Precise point positioning (PPP) of ships using Global Navigation Satellite System (GNSS) data reveals the precise movements of marine vessels. This method may quantify anomalies in sea surface height with implications for oceanographic monitoring, exploration, and tsunami warning. The GNSS PPP data from the R/V Sikuliaq, a research ship of the University of Alaska Fairbanks, were processed to detect a small local tsunami generated by the Lowell Point landslide, which occurred near Seward, Alaska, on 8 May 2022 (UTC). The GNSS receiver aboard the R/V Sikuliaq recorded the waves generated by the landslide, with a maximum wave amplitude of 6 cm and wave periods between 40 and 50 s. These results are consistent with simulations of the landslide event.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112472","usgsCitation":"Manaster, A., Sheehan, A.F., Goldberg, D.E., Barnhart, K.R., and Roth, E., 2025, Detection of landslide-generated tsunami by shipborne GNSS precise point positioning: Geophysical Research Letters, v. 52, e2024GL112472, 9 p., https://doi.org/10.1029/2024GL112472.","productDescription":"e2024GL112472, 9 p.","ipdsId":"IP-169782","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487831,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112472","text":"Publisher Index Page"},{"id":485128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Seward","otherGeospatial":"Resurrection Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.60008399844827,\n 60.18626730817803\n ],\n [\n -149.60008399844827,\n 59.864306105004715\n ],\n [\n -149.1691851387203,\n 59.864306105004715\n ],\n [\n -149.1691851387203,\n 60.18626730817803\n ],\n [\n -149.60008399844827,\n 60.18626730817803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Manaster, Adam E. 0000-0001-8183-4274","orcid":"https://orcid.org/0000-0001-8183-4274","contributorId":215663,"corporation":false,"usgs":true,"family":"Manaster","given":"Adam E.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheehan, Anne F 0000-0002-9629-1687","orcid":"https://orcid.org/0000-0002-9629-1687","contributorId":224234,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","email":"","middleInitial":"F","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":934769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldberg, Dara Elyse 0000-0002-0923-3180","orcid":"https://orcid.org/0000-0002-0923-3180","contributorId":289891,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dara","email":"","middleInitial":"Elyse","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":934770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":934771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Ethan F.","contributorId":353914,"corporation":false,"usgs":false,"family":"Roth","given":"Ethan F.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":934772,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273501,"text":"70273501 - 2025 - Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery","interactions":[],"lastModifiedDate":"2026-01-20T15:43:08.696041","indexId":"70273501","displayToPublicDate":"2025-04-25T08:38:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery","docAbstract":"Mapping the land area used for mining in the past is essential for guiding the remediation of affected landscapes and assessing the resource potential of related waste products. Despite significant recent progress delineating footprints of active and inactive mining globally, the known inventory of such mine lands remains incomplete. Here, I describe a new map dataset of footprints of land surface disturbance and waste at sites of past mining in Alaska (USA) based on visual interpretation of satellite imagery. This dataset maps 6–14 times the area of previous regional and global mine footprint maps in Alaska and is the first in the region to explicitly delineate mine waste landforms (e.g., tailings piles). The data are publicly available from the U.S. Geological Survey under a “no rights reserved” Creative Commons (CC0) license agreement.
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Adrian 0000-0001-7469-1957","orcid":"https://orcid.org/0000-0001-7469-1957","contributorId":219952,"corporation":false,"usgs":true,"family":"Bender","given":"Adrian","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":954030,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70266282,"text":"70266282 - 2025 - Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","interactions":[],"lastModifiedDate":"2025-05-02T14:54:35.492386","indexId":"70266282","displayToPublicDate":"2025-04-24T09:53:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","docAbstract":"Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-19-1675-2025","usgsCitation":"Aberle, R., Enderlin, E., O'Neel, S., Florentine, C., Sass, L., Dickson, A., Marshall, H., and Flores, A., 2025, Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning: The Cryosphere, v. 19, p. 1675-1693, https://doi.org/10.5194/tc-19-1675-2025.","productDescription":"19 p.","startPage":"1675","endPage":"1693","ipdsId":"IP-161789","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-19-1675-2025","text":"Publisher Index Page"},{"id":485326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Unite States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.76360091465392,\n 47.06180837633883\n ],\n [\n -121.3176201884703,\n 48.805343460206615\n ],\n [\n -120.0015482436147,\n 50.335300241584264\n ],\n [\n -130.45623413084917,\n 62.431155673423405\n ],\n [\n -144.1767604606605,\n 64.77803371822662\n ],\n [\n -156.74065293077143,\n 62.94965967944643\n ],\n [\n -153.58716512013862,\n 58.34813914012679\n ],\n [\n -151.25294354192766,\n 56.670611414741046\n ],\n [\n -147.2303378681512,\n 59.785680900702204\n ],\n [\n -139.72127967128716,\n 59.315677918279675\n ],\n [\n -134.82069406634898,\n 55.48786621236388\n ],\n [\n -131.59648302534626,\n 51.752911462025\n ],\n [\n -123.76360091465392,\n 47.06180837633883\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Aberle, Rainey","contributorId":354302,"corporation":false,"usgs":false,"family":"Aberle","given":"Rainey","affiliations":[],"preferred":false,"id":935376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":935377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":935378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickson, Adam","contributorId":354305,"corporation":false,"usgs":false,"family":"Dickson","given":"Adam","affiliations":[],"preferred":false,"id":935383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Hans-Peter","contributorId":330964,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":935379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":935382,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266843,"text":"70266843 - 2025 - Does the Lost Jim lava flow (Alaska) really preserve evidence of interaction with permafrost?","interactions":[],"lastModifiedDate":"2025-05-13T16:35:06.894023","indexId":"70266843","displayToPublicDate":"2025-04-16T09:29:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Does the Lost Jim lava flow (Alaska) really preserve evidence of interaction with permafrost?","docAbstract":"The basaltic Lost Jim lava flow, the youngest member of the Imuruk Lake volcanic field, Alaska, is reported to have interacted with underlying permafrost by thawing it and forming cavities into which the lava flow collapsed, forming pits and other depressions on the lava flow's surface. Our field observations contradict this hypothesis. The Lost Jim lava flow exhibits surface features typical of an inflated pāhoehoe flow, and we propose instead that most of the pits are unambiguously the result of flow inflation (i.e., lava-rise pits). These pits are found on elevated, relatively level surfaces, and their inner walls preserve features like rotated surface slabs and fine-scale flow banding on exposed crack surfaces, both of which are hallmarks of lava flow inflation. While collapse pits do exist on the Lost Jim lava flow, they are morphologically distinct and formed by crustal failure into drained lava tubes.
