{"pageNumber":"15","pageRowStart":"350","pageSize":"25","recordCount":11370,"records":[{"id":70250271,"text":"70250271 - 2023 - Investigating permafrost carbon dynamics in Alaska with artificial intelligence","interactions":[],"lastModifiedDate":"2023-11-30T13:12:50.071693","indexId":"70250271","displayToPublicDate":"2023-11-16T07:09:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Investigating permafrost carbon dynamics in Alaska with artificial intelligence","docAbstract":"

Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integrates in-situ measurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO2), and methane flux (CH4)—with high precision and minimal loss (i.e. ALTRMSE: 1.327 cm [1969–2022]; CO2RMSE: 0.697 µmolCO2m−2s−1 [2003–2021]; CH4RMSE: 0.715 nmolCH4m−2s−1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ad0607","usgsCitation":"Gay, B., Pastick, N., Zufle, A., Armstrong, A., Miner, K., and Qu, J., 2023, Investigating permafrost carbon dynamics in Alaska with artificial intelligence: Environmental Research Letters, v. 18, no. 12, 125001, 20 p., https://doi.org/10.1088/1748-9326/ad0607.","productDescription":"125001, 20 p.","ipdsId":"IP-158731","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":441585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad0607","text":"Publisher Index Page"},{"id":423088,"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 -157.71401031875757,\n 58.963334167122895\n ],\n [\n -152.79213531875774,\n 62.03364889814105\n ],\n [\n -147.07924469375772,\n 63.08656488912206\n ],\n [\n -142.24526031875774,\n 62.44303153277835\n ],\n [\n -141.01479156875783,\n 62.198069009088584\n ],\n [\n -141.01479156875783,\n 66.69800816270453\n ],\n [\n -141.10268219375766,\n 70.02941490604613\n ],\n [\n -149.89174469375754,\n 70.79541190510929\n ],\n [\n -156.39565094375757,\n 71.42141172305344\n ],\n [\n -162.54799469375754,\n 70.7375061891758\n ],\n [\n -165.53627594375757,\n 69.32744461858817\n ],\n [\n -167.11830719375752,\n 68.01724610188819\n ],\n [\n -167.38197906875752,\n 64.78513626012426\n ],\n [\n -166.94252594375763,\n 61.11327463769916\n ],\n [\n -166.67885406875763,\n 59.45820927210812\n ],\n [\n -164.04213531875743,\n 58.64466553350218\n ],\n [\n -159.73547175467303,\n 57.905238894122505\n ],\n [\n -157.53820612967297,\n 57.85851039771879\n ],\n [\n -157.71401031875757,\n 58.963334167122895\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gay, Bradley 0000-0003-2617-2559","orcid":"https://orcid.org/0000-0003-2617-2559","contributorId":332010,"corporation":false,"usgs":false,"family":"Gay","given":"Bradley","email":"","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":889234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pastick, Neal 0000-0002-4321-6739","orcid":"https://orcid.org/0000-0002-4321-6739","contributorId":222683,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":889235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zufle, Andreas 0000-0001-7001-4123","orcid":"https://orcid.org/0000-0001-7001-4123","contributorId":332011,"corporation":false,"usgs":false,"family":"Zufle","given":"Andreas","email":"","affiliations":[{"id":40432,"text":"Emory University","active":true,"usgs":false}],"preferred":false,"id":889236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Armstrong, Amanda 0000-0002-9123-8924","orcid":"https://orcid.org/0000-0002-9123-8924","contributorId":332012,"corporation":false,"usgs":false,"family":"Armstrong","given":"Amanda","email":"","affiliations":[{"id":40052,"text":"NASA Goddard","active":true,"usgs":false}],"preferred":false,"id":889237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miner, Kimberly 0000-0002-1006-1283","orcid":"https://orcid.org/0000-0002-1006-1283","contributorId":329027,"corporation":false,"usgs":false,"family":"Miner","given":"Kimberly","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":889238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Qu, J.J.","contributorId":182468,"corporation":false,"usgs":false,"family":"Qu","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":889239,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250005,"text":"70250005 - 2023 - Kinematic evolution of a large paraglacial landslide in the Barry Arm fjord of Alaska","interactions":[],"lastModifiedDate":"2023-11-12T13:40:42.228675","indexId":"70250005","displayToPublicDate":"2023-11-09T07:36:48","publicationYear":"2023","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":"Kinematic evolution of a large paraglacial landslide in the Barry Arm fjord of Alaska","docAbstract":"

Our warming climate is adversely affecting cryospheric landscapes via glacial retreat, permafrost degradation, and associated slope destabilization. In Prince William Sound, Alaska, the rapid retreat of Barry Glacier has destabilized the slopes flanking the glacier, resulting in numerous landslides. The largest of these landslides (∼500 Mm3 in volume) is more than 2 km wide and has the potential to generate a tsunami that could affect nearby recreationists, marine traffic, infrastructure, natural and cultural resources, and the community of Whittier, located 60 km from the landslide. Here, we combine landslide structural and kinematic element mapping with data acquired from bi-yearly airborne lidar, multi-week satellite-based synthetic aperture radar (SAR), sub-hourly ground-based SAR, and seismic monitoring from 2020 to 2022 to characterize this landslide and examine its evolution. While some methods serve as a snapshot in time that is a culmination of events, others emphasize the ever-evolving nature of the landslide and associated hazards. Four major kinematic elements define the overall structure of the landslide, which vary in deformation type and rate, from creep (5 mm per day over several months) to episodic movement (2 m in 30 days) and landslide-wide to localized events. In some areas of the landslide, short-term deformation deviates from structures formed by cumulative movement, implying structural and kinematic evolution associated with glacier retreat. These insights are important for assessing landslide hazards and hazard evolution for large, slow-moving bedrock landslides in actively deglaciating environments.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JF007119","usgsCitation":"Schaefer, L.N., Coe, J.A., Wikstrom Jones, K., Collins, B.D., Staley, D.M., West, M.E., Karasozen, E., Miles, C.P., Wolken, G.J., Daanan, R.P., and Baxstrom, K.W., 2023, Kinematic evolution of a large paraglacial landslide in the Barry Arm fjord of Alaska: Journal of Geophysical Research: Earth Surface, v. 128, no. 11, e2023JF007119, 24 p., https://doi.org/10.1029/2023JF007119.","productDescription":"e2023JF007119, 24 p.","ipdsId":"IP-144624","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":441632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jf007119","text":"Publisher Index Page"},{"id":422518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm Fjord","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.57375471849628,\n 61.21073282428921\n ],\n [\n -148.57375471849628,\n 60.89167463593492\n ],\n [\n -147.89534895677747,\n 60.89167463593492\n ],\n [\n -147.89534895677747,\n 61.21073282428921\n ],\n [\n -148.57375471849628,\n 61.21073282428921\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-09","publicationStatus":"PW","contributors":{"authors":[{"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":887947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":887948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wikstrom Jones, Katreen","contributorId":331515,"corporation":false,"usgs":false,"family":"Wikstrom Jones","given":"Katreen","email":"","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":887949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":887950,"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":887951,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"West, Michael E.","contributorId":147407,"corporation":false,"usgs":false,"family":"West","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":887952,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karasozen, Ezgi","contributorId":331516,"corporation":false,"usgs":false,"family":"Karasozen","given":"Ezgi","affiliations":[{"id":79224,"text":"Alaska Earthquake Center","active":true,"usgs":false}],"preferred":false,"id":887953,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miles, Charles Prentice-James 0000-0001-8381-6803","orcid":"https://orcid.org/0000-0001-8381-6803","contributorId":331517,"corporation":false,"usgs":true,"family":"Miles","given":"Charles","email":"","middleInitial":"Prentice-James","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":887954,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wolken, Gabriel J.","contributorId":221149,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","email":"","middleInitial":"J.","affiliations":[{"id":40336,"text":"Alaska Department of Natural Resources: Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":887955,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Daanan, Ronald P.","contributorId":331518,"corporation":false,"usgs":false,"family":"Daanan","given":"Ronald","email":"","middleInitial":"P.","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":887956,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Baxstrom, Kelli Wadsworth 0000-0003-1409-0492","orcid":"https://orcid.org/0000-0003-1409-0492","contributorId":261748,"corporation":false,"usgs":true,"family":"Baxstrom","given":"Kelli","email":"","middleInitial":"Wadsworth","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":887957,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70249893,"text":"70249893 - 2023 - Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc","interactions":[],"lastModifiedDate":"2023-11-04T13:41:33.169601","indexId":"70249893","displayToPublicDate":"2023-11-03T08:38:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc","docAbstract":"

