166.77052015372473,\n 71.6150463599952\n ],\n [\n -166.77052015372473,\n 68.2773239828885\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
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Alaska Mineral Resources
Alaska Science Center
The ongoing panzootic of highly pathogenic H5 clade 2.3.4.4b avian influenza (HPAI) spread to North America in late 2021, with detections of HPAI viruses in Alaska beginning in April 2022. HPAI viruses have since spread across the state, affecting many species of wild birds as well as domestic poultry and wild mammals. To better understand the dissemination of HPAI viruses spatiotemporally and among hosts in Alaska and adjacent regions, we compared the genomes of 177 confirmed HPAI viruses detected in Alaska during April – December 2022. Results suggest multiple viral introductions into Alaska between November 2021 and August or September 2022, as well as dissemination to areas within and outside of the state. Viral genotypes differed in their spatiotemporal spread, likely influenced by timing of introductions relative to population immunity. We found evidence for dissemination of HPAI viruses between wild bird species, wild birds and domestic poultry, as well as wild birds and wild mammals. Continued monitoring for and genomic characterization of HPAI viruses in Alaska can improve our understanding of the evolution and dispersal of these economically costly and ecologically relevant pathogens.
Environment and behavior are widely understood to affect bird morphology, which can lead to differences among subspecies or populations within a wide-ranging species. Several patterns of latitudinal gradients in morphology have been described, though Allen's and Bergmann's rules are the most well-known and have been tested and confirmed across a diversity of taxa and species. These state that individuals at higher latitudes will have larger bodies (Bergmann's Rule) but smaller extremities (Allen's Rule) to conserve heat in colder climates. Migratory behavior also can influence avian morphology, particularly wing shape, where migratory birds tend to have longer, more pointed wings than residents. The Black Oystercatcher (Haematopus bachmani) is a large, partially migratory shorebird species restricted to intertidal habitats and distributed from Alaska to Baja California, spanning about 35° of latitude. A large proportion of Black Oystercatchers that breed in Alaska are migratory, where nearly all individuals breeding in British Columbia through the southern end of their range remain resident through the annual cycle. Their broad latitudinal range and diversity in migratory behavior may drive geographic variation in morphology. Here we evaluate three explanations for geographic variation in morphology of the Black Oystercatcher using data from seven sites across two regions: Alaska and British Columbia. We found evidence consistent with Allen's but not Bergmann's rule; birds in Alaska have shorter bills than those in British Columbia, and these findings held when controlling for body size using wing length. Despite regional differences in migratory behavior, we detected no difference in the wing shape of birds in Alaska and British Columbia. Differences between sexes and among sites suggest that multiple factors drive patterns of morphological variation in the Black Oystercatcher.
Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database (N = 613,724), a high-resolution digital elevation model (10-m), and high-performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. We down-sample our results to a 90-m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.
Migratory shorebirds are one of the fastest declining groups of North American avifauna. Yet, relatively little is known about how these species select habitat during migration. We explored the habitat selection of Buff-breasted Sandpipers (Calidris subruficollis) during spring and fall migration through the Texas Coastal Plain, a major stopover region for this species. Using tracking data from 118 birds compiled over 4 years, we found Buff-breasted Sandpipers selected intensively managed crops such as sod and short-stature crop fields, but generally avoided rangeland and areas near trees and shrubs. This work supports prior studies that also indicate the importance of short-stature vegetation for this species. Use of sod and corn varied by season, with birds preferring sod in spring, and avoiding corn when it is tall, but selecting for corn in fall after harvest. This dependence on cropland in the Texas Coastal Plain is contrary to habitat use observed in other parts of their non-breeding range, where rangelands are used extensively. The species' almost complete reliance on a highly specialized crop, sod, at this critical stopover site raises concerns about potential exposure to contaminants as well as questions about whether current management practices are providing suitable conditions for migratory grassland birds.