Satellite images of the Lost Jim lava flow show similarities in the size and distribution of pits within other young pāhoehoe lava flows scattered across the globe. The small diameter of many of the pits (<10 m), compared to flow thickness (≥10 m), also argues against collapse—numerical modeling shows that the relatively high tensile strength of a coherent lava flow would have prevented its collapse into cavities similar in diameter to the lava flow's thickness. Finally, the pits are found scattered across the Lost Jim lava flow, including in locations where the lava flow rests directly on bedrock, which consists of older lava flows. Segregated ice lenses and soil expansion—necessary components for thermokarst formation when thawed—do not exist in such locations. Altogether, these factors show that the Lost Jim lava flow is an inflated lava flow, and permafrost played no significant role during or after its emplacement.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108347","usgsCitation":"Orr, T., Coombs, W., Rader, E., and Larsen, J., 2025, Does the Lost Jim lava flow (Alaska) really preserve evidence of interaction with permafrost?: Journal of Volcanology and Geothermal Research, v. 464, 108347, 12 p., https://doi.org/10.1016/j.jvolgeores.2025.108347.","productDescription":"108347, 12 p.","ipdsId":"IP-156249","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488269,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2025.108347","text":"Publisher Index Page"},{"id":485838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lost Jim lava flow","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -163.99392503271278,\n 65.97766449153795\n ],\n [\n -163.99392503271278,\n 65.7063086880865\n ],\n [\n -162.3815948765932,\n 65.7063086880865\n ],\n [\n -162.3815948765932,\n 65.97766449153795\n ],\n [\n -163.99392503271278,\n 65.97766449153795\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"464","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Orr, Tim R. 0000-0003-1157-7588","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":26365,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":936886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coombs, William M. 0000-0003-2099-1676","orcid":"https://orcid.org/0000-0003-2099-1676","contributorId":355121,"corporation":false,"usgs":false,"family":"Coombs","given":"William M.","affiliations":[{"id":35079,"text":"Durham University, Durham, UK","active":true,"usgs":false}],"preferred":false,"id":936887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rader, Erika 0000-0001-8205-3461","orcid":"https://orcid.org/0000-0001-8205-3461","contributorId":331813,"corporation":false,"usgs":false,"family":"Rader","given":"Erika","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":936888,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larsen, Jessica 0000-0003-1171-129X","orcid":"https://orcid.org/0000-0003-1171-129X","contributorId":242808,"corporation":false,"usgs":false,"family":"Larsen","given":"Jessica","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":936889,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268972,"text":"70268972 - 2025 - Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska","interactions":[],"lastModifiedDate":"2025-07-11T13:56:07.175736","indexId":"70268972","displayToPublicDate":"2025-04-16T08:51:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska","docAbstract":"The 2018–2023 phreatomagmatic eruptions at Semisopochnoi Island, Alaska produced abundant long-period (LP) seismicity, harmonic and broadband tremor, and explosion signals over several well-monitored periods of eruption and quiescence. The corresponding dataset provides an excellent opportunity to investigate precursory and syn-eruptive geophysical signals of long-lived phreatomagmatic eruptions using multiparameter observations. We generated explosion and LP event catalogs through novel implementations of the REDPy (Hotovec-Ellis, 2024) repeating event detector in mid-2021 following a network upgrade and the onset of a new phase of the eruption. The hundreds of detected explosions show a high degree of infrasound waveform similarity over more than a year, indicating a repeating source mechanism likely associated with explosive magma-water interaction. The seismic LP catalog shows that events began over a month prior to renewed explosive activity at the beginning of August 2021, and that lower frequency index (FI) LPs were generated in the week prior to the onset of explosions. We applied a recently developed machine learning tool (VOISS-Net, Tan et al., 2024) to catalog abundant broadband and harmonic seismic tremor recorded before and during the renewed explosive activity, along with LPs and explosions. The tremor catalogs complement the LP and explosion catalogs by filling out the seismic sequence with the dominant signal types. Together, these catalogs reveal a seismic sequence of renewed unrest that started with several weeks of LP events, followed by LPs with lower FI values and harmonic tremor in the days prior to explosive activity, and finally the onset of discrete explosions and broadband eruption tremor. We interpret this sequence as the ascent of a new pulse of magma that first interacted with the hydrothermal/groundwater system to produce LPs, followed by harmonic tremor, and that ultimately drove explosive magma-water interactions and periods of continuous ash emissions. The 2021 seismic sequence, in combination with long-term records of satellite SO2 emissions, deformation from interferometric synthetic aperture radar (InSAR) analysis, ash sample analysis, infrasound, and volcano tectonic seismicity, allows us to interpret the entire 9-year period of unrest and eruption that began with an intrusion and earthquake swarm in 2014.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2025.108329","usgsCitation":"Lyons, J.J., Tan, D., Angarita, M., Loewen, M.W., Lopez, T., Grapenthin, R., Hotovec-Ellis, A.J., Fee, D., and Haney, M.M., 2025, Identifying precursors and tracking pulses of magma ascent in multidisciplinary data during the 2018–2023 phreatomagmatic eruption at Semisopochnoi Island, Alaska: Journal of Volcanology and Geothermal Research, v. 463, 108329, 20 p., https://doi.org/10.1016/j.jvolgeores.2025.108329.","productDescription":"108329, 20 p.","ipdsId":"IP-176345","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":492468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2025.108329","text":"Publisher Index Page"},{"id":492125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Semisopochnoi Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 179.45548299275004,\n 52.040459864240546\n ],\n [\n 179.45548299275004,\n 51.86149191597676\n ],\n [\n 179.7966363352781,\n 51.86149191597676\n ],\n [\n 179.7966363352781,\n 52.040459864240546\n ],\n [\n 179.45548299275004,\n 52.040459864240546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"463","noUsgsAuthors":false,"publicationDate":"2025-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards 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0000-0002-5621-285X","orcid":"https://orcid.org/0000-0002-5621-285X","contributorId":213321,"corporation":false,"usgs":true,"family":"Loewen","given":"Matthew","email":"","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942756,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez, Taryn","contributorId":237830,"corporation":false,"usgs":false,"family":"Lopez","given":"Taryn","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":942757,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grapenthin, Ronni","contributorId":257035,"corporation":false,"usgs":false,"family":"Grapenthin","given":"Ronni","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":942758,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hotovec-Ellis, Alicia J. 0000-0003-1917-0205","orcid":"https://orcid.org/0000-0003-1917-0205","contributorId":211785,"corporation":false,"usgs":true,"family":"Hotovec-Ellis","given":"Alicia","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942759,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":942760,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942761,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70266250,"text":"70266250 - 2025 - Detection of Giardia and Cryptosporidium in surface water of a subarctic city","interactions":[],"lastModifiedDate":"2025-05-02T14:34:36.525786","indexId":"70266250","displayToPublicDate":"2025-04-11T09:31:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16699,"text":"Food and Waterborne Parasitology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Detection of Giardia and Cryptosporidium in surface water of a subarctic city","title":"Detection of Giardia and Cryptosporidium in surface water of a subarctic city","docAbstract":"Giardia and Cryptosporidium spp. are globally distributed protozoan parasites that can cause gastrointestinal disease in humans and animals. These zoonotic parasites and their ecological relationships have been understudied in Alaska and elsewhere, despite being identified as priority zoonotic pathogens. We aimed to detect and characterize Giardia and Cryptosporidium spp. in waterbodies within Anchorage, Alaska, USA using two methods, including the Environmental Protection Agency (EPA) Method 1623 that relies on microscopy and a molecular detection approach. The molecular approach was ultimately unsuccessful and therefore only data obtained using Method 1623 are presented. Giardia or Cryptosporidium spp. was detected from nine of 15 urban streams and lakes sampled (60%), six of which were positive for both parasites (40%). Fewer than 10 cysts or oocysts were detected in 10 L of surface water. Further research to characterize Giardia and Cryptosporidium beyond the genus level would help elucidate the zoonotic potential and ecology of these parasites within the region and more broadly in Alaska.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fawpar.2025.e00262","usgsCitation":"Ahlstrom, C., Carey, M.P., Menning, D.M., O’Donnell, J.A., and Ramey, A.M., 2025, Detection of Giardia and Cryptosporidium in surface water of a subarctic city: Food and Waterborne Parasitology, v. 39, e00262, 6 p., https://doi.org/10.1016/j.fawpar.2025.e00262.","productDescription":"e00262, 6 p.","ipdsId":"IP-173920","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":487921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fawpar.2025.e00262","text":"Publisher Index Page"},{"id":485323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.71809212582164,\n 61.26288495513296\n ],\n [\n -150.13097003492274,\n 61.26288495513296\n ],\n [\n -150.13097003492274,\n 61.05787266884491\n ],\n [\n -149.71809212582164,\n 61.05787266884491\n ],\n [\n -149.71809212582164,\n 61.26288495513296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2025-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":935084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":935085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Menning, Damian M. 0000-0003-3547-3062 dmenning@usgs.gov","orcid":"https://orcid.org/0000-0003-3547-3062","contributorId":205131,"corporation":false,"usgs":true,"family":"Menning","given":"Damian","email":"dmenning@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":935086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Jonathan A. 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":191423,"corporation":false,"usgs":false,"family":"O’Donnell","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":935087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","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":935088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265768,"text":"70265768 - 2025 - Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus)","interactions":[],"lastModifiedDate":"2025-04-15T14:34:50.502337","indexId":"70265768","displayToPublicDate":"2025-04-11T09:30:32","publicationYear":"2025","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}},"displayTitle":"Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus)","title":"Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus)","docAbstract":"The management and conservation of large mammals, such as black bears (Ursus americanus), have long been informed by genetic estimates of population size and individual dispersal. Amplicon sequencing methods, also known as ‘genotyping-in-thousands-by sequencing’ (GT-seq), now enable the efficient and cost-effective genotyping of hundreds of loci and individuals in the same sequencing run. Here, we develop a GT-seq panel for individual identification and kinship inference in Alaska black bears. Using genomic data from restriction site-associated DNA sequencing of hunter-harvested bears from Southcentral Alaska (n = 85), we identified 170 microhaplotype and single nucleotide polymorphism (SNP) loci that were highly heterozygous in local populations. To enable sexing of individuals, we also included a previously published sex-linked locus in the GT-seq panel. We empirically validated the GT-seq panel using samples collected at different spatial scales. These samples included tissues (n = 82) obtained from bears within a small geographic area in Anchorage, Alaska, which were likely to be relatives as well as the hunter-harvested samples collected from geographically widespread locations throughout Southcentral Alaska. Empirical validation indicated high genotyping success and genotype reproducibility across replicate subsamples. Computer simulations demonstrated that the GT-seq panel had ample statistical power for distinguishing distinct individuals and first-order relatives (parent-offspring and full-sibling pairs) from unrelated individuals. As a final proof of concept, the panel was used to identify individual bears and close kin sampled from urban and wild habitats in Anchorage, Alaska. We anticipate that the GT-seq panel will be a useful genomic resource for the monitoring and management of Alaska black bear populations.ons.