Alaska contains over 130 volcanoes and volcanic fields that have been active within the last 2 million years. Of these, roughly 90 have erupted during the Holocene, with many characterized by at least one large explosive eruption. These large tephra-producing eruptions (LTPEs) generate orders of magnitude more erupted material than a “typical” arc explosive eruption and distribute ash thousands of kilometers from their source. Because LTPEs occur infrequently, and the proximal explosive deposit record in Alaska is generally limited to the Holocene, we require a method that links distal deposits to a source volcano where the correlative proximal deposits from that eruption are no longer preserved. We present a model that accurately and confidently identifies LTPE volcanic sources in the Alaska-Aleutian arc using only in situ geochemistry. The model is a voting ensemble classifier comprised of six conceptually different machine learning algorithms trained on proximal tephra deposits that have had their source positively identified. We show that incompatible trace element ratios (e.g., Nb/U, Th/La, Rb/Sm) help produce a feature space that contains significantly more variance than one produced by major element concentrations, ultimately creating a model that can achieve high accuracy, precision, and recall on predicted volcanic sources, regardless of the perceived 2D data distribution (i.e., bimodal, uniform, normal) or composition (i.e., andesite, trachyte, rhyolite) of that source. Finally, we apply our model to unidentified distal marine tephra deposits in the region to better understand explosive volcanism in the Alaska-Aleutian arc, specifically its pre-Holocene spatiotemporal distribution.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC011037","usgsCitation":"Lubbers, J.E., Loewen, M.W., Wallace, K.L., Coombs, M.L., and Addison, J.A., 2023, Probabilistic source classification of large tephra producing eruptions using supervised machine learning: An example from the Alaska-Aleutian arc: Geochemistry, Geophysics, Geosystems, v. 24, no. 11, e2023GC011037, 32 p., https://doi.org/10.1029/2023GC011037.","productDescription":"e2023GC011037, 32 p.","ipdsId":"IP-153612","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":441693,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc011037","text":"Publisher Index Page"},{"id":422393,"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 -133.08003662231607,\n 57.22654031267055\n ],\n [\n -136.06831787231613,\n 59.75177682633975\n ],\n [\n -139.75972412231607,\n 61.223543890524866\n ],\n [\n -145.03316162231616,\n 63.07071371777923\n ],\n [\n -153.2069897473162,\n 62.26365593788125\n ],\n [\n -156.89839599731616,\n 59.30608826772951\n ],\n [\n -164.36909912231613,\n 54.921873210791034\n ],\n [\n -169.99409912231616,\n 53.63920253972526\n ],\n [\n -174.47652099731624,\n 52.690804333646184\n ],\n [\n -175.17964599731627,\n 51.99276521187923\n ],\n [\n -171.57613037231627,\n 51.393485235565834\n ],\n [\n -155.93159912231624,\n 55.27389283425282\n ],\n [\n -148.0214428723162,\n 59.35092259210174\n ],\n [\n -145.4726147473162,\n 59.79602215522897\n ],\n [\n -139.23238037231607,\n 58.8999102448233\n ],\n [\n -136.15620849731607,\n 56.939973009246756\n ],\n [\n -135.1894116223162,\n 55.32392853293092\n ],\n [\n -132.99214599731613,\n 55.07311840210144\n ],\n [\n -131.14644287231613,\n 55.771416187262616\n ],\n [\n -133.08003662231607,\n 57.22654031267055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-11-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lubbers, Jordan Edward 0000-0002-3566-5091","orcid":"https://orcid.org/0000-0002-3566-5091","contributorId":330466,"corporation":false,"usgs":true,"family":"Lubbers","given":"Jordan","email":"","middleInitial":"Edward","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":887574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loewen, Matthew W. 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":887575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":887576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":887577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Addison, Jason A. 0000-0003-2416-9743 jaddison@usgs.gov","orcid":"https://orcid.org/0000-0003-2416-9743","contributorId":4192,"corporation":false,"usgs":true,"family":"Addison","given":"Jason","email":"jaddison@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":887578,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245198,"text":"70245198 - 2023 - Reconnaissance mineral and cathodoluminescence studies of gold occurrences in the Pogo-Black Mountain area, eastern interior Alaska, USA","interactions":[],"lastModifiedDate":"2024-01-26T17:23:09.372104","indexId":"70245198","displayToPublicDate":"2023-11-01T11:17:57","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Reconnaissance mineral and cathodoluminescence studies of gold occurrences in the Pogo-Black Mountain area, eastern interior Alaska, USA","docAbstract":"The Pogo Au deposit is the largest of a number of gold occurrences in eastern interior Alaska, that occur along a broad trend from west of Pogo to Black Mountain. Some of these occurrences are hosted in amphibolite facies gneisses and others in mid-Cretaceous igneous rocks that intruded the older metamorphic rocks. All occurrences contain arsenopyrite and pyrite. Whole rock geochemical trends distinguish most metamorphic rock-hosted vein prospects (strong Bi-Te-Au correlations) and intrusion-hosted occurrences (weak As-Au correlations). Brecciated quartz veins in metamorphic rocks have paragentically late Bi-Te (±S) + Au that post-dates Fe-As sulphide deposition. High grade vein samples from the Tibbs Creek intrusion-hosted deposits contain pyrite and arsenopyrite, generally lack Bi-Te minerals, but can contain paragentically younger euhedral quartz, stibnite and carbonate. Cathodoluminescence studies of gold-rich samples indicate that quartz dissolution occurred during the syn- to post-tectonic Bi-Te-Au deposition, and the later stibnite event. In the case of metamorphic rock-hosted deposits (e.g., Pogo, Gray Lead), Bi-Te and gold deposition commonly occurs in microfractures within quartz veins; the limited quartz in these fractures have distinctive CL response. We propose that gold deposition is related to changes in P-T conditions rather than fluid-rock chemical reactions. Similar quartz dissolution textures affect the void-filling euhedral quartz before or during stibnite and carbonate mineralization in the high-grade Au samples from Blue Lead.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 17th SGA biennial meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"17th Biennial SGA Meeting","conferenceDate":"August 28 - September 1, 2023","conferenceLocation":"Zurich, Switzerland","language":"English","publisher":"Society for Geology Applied to Mineral Deposits","usgsCitation":"Graham, G.E., Marsh, E.E., Lowers, H.A., and Taylor, R., 2023, Reconnaissance mineral and cathodoluminescence studies of gold occurrences in the Pogo-Black Mountain area, eastern interior Alaska, USA, in Proceedings of the 17th SGA biennial meeting, v. 2, Zurich, Switzerland, August 28 - September 1, 2023, p. 142-145.","productDescription":"4 p.","startPage":"142","endPage":"145","ipdsId":"IP-151250","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":425025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425024,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sga2023.ch/programme/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alaska","otherGeospatial":"Pogo-Black Mountains area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -145,\n 64.5\n ],\n [\n -145,\n 64.33\n ],\n [\n -144.5,\n 64.33\n ],\n [\n -144.5,\n 64.5\n ],\n [\n -145,\n 64.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":875825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marsh, Erin E. 0000-0001-5245-9532 emarsh@usgs.gov","orcid":"https://orcid.org/0000-0001-5245-9532","contributorId":1250,"corporation":false,"usgs":true,"family":"Marsh","given":"Erin","email":"emarsh@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":875826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":875827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":201948,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":875828,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70245199,"text":"70245199 - 2023 - Critical minerals: Germanium and cobalt in the Bornite deposit, southwestern Brooks Range, Alaska","interactions":[],"lastModifiedDate":"2024-01-26T17:16:14.966507","indexId":"70245199","displayToPublicDate":"2023-11-01T11:10:22","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Critical minerals: Germanium and cobalt in the Bornite deposit, southwestern Brooks Range, Alaska","docAbstract":"Increasing demand for Ge and Co has led to a renewed interest and focus on advancing our understanding of the occurrence, distribution, and sequestration of these critical elements in known ore deposits. A workflow using a variety of analytical techniques and co-registered datasets has been developed and was applied to the carbonate hosted Bornite Cu-Co (Zn,-Ge) deposit in Alaska, where a new orebody (“the South Reef”) was discovered in 2011. The South Reef is host to substantial amounts of high-grade Cu, Co, and appreciable Zn, but also contains localised elevated concentrations of Ge. We describe the mineralogy and paragenesis of the South Reef based on detailed optical petrography, scanning electron microscopy, scanning electron microscopy-based automated mineralogy, and mapping μ-X-ray fluorescence analysis. Germanium was found to exist in renierite, white mica, illite, galena and feldspars within the South Reef. 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As the Arctic and its rivers continue to warm, a better understanding of the possible future impacts on people would benefit from close partnership with Indigenous communities and scientists from diverse fields of study. We present efforts by the Arctic Rivers Project to conduct community-engaged research to increase collective understanding of the historical and potential future impacts of climate change on rivers, fish, and Indigenous communities. Working in central to northern Alaska and the Yukon Territory in Canada, the project seeks to engage with Indigenous communities in ethical and equitable ways to produces science that is useful, useable, and used that may serve as an example for future research efforts. Toward this goal, we formed an Indigenous Advisory Council and together developed project-specific knowledge co-production protocols. This paper provides a novel model of design and implementation to co-produce knowledge with communities across a large study domain.