Climate is the primary driver of environmental change and is a key consideration in defining science priorities conducted across all mission areas in the U.S. Geological Survey (USGS). Recognizing the importance of climate change to its future research agenda, the USGS’s Climate Science Steering Committee requested the development of a Climate Science Plan to identify future research directions. Subject matter experts from across the Bureau formed the USGS Climate Science Plan Writing Team, which convened in September 2022 to identify and outline the major climate science topics of future concern and develop an integrated approach to conducting climate science in support of the USGS and U.S. Department of the Interior missions.
The resulting USGS Climate Science Plan identifies three major priorities under which USGS climate science proceeds: (1) characterize climate change and associated impacts, (2) assess climate change risks and develop approaches to mitigate climate change, and (3) provide climate science tools and support. The Climate Science Plan identifies 12 specific goals to achieve the outcomes of the three priorities.
To achieve these goals, the USGS Climate Science Plan also outlines climate science guidelines—key elements for conducting climate-based research—as well as emerging opportunities to support successful climate science. The USGS Climate Science Plan provided in this circular will guide future research priorities and science-support investments, as well as continued development of the climate workforce for decades to come, ensuring that the USGS continues to serve as one of the Nation’s leading climate science agencies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1526","usgsCitation":"Wilson, T., Boyles, R.P., DeCrappeo, N., Drexler, J.Z., Kroeger, K.D., Loehman, R.A., Pearce, J.M., Waldrop, M.P., Warwick, P.D., Wein, A.M., Zeigler, S.L., and Beard, T.D., Jr., 2024, U.S. Geological Survey climate science plan—Future research directions: U.S. Geological Survey Circular 1526, 30 p., https://doi.org/10.3133/cir1526.","productDescription":"iv, 30 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163273","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true},{"id":49226,"text":"Northwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":434762,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1526/images/"},{"id":434791,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1526/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1526 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U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 516
Reston, VA 20192
Email: casc@usgs.gov
Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.
","conferenceTitle":"2024 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 7-12, 2024","conferenceLocation":"Athens, Greece","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS53475.2024.10642436","usgsCitation":"Ravindra, V., Caldwell, D., Chandarana Saephan, M., Duncan, B., Strode, S., Swartz, W., Manies, K.L., Frank, J., Levinson, R., and Turkov, E., 2024, Science target prioritization framework for remote sensing, 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, July 7-12, 2024, p. 689-693, https://doi.org/10.1109/IGARSS53475.2024.10642436.","productDescription":"5 p.","startPage":"689","endPage":"693","ipdsId":"IP-166703","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":501742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ravindra, Vinay","contributorId":340220,"corporation":false,"usgs":false,"family":"Ravindra","given":"Vinay","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell, Douglas","contributorId":340222,"corporation":false,"usgs":false,"family":"Caldwell","given":"Douglas","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandarana Saephan, Meghan","contributorId":340224,"corporation":false,"usgs":false,"family":"Chandarana Saephan","given":"Meghan","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duncan, Bryan","contributorId":340226,"corporation":false,"usgs":false,"family":"Duncan","given":"Bryan","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":906634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strode, Sarah A.","contributorId":151018,"corporation":false,"usgs":false,"family":"Strode","given":"Sarah A.","affiliations":[{"id":17844,"text":"University of Washington, Seattle, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":906635,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Swartz, William","contributorId":340228,"corporation":false,"usgs":false,"family":"Swartz","given":"William","affiliations":[{"id":81510,"text":"John Hopkins University Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":906636,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":906637,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frank, Jeremy","contributorId":340229,"corporation":false,"usgs":false,"family":"Frank","given":"Jeremy","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906638,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Levinson, Richard","contributorId":340230,"corporation":false,"usgs":false,"family":"Levinson","given":"Richard","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906639,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Turkov, Eugene","contributorId":340231,"corporation":false,"usgs":false,"family":"Turkov","given":"Eugene","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":906640,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70257580,"text":"pp1888 - 2024 - New U-Pb geochronology and geochemistry of Paleozoic metaigneous rocks from western Yukon and eastern Alaska, cross-border synthesis, and implications for tectonic models","interactions":[],"lastModifiedDate":"2025-08-15T16:38:04.454212","indexId":"pp1888","displayToPublicDate":"2024-09-04T09:21:34","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1888","displayTitle":"New U-Pb Geochronology and Geochemistry of Paleozoic Metaigneous Rocks from Western Yukon and Eastern Alaska, Cross-Border Synthesis, and Implications for Tectonic Models","title":"New U-Pb geochronology and geochemistry of Paleozoic metaigneous rocks from western Yukon and eastern Alaska, cross-border synthesis, and implications for tectonic models","docAbstract":"The tectonic evolution of and relation between the Yukon-Tanana terrane and the Lake George assemblage, as well as other associated tectonic assemblages in western Yukon and eastern Alaska, have been debated for decades. The Yukon-Tanana terrane is widely considered to be an allochthonous rifted fragment derived from the Laurentian continental margin, whereas the Lake George assemblage and associated assemblages are currently interpreted to be part of the parautochthonous continental margin of western North America (Laurentia). To address these topics, we present 40 new U-Pb zircon ages and 20 new whole-rock geochemical analyses. We incorporate these data into a new compilation of available geological mapping for a large area that straddles the Alaska-Yukon border, together with 34 previously published U-Pb age determinations and an extensive geochemical database of metaigneous rocks from Late Devonian to Early Mississippian and middle to late Permian assemblages in this area.