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71273","usgsCitation":"Petrou, E., Brandt, C., Spivey, T., Gruenthal, K., Mckeeman, C.M., Farley, S.D., Battle, D., Stantorf, C., and Ramey, A.M., 2025, Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus): Ecology and Evolution, v. 15, no. 4, e71273, 13 p., https://doi.org/10.1002/ece3.71273.","productDescription":"e71273, 13 p.","ipdsId":"IP-173133","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488245,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71273","text":"Publisher Index Page"},{"id":484575,"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\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.37908579144883,\n 60.1880549532429\n ],\n [\n -145.33789769841525,\n 61.28084387847426\n ],\n [\n -147.5603688778254,\n 61.761383856172614\n ],\n [\n -150.36520049953535,\n 61.47257901289217\n ],\n [\n -151.5611076733203,\n 60.44966814671875\n ],\n [\n -152.16087869317707,\n 59.10929453238546\n ],\n [\n -150.57556260378138,\n 59.05220379442184\n ],\n [\n -144.37908579144883,\n 60.1880549532429\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Petrou, Eleni Leto 0000-0001-7811-9288","orcid":"https://orcid.org/0000-0001-7811-9288","contributorId":334653,"corporation":false,"usgs":true,"family":"Petrou","given":"Eleni Leto","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":933480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, Colette D.","contributorId":353400,"corporation":false,"usgs":false,"family":"Brandt","given":"Colette D.","affiliations":[{"id":84395,"text":"Department of the Air Force","active":true,"usgs":false}],"preferred":false,"id":933481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spivey, Timothy J.","contributorId":353401,"corporation":false,"usgs":false,"family":"Spivey","given":"Timothy J.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gruenthal, Kristen M.","contributorId":353402,"corporation":false,"usgs":false,"family":"Gruenthal","given":"Kristen M.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mckeeman, Cherie Marie 0000-0001-9868-2502","orcid":"https://orcid.org/0000-0001-9868-2502","contributorId":334651,"corporation":false,"usgs":true,"family":"Mckeeman","given":"Cherie","email":"","middleInitial":"Marie","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":933484,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Farley, Sean D.","contributorId":340801,"corporation":false,"usgs":false,"family":"Farley","given":"Sean","email":"","middleInitial":"D.","affiliations":[{"id":81667,"text":"Alaska Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":933485,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Battle, David","contributorId":353403,"corporation":false,"usgs":false,"family":"Battle","given":"David","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933486,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stantorf, Cory","contributorId":353404,"corporation":false,"usgs":false,"family":"Stantorf","given":"Cory","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":933487,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":933488,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70265494,"text":"70265494 - 2025 - Equilibrium line altitudes, accumulation areas, and the vulnerability of glaciers in Alaska","interactions":[],"lastModifiedDate":"2025-04-08T15:11:34.579076","indexId":"70265494","displayToPublicDate":"2025-04-07T08:01:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2328,"text":"Journal of Glaciology","active":true,"publicationSubtype":{"id":10}},"title":"Equilibrium line altitudes, accumulation areas, and the vulnerability of glaciers in Alaska","docAbstract":"The accumulation area ratio (AAR) of a glacier reflects its current state of equilibrium, or disequilibrium, with climate and its vulnerability to future climate change. Here, we present an inventory of glacier-specific annual accumulation areas and equilibrium line altitudes (ELAs) for over 3000 glaciers in Alaska and northwest Canada (88% of the regional glacier area) from 2018 to 2022 derived from Sentinel-2 imagery. We find that the 5 year average AAR of the entire study area is 0.41, with an inter-annual range of 0.25–0.49. More than 1000 glaciers, representing 8% of the investigated glacier area, were found to have effectively no accumulation area. Summer temperature and winter precipitation from ERA5-Land explained nearly 50% of the inter-annual ELA variability across the entire study region (R2 = 0.47). An analysis of future climate scenarios (SSP2-4.5) projects that ELAs will rise by ∼170 m on average by the end of the 21st century. Such changes would result in a loss of 25% of the modern accumulation area, leaving a total of 1900 glaciers (22% of the investigated area) with no accumulation area. These results highlight the current state of glacier disequilibrium with modern climate, as well as glacier vulnerability to projected future warming.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/jog.2024.65","usgsCitation":"Zeller, L., McGrath, D.J., Sass, L., Florentine, C., and Downs, J., 2025, Equilibrium line altitudes, accumulation areas, and the vulnerability of glaciers in Alaska: Journal of Glaciology, v. 71, e28, 13 p., https://doi.org/10.1017/jog.2024.65.","productDescription":"e28, 13 p.","ipdsId":"IP-163494","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":488628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/jog.2024.65","text":"Publisher Index Page"},{"id":484323,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":932825,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":932826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Downs, Jacob 0000-0002-5588-7767","orcid":"https://orcid.org/0000-0002-5588-7767","contributorId":336920,"corporation":false,"usgs":false,"family":"Downs","given":"Jacob","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":932827,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267524,"text":"70267524 - 2025 - Streamflow response to glacier mass loss varies with basin precipitation across Alaska","interactions":[],"lastModifiedDate":"2025-05-28T14:28:22.882379","indexId":"70267524","displayToPublicDate":"2025-04-04T09:24:54","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Streamflow response to glacier mass loss varies with basin precipitation across Alaska","docAbstract":"Diminishing glaciers affect streamflow, and given the extent of glaciers in Alaska and adjacent Canada, continued glacier mass loss is likely to have profound effects on ecosystems sensitive to runoff. The effects of glacier mass loss on streamflow are likely to vary across the wide ranges of basin size, glacier cover, and precipitation in this region. In this study, we use U.S. Geological Survey (USGS) streamflow data with satellite-based glacier volume change estimates to quantify how glacier mass loss subsidized streamflow over the 2000–2019 period for 116 glacierized basins. We examine interannual variability in that subsidy at three USGS-monitored glaciers to explore the ability of the subsidy to buffer streamflow derived solely from precipitation. We found the relative importance of percent glacier cover on streamflow magnitude increases in drier basins. In the driest basins, glaciers produced 40 times greater percent glacier mass loss subsidies to streamflow for the percent glacier cover compared to the wettest basins. While the subsidy from glacier mass loss buffers interannual variability in streamflow to varying degrees, it can also increase streamflow variability. Smaller amounts of percent glacier cover are needed to produce summer-melt-dominated seasonal flow regimes in drier basins than in wetter basins. Decreasing glacier cover will eventually decrease summer streamflow, increasing spring streamflow in drier basins, and attenuating seasonality with increasing spring and autumnal streamflow in wetter basins. Quantifying the downstream effects of continued glacier mass loss without the computational expense of a hydrological model is broadly applicable in this changing climate.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR037859","usgsCitation":"Curran, J.H., Rick, B., Littell, J., and Sass, L., 2025, Streamflow response to glacier mass loss varies with basin precipitation across Alaska: Water Resources Research, v. 61, no. 4, e2024WR037859, 18 p., https://doi.org/10.1029/2024WR037859.","productDescription":"e2024WR037859, 18 p.","ipdsId":"IP-165191","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":490156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr037859","text":"Publisher Index Page"},{"id":486640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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hazards","interactions":[],"lastModifiedDate":"2025-04-01T15:08:00.852625","indexId":"70265056","displayToPublicDate":"2025-04-01T08:03:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty reduction for subaerial landslide-tsunami hazards","docAbstract":"Subaerial rock slopes may generate a tsunami by rapidly moving into the water. Large uncertainty in landslide characteristics propagates into large uncertainty in tsunami hazard, making hazard assessment more difficult for land and emergency managers. Once a potentially tsunamigenic landslide is identified, it may not be clear which landslide characteristics contribute most significantly to uncertainty in the tsunami hazard. Our aim is to document the relative worth of different landslide characteristics (e.g., size, material properties) for reducing uncertainty in landslide-tsunami hazard assessments. Isolating the relative importance of specific landslide characteristics may inform prioritization of data collection and improve efficiency in understanding hazard. To accomplish this, we generated a set of 288 landslide-tsunami simulations in which we systematically varied the size and material properties of possible failure extents at the Barry Arm landslide complex in northwestern Prince William Sound, Alaska, USA. We find that for landslides smaller than the receiving waterbody, the landslide volume has the strongest effect on resulting wave characteristics and thus the highest leverage on reducing uncertainty in tsunami hazard. In contrast, for landslides substantially larger than the waterbody, the duration of rapid movement of the landslide has the highest leverage. Based on our results, we propose a classification scheme for subaerial landslides based on the relative size of the landslide and waterbody. Additionally, our results support the generation of a tsunami height transfer function between existing tide gages and a nearby coastal city. These results have direct implications for the practice of operational early warning.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JF007906","usgsCitation":"Barnhart, K.R., George, D.L., Collins, A.L., Schaefer, L.N., and Staley, D.M., 2025, Uncertainty reduction for subaerial landslide-tsunami hazards: Journal of Geophysical Research: Earth Surface, v. 130, no. 4, e2024JF007906, 33 p., https://doi.org/10.1029/2024JF007906.","productDescription":"e2024JF007906, 33 p.","ipdsId":"IP-167063","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":488660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jf007906","text":"Publisher Index Page"},{"id":484066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.7784819894945,\n 61.26414641973446\n ],\n [\n -148.7784819894945,\n 59.85768506370988\n ],\n [\n -145.67415454216552,\n 59.85768506370988\n ],\n [\n -145.67415454216552,\n 61.26414641973446\n ],\n [\n -148.7784819894945,\n 61.26414641973446\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Andrew L. 0000-0003-4751-7333","orcid":"https://orcid.org/0000-0003-4751-7333","contributorId":332093,"corporation":false,"usgs":true,"family":"Collins","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932436,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265042,"text":"70265042 - 2025 - A generalized framework for inferring river bathymetry from image-derived velocity fields","interactions":[],"lastModifiedDate":"2026-04-01T14:47:45.336764","indexId":"70265042","displayToPublicDate":"2025-03-28T09:22:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"A generalized framework for inferring river bathymetry from image-derived velocity fields","docAbstract":"Although established techniques for remote sensing of river bathymetry perform poorly in turbid water, image velocimetry can be effective under these conditions. 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Critical minerals are commodities essential to modern industrial and strategic technologies and are highly vulnerable to supply chain disruption. The Critical Minerals Mapping Initiative (CMMI) is a collaboration among the U.S. Geological Survey (USGS), the Geological Survey of Canada, and Geoscience Australia that aims to deepen global understanding of where critical minerals are located. A key output of this initiative is the Critical Minerals in Ores (CMiO) database that is advancing our collective understanding of critical minerals distributions. For instance, publicly available data on the concentrations of many critical minerals are sparse because these commodities can only be produced in small, yet essential, quantities compared to the primary commodities like copper and zinc. The CMiO database helps bridge this gap by offering high-quality, multielement geochemical data from a wide variety of critical mineral-bearing deposits around the world. Importantly, it uses a novel consensus deposit environment, group, and type classification scheme developed by the agencies that allows comparisons among ore deposits from different regions. The CMiO database contains geochemical data for more than 20,000 samples from more than 100 deposit types comprising 10 deposit environments.