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Updating predictions of the response of high-profile, at-risk species to climate change and anthropogenic stressors is vital for informing effective conservation action. Here, we review two prior generations of Bayesian network probability models predicting changes in global polar bear (Ursus maritimus) population status, and provide a contemporary update based on recent research findings and sea-ice projections by newer climate models. We compare predictions of polar bear population response from all 3 models among four circumpolar Arctic ecoregions, using sea ice projections based on three IPCC greenhouse gas emissions scenarios (SSP2.6, 4.5, 8.5). Consistent with the previous two model generations, polar bears will continue to experience increasing probability of declining or greatly declining populations throughout the 21st century, varying by emission scenario. Populations within the Polar Basin Divergent Ice Ecoregion have the highest predicted probability of declines, but predictions were slightly less dire relative to the previous model generation. Most of the influence, denoted by model sensitivity analysis, is from expected degradation and loss of sea ice and reduced access to marine prey. The lack of terrestrial prey adequate to substitute for loss of access to marine prey, as well as human-caused bear morality associated with hunting and defense of life and property encountered when polar bears are increasingly forced ashore also contributed to predicted declines. Although some tidewater glacial fjords and other localized onshore resources may provide local refugia, their benefit is transient. Our findings continue to inform priorities for inventory, monitoring, and research needs, and suggest that similar updates to models of other at-risk species can capitalize on the comparison framework we present here.

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Pacific Northwest, U.S.A","docAbstract":"

Water resources in the Pacific Northwest (PNW) are characterized by significant interannual to interdecadal variation. Paleo-proxy reconstructions such as those derived from tree-rings provide longer-term context and supplement information on this expected range of variability, which can improve planning, management, and response related to extreme events and hydrologic change. However, existing paleo-proxy reconstructions have yet to address the potential for pronounced within- and among-basin variations in the PNW due to a lack of spatial coverage. Here we develop methodologically consistent reconstructions for 36 gages in the PNW, including the Columbia and Snake River drainages, as well as key coastal watersheds. These reconstructions extend back at least to the 1500s coefficient of efficiency. Reconstruction skill is relatively high (mean R2 = 0.63), and snowpack- or winter precipitation-sensitive chronologies from high-elevation sites provide important contributions to reconstruction skill. At the whole-region scale, reconstructed variability indicates evidence for drier and wetter years, more persistent decadal variability, and correspondingly longer episodes of deficit and surplus compared to instrumental records. Within the region, this expanded range of extremes appears especially prevalent in the Snake River and southern PNW watersheds. Regionally, cumulative deficits in the early 1500s and mid 1600s rival those of the early 20th century, though persistence and timing vary widely among basins. These reconstructions suggest that considering within-region variability will be key for water management and planning under climate change, and that sub-regional adaptation strategies are likely to be advantageous.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035255","usgsCitation":"Littell, J.S., Pederson, G.T., Martin, J.T., and Gray, S.T., 2023, Networks of tree-ring based streamflow reconstructions for the Pacific Northwest, U.S.A: Water Resources Research, v. 59, no. 11, e2023WR035255, 19 p., https://doi.org/10.1029/2023WR035255.","productDescription":"e2023WR035255, 19 p.","ipdsId":"IP-157562","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":501606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr035255","text":"Publisher Index Page"},{"id":501578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Pacific Northwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.71989106589547,\n 51.23952552193842\n ],\n [\n -119.80255789994311,\n 50.58241223780449\n ],\n [\n -120.03798004087265,\n 48.335839842710136\n ],\n [\n -124.01371557903101,\n 45.96848992109024\n ],\n [\n -123.89678202651015,\n 43.17034661083997\n ],\n [\n -117.55123466152142,\n 43.759998649017774\n ],\n [\n -116.75453238797795,\n 41.1626488502026\n ],\n [\n -107.8814009064086,\n 41.44779467194607\n ],\n [\n -111.20892445986055,\n 45.51412922045732\n ],\n [\n -113.15663189749853,\n 52.616945225148044\n ],\n [\n -114.23620971604478,\n 53.31116037143961\n ],\n [\n -117.71989106589547,\n 51.23952552193842\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-10-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":957851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":957852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":957853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gray, Stephen T. 0000-0002-0959-3418 sgray@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-3418","contributorId":209851,"corporation":false,"usgs":true,"family":"Gray","given":"Stephen","email":"sgray@usgs.gov","middleInitial":"T.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":957854,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255038,"text":"70255038 - 2023 - Stream hydrology and a pulse subsidy shape patterns of fish foraging","interactions":[],"lastModifiedDate":"2024-06-17T15:25:51.081928","indexId":"70255038","displayToPublicDate":"2023-10-30T10:18:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Stream hydrology and a pulse subsidy shape patterns of fish foraging","docAbstract":"
  1. Pulsed subsidy events create ephemeral fluxes of hyper-abundant resources that can shape annual patterns of consumption and growth for recipient consumers. However, environmental conditions strongly affect local resource availability for much of the year, and can heavily impact consumer foraging and growth patterns prior to pulsed subsidy events. Thus, a consumer's capacity to exploit pulse subsidy resources may be influenced by antecedent environmental conditions, but this has rarely been shown in nature and is unknown in aquatic ecosystems.
  2. Here, we sought to understand the importance of hydrologic variation and a salmon pulse subsidy on the foraging and growth patterns of two stream salmonids in a coastal southeast Alaska drainage.
  3. To do this, we sampled fish stomach contents at a high temporal frequency (daily–weekly measurements) and analyzed fish consumption rates in relation to streamflow and pulse subsidy resource availability. We then explored the influence of interannual hydrologic variation on access to pulse subsidy resources (i.e. whether fish exceeded an egg consumption gape limit) in a bioenergetic simulation.
  4. Prior to Pink Salmon spawning, Dolly Varden and Coho Salmon displayed distinct and nonlinear flow-foraging relationships, where forage for both species consisted primarily of macroinvertebrates. During this time period, consumption maxima coincided with baseflow and the highest observed flow conditions, and consumption minima were observed at severe low-water and intermediate flow values. After salmon spawning began, forage was not significantly related to flow and consisted primarily of salmon eggs. Further, consumption rates increased overall, and foraging patterns did not appear to be affected by flow in either species. Bioenergetic simulations revealed that patterns of interannual hydrologic variation may shift Coho Salmon growth trajectories among years.
  5. Together, our results suggest that access to marine pulse subsidy resources may depend on whether antecedent hydrologic conditions are suitable for juvenile salmonids to grow large enough to consume salmon eggs by the onset of spawning.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.14018","usgsCitation":"Fitzgerald, K., Bellmore, J.R., Fellman, J.B., Cheng, M.L., Delbecq, C., and Falke, J.A., 2023, Stream hydrology and a pulse subsidy shape patterns of fish foraging: Journal of Animal Ecology, v. 92, no. 12, p. 2386-2398, https://doi.org/10.1111/1365-2656.14018.","productDescription":"13 p.","startPage":"2386","endPage":"2398","ipdsId":"IP-150442","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":441747,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/1365-2656.14018","text":"External Repository"},{"id":430279,"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 -134.69532259029916,\n 58.4891103778352\n ],\n [\n -134.69532259029916,\n 58.384723457070805\n ],\n [\n -134.53360309034514,\n 58.384723457070805\n ],\n [\n -134.53360309034514,\n 58.4891103778352\n ],\n [\n -134.69532259029916,\n 58.4891103778352\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzgerald, Kevin","contributorId":332288,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Kevin","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":903215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellmore, J. Ryan","contributorId":271034,"corporation":false,"usgs":false,"family":"Bellmore","given":"J.","email":"","middleInitial":"Ryan","affiliations":[{"id":56260,"text":"U.S. Forest Service, Pacific Northwest Research Station, 11175 Auke Lake Way, Juneau, Alaska, 99801","active":true,"usgs":false}],"preferred":false,"id":903216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fellman, Jason B.","contributorId":198741,"corporation":false,"usgs":false,"family":"Fellman","given":"Jason","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":903217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cheng, Matthew L. H.","contributorId":339456,"corporation":false,"usgs":false,"family":"Cheng","given":"Matthew","email":"","middleInitial":"L. H.","affiliations":[],"preferred":false,"id":903218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Delbecq, Claire","contributorId":337162,"corporation":false,"usgs":false,"family":"Delbecq","given":"Claire","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":903219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903220,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70273450,"text":"70273450 - 2023 - Dating the penultimate great earthquake in south-central Alaska using tree-ring crossdating and radiocarbon wiggle-matching","interactions":[],"lastModifiedDate":"2026-01-14T15:59:24.616609","indexId":"70273450","displayToPublicDate":"2023-10-30T08:53:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7169,"text":"Quaternary Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Dating the penultimate great earthquake in south-central Alaska using tree-ring crossdating and radiocarbon wiggle-matching","docAbstract":"

