This report provides analysis to extend the 2040 forecasts of polar bear (Ursus maritimus) land use for the southern Beaufort and Chukchi Sea populations presented in a recent publication (Rode and others, 2022) through the year 2065. To inform long-term polar bear management considerations, we provide point-estimate forecasts and 95-percent prediction intervals of the proportion of polar bear populations summering onshore for 21 days or more (≥) and their duration onshore every 5 years from 2040 to 2065. Because sea-ice projections based on earth system models show greater divergence with emission scenarios after 2040, we have provided forecasts for three greenhouse gas emission scenarios, SSP1-2.6, SSP2-4.5 and SSP5-8.5, compared to the two emission scenarios used in Rode and others (2022). Our forecasting methods estimated that 61–97 percent of polar bears in the southern Beaufort Sea and 80–100 percent of polar bears in the Chukchi Sea populations may summer onshore for ≥21 days by 2065. Forecasts of mean duration onshore were 105–158 days for polar bears in the southern Beaufort Sea and 111–178 days in the Chukchi Sea populations by 2065. Sea ice conditions projected to occur by 2065 could alter the current patterns of bear behavior from what has been observed over the past 30 years. As a result, these extended projections are associated with a higher degree of uncertainty than estimates through 2040, especially under the SSP5-8.5 scenario.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231048","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rode, K.D., Douglas, D.C., Atwood, T.C., and Wilson, R.R., Forecasts of polar bear (Ursus maritimus) land use in the southern Beaufort and Chukchi Seas, 2040–65: U.S. Geological Survey Open-File Report 2023–1048, 7 p., https://doi.org/10.3133/ofr20231048.","productDescription":"Report: vi, 7 p.; Data Release","numberOfPages":"7","onlineOnly":"Y","ipdsId":"IP-148069","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":418953,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211048/full"},{"id":418955,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1048/images"},{"id":418954,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1048/ofr20231048.xml"},{"id":418956,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XEOBWV","text":"Polar bear Continuous Time-Correlated Random Walk (CTCRW) location data derived from satellite location data, Chukchi and Beaufort Seas, July-November 1985-2017","description":"Rode, K. D., Douglas, D. C., Atwood, T. C., Durner, G. M., Wilson, R. R., Bromaghin, J. F., Pagano, A. M. and Simac, K. S., 2022, Polar bear Continuous Time-Correlated Random Walk (CTCRW) location data derived from satellite location data, Chukchi and Beaufort Seas, July-November 1985-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9XEOBWV."},{"id":418951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1048/covrthb.jpg"},{"id":418952,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1048/ofr20231048.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Citing published reports and their own diagnostic data, Kaleta and Taday (Citation2003) (https://doi.org/10.1080/03079450310001593613) reported that 469 domestic and free-living bird species were determined to be chlamydia-positive, based on isolation of the organism and antigen detection or on serological detection of circulating antibodies. However, I was unable to reconcile the designation of chlamydia-positive in some of the species listed by Kaleta and Taday (Citation2003) with the information provided in the corresponding references cited. For example, Eddie et al. (Citation1966) tested sera from 24 species of birds in Alaska (see their ) by “direct and indirect complement fixation techniques in the presence of the standard psittacosis antigen.” Eddie et al. (Citation1966) reported that serum samples from only two species reacted, and the authors considered those titres too low to be of diagnostic significance. However, Kaleta and Taday (Citation2003) listed 20 bird species from Eddie et al. (Citation1966) as being positive for chlamydia. Additional apparent discrepancies are listed in of the current article.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/03079457.2023.2225978","usgsCitation":"Franson, J.C., 2023, Apparent discrepancies in the review “Avian host range of Chlamydophila spp. based on isolation, antigen detection and serology” by Kaleta, E.F. & Taday, E.M.A. (2003), Avian Pathology, 32, 435–462: Avian Pathology, v. 52, no. 4, p. 283-284, https://doi.org/10.1080/03079457.2023.2225978.","productDescription":"2 p.","startPage":"283","endPage":"284","ipdsId":"IP-129523","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":418809,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Franson, J. Christian 0000-0002-0251-4238 jfranson@usgs.gov","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":177499,"corporation":false,"usgs":true,"family":"Franson","given":"J.","email":"jfranson@usgs.gov","middleInitial":"Christian","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":877142,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70246650,"text":"70246650 - 2023 - Changes in abiotic drivers of green sea urchin demographics following the loss of a keystone predator","interactions":[],"lastModifiedDate":"2023-07-12T15:00:47.316627","indexId":"70246650","displayToPublicDate":"2023-07-06T09:47:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16299,"text":"Journal