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U.S. Geological Survey
913 National Center
Reston, VA 20192
Since the 1980s, Pacific Black Brant (Branta bernicla nigricans, hereafter brant) have shifted their winter distribution northward from Mexico to Alaska (approximately 4500 km) with changes in climate. Alongside this shift, the primary breeding population of brant has declined. To understand the population-level implications of the changing migration strategy of brant, it is important to connect movement and demographic data. Our objectives were to calculate migratory connectivity, a measure of spatial and temporal overlap during the non-breeding period, for Arctic and subarctic breeding populations of brant, and to determine if variation in migration strategies affected nesting phenology and nest survival.
We derived a migratory network using light-level geolocator migration tracks from an Arctic site (Colville River Delta) and a subarctic site (Tutakoke River) in Alaska. Using this network, we quantified the migratory connectivity of the two populations during the winter. We also compared nest success rates among brant that used different combinations of winter sites and breeding sites.
The two breeding populations were well mixed during the winter, as indicated by a migratory connectivity score close to 0 (− 0.06) at the primary wintering sites of Izembek Lagoon, Alaska (n = 11 brant) and Baja California, Mexico (n = 48). However, Arctic birds were more likely to migrate the shorter distance to Izembek (transition probability = 0.24) compared to subarctic birds (transition probability = 0.09). Nest survival for both breeding populations was relatively high (0.88–0.92), and we did not detect an effect of wintering site on nest success the following year.
Nest survival of brant did not differ among brant that used wintering sites despite a 4500 km difference in migration distances. Our results also suggested that the growing Arctic breeding population is unlikely to compensate for declines in the larger breeding population of brant in the subarctic. However, this study took place in 2011–2014 and wintering at Izembek Lagoon may have greater implications for reproductive success under future climate conditions.
","language":"English","publisher":"Biomed Central","doi":"10.1186/s40462-025-00530-z","usgsCitation":"Matsuoka, T., Patil, V.P., Hupp, J., Leach, A.G., Reed, J., Sedinger, J.S., and Ward, D., 2025, Shortening migration by 4500 km does not affect nesting phenology or increase nest success for black brant (Branta bernicla nigricans) breeding in Arctic and subarctic Alaska: Movement Ecology, v. 13, 21, 13 p., https://doi.org/10.1186/s40462-025-00530-z.","productDescription":"21, 13 p.","ipdsId":"IP-165239","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-025-00530-z","text":"Publisher Index Page"},{"id":483879,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.81790807363556,\n 25.109390075267115\n ],\n [\n -111.12577320220554,\n 27.35015894013445\n ],\n [\n -120.0723516818312,\n 35.91671872458075\n ],\n [\n -123.33332088662812,\n 40.09627486987472\n ],\n [\n -121.76064748127651,\n 47.98998229927324\n ],\n [\n -130.2791770051045,\n 55.04657100955589\n ],\n [\n -136.74332020250924,\n 60.0900518762904\n ],\n [\n -151.50218954211437,\n 61.77599779101939\n ],\n [\n -149.6269528576101,\n 71.05149390049743\n ],\n [\n -170.1204062110206,\n 69.1137478231604\n ],\n [\n -168.34559408430363,\n 64.82390470369859\n ],\n [\n -168.00886131353556,\n 59.730643122555506\n ],\n [\n -161.62008391184636,\n 54.513433513042116\n ],\n [\n -144.88512099831664,\n 59.17828589453629\n ],\n [\n -135.41036792441935,\n 55.18200025222632\n ],\n [\n -131.56466235414842,\n 51.06113369117236\n ],\n [\n -126.52375383157013,\n 47.127718603387564\n ],\n [\n -125.86832751079751,\n 39.580339094365684\n ],\n [\n -119.94329894845731,\n 31.85072142642305\n ],\n [\n -113.81790807363556,\n 25.109390075267115\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2025-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Matsuoka, Toshio Doroff 0009-0009-4235-2614","orcid":"https://orcid.org/0009-0009-4235-2614","contributorId":352705,"corporation":false,"usgs":true,"family":"Matsuoka","given":"Toshio Doroff","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":932067,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":932068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hupp, Jerry W. 0000-0002-6439-3910","orcid":"https://orcid.org/0000-0002-6439-3910","contributorId":339472,"corporation":false,"usgs":false,"family":"Hupp","given":"Jerry W.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":932069,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leach, Alan G.","contributorId":203591,"corporation":false,"usgs":false,"family":"Leach","given":"Alan","email":"","middleInitial":"G.","affiliations":[{"id":36666,"text":"Department of Natural Resources and Environmental Science, University of Nevada-Reno","active":true,"usgs":false}],"preferred":false,"id":932070,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, John 0000-0002-3239-6906","orcid":"https://orcid.org/0000-0002-3239-6906","contributorId":214852,"corporation":false,"usgs":true,"family":"Reed","given":"John","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":932071,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sedinger, James S.","contributorId":84861,"corporation":false,"usgs":false,"family":"Sedinger","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":932072,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ward, David H.","contributorId":352708,"corporation":false,"usgs":false,"family":"Ward","given":"David H.","affiliations":[{"id":84288,"text":"U.S. Geological Survey Alaska Science Center (Emeritus)","active":true,"usgs":false}],"preferred":false,"id":932073,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264734,"text":"70264734 - 2025 - Reconstruction of Holocene and Last Interglacial vegetation dynamics and wildfire activity in Southern Siberia","interactions":[],"lastModifiedDate":"2025-03-21T15:27:48.558095","indexId":"70264734","displayToPublicDate":"2025-03-20T08:09:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Reconstruction of Holocene and Last Interglacial vegetation dynamics and wildfire activity in Southern Siberia","docAbstract":"Wildfires are a rapidly increasing threat to boreal forests. While our understanding of the drivers behind wildfires and their environmental impact is growing, it is mostly limited to the observational period. Here we focus on the boreal forests of southern Siberia and exploit a U–Th-dated stalagmite from Botovskaya Cave, located in the upper Lena region of southern Siberia, to document wildfire activity and vegetation dynamics during parts of two warm periods: the Last Interglacial (LIG; specifically part of the Last Interglacial maximum between 124.1 and 118.8 ka) and the Holocene (10–0 ka). Our record is based on levoglucosan (Lev), a biomarker sensitive to biomass burning, and on lignin oxidation products (LOPs) that discriminate between open and closed forest and hard- or softwood vegetation. In addition, we used carbonate carbon stable isotope ratios (δ13C), which reflect a dominant control of the host rock, to evaluate soil respiration and local infiltration changes. Our LOP data suggest that, during the Last Interglacial, the region around Botovskaya Cave was characterised by open forest, which by ca. 121.5 ka underwent a transition from fire-resistant hardwood to fire-prone softwood. The Lev record indicates that fire activity was high and increased towards the end of Last Interglacial just before 119 ka. In contrast, the Holocene was characterised by a closed-forest environment with mixed hard- and softwood vegetation. Holocene fire activity varied but at a much lower level than during the Last Interglacial. We attribute the changes in wildfire activity during the intervals of interest to the interplay between vegetation and climate. The open forests of the Last Interglacial were more likely to ignite than their closed Holocene equivalents, and their flammability was aided by warmer and drier summers and a stronger seasonal temperature contrast due to the increase in seasonal insolation difference compared to the Holocene. Our comparison of the last two interglacial intervals suggests that, with increasing global temperatures, the boreal forest of southern Siberia may become progressively more vulnerable to higher wildfire activity.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-21-661-2025","usgsCitation":"Margerum, J., Homann, J., Umbo, S., Nehrke, G., Hoffmann, T., Vaks, A., Kononov, A., Osintsev, A., Giesche, A., Mason, A., Lechleitner, F., Henderson, G., Kwiecien, O., and Breitenbach, S., 2025, Reconstruction of Holocene and Last Interglacial vegetation dynamics and wildfire activity in Southern Siberia: Climate of the Past, v. 21, no. 3, p. 661-677, https://doi.org/10.5194/cp-21-661-2025.","productDescription":"17 p.","startPage":"661","endPage":"677","ipdsId":"IP-165953","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":488363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-21-661-2025","text":"Publisher Index Page"},{"id":483662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Botovskaya Cave, Siberia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 104.97249739324332,\n 55.0006548103033\n ],\n [\n 104.97249739324332,\n 54.87985401356909\n ],\n [\n 105.12004339826586,\n 54.87985401356909\n ],\n [\n 105.12004339826586,\n 55.0006548103033\n ],\n [\n 104.97249739324332,\n 55.0006548103033\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Margerum, Jade","contributorId":352494,"corporation":false,"usgs":false,"family":"Margerum","given":"Jade","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":931488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homann, Julia","contributorId":352495,"corporation":false,"usgs":false,"family":"Homann","given":"Julia","affiliations":[{"id":84241,"text":"Department Chemie, Johannes Gutenberg-Universität Mainz, Duesbergweg 10-14, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":931489,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Umbo, Stuart","contributorId":352496,"corporation":false,"usgs":false,"family":"Umbo","given":"Stuart","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":931490,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nehrke, Gernot","contributorId":352497,"corporation":false,"usgs":false,"family":"Nehrke","given":"Gernot","affiliations":[{"id":84242,"text":"Alfred