A forest bed of tree stumps currently in the intertidal zone at Girdwood, south-central Alaska, records coseismic submergence during the penultimate great earthquake. Tree-ring samples from ten spruce stumps were crossdated to develop a 149-year-long ring-width chronology. Radiocarbon wiggle-matching found that single-ring ages from the chronology were offset 28 ± 7 years older than the IntCal20 calibration curve and that the last ring of the chronology dated as 1169 to 1189 CE (781–761 cal. yr. BP) at the 95% confidence level. Bark was observed on some stumps, six samples had the same year for the last growth ring, and so this wiggle-match date is also the best estimate of the date of the penultimate great earthquake. This date is in good agreement with a date for this event in a seismo-turbidite record from Skilak Lake but not with previous dates from Bayesian models of maximum- and minimum-limiting ages from coastal salt marshes. Reanalysis of the coastal salt marsh ages with the data grouped by area, context and material found that outer wood samples from stumps at coseismic submergence sites and a Bayesian limiting age model based on just herbaceous plant ages from Turnagain Arm and the Copper River area are both consistent with our wiggle-match date. Furthermore, coseismic emergence ages from Cape Suckling and Yakataga are older than the penultimate earthquake and so likely relate to an earlier uplift event in this eastern area. The rupture extent during the penultimate great earthquake appears to have been less than in the 1964 great earthquake and the interseismic interval between these two events was 785 ± 10 years.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.qsa.2023.100142","usgsCitation":"Barclay, D.J., Haeussler, P., and Witter, R.C., 2023, Dating the penultimate great earthquake in south-central Alaska using tree-ring crossdating and radiocarbon wiggle-matching: Quaternary Science Advances, v. 13, 100142, 13 p., https://doi.org/10.1016/j.qsa.2023.100142.","productDescription":"100142, 13 p.","ipdsId":"IP-158222","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":498704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.qsa.2023.100142","text":"Publisher Index Page"},{"id":498618,"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 -156.97392172569602,\n 60.587785877878815\n ],\n [\n -156.97392172569602,\n 56.48596044935496\n ],\n [\n -140.95577716118677,\n 56.48596044935496\n ],\n [\n -140.95577716118677,\n 60.587785877878815\n ],\n [\n -156.97392172569602,\n 60.587785877878815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barclay, David J 0009-0007-9629-3731","orcid":"https://orcid.org/0009-0007-9629-3731","contributorId":365136,"corporation":false,"usgs":false,"family":"Barclay","given":"David","middleInitial":"J","affiliations":[{"id":87054,"text":"SUNY Cortland, Cortland, NY","active":true,"usgs":false}],"preferred":false,"id":953743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":953744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":219962,"corporation":false,"usgs":true,"family":"Witter","given":"Robert","email":"rwitter@usgs.gov","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":953745,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70260094,"text":"70260094 - 2023 - Glass electron microprobe analyses methods, precision and accuracy for tephra studies in Alaska","interactions":[],"lastModifiedDate":"2024-10-28T12:05:52.765967","indexId":"70260094","displayToPublicDate":"2023-10-28T07:04:16","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Glass electron microprobe analyses methods, precision and accuracy for tephra studies in Alaska","docAbstract":"

No abstract available.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Alaska Division of Geological & Geophysical Surveys Miscellaneous Publication 174","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"Alaska Division of Geological & Geophysical Surveys","doi":"10.14509/31045","collaboration":"University of Alaska Fairbanks, Alaska Division of Geology and Geophysical Surveys","usgsCitation":"Loewen, M.W., Wallace, K.L., Lubbers, J.E., Ruth, D.C., Izbekov, P., Larsen, J., and Graham, N., 2023, Glass electron microprobe analyses methods, precision and accuracy for tephra studies in Alaska, v. 174, 20 p., https://doi.org/10.14509/31045.","productDescription":"20 p.","ipdsId":"IP-152644","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.14509/31045","text":"Publisher Index Page"},{"id":463241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"174","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Loewen, Matthew W. 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":916950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":916951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lubbers, Jordan Edward 0000-0002-3566-5091","orcid":"https://orcid.org/0000-0002-3566-5091","contributorId":330466,"corporation":false,"usgs":true,"family":"Lubbers","given":"Jordan","email":"","middleInitial":"Edward","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruth, Dawn Catherine Sweeney 0000-0001-9369-9364","orcid":"https://orcid.org/0000-0001-9369-9364","contributorId":334908,"corporation":false,"usgs":true,"family":"Ruth","given":"Dawn","email":"","middleInitial":"Catherine Sweeney","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Izbekov, Pavel","contributorId":237833,"corporation":false,"usgs":false,"family":"Izbekov","given":"Pavel","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":916954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":916955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Graham, Nathan 0000-0002-8100-207X","orcid":"https://orcid.org/0000-0002-8100-207X","contributorId":242809,"corporation":false,"usgs":false,"family":"Graham","given":"Nathan","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":916956,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247964,"text":"70247964 - 2023 - High pathogenicity avian influenza represents an unprecedented conservation challenge for globally declining seabird populations. Response to: Editorial, Protect wildlife from livestock diseases (Thijs Kuiken & Ruth Cromie)","interactions":[],"lastModifiedDate":"2023-10-25T10:46:49.035171","indexId":"70247964","displayToPublicDate":"2023-10-24T11:55:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"High pathogenicity avian influenza represents an unprecedented conservation challenge for globally declining seabird populations. Response to: Editorial, Protect wildlife from livestock diseases (Thijs Kuiken & Ruth Cromie)","docAbstract":"

No abstract available.

","language":"English","publisher":"AAAS","doi":"10.1126/science.adf0956","usgsCitation":"Wille, M., Avery-Gomm, S., Caliendo, V., Camphuysen, K., Humphreys, E., Lang, A., Philip, E., Ramey, A.M., Rijks, J.M., Robertson, G., and Robertson, L., 2023, High pathogenicity avian influenza represents an unprecedented conservation challenge for globally declining seabird populations. Response to: Editorial, Protect wildlife from livestock diseases (Thijs Kuiken & Ruth Cromie): Science, v. 378, no. 6615, p. 5-5, https://doi.org/10.1126/science.adf0956.","productDescription":"1 p.","startPage":"5","endPage":"5","ipdsId":"IP-143925","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":441778,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/science.adf0956","text":"Publisher Index Page"},{"id":422070,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"378","issue":"6615","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wille, Michelle","contributorId":173881,"corporation":false,"usgs":false,"family":"Wille","given":"Michelle","email":"","affiliations":[{"id":27309,"text":"Memorial University of Newfoundland, St. John’s, NL A1B 3X9, Canada","active":true,"usgs":false}],"preferred":false,"id":881349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":886703,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caliendo, Valentina","contributorId":238045,"corporation":false,"usgs":false,"family":"Caliendo","given":"Valentina","email":"","affiliations":[],"preferred":false,"id":886704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Camphuysen, Kees","contributorId":331074,"corporation":false,"usgs":false,"family":"Camphuysen","given":"Kees","email":"","affiliations":[],"preferred":false,"id":886705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Humphreys, E.","contributorId":43112,"corporation":false,"usgs":true,"family":"Humphreys","given":"E.","email":"","affiliations":[],"preferred":false,"id":886706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lang, Andrew","contributorId":331075,"corporation":false,"usgs":false,"family":"Lang","given":"Andrew","affiliations":[],"preferred":false,"id":886707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Philip, Emma","contributorId":331076,"corporation":false,"usgs":false,"family":"Philip","given":"Emma","email":"","affiliations":[],"preferred":false,"id":886708,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":881350,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rijks, Jolianne M.","contributorId":191780,"corporation":false,"usgs":false,"family":"Rijks","given":"Jolianne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":886709,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Robertson, Gregory","contributorId":331077,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory","affiliations":[],"preferred":false,"id":886710,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robertson, Laura","contributorId":193275,"corporation":false,"usgs":false,"family":"Robertson","given":"Laura","affiliations":[],"preferred":false,"id":881351,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70255728,"text":"70255728 - 2023 - Climate, heatwaves, nearshore ecosystems and the sunflower sea star","interactions":[],"lastModifiedDate":"2024-07-02T13:26:36.418434","indexId":"70255728","displayToPublicDate":"2023-10-24T08:22:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17473,"text":"Open Access Government","active":true,"publicationSubtype":{"id":10}},"title":"Climate, heatwaves, nearshore ecosystems and the sunflower sea star","docAbstract":"At the nexus of the ocean and the land lies the nearshore ecosystem, which includes the intertidal zone, an area exposed during low tides and submerged during high tides. Species in the intertidal are adapted to a high level of environmental variability including high temperatures, waves, and salinity variability. Species’ tolerance to desiccation in part determines the elevation within the intertidal zone they occupy. Biological interactions also determine the presence and abundance of species. Predators like sea stars and competition for space can influence numbers of barnacles and mussels. 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No abstract available.