Magmatism in the Lake George assemblage and related assemblages occurred in two pulses from about 371 to 360 and from about 358 to 347 million years ago (Ma); geochemical discrimination diagrams indicate a large crustal component, possibly indicative of arc magmatism, for felsic metaigneous rocks and a range of tectonic environments for mafic rocks. Magmatism in the Fortymile River and related assemblages, and parts of the Nasina assemblage—all parts of the Yukon-Tanana terrane—are mainly Early Mississippian and span a crystallization age range from about 361 to 343 Ma; geochemical discrimination diagrams for these rocks indicate primarily arc geochemical signatures for both mafic and felsic rocks. Middle to late Permian crystallization ages (about 261–253 Ma) are indicated for felsic metaigneous rocks in the Klondike assemblage and some of the felsic metaigneous rocks in the Nasina assemblage. Based on our mapping, we propose the existence of a possible unconformity between the Mississippian and Permian felsic metavolcanic rocks within the Nasina assemblage that is marked by sporadic occurrences of stretched-pebble conglomerate.
Our combined database supports the well-established model of a magmatic arc comprising the Fortymile River and Finlayson assemblages of the rifted Yukon-Tanana terrane continental fragment on which a middle to late Permian arc (Klondike assemblage) was later built. The assemblages of the Yukon-Tanana terrane were subsequently intruded by Late Triassic to Early Jurassic granitoids, presumably during reaccretion of the Yukon-Tanana terrane to the continental margin. Permian and Late Triassic to Early Jurassic intrusions have not been mapped in the now structurally lower plate Lake George assemblage; their absence is one of the lines of evidence that have been used to support the parautochthonous, rather than allochthonous, origin of the Lake George assemblage and related assemblages. Our new data, together with previously published ranges of igneous crystallization ages and geochemical tectonic signatures of the Late Devonian to Early Mississippian magmatic rocks in the Lake George assemblage and associated assemblages and in the Fortymile River, Nasina, and correlated assemblages of the Yukon-Tanana terrane, indicate that the currently accepted interpretation of the Lake George assemblage and associated rocks being part of parauthochthonous North America is not the only possible interpretation of this tectonic entity. Approximately half of the dated intrusive rocks in the Lake George assemblage are contemporaneous with the metaigneous rocks of the Yukon-Tanana terrane arc (<361 Ma). We speculate that our approximately 361 Ma U-Pb age for quartz syenite in part of the North American continental margin in south-central Yukon defines the beginning of rifting of the Laurentian margin. Although the currently favored model of prolonged middle Paleozoic subduction and extension in both the Yukon-Tanana terrane and parautochthonous North America allows for simultaneous middle Paleozoic magmatism on both sides of the Slide Mountain Ocean, we now propose an alternative hypothesis in which the Lake George assemblage represents a deeper part of the rifted Yukon-Tanana terrane arc. If this is the case, the absence of Permian and Late Triassic to Early Jurassic arc rocks in the Lake George assemblage could be explained either by the arcs of these ages not being wide enough to have affected the Lake George assemblage or by tectonic displacement of these arc rocks away from the Lake George assemblage.