of Marine Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Changes in abiotic drivers of green sea urchin demographics following the loss of a keystone predator","docAbstract":"Sea urchin population demographics can respond to changes in keystone species abundances, with the magnitude of these responses varying depending on environmental influences. In this study, sea urchin populations were surveyed across 15 Aleutian archipelago islands over a 30-year period to understand how patterns of sea urchin demography (density, biomass, and size structure) varied through different ecological regimes that were caused by changes in the abundance of sea otters, a keystone species in this system. To examine long-term changes in sea urchin demographics, four time periods across the recent decline of sea otters were examined: during sea otter presence (1987-1994), nearing absence at the end of the decline (1997-2000), 10 years postdecline (2008-2010), and 15-20 years following the loss of sea otters from the ecosystem (2014-2017). Our results show that when sea otters were broadly present, sea urchin demographics were generally similar across the archipelago, with few urchins that had large-sized bodies. During this time, bottom-up environmental controls were muted relative to top-down forces from keystone predation. However, as sea otters declined and remained absent from the system, abiotic factors became more influential on sea urchin biomass, density, and size structure. In particular, differences among island groups during these periods were correlated with variation in ocean temperature, bathymetric complexity, and habitat availability. Sea urchin recruitment also varied among island groups, corresponding to ecoregions delineated by oceanic passes across the archipelago. The functional extinction of sea otters revealed an increasing influence of abiotic forcing in the absence of top-down control. This study further highlights the importance of understanding how keystone predators regulate herbivore demographics.
","language":"English","publisher":"Hindawi","doi":"10.1155/2023/1198953","usgsCitation":"Weitzman, B., Konar, B., Edwards, M.S., Rasher, D., Kenner, M.C., Tinker, M.T., and Estes, J., 2023, Changes in abiotic drivers of green sea urchin demographics following the loss of a keystone predator: Journal of Marine Sciences, v. 2023, 1198953, 18 p., https://doi.org/10.1155/2023/1198953.","productDescription":"1198953, 18 p.","ipdsId":"IP-110692","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":442841,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/2023/1198953","text":"Publisher Index Page"},{"id":418898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian archipelago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -165.28604507308413,\n 54\n ],\n [\n -179.9,\n 54\n ],\n [\n -179.9,\n 47.0490457932118\n ],\n [\n -165.28604507308413,\n 47.0490457932118\n ],\n [\n -165.28604507308413,\n 54\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 167,\n 54\n ],\n [\n 167,\n 47.07788267220468\n ],\n [\n 179.9,\n 47.07788267220468\n ],\n [\n 179.9,\n 54\n ],\n [\n 167,\n 54\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2023","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weitzman, Ben P. 0000-0001-7559-3654 bweitzman@usgs.gov","orcid":"https://orcid.org/0000-0001-7559-3654","contributorId":5123,"corporation":false,"usgs":true,"family":"Weitzman","given":"Ben P.","email":"bweitzman@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":877757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Konar, B.","contributorId":93658,"corporation":false,"usgs":true,"family":"Konar","given":"B.","email":"","affiliations":[],"preferred":false,"id":877758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edwards, M. S.","contributorId":316595,"corporation":false,"usgs":false,"family":"Edwards","given":"M.","email":"","middleInitial":"S.","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":877797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rasher, D. B.","contributorId":316596,"corporation":false,"usgs":false,"family":"Rasher","given":"D. B.","affiliations":[{"id":13692,"text":"Bigelow Laboratory for Ocean Sciences","active":true,"usgs":false}],"preferred":false,"id":877798,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kenner, Michael C. 0000-0003-4659-461X","orcid":"https://orcid.org/0000-0003-4659-461X","contributorId":208151,"corporation":false,"usgs":true,"family":"Kenner","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":877760,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tinker, M. T. 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":54152,"corporation":false,"usgs":false,"family":"Tinker","given":"M.","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":877761,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Estes, J. A.","contributorId":316594,"corporation":false,"usgs":false,"family":"Estes","given":"J. A.