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H.","affiliations":[{"id":39689,"text":"Alaska Division of Geological & Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":884857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orr, Tim R. 0000-0003-1157-7588 torr@usgs.gov","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":149803,"corporation":false,"usgs":true,"family":"Orr","given":"Tim","email":"torr@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":884858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loewen, Matthew W. 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":884859,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263958,"text":"70263958 - 2023 - Regional crustal structure of Indonesia from receiver functions","interactions":[],"lastModifiedDate":"2025-03-04T14:09:43.092705","indexId":"70263958","displayToPublicDate":"2023-10-20T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Regional crustal structure of Indonesia from receiver functions","docAbstract":"

Characterizing the crustal structure of Indonesia is important to gain a better understanding of its geodynamic evolution and improve seismic hazard assessments in the area. However, a unified crustal model of the entire Indonesian region and its surroundings is lacking. We present new maps of crustal thickness and bulk Vp/Vs ratio in Indonesia and the surrounding area that are obtained using P-wave receiver functions at 36 seismic stations from several permanent regional networks. The measured crustal thickness varies from ∼24 km to ∼38 km. The thickest crust, ∼38 km, is beneath Flores Island, southern Maluku, and neighboring northernmost Australia, whereas the thinnest crust, ∼24 km, is found under eastern Malaysia. Thus, crustal thickness varies by ∼14 km (from ∼24 km to ∼38 km) despite the small changes in elevation at the measurement points. The Vp/Vs ratios are 1.79±\">±0.11, with high values (>1.85) found along the Banda-Sunda arc-trench system. We attribute these high values to: (1) the presence of mafic island arc and oceanic crust and (2) partial melting within this volcanic region, which causes a larger decrease in S-wave velocities compared with P-wave velocities. The comparison of the seismic properties of Indonesian island arc crust, particularly the Vp/Vs ratio, with laboratory measurements and the petrology of the exhumed Talkeetna island arc, Alaska, allows us to infer the crustal composition of Indonesian island arc crust.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2023.230033","usgsCitation":"Zhang, Y., and Mooney, W.D., 2023, Regional crustal structure of Indonesia from receiver functions: Tectonophysics, v. 865, 230033, 12 p., https://doi.org/10.1016/j.tecto.2023.230033.","productDescription":"230033, 12 p.","ipdsId":"IP-151963","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489974,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2023.230033","text":"Publisher Index 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Ying","contributorId":351735,"corporation":false,"usgs":false,"family":"Zhang","given":"Ying","affiliations":[{"id":36391,"text":"University of Houston","active":true,"usgs":false}],"preferred":false,"id":929348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mooney, Walter D. 0000-0002-5310-3631 mooney@usgs.gov","orcid":"https://orcid.org/0000-0002-5310-3631","contributorId":3194,"corporation":false,"usgs":true,"family":"Mooney","given":"Walter","email":"mooney@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":929349,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241162,"text":"70241162 - 2023 - Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA","interactions":[],"lastModifiedDate":"2023-11-08T11:48:36.011532","indexId":"70241162","displayToPublicDate":"2023-10-18T11:44:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA","docAbstract":"

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-23-3261-2023","usgsCitation":"Patton, A., Luna, L., Roering, J.J., Jacobs, A., Korup, O., and Mirus, B., 2023, Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA: Natural Hazards and Earth System Sciences, v. 23, no. 10, p. 3261-3284, https://doi.org/10.5194/nhess-23-3261-2023.","productDescription":"24 p.","startPage":"3261","endPage":"3284","ipdsId":"IP-148647","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":441845,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-23-3261-2023","text":"Publisher Index Page"},{"id":422429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Sitka","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -135.5083890264902,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 57.18995904083906\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Annette","contributorId":303028,"corporation":false,"usgs":false,"family":"Patton","given":"Annette","email":"","affiliations":[{"id":65615,"text":"Sitka Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":866314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luna, Lisa","contributorId":303029,"corporation":false,"usgs":false,"family":"Luna","given":"Lisa","email":"","affiliations":[{"id":52955,"text":"University of Potsdam","active":true,"usgs":false}],"preferred":false,"id":866315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roering, Josh J.","contributorId":303030,"corporation":false,"usgs":false,"family":"Roering","given":"Josh","email":"","middleInitial":"J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":866316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobs, Aaron","contributorId":204855,"corporation":false,"usgs":false,"family":"Jacobs","given":"Aaron","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":866317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Korup, Oliver","contributorId":218071,"corporation":false,"usgs":false,"family":"Korup","given":"Oliver","email":"","affiliations":[{"id":39735,"text":"Institute of Earth and Environmental Science, University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":866318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866319,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252214,"text":"70252214 - 2023 - Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast","interactions":[],"lastModifiedDate":"2024-03-20T11:53:06.811606","indexId":"70252214","displayToPublicDate":"2023-10-18T06:50:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast","docAbstract":"

Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ad04b8","usgsCitation":"Piliouras, A., Jones, B.M., Clevenger, T., Gibbs, A.E., and Rowland, J.C., 2023, Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast: Environmental Research Letters, v. 18, 114050, 10 p., https://doi.org/10.1088/1748-9326/ad04b8.","productDescription":"114050, 10 p.","ipdsId":"IP-141537","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441857,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad04b8","text":"Publisher Index Page"},{"id":426794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.82972953158225,\n 72.33792024202972\n ],\n [\n -160.82972953158225,\n 68.87395946820305\n ],\n [\n -140.35121390658202,\n 68.87395946820305\n ],\n [\n -140.35121390658202,\n 72.33792024202972\n ],\n [\n -160.82972953158225,\n 72.33792024202972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2023-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Piliouras, Anastasia","contributorId":334927,"corporation":false,"usgs":false,"family":"Piliouras","given":"Anastasia","email":"","affiliations":[{"id":80287,"text":"Department of Geosciences, Pennsylvania State University, University Park, PA","active":true,"usgs":false}],"preferred":false,"id":896945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M.","contributorId":305542,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":896946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clevenger, Tabatha","contributorId":334928,"corporation":false,"usgs":false,"family":"Clevenger","given":"Tabatha","email":"","affiliations":[{"id":80288,"text":"Department of Earth Science and Geography, Vassar College, Poughkeepsie, NY","active":true,"usgs":false}],"preferred":false,"id":896947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":896948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowland, Joel C.","contributorId":169046,"corporation":false,"usgs":false,"family":"Rowland","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":896949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249575,"text":"70249575 - 2023 - Evaluation of breeding distribution and chronology of North American scoters","interactions":[],"lastModifiedDate":"2024-01-08T17:21:40.311151","indexId":"70249575","displayToPublicDate":"2023-10-14T07:01:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of breeding distribution and chronology of North American scoters","docAbstract":"

North America's scoter species are poorly monitored relative to other waterfowl. Black Melanitta americana, surf M. perspicillata, and white-winged M. deglandi scoter abundance and trend estimates are thus uncertain in many parts of these species' ranges. The most extensive source of waterfowl abundance and distribution data in North America is the Waterfowl breeding population and habitat survey (WBPHS). Although the WBPHS effectively monitors most species, both its timing and geographic coverage may preclude accurate scoter monitoring. Therefore, our goal was to better define when and where scoters breed to help interpret survey results and optimize potential supplemental survey efforts for scoters. We integrated satellite telemetry tracking data from scoters marked at multiple molting, staging, breeding, and wintering areas along the Atlantic and Pacific coasts to quantify continent-wide breeding chronology and distribution. We also examined possible drivers of variation in timing of arrival, length of stay, and departure at nesting locations. We documented a northwest to southeast distribution of estimated breeding sites across Alaska and Canada. On average, scoters arrived at nest sites on 1 June. Surf scoters and Pacific black scoters arrived earliest and departed earliest. Pacific-wintering black and white-winged scoters began breeding earlier than Atlantic-wintering birds. Additionally, birds arrived at nesting locations earlier in years with earlier snowmelt, and later snowmelt reduced lengths of stay for males. Breeding chronology also varied by age group, with adults arriving earlier than subadults. Our study is the first to comprehensively describe spatial variation in timing of breeding of both Atlantic and Pacific populations of all three scoter species across North America. Our results increase our understanding of how current surveys enumerate scoters and will inform possible supplemental efforts to improve continental monitoring of scoter populations.