Our approximately 259 Ma U-Pb zircon age and geochemical analyses of metarhyolite in the Seventymile terrane in Alaska, which comprises remnants of the back-arc basin that separated the Yukon-Tanana terrane from the Laurentian continental margin, confirm the presence of a late middle Permian volcanic arc component to the terrane. Our approximately 319 Ma U-Pb zircon age from the Chicken assemblage (as redefined in this study) in eastern Alaska, combined with previously reported fossil ages and a U-Pb zircon age from this assemblage, indicate that it is a Late Mississippian to Early Pennsylvanian arc assemblage. We propose several other relatively young, locally developed arc assemblages outboard of the ancient continental margin of Laurentia that may correlate with the Chicken assemblage, but we consider its origin to remain an enigma.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1888","usgsCitation":"Dusel-Bacon, C., and Mortensen, J.K., 2024, New U-Pb geochronology and geochemistry of Paleozoic metaigneous rocks from western Yukon and eastern Alaska, cross-border synthesis, and implications for tectonic models (ver. 1.1, December 2024): U.S. Geological Survey Professional Paper 1888, 100 p., https://doi.org/10.3133/pp1888.","productDescription":"Report: vi, 100 p.; Data Release","numberOfPages":"100","onlineOnly":"Y","ipdsId":"IP-120238","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494234,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117305.htm","linkFileType":{"id":5,"text":"html"}},{"id":432900,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/pp/1888/images"},{"id":432899,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/pp/1888/pp1888.xml"},{"id":432898,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1888/pp1888.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432896,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93ZWGA1","text":"USGS Data Release","description":"Dusel-Bacon, C., and Mortensen, J.K., 2023, New U-Pb geochronology and geochemistry of Paleozoic metaigneous rocks from western Yukon and eastern Alaska: U.S. Geological Survey data release, https://doi.org/10.5066/P93ZWGA1.","linkHelpText":"New U-Pb geochronology and geochemistry of Paleozoic metaigneous rocks from western Yukon and eastern Alaska"},{"id":432901,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/pp1888/full"},{"id":432897,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1888/covrthb.jpg"},{"id":465115,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/1888/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"Canada, United States","state":"Alaska","otherGeospatial":"Yukon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -165.8250144876443,\n 71.34744883614135\n ],\n [\n -165.8250144876443,\n 53.1023500477161\n ],\n [\n -121.35235823764475,\n 53.1023500477161\n ],\n [\n -121.35235823764475,\n 71.34744883614135\n ],\n [\n -165.8250144876443,\n 71.34744883614135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"ver. 1.0: September 4, 2024; ver. 1.1: December 16, 2024","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
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Two contrasting age models for initial mountain building in the northeastern (NE) Tibetan Plateau (Paleocene-early Eocene versus late Oligocene-early Miocene) have led to the debate on how the deformed continental lithosphere absorbs plate convergence in general. The initial compressional deformation in the West Qinling (WQL) of the NE Tibetan Plateau figures prominently in this ongoing debate. Here, apatite (U-Th)/He (AHe) thermochronology combined with geomorphological analysis are used to refine the onset of compressional deformation in the WQL. New AHe ages from two vertical transects and an updated reconstruction of an obliquely-tilted erosion surface document the accelerated exhumation in the northern WQL at 23-22 Ma, interpreted as the onset of north-vergent thrusting. The AHe results, together with sedimentary records in the intermontane and foreland basins, suggest that the entire WQL began experiencing compressional deformation in the late Oligocene-early Miocene. When integrated with previous studies, our findings show that the northern plateau boundary has not remained stationary since the collision, but has instead experienced ∼750 km of outward expansion during the late Oligocene to middle Miocene. This phase of rapid plateau growth is coeval with the ∼30–50 % reduction of the India-Eurasia convergence rate, which suggests that the increased gravitational potential energy of orogenic belts played a key role in plate motion changes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2024.118966","usgsCitation":"Li, C., Zheng, D., Yu, J., Lease, R.O., Wang, Y., Pang, J., Wang, Y., Hao, Y., and Xu, Y., 2024, Revised timing of rapid exhumation in the West Qinling: Implications for geodynamics of Oligocene-Miocene