Wegener Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Section Marine BioGeoSciences, 27570 Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":931491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoffmann, Thorsten","contributorId":352498,"corporation":false,"usgs":false,"family":"Hoffmann","given":"Thorsten","affiliations":[{"id":84241,"text":"Department Chemie, Johannes Gutenberg-Universität Mainz, Duesbergweg 10-14, 55128 Mainz, Germany","active":true,"usgs":false}],"preferred":false,"id":931492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vaks, Anton","contributorId":352499,"corporation":false,"usgs":false,"family":"Vaks","given":"Anton","affiliations":[{"id":84243,"text":"Geological Survey of Israel, 32 Yeshayahu Leibowitz Street, 9692100 Jerusalem, Israel","active":true,"usgs":false}],"preferred":false,"id":931493,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kononov, Aleksandr","contributorId":352500,"corporation":false,"usgs":false,"family":"Kononov","given":"Aleksandr","affiliations":[{"id":84244,"text":"Irkutsk Nation al Research Technical University, Irkutsk, 664074, Russia; 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An invasive species is an organism that is not native to a locality and causes (or is likely to cause) harm. An introduced species is one that is nonnative to a locality and occurs there because of human activities or their consequences, including the species’ intentional or unintentional escape, release, dissemination, or placement. The United States Register of Introduced and Invasive Species (US-RIIS, ver. 2.0) contains 14,700 records for three localities: Alaska (545 records), Hawaii (5,628 records), and the conterminous United States (L48; 8,527 records). For these localities, the US-RIIS catalogs introduced species that have become established, thus providing a basis for their prioritization and management. To be included on the US-RIIS, a species must be nonnative to the entire locality and reproducing anywhere in the locality. Each US-RIIS record has information on taxonomy, dates of introduction (where available; version 2.0 for 47 percent of the records), invasion status (invasive or introduced), use for biocontrol (if applicable), and a citation for the information source(s). The US-RIIS was designed to be compatible with country contributions to the Global Register of Introduced and Invasive Species Initiative, which compiles annotated and verified country-wide inventories of introduced and invasive species. Within the US-RIIS, the density of introduced species per 10,000 square kilometers among the localities ranges markedly, from 3 in Alaska to 1,988 in Hawaii (11 in the L48). The comparative taxonomic composition of the largest groups in the sublists also varies: the Alaska sublist has a majority of flowering plants; Hawaii has a majority of insects; and the L48 is about equally divided between insects and flowering plants. 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U.S. Geological Survey
Box 25046, MS 302
Denver, CO 80225-0046
Great Aleutian underthrusting earthquakes produced destructive tsunamis impacting Hawaiʻi in 1946 and 1957. Prior modeling of the 1957 tsunami deposit and runup records on eastern Aleutian and Hawaiian Islands jointly with tide-gauge observations across the Pacific Ocean constrained a rupture model with shallow slip up to 26 m along 600 km of the plate boundary. Here we implement this modeling approach to older deposits and show alternating deep and shallow megathrust slip up to 26, 32, and 22 m for great earthquakes along the same segment in the 18th, 15th, and 14th centuries. All three modeled prehistoric Aleutian earthquakes produce tsunami inundation in Hawaiʻi with the most severe, 14th century event having impacts exceeding the 1957 event. The along-dip variability of these four ruptures spanning seven centuries provides insights on earthquake cycles for engineering design and hazard assessment. The 15th century and 1957 rupture models provide evidence for recurrence of tsunami earthquakes, which can produce disproportionately large tsunamis for a given moment magnitude due to reduced rigidity in the shallow megathrust. The 14th and 18th century events likely ruptured deeper regions that did not slip in 1957, suggesting potential for corresponding deeper failure in the next great eastern Aleutian earthquake.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-025-57802-w","usgsCitation":"Yamazaki, Y., Cheung, K.F., Lay, T., La Selle, S., Witter, R., and Jaffe, B., 2025, A 700-year rupture sequence of great eastern Aleutian earthquakes from tsunami modeling of stratigraphic records: Nature Communications, v. 16, 2638, 16 p., https://doi.org/10.1038/s41467-025-57802-w.","productDescription":"2638, 16 p.","ipdsId":"IP-169333","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-025-57802-w","text":"Publisher Index Page"},{"id":484585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Hawaii","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -146.41033794940356,\n 60.59031062994006\n ],\n [\n -179.99,\n 60.59031062994006\n ],\n [\n -179.99,\n 18\n ],\n [\n -146.41033794940356,\n 18\n ],\n [\n -146.41033794940356,\n 60.59031062994006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 172.7792582818936,\n 55.23104605282941\n ],\n [\n 172.7792582818936,\n 48.90879653399284\n ],\n [\n 179.99,\n 48.90879653399284\n ],\n [\n 179.99,\n 55.23104605282941\n ],\n [\n 172.7792582818936,\n 55.23104605282941\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Yamazaki, Yoshiki","contributorId":216792,"corporation":false,"usgs":false,"family":"Yamazaki","given":"Yoshiki","email":"","affiliations":[{"id":39517,"text":"University of Hawaii at Mano","active":true,"usgs":false}],"preferred":false,"id":933512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheung, Kwok Fai","contributorId":329690,"corporation":false,"usgs":false,"family":"Cheung","given":"Kwok","email":"","middleInitial":"Fai","affiliations":[{"id":78685,"text":"University of Hawai'i at Manoa","active":true,"usgs":false}],"preferred":false,"id":933513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":328838,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":933514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Selle, SeanPaul 0000-0002-4500-7885 slaselle@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-7885","contributorId":181565,"corporation":false,"usgs":true,"family":"La Selle","given":"SeanPaul","email":"slaselle@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":933515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":933516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaffe, Bruce E. 0000-0002-8816-5920","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":335664,"corporation":false,"usgs":false,"family":"Jaffe","given":"Bruce E.","affiliations":[{"id":80462,"text":"former USGS PCMSC employee","active":true,"usgs":false}],"preferred":false,"id":933517,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264664,"text":"70264664 - 2025 - Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus","interactions":[],"lastModifiedDate":"2025-03-26T16:09:33.212626","indexId":"70264664","displayToPublicDate":"2025-03-13T09:40:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1493,"text":"Emerging Infectious Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Exposure of wild mammals inhabiting Alaska to influenza A(H5N1) virus","docAbstract":"Serum samples from wild mammals inhabiting Alaska, USA, showed that 4 species, including Ursus arctos bears and Vulpes vulpes foxes, were exposed to influenza A(H5N1) viruses. Results indicated some mammals in Alaska survived H5N1 virus infection. Surveillance efforts may be improved by incorporating information on susceptibility and detectable immune responses among wild mammals.
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College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":931172,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70264405,"text":"70264405 - 2025 - Delineating ecologically-distinct groups for annual cycle management of a declining shorebird","interactions":[],"lastModifiedDate":"2025-05-12T15:40:26.81971","indexId":"70264405","displayToPublicDate":"2025-03-12T08:01:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Delineating ecologically-distinct groups for annual cycle management of a declining shorebird","docAbstract":"1. Patterns of migratory connectivity are increasingly used to understand and manage threats throughout the annual cycle of migratory species. Strong migratory connectivity refers to when individuals from different populations remain spatially separated across the annual cycle, which may expose populations to unique sets of threats and conditions that cause differential population trends. However, the populations or groups used for species’ management are often defined a priori based on expert knowledge and/or management units, which may mask important population segregation and obscure differential population trends and their drivers.
2. We compared three approaches to defining management groups of a declining shorebird, the long-billed curlew (Numenius americanus), for annual cycle management: by expert-opinion, according to management flyways, and with unsupervised clustering of satellite tracking data that maximizes the strength of migratory connectivity.
3. Despite the curlews having a continuous breeding range and a pattern of parallel migration, all three approaches identified groups with different population trends, movement behaviours and habitat selection across the annual cycle, suggesting these are meaningful ecological groups. The expert and clustering approaches resulted in similar group structure, strong estimates of migratory connectivity (measured as MC = 0.64 across seasons), movement behaviour and habitat selection; however, the expert approach identified an additional divide between the easternmost grouping, which revealed strongly negative population trends in the group occupying the Chihuahuan desert during the stationary nonbreeding season. In contrast, the flyway delineation resulted in weaker estimates of migratory connectivity, marginal differences in population trends and less between-group differences in movement behaviour and habitat selection.