","language":"English","publisher":"Wiley","doi":"10.1002/wlb3.01099","usgsCitation":"Bianchini, K., Gilliland, S.G., Berlin, A., Bowman, T.D., Boyd, W., De La Cruz, S.E., Esler, D., Evenson, J.R., Flint, P.L., Lepage, C., McWilliams, S.R., Meattey, D.E., Osenkowski, J.E., Perry, M., Poulin, J., Reed, E.T., Roy, C., Savard, J.L., Savoy, L., Schamber, J.L., Spiegel, C.S., Takekawa, J., Ward, D.H., and Mallory, M.L., 2023, Evaluation of breeding distribution and chronology of North American scoters: Wildlife Biology, v. 2023, e01099, 19 p., https://doi.org/10.1002/wlb3.01099.","productDescription":"e01099, 19 p.","ipdsId":"IP-140908","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":441881,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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Barrow’s goldeneyes across western North America have been shown to have a high degree of subpopulation independence using several data types. However, evidence for structured populations based on mitochondrial DNA, band recoveries, and tracking of adults is discordant with evidence from autosomal DNA. We used satellite tracking data from both juveniles and adults marked on natal and breeding grounds, respectively, in British Columbia, Canada to evaluate the hypothesis that male-biased juvenile dispersal maintains genetic panmixia of Pacific Barrow’s goldeneyes otherwise structured by migratory movements and high winter and breeding site fidelity of adults. We found that juvenile males traveled to overwintering sites located within the range of the overwintering sites of juvenile females, adult males, and adult females. Juvenile males migrated at the same time, travelled the same distance when moving between natal and overwintering sites, and had the same winter dispersion as juvenile females. Although juveniles did not travel with attendant females, all juveniles overwintered within the wintering range of adults. We tracked some juveniles into the following spring/summer and even second winter. Prospecting juveniles of both sexes travelled from their wintering grounds to potential breeding sites in the proximity of Riske Creek and within the bounds of the breeding locations used by adults. Juveniles tracked for more than a year also showed relatively high winter site fidelity. Because Barrow’s goldeneyes pair on wintering grounds, our tracking data are not consistent with the hypothesis that male-biased juvenile dispersal explains the genetic structure in the mitochondrial DNA and panmixia in the autosomal DNA of Barrow’s goldeneye. We suggest that uncommon or episodic dispersal of males might be enough to homogenize autosomal DNA but is unlikely to influence demographic population structure relevant to contemporary population management.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-023-00423-z","usgsCitation":"Forstner, T., Boyd, S., Esler, D., and Green, D., 2023, Dispersal of juvenile Barrow’s goldeneyes (Bucephala islandica) mirrors that of breeding adults: Movement Ecology, v. 11, 62, 11 p., https://doi.org/10.1186/s40462-023-00423-z.","productDescription":"62, 11 p.","ipdsId":"IP-152532","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":441891,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1186/s40462-023-00423-z","text":"Publisher Index Page"},{"id":421954,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -130.9100680281842,\n 53.167292643909775\n ],\n [\n -130.9100680281842,\n 47.0335576933322\n ],\n [\n -118.34170865318431,\n 47.0335576933322\n ],\n [\n -118.34170865318431,\n 53.167292643909775\n ],\n [\n -130.9100680281842,\n 53.167292643909775\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Forstner, Tess","contributorId":330968,"corporation":false,"usgs":false,"family":"Forstner","given":"Tess","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":886352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyd, Sean","contributorId":76672,"corporation":false,"usgs":false,"family":"Boyd","given":"Sean","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":886353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":886354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Green, David","contributorId":167789,"corporation":false,"usgs":false,"family":"Green","given":"David","affiliations":[],"preferred":false,"id":886355,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254456,"text":"70254456 - 2023 - Mortality thresholds of juvenile trees to drought and heatwaves: Implications for forest regeneration across a landscape gradient","interactions":[],"lastModifiedDate":"2024-05-28T11:43:30.086705","indexId":"70254456","displayToPublicDate":"2023-10-12T06:41:38","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"Mortality thresholds of juvenile trees to drought and heatwaves: Implications for forest regeneration across a landscape gradient","docAbstract":"

Tree loss is increasing rapidly due to drought- and heat-related mortality and intensifying fire activity. Consequently, the fate of many forests depends on the ability of juvenile trees to withstand heightened climate and disturbance anomalies. Extreme climatic events, such as droughts and heatwaves, are increasing in frequency and severity, and trees in mountainous regions must contend with these landscape-level climate episodes. Recent research focuses on how mortality of individual tree species may be driven by drought and heatwaves, but how juvenile mortality under these conditions would vary among species spanning an elevational gradient—given concurrent variation in climate, ecohydrology, and physiology–remains unclear. We address this knowledge gap by implementing a growth chamber study, imposing extreme drought with and without a compounding heatwave, for juveniles of five species that span a forested life zones in the Southwestern United States. Overall, the length of a progressive drought required to trigger mortality differed by up to 20 weeks among species. Inclusion of a heatwave hastened mean time to mortality for all species by about 1 week. Lower-elevation species that grow in warmer ambient conditions died earlier (Pinus ponderosa in 10 weeks, Pinus edulis in 14 weeks) than did higher-elevation species from cooler ambient conditions (Picea engelmannii and Pseudotsuga menziesii in 19 weeks, and Pinus flexilis in 30 weeks). When exposed to a heatwave in conjunction with drought, mortality advanced significantly only for species from cooler ambient conditions (Pinus flexilis: 2.7 weeks earlier; Pseudotsuga menziesii: 2.0 weeks earlier). Cooler ambient temperatures may have buffered against moisture loss during drought, resulting in longer survival of higher-elevation species despite expected drought tolerance of lower-elevation species due to tree physiology. Our study suggests that droughts will play a leading role in juvenile tree mortality and will most directly impact species at warmer climate thresholds, with heatwaves in tandem with drought potentially exacerbating mortality especially of high elevation species. These responses are relevant for assessing the potential success of both natural and managed reforestation, as differential juvenile survival following episodic extreme events will determine future landscape-scale vegetation trajectories under changing climate.