Tibetan plateau outward expansion: Earth and Planetary Science Letters, v. 646, 118966, 9 p., https://doi.org/10.1016/j.epsl.2024.118966.","productDescription":"118966, 9 p.","ipdsId":"IP-165148","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":485826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Tibetan plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 101.5,\n 36\n ],\n [\n 101.5,\n 35\n ],\n [\n 104,\n 35\n ],\n [\n 104,\n 36\n ],\n [\n 101.5,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"646","noUsgsAuthors":false,"publicationDate":"2024-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Chaopeng","contributorId":355149,"corporation":false,"usgs":false,"family":"Li","given":"Chaopeng","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zheng, Dewen","contributorId":355150,"corporation":false,"usgs":false,"family":"Zheng","given":"Dewen","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yu, Jingxing","contributorId":355151,"corporation":false,"usgs":false,"family":"Yu","given":"Jingxing","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936939,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lease, Richard O. 0000-0003-2582-8966 rlease@usgs.gov","orcid":"https://orcid.org/0000-0003-2582-8966","contributorId":5098,"corporation":false,"usgs":true,"family":"Lease","given":"Richard","email":"rlease@usgs.gov","middleInitial":"O.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":936940,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Yizhou","contributorId":355152,"corporation":false,"usgs":false,"family":"Wang","given":"Yizhou","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936941,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pang, Jianzhang","contributorId":355153,"corporation":false,"usgs":false,"family":"Pang","given":"Jianzhang","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Ying","contributorId":355154,"corporation":false,"usgs":false,"family":"Wang","given":"Ying","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936943,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hao, Yuqi","contributorId":355155,"corporation":false,"usgs":false,"family":"Hao","given":"Yuqi","affiliations":[{"id":84718,"text":"Institute of Geology, China Earthquake Administration","active":true,"usgs":false}],"preferred":false,"id":936944,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Xu, Yigang","contributorId":355156,"corporation":false,"usgs":false,"family":"Xu","given":"Yigang","affiliations":[{"id":84719,"text":"Guangzhou Institute of Geochemistry, Chinese Academy of Science","active":true,"usgs":false}],"preferred":false,"id":936945,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70259258,"text":"70259258 - 2024 - From field station to forecast: Managing data at the Alaska Volcano Observatory","interactions":[],"lastModifiedDate":"2024-10-02T14:06:15.606438","indexId":"70259258","displayToPublicDate":"2024-08-28T08:56:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"From field station to forecast: Managing data at the Alaska Volcano Observatory","docAbstract":"The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-024-01766-0","usgsCitation":"Coombs, M.L., Cameron, C., Dietterich, H., Boyce, E., Wech, A., Grapenthin, R., Wallace, K.L., Parker, T., Lopez, T., Crass, S., Fee, D., Haney, M.M., Ketner, D.M., Loewen, M.W., Lyons, J.J., Nakai, J.S., Power, J., Botnick, S.M., Brewster, I., Enders, M.L., Harmon, D., Kelly, P.J., and Randall, M., 2024, From field station to forecast: Managing data at the Alaska Volcano Observatory: Bulletin of Volcanology, v. 86, 79, 22 p., https://doi.org/10.1007/s00445-024-01766-0.","productDescription":"79, 22 p.","ipdsId":"IP-163867","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": 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Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":914590,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914591,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Randall, Michael J. 0000-0001-7750-9612","orcid":"https://orcid.org/0000-0001-7750-9612","contributorId":44819,"corporation":false,"usgs":true,"family":"Randall","given":"Michael J.","affiliations":[],"preferred":false,"id":914592,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70257818,"text":"70257818 - 2024 - Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","interactions":[],"lastModifiedDate":"2024-09-11T16:26:52.60017","indexId":"70257818","displayToPublicDate":"2024-08-27T07:07:37","publicationYear":"2024","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":"Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","docAbstract":"Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real‐time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P‐ and S‐wave arrival‐time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey‐published 1D P‐wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.