4. Synthesis and applications. Using measurements of migratory connectivity in concert with expert opinion can define ecologically distinct groups for wildlife management that differ in the environmental conditions they experience across seasons of the annual cycle, which is a key component for understanding and reversing declines of migratory species.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14885","usgsCitation":"Knight, E., Carlisle, J.D., Boyce, A., Bradley, D., Cimprich, P., Coates, S., Dinsmore, S., Gregory, C., Jorgensen, J., Kelly, J., Newstead, D., Olalla, A., Powell, L., Scarpignato, A., Tibbitts, T., Warnock, N., Wehtje, W., Marra, P., and Harrison, A., 2025, Delineating ecologically-distinct groups for annual cycle management of a declining shorebird: Journal of Applied Ecology, v. 62, no. 5, p. 1152-1165, https://doi.org/10.1111/1365-2664.14885.","productDescription":"14 p.","startPage":"1152","endPage":"1165","ipdsId":"IP-162500","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488308,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.14885","text":"Publisher Index Page"},{"id":483352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -130.61742203243398,\n 55.06220192129385\n ],\n [\n -125.78642691212771,\n 37.96714257094378\n ],\n [\n -112.79332255145798,\n 19.6607075263222\n ],\n [\n -96.55986873218092,\n 19.42104945978697\n ],\n [\n -98.16768517783267,\n 27.617354166317778\n ],\n [\n -93.88189635105832,\n 30.28935268993333\n ],\n [\n -80.97632580516836,\n 30.315920278541377\n ],\n [\n -95.50678240297971,\n 43.55365071845705\n ],\n [\n -100.13328021048312,\n 54.69226523920972\n ],\n [\n -130.61742203243398,\n 55.06220192129385\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"62","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Knight, Elly C.","contributorId":352283,"corporation":false,"usgs":false,"family":"Knight","given":"Elly C.","affiliations":[{"id":84154,"text":"Migratory Bird Center, Smithsonian’s National Zoo and Conservation Biology Institute, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":930653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlisle, J. 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Even though the sub-population is likely decreasing, it remains difficult to link individual polar bear health and physiological change to sub-population effects. We developed an index of allostatic load, which represents potential physiological dysregulation. The allostatic load index included blood- and hair-based analytes measured in physically captured southern Beaufort bears in spring. We examined allostatic load in relation to bear body condition, age, terrestrial habitat use and, over time, for bear demographic groups. Overall, allostatic load had no relationship with body condition. However, allostatic load was higher in adult females without cubs that used terrestrial habitats the prior year, indicating potential physiological dysregulation with land use. Allostatic load declined with age in adult females without cubs. Sub-adult males demonstrated decreased allostatic load over time. Our study is one of the first attempts to develop a health scoring system for free-ranging polar bears, and our findings highlight the complexity of using allostatic load as an index of health in a wild species. Establishing links between individual bear health and population dynamics is important for advancing conservation efforts.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coaf013","usgsCitation":"Teman, S., Atwood, T.C., Converse, S.J., Fry, T., and Laidre, K.L., 2025, Measuring polar bear health using allostatic load: Conservation Physiology, v. 13, no. 1, coaf013, 14 p., https://doi.org/10.1093/conphys/coaf013.","productDescription":"coaf013, 14 p.","ipdsId":"IP-170326","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":487727,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaf013","text":"Publisher Index Page"},{"id":483055,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.59695705634383,\n 74.06388252003191\n ],\n [\n -156.59695705634383,\n 69.28231129261928\n ],\n [\n -140.99324520225554,\n 69.28231129261928\n ],\n [\n -140.99324520225554,\n 74.06388252003191\n ],\n [\n -156.59695705634383,\n 74.06388252003191\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Teman, Sarah J.","contributorId":352066,"corporation":false,"usgs":false,"family":"Teman","given":"Sarah","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929976,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":929977,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929978,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fry, Tricia","contributorId":222310,"corporation":false,"usgs":false,"family":"Fry","given":"Tricia","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":929979,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":929980,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264002,"text":"70264002 - 2025 - Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models","interactions":[],"lastModifiedDate":"2025-03-27T13:17:10.387768","indexId":"70264002","displayToPublicDate":"2025-03-03T09:34:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models","docAbstract":"Mortality risk for animals often varies spatially and can be linked to how animals use landscapes. While numerous studies collect telemetry data on animals, the focus is typically on the period when animals are alive, even though there is important information that could be gleaned about mortality risk. We introduce a thinned spatial point process (SPP) modelling framework that couples relative abundance and space use with a mortality process to formally treat the occurrence of mortality events across the landscape as a spatial process. We show how this model can be embedded in a hierarchical statistical framework and fit to telemetry data to make inferences about how spatial covariates drive both space use and mortality risk. We apply the method to two data sets to study the effects of roads and habitat on spatially explicit mortality risk: (1) VHF telemetry data collected for willow ptarmigan in Alaska, and (2) hourly GPS telemetry data collected for black bears in Colorado. These case studies demonstrate the applicability of this method for different species and data types, making it broadly useful in enabling inferences about the mechanisms influencing animal survival and spatial population processes while formally treating survival as a spatial process, especially as the development and implementation of joint analyses continue to progress.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.70092","usgsCitation":"Eisaguirre, J.M., Lohman, M., Frye, G., Johnson, H.E., Riecke, T., and Williams, P.J., 2025, Estimating spatially explicit survival and mortality risk from telemetry data with thinned point process models: Ecology Letters, v. 28, no. 3, e70092, 11 p., https://doi.org/10.1111/ele.70092.","productDescription":"e70092, 11 p.","ipdsId":"IP-160909","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":496394,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.70092","text":"Publisher Index Page"},{"id":492795,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HSN8PV","text":"USGS data release","linkHelpText":"Thinned Point Process Models for Telemetry Data"},{"id":482801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph Michael 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":301980,"corporation":false,"usgs":true,"family":"Eisaguirre","given":"Joseph","email":"","middleInitial":"Michael","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":929454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lohman, Medeleine G.","contributorId":351785,"corporation":false,"usgs":false,"family":"Lohman","given":"Medeleine G.","affiliations":[{"id":12742,"text":"University of Nevada Reno","active":true,"usgs":false}],"preferred":false,"id":929455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frye, Graham G.","contributorId":351786,"corporation":false,"usgs":false,"family":"Frye","given":"Graham G.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":929456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"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":929457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riecke, Thomas V.","contributorId":171482,"corporation":false,"usgs":false,"family":"Riecke","given":"Thomas V.","affiliations":[],"preferred":false,"id":929458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":929459,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275008,"text":"70275008 - 2025 - Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska","interactions":[],"lastModifiedDate":"2026-04-10T15:08:45.046416","indexId":"70275008","displayToPublicDate":"2025-03-01T09:55:18","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"BOEM 2025-019","title":"Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska","docAbstract":"Following near extirpation from the fur trade, sea otters (Enhydra lutris) have returned to occupy lower Cook Inlet since the 1950s, or earlier, with numbers increasing to ~11,000 and ~9,000 on the west and east side, respectively, by 2017. Northward range expansion on the west side has been negligible for decades with few animals found north of Kamishak Bay, while northward expansion on the east side has been more pronounced in recent decades. The reasons for these contrasting distribution patterns are not certain. Possible explanations for lack of expansion on the west side included 1) poor sea otter habitat north of Kamishak Bay; 2) adequate habitat north of Kamishak Bay but no incentive for sea otters to move north because of abundant food in Kamishak Bay, and/or sea otters discouraged from moving north of Kamishak Bay; 3) seasonal ice formation; or 4) seasonal presence of killer whales. This project was designed to document current sea otter abundance and distribution in lower Cook Inlet, including seasonal variation, and evaluate drivers of habitat use and foraging conditions, including how these overlap with Bureau of Ocean Energy Management (BOEM) Lease Sale blocks.
We found that the probability of sea otter presence in lower Cook Inlet was significantly related to depth and distance from shore with the highest probabilities of sea otter presence in areas ~7–8 kilometers (km) from shore in water ~20–30 meters (m) deep. Historical survey data suggest sea otter distribution has not changed dramatically since 2002, and while we detected significant seasonal changes including due to presence of heavy sea ice, the seasonal changes were modest and primarily related to locations with consistent winter ice formation on the west side of lower Cook Inlet. Overall, throughout the year, sea otters appeared to be utilizing most of the habitat within the 40-m depth contour on the east side of lower Cook Inlet, including Kachemak Bay. Sea otters on the west side reside largely within Kamishak Bay. Because of the shallow bathymetry of lower Cook Inlet, sea otters consistently occurred farther offshore than in many other areas of Alaska. The presence of sea otters, including females with pups, in these offshore waters indicates that sea otters can forage productively in these open water areas. Importantly, we documented that sea otters, including females with pups, occurred in most of the eastern BOEM lease blocks including the original Lease Sale 244 blocks 7064, 7114, 6162, 6310, 6360, 6410, 6458 and 6457. The relinquishments for these 7 leases were effective September 17, 2024: OCS-Y-02434 (block # 7064), OCS-Y-02435 (7114), OCS-Y-02436 (6162), OCS-Y-02438 (6357), OCS-Y-02442 (6407), OCS-Y02446 (6457), OCS-Y-02447 (6458) https://www.boem.gov/sites/default/files/documents/environment/Map%20of%20Active%20Leases%20 Cook%20Inlet%20OCS_0.pdf
Regarding differential northward expansion on the east and west side of lower Cook Inlet, we found that wind and water circulation patterns make winter sea ice more prominent on the west side, but this had minimal effects on sea otter distribution. In addition, although there have been observations of killer whale (Orcinus orca) predation on sea otters in Cook Inlet, we did not see behavioral or distributional evidence that it was prevalent enough to have strong effects on sea otter habitat use. Benthic surveys using a remotely operated vehicle (ROV) indicated that epibenthic substrate and biological community heterogeneity differed between areas with and without otters, suggesting that these factors may explain the current distribution of otters within lower Cook Inlet. However, ROV surveys cannot assess infaunal prey abundance, making assessments of the role of prey availability difficult. The existence of a healthy Pacific razor clam (Siliqua patula) fishery along western lower Cook Inlet along the Lake Clark coast north of Kamishak Bay suggests there may still be a resource base for eventual expansion of sea otters into this area.