","language":"English","publisher":"Frontiers","doi":"10.3389/ffgc.2023.1198156","usgsCitation":"Lalor, A., Law, D.J., Breshears, D.D., Falk, D.A., Field, J.P., Loehman, R.A., Triepke, J., and Barron-Gafford, G.A., 2023, Mortality thresholds of juvenile trees to drought and heatwaves: Implications for forest regeneration across a landscape gradient: Frontiers in Forests and Global Change, v. 6, 1198156, 16 p., https://doi.org/10.3389/ffgc.2023.1198156.","productDescription":"1198156, 16 p.","ipdsId":"IP-152077","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":441898,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2023.1198156","text":"Publisher Index Page"},{"id":429317,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","noUsgsAuthors":false,"publicationDate":"2023-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Lalor, Alexadra","contributorId":336932,"corporation":false,"usgs":false,"family":"Lalor","given":"Alexadra","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":901439,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Law, Darin J.","contributorId":216390,"corporation":false,"usgs":false,"family":"Law","given":"Darin","email":"","middleInitial":"J.","affiliations":[{"id":39400,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":901440,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breshears, David D.","contributorId":51620,"corporation":false,"usgs":false,"family":"Breshears","given":"David","email":"","middleInitial":"D.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":901441,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falk, Donald A.","contributorId":197570,"corporation":false,"usgs":false,"family":"Falk","given":"Donald","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":901442,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Field, Jason P.","contributorId":216389,"corporation":false,"usgs":false,"family":"Field","given":"Jason","email":"","middleInitial":"P.","affiliations":[{"id":39400,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":901443,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":false,"id":901444,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Triepke, Jack","contributorId":336933,"corporation":false,"usgs":false,"family":"Triepke","given":"Jack","email":"","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":901445,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barron-Gafford, Greg A.","contributorId":19058,"corporation":false,"usgs":false,"family":"Barron-Gafford","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":901446,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249403,"text":"fs20233044 - 2023 - LANDFIRE","interactions":[],"lastModifiedDate":"2023-10-10T21:18:58.443597","indexId":"fs20233044","displayToPublicDate":"2023-10-10T15:02:29","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3044","displayTitle":"LANDFIRE","title":"LANDFIRE","docAbstract":"Landscape Fire and Resource Management Planning Tools (LANDFIRE) is a key national geospatial data source for strategic fire and resource management planning and analysis. LANDFIRE is the first complete, nationally consistent collection of more than 25 geospatial layers, databases, and ecological models at a 30-meter resolution that describe disturbance, vegetation, fire, and fuel characteristics. Because fires do not stop at ownership borders, LANDFIRE products by design support cross-boundary planning, management, and operations across all lands of the conterminous United States (CONUS), Alaska, Hawaii, and insular areas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233044","usgsCitation":"Long, J.L., and Hatten, T.D., 2023, LANDFIRE: U.S. Geological Survey Fact Sheet 2023–3044, 4 p., https://doi.org/10.3133/fs20233044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-146927","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":501266,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XVKXRL","text":"USGS data release","linkHelpText":"LANDFIRE 2024 Update (ver. 1.1, March 2026)"},{"id":421688,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3044/fs20233044.XML","linkFileType":{"id":8,"text":"xml"}},{"id":421689,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233044/full","linkFileType":{"id":5,"text":"html"}},{"id":421687,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3044/fs20233044.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3044"},{"id":421694,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3044/images/"},{"id":421686,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3044/coverthb.jpg"}],"contact":"

LANDFIRE Help Desk
Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-10-10","noUsgsAuthors":false,"publicationDate":"2023-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Jennifer L. 0000-0002-0698-2303","orcid":"https://orcid.org/0000-0002-0698-2303","contributorId":330641,"corporation":false,"usgs":false,"family":"Long","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":63244,"text":"KBR Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":885493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, Timothy D. 0000-0003-3413-4325","orcid":"https://orcid.org/0000-0003-3413-4325","contributorId":291959,"corporation":false,"usgs":false,"family":"Hatten","given":"Timothy D.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":885494,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249594,"text":"70249594 - 2023 - Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types","interactions":[],"lastModifiedDate":"2023-10-18T14:32:35.897212","indexId":"70249594","displayToPublicDate":"2023-10-08T08:28:37","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types","docAbstract":"

Large-magnitude snow avalanches pose a hazard to humans and infrastructure worldwide. Analyzing the spatiotemporal behavior of avalanches and the contributory climate factors is important for understanding historical variability in climate-avalanche relationships as well as improving avalanche forecasting. This study uses established dendrochronological methods to develop long-term regional avalanche chronologies for three different climate types: high-latitude maritime climate of southeast Alaska, intermountain climate of the northern Rocky Mountains, and continental climate of Colorado. In the maritime study area, we collected 434 cross sections throughout six avalanche paths near Juneau, Alaska. This resulted in 2706 identified avalanche growth disturbances between year 1720 and 2018 Common Era (CE), which allowed us to reconstruct 82 years with large magnitude avalanche activity across three sub-regions. By combining this tree-ring derived avalanche dataset with a suite of climate and atmospheric variables and applying a generalized linear model to fit a binomial regression, we found February and March precipitation and the Oceanic Niño Index (ONI) were significant predictors of large magnitude avalanche activity in the southeast Alaska study area. In the intermountain climate study area, tree-rings from 647 trees exhibited 2134 avalanche-related growth disturbances in the northern Rocky Mountains of northwest Montana from 1867 to 2019. The data show that the amount of snowpack across the northern Rocky Mountain region is directly related to avalanche probability. Coincident with warming and regional snowpack reductions, a decline of ~ 14% (~ 2% per decade) in overall large magnitude avalanche probability is apparent through the period 1950–2017 CE. In the continental climate of Colorado, we sampled 24 avalanche paths throughout the state and collected 1188 total samples with 4135 identified growth disturbances from 1698 to 2019. Preliminary results suggest years with large magnitude avalanche activity across the sub-regions of this study area are generally characterized by stormy winters with above average snowpack development but that early and late winter temperature and precipitation also play an important role in large avalanche activity. Characterizing historical climate-avalanche relationships across different climate types provides a broad baseline for understanding potential future changes in avalanche activity. Overall, this work helps forecasters and planners better understand the influence of climate on large magnitude avalanche frequency, and how potential changes in avalanche character and occurrence will affect their operations in the context of a warming climate.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Peitzsch, E.H., Pederson, G.T., Martin, J.T., Hood, E., Greene, E.M., Birkeland, K.W., Elder, K., Wolken, G., Kichas, N.E., Stahle, D.K., and Harley, J., 2023, Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 547-553.","productDescription":"7 p.","startPage":"547","endPage":"553","ipdsId":"IP-156827","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science 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Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886375,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886376,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hood, 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Kelly","contributorId":174398,"corporation":false,"usgs":false,"family":"Elder","given":"Kelly","email":"","affiliations":[{"id":5121,"text":"U.S. Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843","active":true,"usgs":false}],"preferred":false,"id":886380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolken, Gabriel","contributorId":305685,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":886381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kichas, Nickolas E.","contributorId":221182,"corporation":false,"usgs":false,"family":"Kichas","given":"Nickolas","email":"","middleInitial":"E.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886383,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stahle, Daniel Kent 0000-0003-1252-5990","orcid":"https://orcid.org/0000-0003-1252-5990","contributorId":224403,"corporation":false,"usgs":true,"family":"Stahle","given":"Daniel","email":"","middleInitial":"Kent","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886382,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Harley, John","contributorId":292933,"corporation":false,"usgs":false,"family":"Harley","given":"John","email":"","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":886384,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70249366,"text":"sir20235082 - 2023 - Critical minerals in subduction-related magmatic-hydrothermal systems of the United States","interactions":[],"lastModifiedDate":"2026-03-12T20:56:42.28817","indexId":"sir20235082","displayToPublicDate":"2023-10-05T10:01:48","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5082","displayTitle":"Critical Minerals in Subduction-related Magmatic-Hydrothermal Systems of the United States","title":"Critical minerals in subduction-related magmatic-hydrothermal systems of the United States","docAbstract":"

During the World War and Cold War eras (1910s–1990s), domestic consumption of numerous mineral commodities relied increasingly on imported supplies. Consumption reliance has since expanded to include 50 “critical minerals” (elements and mineral commodities) that are mostly to entirely imported and subject to curtailment by suppliers or supply chain disruption. New domestic supplies of critical minerals are being pursued by mining companies and by several federal departments and agencies. Information on domestic deposits and resources of critical minerals is being compiled by the U.S. Geological Survey Mineral Resources Program, which has organized investigations by mineral system, deposit type, and commodity.

Production, reserves, resources, and inventories of 21 critical minerals in domestic magmatic-hydrothermal deposits related to subduction-generated magmatism, and in tailings, slag, slimes, and electrolyte from copper concentrators, smelters, and refineries that processed some deposits, are largely restricted to Western States and Alaska. The critical mineral commodities Al, Sb, As, Bi, Co, fluorite, Ga, Ge, In, Mn, Ni, Nb, Pd, Pt, potash, Re, Ta, Te, Sn, W, and V are variably concentrated in porphyry/skarn copper-(molybdenum), skarn-replacement-vein (S-R-V) tungsten, polymetallic sulfide S-R-V intermediate sulfidation (IS), high-sulfidation gold-silver, low-sulfidation gold-silver, and lithocap alunite deposit types. These deposit types occur in porphyry copper-molybdenum-gold, alkalic porphyry, porphyry tin (granite related), and reduced intrusion-related mineral systems.

Production, reserves, and resources of Co, Ni, Nb, Pd, Pt, Ta, Sn, and V in subduction-related deposits in Western States are insignificant to small, mostly equivalent to months to a few years of recent annual domestic consumption (2016–2020). Significant inventories, equivalent to 2 or more years of consumption of aluminum, antimony, potash, and tungsten in unmined S-R-V tungsten, polymetallic sulfide S-R-V-IS, and lithocap alunite deposits vary from approximately 2 to 8 years. Several decades of consumption of arsenic, bismuth, fluorite, gallium, germanium, and indium exist in some polymetallic sulfide S-R-V-IS and lithocap alunite deposit types.

Based on concentrations of critical minerals in reserves, resources, drill holes, and deposit domains (ore types), and in captive refinery records, the largest domestic inventories of Sb, As, Bi, Re, and Te, and possibly Ga, Ge, In, Sn, and W, are in porphyry copper-molybdenum (Cu-Mo) deposits in Alaska, Idaho, Utah, and Arizona, and in interim products of processing porphyry Cu-Mo deposit ores for recovery of copper and molybdenum. Concentrations of critical minerals in archival specimens and sample collections, although somewhat biased by collection and conservation decisions and categorization, are broadly proportionate to those in reserves, resources, and drill holes. These concentrations imply significant inventories of some critical minerals in deposits for which production, resources, and refinery records are unavailable or incomplete.

Because of the large masses of ores mined and processed annually (hundreds of millions of metric tons) and in reserves and resources (hundreds of millions of metric tons to billions of metric tons), calculated inventories of critical minerals in porphyry Cu-Mo deposits are equivalent to decades and centuries of recent consumption. However, these inventories should not be considered consumable supplies without reserve definition and development of economically viable mining plans and recovery techniques. An expeditious strategy for elimination or reduction of import reliance is recovery, and improved recovery efficiency, of Sb, As, Bi, Re, and Te, and possibly Ga, Ge, In, Ni, Sn, Ti, and W; during concentration and refining of copper and molybdenum minerals in ores of operating porphyry Cu-Mo mines; and in unmined porphyry Cu-Mo resources. These chalcophile, siderophile, and lithophile critical minerals, often undetectable in ore, are concentrated (hundreds of parts per million to percents) in slimes and electrolyte during copper electrorefining or could be recovered, in part, during sulfide concentration and smelting. Other than rhenium (recovered during molybdenum refining) and tellurium, all have been routinely discarded.

Subsidization (for example, commodity price guarantees, tax credits, recovery technology development), political initiative, and (or) sustained market favorability could support new production of critical mineral commodities from subduction-related magmatic-hydrothermal deposits in Western States. In addition, insufficient domestic refining capacity could relegate the large inventories of critical minerals in porphyry Cu-Mo reserves and resources (for example, Pebble, Alaska; Resolution and Copper World [Rosemont], Arizona) to exportation in concentrates and importation insecurity, fortifying their present status.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235082","usgsCitation":"Vikre, P., John, D., Wintzer, N.E., Koutz, F., Graybeal, F., Dail, C., and Annis, D.C., 2023, Critical minerals in subduction-related magmatic-hydrothermal systems of the United States: U.S. Geological Survey Scientific Investigations Report 2023–5082, 110 p., https://doi.org/10.3133/sir20235082.","productDescription":"x, 110 p.","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-137402","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":501045,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115449.htm","linkFileType":{"id":5,"text":"html"}},{"id":421606,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5082/images"},{"id":421605,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235082/full"},{"id":421604,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5082/sir20235082.xml"},{"id":421603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5082/sir20235082.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":421602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5082/covrthb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -126.97828338393356,\n 50.4388592678921\n ],\n [\n -126.97828338393356,\n 27.657631239110117\n ],\n [\n -95.77838522885939,\n 27.657631239110117\n ],\n [\n -95.77838522885939,\n 50.4388592678921\n ],\n [\n -126.97828338393356,\n 50.4388592678921\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-10-05","noUsgsAuthors":false,"publicationDate":"2023-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Vikre, Peter 0000-0001-7895-5972 pvikre@usgs.gov","orcid":"https://orcid.org/0000-0001-7895-5972","contributorId":267885,"corporation":false,"usgs":true,"family":"Vikre","given":"Peter","email":"pvikre@usgs.gov","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":885345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":885346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wintzer, Niki E. 0000-0003-3085-435X nwintzer@usgs.gov","orcid":"https://orcid.org/0000-0003-3085-435X","contributorId":5297,"corporation":false,"usgs":true,"family":"Wintzer","given":"Niki","email":"nwintzer@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":885347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koutz, Fleetwood","contributorId":30902,"corporation":false,"usgs":true,"family":"Koutz","given":"Fleetwood","affiliations":[],"preferred":false,"id":885348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graybeal, Frederick","contributorId":139000,"corporation":false,"usgs":false,"family":"Graybeal","given":"Frederick","email":"","affiliations":[{"id":12586,"text":"Consultant","active":true,"usgs":false}],"preferred":true,"id":885349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dail, Chris","contributorId":330577,"corporation":false,"usgs":false,"family":"Dail","given":"Chris","email":"","affiliations":[{"id":12586,"text":"Consultant","active":true,"usgs":false}],"preferred":true,"id":885350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Annis, David C.","contributorId":330578,"corporation":false,"usgs":false,"family":"Annis","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":true,"id":885351,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250109,"text":"70250109 - 2023 - Effects of vehicle traffic on space use and road crossings of caribou in the Arctic","interactions":[],"lastModifiedDate":"2023-12-04T17:27:48.321119","indexId":"70250109","displayToPublicDate":"2023-10-03T09:30:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Effects of vehicle traffic on space use and road crossings of caribou in the Arctic","docAbstract":"

Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou (Rangifer tarandus granti). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska. We first modeled spatiotemporal variation in hourly traffic volumes across the road system from traffic counter data using gradient-boosted regression trees. We then used generalized additive models to estimate nonlinear step selection functions and road-crossing probabilities from collared female caribou during the post-calving and insect harassment seasons, when they primarily interact with roads. Step selection analyses revealed that caribou selected areas further from roads (~1–3 km) during the post-calving and mosquito seasons and selected areas with lower traffic volumes during all seasons, with selection probabilities peaking when traffic was <5 vehicles/h. Using road-crossing models, we found that caribou were less likely to cross roads during the insect seasons as traffic increased, but that response dissipated as insect harassment became more severe. Past studies suggested that caribou exhibit behavioral responses when traffic exceeds 15 vehicles/h, but our results demonstrate behavioral responses at much lower traffic levels. Our results illustrate that vehicle activity mediates caribou responses to road infrastructure, information that can be used in future land-use planning to minimize the behavioral responses of caribou to industrial development in sensitive Arctic landscapes.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2923","usgsCitation":"Severson, J.P., Johnson, H.E., and Vosburgh, T.C., 2023, Effects of vehicle traffic on space use and road crossings of caribou in the Arctic: Ecological Applications, v. 33, no. 8, e2923, 21 p., https://doi.org/10.1002/eap.2923.","productDescription":"e2923, 21 p.","ipdsId":"IP-145608","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":441958,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2923","text":"Publisher Index Page"},{"id":435163,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HXW3N5","text":"USGS data release","linkHelpText":"Hourly Vehicle Traffic Data Associated with Industrial Activity on the North Slope of Alaska During Summers 2019-2020"},{"id":422728,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.86133804316822,\n 70.5106570028494\n ],\n [\n -150.1022371487244,\n 70.43506564845134\n ],\n [\n -150.2251448556409,\n 70.43341923336993\n ],\n [\n -150.57911905156016,\n 70.3773618530881\n ],\n [\n -150.74135722468978,\n 70.32611628176022\n ],\n [\n -150.84459969849965,\n 70.2448529934669\n ],\n [\n -150.83476708194632,\n 70.20992730257379\n ],\n [\n -150.71677568330648,\n 70.143238146334\n ],\n [\n -151.12482927026898,\n 69.73513298795481\n ],\n [\n -150.58895166811348,\n 69.72322531382761\n ],\n [\n -150.42671349498383,\n 69.76237009655026\n ],\n [\n -150.24972639702415,\n 69.86563513788099\n ],\n [\n -150.16123284804445,\n 69.94084273688509\n ],\n [\n -149.85642173489157,\n 69.9705480654984\n ],\n [\n -149.4606589186207,\n 70.04604045538369\n ],\n [\n -149.24679950858615,\n 70.08973159573645\n ],\n [\n -148.97640255337,\n 70.26312377942679\n ],\n [\n -149.04031456096658,\n 70.34927534019323\n ],\n [\n -148.91740685405023,\n 70.41529987953601\n ],\n [\n -149.0501471775199,\n 70.48111126247821\n ],\n [\n -149.46311707275905,\n 70.52213540068598\n ],\n [\n -149.649936787272,\n 70.5106570028494\n ],\n [\n -149.86133804316822,\n 70.5106570028494\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Severson, John P. 0000-0002-1754-6689","orcid":"https://orcid.org/0000-0002-1754-6689","contributorId":213469,"corporation":false,"usgs":true,"family":"Severson","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":888389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":888390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vosburgh, Timothy C.","contributorId":331661,"corporation":false,"usgs":false,"family":"Vosburgh","given":"Timothy","email":"","middleInitial":"C.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":888391,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}