Our shore-based forage observations indicated that sea otters exist near carrying capacity densities relative to nearshore prey resources. However, our distribution model suggests the bulk of the population lives offshore beyond our ability to observe their feeding activity. The fact that sea otters are a consistent presence in offshore areas suggests that prey resources in these areas were relatively abundant in comparison to nearshore prey resources, making foraging in offshore areas, with water depths of 0–40 m, energetically profitable. Prey types in these offshore areas of lower Cook Inlet likely included epifauna such as crabs and large urchins, in addition to infaunal clams based on the soft substrate habitat types that characterize lower Cook Inlet.
Collectively, our results suggest that sea otters occupy most areas of lower Cook Inlet with appropriate benthic habitat types and prey resources, which includes areas within BOEM Lease Sale blocks. With the exception of the Lake Clark coast, sea otters may be approaching a food- and habitat-limited distribution and carrying capacity, suggesting that the current status of sea otters in lower Cook Inlet is likely to remain similar in the absence of significant changes to prey, habitat, predation, or anthropogenic disturbance.
","language":"English","publisher":"Bureau of Ocean Energy Management (BOEM)","usgsCitation":"Monson, D., Kloecker, K.A., LaRoche, N., Power, C., Geissinger, L., Hasan, E., Jones, T., and Weitzman, B., 2025, Quantifying sea otter abundance, distribution, habitat use, and foraging intake in Cook Inlet, Alaska: OCS Study BOEM 2025-019, viii, 73 p.","productDescription":"viii, 73 p.","ipdsId":"IP-176659","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":502683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":502670,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2025-019.pdf"}],"country":"United States","state":"Alaska","otherGeospatial":"Cook Inlet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.3155651543558,\n 60.79849962080206\n ],\n [\n -154.3155651543558,\n 58.54443596757534\n ],\n [\n -149.77946397731765,\n 58.54443596757534\n ],\n [\n -149.77946397731765,\n 60.79849962080206\n ],\n [\n -154.3155651543558,\n 60.79849962080206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":959185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kloecker, Kimberly A. 0000-0002-2461-968X kkloecker@usgs.gov","orcid":"https://orcid.org/0000-0002-2461-968X","contributorId":3442,"corporation":false,"usgs":true,"family":"Kloecker","given":"Kimberly","email":"kkloecker@usgs.gov","middleInitial":"A.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":959186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRoche, Nicole 0000-0003-3737-5714","orcid":"https://orcid.org/0000-0003-3737-5714","contributorId":178395,"corporation":false,"usgs":false,"family":"LaRoche","given":"Nicole","email":"","affiliations":[{"id":18890,"text":"formerly USGS Western Ecological Research Center, Santa Cruz Field Station","active":true,"usgs":false}],"preferred":false,"id":959187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Power, Collin","contributorId":303954,"corporation":false,"usgs":false,"family":"Power","given":"Collin","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":959188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geissinger, Laura","contributorId":369804,"corporation":false,"usgs":false,"family":"Geissinger","given":"Laura","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":959189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hasan, Elizabeth","contributorId":369805,"corporation":false,"usgs":false,"family":"Hasan","given":"Elizabeth","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":959190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Tahzay","contributorId":369806,"corporation":false,"usgs":false,"family":"Jones","given":"Tahzay","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":959191,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weitzman, Ben","contributorId":252838,"corporation":false,"usgs":false,"family":"Weitzman","given":"Ben","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":959192,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267354,"text":"70267354 - 2025 - Mount Spurr Volcano","interactions":[],"lastModifiedDate":"2025-05-20T14:15:30.773618","indexId":"70267354","displayToPublicDate":"2025-03-01T09:13:56","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":17066,"text":"Alaska Division of Geological & Geophysical Surveys Information Circular","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"98","title":"Mount Spurr Volcano","docAbstract":"No abstract available.
","language":"English","publisher":"Alaska Division of Geological & Geophysical Survey's","doi":"10.14509/31518","usgsCitation":"Wallace, K.L., Waythomas, C.F., Coombs, M.L., and Nastan, A., 2025, Mount Spurr Volcano: Alaska Division of Geological & Geophysical Surveys Information Circular 98, 2 p., https://doi.org/10.14509/31518.","productDescription":"2 p.","ipdsId":"IP-176693","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":486209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Mount Spurr","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -152.3651677380237,\n 61.355151108267876\n ],\n [\n -152.3651677380237,\n 61.25127738570461\n ],\n [\n -152.15144253360808,\n 61.25127738570461\n ],\n [\n -152.15144253360808,\n 61.355151108267876\n ],\n [\n -152.3651677380237,\n 61.355151108267876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, Kristi L. 0000-0002-0962-048X kwallace@usgs.gov","orcid":"https://orcid.org/0000-0002-0962-048X","contributorId":3454,"corporation":false,"usgs":true,"family":"Wallace","given":"Kristi","email":"kwallace@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":937946,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":937947,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coombs, Michelle L. 0000-0002-6002-6806 mcoombs@usgs.gov","orcid":"https://orcid.org/0000-0002-6002-6806","contributorId":2809,"corporation":false,"usgs":true,"family":"Coombs","given":"Michelle","email":"mcoombs@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":937948,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nastan, A.M.","contributorId":355697,"corporation":false,"usgs":false,"family":"Nastan","given":"A.M.","affiliations":[],"preferred":false,"id":937951,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263997,"text":"70263997 - 2025 - A practical decision tool for marine bird mortality assessments","interactions":[],"lastModifiedDate":"2025-03-04T14:44:08.99165","indexId":"70263997","displayToPublicDate":"2025-02-28T08:35:15","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18754,"text":"EcoEvoRxiv","active":true,"publicationSubtype":{"id":32}},"title":"A practical decision tool for marine bird mortality assessments","docAbstract":"Given the rise in anthropogenic, environmental, and disease events contributing to marine bird mortality, there is a critical need to improve the rigor of mortality assessments. Deficits in data collection and mortality estimation can hinder a manager’s ability to document event scales and inform population level impacts. Therefore, to inform decisions required during activities such as conservation status assessments or harvest management, organizations may choose to incorporate mortality assessments into response plans. Resources, capacity, and assets to assess mortality vary across jurisdictions (federal, state, Indigenous, local, etc.), and clear guidance to support mortality assessments is often unavailable or not clearly addressed. Here, we present a decision support tool to help managers identify and evaluate survey options to assess bird mortality in a diverse array of scenarios. The objective of the decision tool is to improve data collection and availability which will increase the ability to robustly estimate mortality, given situation-specific attributes and constraints. This decision tool is designed to guide the response when a mortality event is initially encountered and offers suggestions for assessment and reporting procedures in the absence of other guidance or to complement existing protocols. The decision tool is also meant to inform decision making for response determination and resource allocation. The tool facilitates examination of options for further assessment and monitoring which users determine by examining questions pertaining to species prioritization, mortality spatial extent, and the potential magnitude of impacts on affected species. Finally, identification of appropriate survey methods, that address imperfect detection when a complete census is not possible, are determined by exploring location, spatial and temporal extent, and the type of species affected. Ultimately, this tool aims to facilitate and improve the standardization of mortality assessments, equipping managers with a practical resource to navigate the decision-making process for marine bird mortality estimation.
","language":"English","publisher":"EcoEvoRxiv","doi":"10.32942/X23S6R","usgsCitation":"Harvey, J., Ramey, A.M., Avery-Gomm, S., Robertson, G., Romano, M., Mullinax, J.M., Boldenow, M., Atkinson, P., and Prosser, D.J., 2025, A practical decision tool for marine bird mortality assessments: EcoEvoRxiv, https://doi.org/10.32942/X23S6R.","productDescription":"48 p.","ipdsId":"IP-168193","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":487279,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.32942/x23s6r","text":"Publisher Index Page"},{"id":482790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harvey, Johanna Alexandra 0000-0003-4504-6777","orcid":"https://orcid.org/0000-0003-4504-6777","contributorId":351781,"corporation":false,"usgs":true,"family":"Harvey","given":"Johanna Alexandra","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":929443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":929444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Avery-Gomm, Stephanie","contributorId":213093,"corporation":false,"usgs":false,"family":"Avery-Gomm","given":"Stephanie","email":"","affiliations":[{"id":12552,"text":"University of Queensland","active":true,"usgs":false}],"preferred":false,"id":929445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robertson, Gregory","contributorId":331077,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory","affiliations":[],"preferred":false,"id":929446,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romano, Marc","contributorId":200806,"corporation":false,"usgs":false,"family":"Romano","given":"Marc","affiliations":[],"preferred":false,"id":929447,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":929448,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boldenow, Megan","contributorId":169336,"corporation":false,"usgs":false,"family":"Boldenow","given":"Megan","affiliations":[],"preferred":false,"id":929449,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Atkinson, Philip W.","contributorId":295425,"corporation":false,"usgs":false,"family":"Atkinson","given":"Philip W.","affiliations":[{"id":38864,"text":"British Trust for Ornithology","active":true,"usgs":false}],"preferred":false,"id":929450,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":929451,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263870,"text":"ofr20251003 - 2025 - Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska","interactions":[],"lastModifiedDate":"2025-07-21T18:27:29.016147","indexId":"ofr20251003","displayToPublicDate":"2025-02-27T11:50:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1003","displayTitle":"Phase 1 Technical Implementation Plan for the Expansion of the ShakeAlert Earthquake Early Warning System to Alaska","title":"Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska","docAbstract":"The conference report accompanying the fiscal year (FY) 2022 Consolidated Appropriations Act (Public Law 117–103) for the U.S. Department of the Interior and related agencies directed the U.S. Geological Survey (USGS) to “work with the State of Alaska to develop an implementation plan to be completed within two years in order to put ShakeAlert/Earthquake Early Warning in Alaska” (p. 29). Congress included $1 million in the FY 2022 appropriation to conduct this effort.
The USGS Earthquake Hazards Program, along with partner organizations, has developed the ShakeAlert earthquake early warning (EEW) system for the West Coast, which currently operates in California, Oregon, and Washington. The purpose of the system and its alert delivery partners is to reduce the impact of earthquakes and save lives and property by delivering ShakeAlert-powered alerts that are transmitted to the public via mass notification technologies, and by providing more detailed data streams to institutional users and commercial service providers to trigger automated, user-specific, protective actions.
ShakeAlert was designed in such a way that it could be expanded to other U.S. regions with high earthquake risk, after the build-out of seismic and geodetic networks to support ShakeAlert in a specified region is completed and the necessary funding is secured for long-term operation and maintenance.
When an earthquake occurs, seismic waves radiate from the rupturing fault like waves on a pond. It is these waves that people feel as earthquake shaking and that can cause damage to structures. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Early warnings of impending shaking are then sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking. There will almost always be a region near the earthquake epicenter where alerts arrive after damaging shaking has begun. The ShakeAlert system updates its ground-motion estimates as an earthquake grows larger.
In response to the FY 2022 congressional direction, the USGS worked with the State of Alaska to devise this implementation plan for ShakeAlert expansion to Alaska. The USGS engaged with the Alaska Division of Homeland Security and Emergency Management (DHS&EM) and the Alaska Division of Geological and Geophysical Surveys (DGGS). A cooperative agreement was awarded to the Alaska Earthquake Center (AEC) at the University of Alaska Fairbanks (UAF) for their contributions to the plan and their work coordinating with other networks in Alaska. The USGS engaged with the Alaska Seismic Hazards Safety Commission (ASHSC) throughout the process. The USGS also held a series of Alaska stakeholder engagements. The process of developing the implementation plan was facilitated by contracted staff from Corner Alliance, which is a government consulting firm.
This implementation plan describes the details and estimates the costs for a Phase 1 expansion of the ShakeAlert system to Alaska. A geographically limited Phase 1 goal was chosen that covers the highest risk and most populated areas of Alaska. The areas proposed encompass the State’s main population centers and 90 percent of the State’s population. This Phase 1 design is considered very challenging and ambitious from the viewpoint of network operators. The lessons learned if this plan is implemented could be used to consider subsequent phases to expand EEW beyond Phase 1 in Alaska in the future.
ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System (ANSS). This implementation plan calls for a total of 450 high-quality, real-time EEW-capable ANSS seismic stations in Alaska: 270 new stations, 160 upgraded stations, and 20 existing stations. These seismic station numbers are based on a station spacing of 10 kilometers (km) in urban areas, 20 km in seismic source areas that endanger population centers, and 40 km in other areas. The associated costs also include support for some EEW-capable global navigation satellite system (GNSS) stations, with a focus on improving warnings for large subduction zone earthquakes. For effective EEW, ShakeAlert requires low-latency, high-availability, robust telemetry links to deliver continuous, real-time data from field stations to the data centers.
The Alaska data processing hardware infrastructure would follow the general design for fail-safe operation that is used for the ShakeAlert system on the West Coast. The ShakeAlert architecture uses two independent layers: the production layer for earthquake processing and the alert layer to make alerting decisions and serve alerts to users. This implementation plan includes two geographically separated data centers in Alaska, each with two fully independent production and alert layers using the same system design developed for the West Coast. As of March 2024, the ShakeAlert system is at version 3.0.1, with more advanced versions in the development and testing pipeline. ShakeAlert originally used two algorithms to determine the location and magnitude of earthquakes using seismic data. A third algorithm that can calculate very large magnitudes of very large earthquakes with geodetic data was added in March 2024.
ShakeAlert publishes several data and alert products to meet the needs of different users. All messages include the location of the earthquake, either as a point or a line, and its magnitude. Ground-shaking estimates are published in two forms, as ground-motion contours and a map grid. Providing adequate warning time for strong shaking (the “target threshold”) requires sending alerts at a threshold lower than that strong shaking level (the “alert threshold”). The thresholds for public alerting in Alaska would be a joint USGS and State decision.
To have the greatest benefit, ShakeAlert-powered alerts would be delivered to institutional users and individuals by all practical pathways. The USGS alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission nor the infrastructure and expertise to perform mass notifications to the public or implement automatic actions for end users of the alerts. To meet this need, ShakeAlert recruits private sector “technology enablers” that have the necessary expertise to develop end-user implementations using EEW alerts with the goal of stimulating an EEW industry.
Earthquake early warning alerts are useless if people do not know how to respond to them. Although the alert messages include instructions about what to do (drop, cover, and hold on), alerts are more effective if people have been trained in advance. Messages about ShakeAlert’s capabilities, limitations, and benefits could be integrated with existing earthquake education programs, including State-run programs. Therefore, ShakeAlert would coordinate with both public and private partners and stakeholders through various partnerships and agreements to accomplish consistent and ongoing public earthquake hazard education.
The estimated capital cost of completing the computing infrastructure and sensor networks for the Phase 1 ShakeAlert expansion to Alaska is approximately $66 million in 2024 dollars. The annual operation and maintenance cost of the completed system is estimated to be $12 million per year in 2024 dollars when fully built out.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20251003","programNote":"Earthquake Hazards Program","usgsCitation":"Wolfe, C.J., Ruppert, N.A., Given, D.D., West, M.E., Thomas, V.I., Murray, J.R., and Grapenthin, R., 2025, Phase 1 technical implementation plan for the expansion of the ShakeAlert earthquake early warning system to Alaska: U.S. Geological Survey Open-File Report 2025–1003, 32 p., https://doi.org/10.3133/ofr20251003.","productDescription":"viii, 32 p.","onlineOnly":"Y","ipdsId":"IP-169264","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":482514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1003/coverthb.jpg"},{"id":482516,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1003/ofr20251003.pdf","text":"Report","size":"7.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1003"},{"id":492693,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118457.htm","linkFileType":{"id":5,"text":"html"}},{"id":482829,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251003/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1003"},{"id":482578,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1003/ofr20251003.xml"},{"id":482577,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1003/images"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.95812081792852,\n 56\n ],\n [\n -132.95812081792852,\n 63\n ],\n [\n -163.75172419269705,\n 63\n ],\n [\n -163.75172419269705,\n 56\n ],\n [\n -132.95812081792852,\n 56\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Senior Science Advisor for Earthquake and Geologic Hazards
Earthquake Hazards Program
U.S. Geological Survey
Mail Stop 905
12201 Sunrise Valley Drive
Reston, VA 20192
The origin of the Aleutian Basin is unresolved because its crust is deeply buried beneath sediments. It has been interpreted as forming in the Eocene when the Beringian convergent margin jumped seaward to south of the Aleutian arc, thereby capturing a large sector of Cretaceous Pacific crust. Alternatively, it may have formed by backarc spreading. We present new magnetic and seismic reflection data compilations and review other pertinent data to evaluate these two possibilities. Arguments for entrapment are: 1) Palaeomagnetic and geologic data document that the Aleutian arc formed approximately in place and as a seaward, on-strike continuation of the Alaska Peninsula; 2) basin-central spreading anomalies trend N-S, normal to the Aleutian arc and exhibit amplitudes and lengths typically formed at mid-ocean ridges; 3) seismic reflection profiles document that cross-basin depositional sequences are like those expected of filling a fixed-width basin. Arguments for a backarc spreading origin include: 1) The perpendicular orientation of magnetic anomalies in the Aleutian Basin and the northern Pacific Plate suggest different origins; 2) the sub-parallel orientation of Aleutian Basin spreading magnetic fabric and Palaeogene rift basins on the Bering Shelf suggest a common extensional regime; 3) thinner continental crust beneath outer-shelf basins is consistent with extension; 4) lineated magnetic fabrics on the margins of the basin are similar to early rifting magnetic fabrics found in other backarc basins; 5) basin heat flow is consistent with Palaeogene seafloor spreading; 6) its marginal basin setting is analogous to marginal basins elsewhere that formed by extension and seafloor spreading, not entrapment; and 7) entrapment requires formation of the Aleutian subduction zone by transference, a mode of subduction initiation that has not yet been documented. We present evidence for both interpretations and underscore the need for continued data collection (e.g. scientific drilling) to test competing hypotheses. Similar approaches may be useful for understanding thickly sedimented marginal basins elsewhere.
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