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":877759,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248774,"text":"70248774 - 2023 - Structure of breeding calls in three closely related bird species (Calidris Merrem, 1804; Scolopacidae)","interactions":[],"lastModifiedDate":"2023-10-11T16:02:52.017572","indexId":"70248774","displayToPublicDate":"2023-07-04T07:21:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1176,"text":"Canadian Journal of Zoology","active":true,"publicationSubtype":{"id":10}},"title":"Structure of breeding calls in three closely related bird species (Calidris Merrem, 1804; Scolopacidae)","docAbstract":"The environments of Arctic Ocean nearshore areas experience high intra- and inter-annual variability, making it difficult to evaluate the impact of anthropogenic warming. However, a sediment record from the southern Canadian Beaufort Sea allowed us to reconstruct the impacts of climate and environmental changes over the last 1300 years along the northern Yukon coast, Canada. The coring site (PG2303; 69.513°N, 138.895°W; water depth 32 m) is located in the Herschel Basin, where high sedimentation rates (0.1–0.5 cm a−1) allowed analyses at sub-centennial to decadal resolutions. Benthic foraminiferal, ostracod, and tintinnid assemblages, as well as the stable isotope composition of the foraminifera Elphidium clavatum and Cassidulina reniforme were used as paleoclimatic and ecological indicators, while the age model was based on the combined radiometric data of 14C, 210Pb and 137Cs. From ca 700 to 1050 CE, our data suggest penetration of offshore shelf-break waters inferred by the dominance of C. reniforme followed by the relatively abundant Triloculina trihedra in the foraminiferal assemblages as both species are associated with stable saline conditions. Afterwards, the occurrence of ostracods Kotoracythere arctoborealis and Normanicythere leioderma suggests influx of Pacific-sourced waters until ca. 1150 CE. From ∼1150–1650 CE, persistent frigid waters, limited sediment supply, and low abundances of microfossils suggest cold conditions with pervasive annual sea-ice cover that may have restricted upwelling of oceanic waters. After ∼1800 CE, the co-occurrence of Tintinnopsis fimbriata and bacterial/complex organic carbon feeder foraminifera (Quinqueloculina stalkeri, Textularia earlandi and Stetsonia horvathi), suggest an increased influence of freshwater rich in particulate organic matter, which may be related to the spreading of the Mackenzie River plume and/or increased coastal permafrost erosion during longer ice-free seasons. Based on these proxy data, the shift at ∼1800 CE marks the onset of regional warming, which further intensified after ∼1955 CE, likely in response to the anthropogenic forcing.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2023.111670","usgsCitation":"Falardeau, J., de Vernal, A., Seidenkrantz, M., Fritz, M., Cronin, T.M., Gemery, L., Rochon, A., Carnero-Bravo, V., Hillaire-Marcel, C., Pearce, C., and Archambault, P., 2023, A 1300-year microfaunal record from the Beaufort Sea shelf indicates exceptional climate-related environmental changes over the last two centuries: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 625, 111670, 18 p., https://doi.org/10.1016/j.palaeo.2023.111670.","productDescription":"111670, 18 p.","ipdsId":"IP-146909","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":442984,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orcid.org/0000-0003-4591-7325","text":"External Repository"},{"id":419154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, Yukon","otherGeospatial":"Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -145.2665658621064,\n 71.13218589622568\n ],\n [\n -145.2665658621064,\n 68.83512392360717\n ],\n [\n -136.49812630673208,\n 68.83512392360717\n ],\n [\n -136.49812630673208,\n 71.13218589622568\n ],\n [\n -145.2665658621064,\n 71.13218589622568\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"625","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Falardeau, Jade","contributorId":304651,"corporation":false,"usgs":false,"family":"Falardeau","given":"Jade","affiliations":[{"id":66141,"text":"1. Geotop and Département des sciences de la Terre et de l’atmosphère, Université du Québec à Montréal, Montréal, Canada","active":true,"usgs":false}],"preferred":false,"id":871236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Vernal, Anne","contributorId":304652,"corporation":false,"usgs":false,"family":"de Vernal","given":"Anne","affiliations":[{"id":66142,"text":"Geotop","active":true,"usgs":false}],"preferred":false,"id":871237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seidenkrantz, Marit-Solveig","contributorId":304650,"corporation":false,"usgs":false,"family":"Seidenkrantz","given":"Marit-Solveig","affiliations":[{"id":49183,"text":"Department of Geoscience, Aarhus University, Aarhus, Denmark","active":true,"usgs":false}],"preferred":false,"id":871238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritz, Michael","contributorId":176701,"corporation":false,"usgs":false,"family":"Fritz","given":"Michael","email":"","affiliations":[],"preferred":false,"id":871239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cronin, Thomas M. 0000-0001-9522-3992 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0001-9522-3992","contributorId":304640,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":871240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gemery, Laura 0000-0003-1966-8732","orcid":"https://orcid.org/0000-0003-1966-8732","contributorId":245413,"corporation":false,"usgs":true,"family":"Gemery","given":"Laura","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":871241,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rochon, Andre","contributorId":316792,"corporation":false,"usgs":false,"family":"Rochon","given":"Andre","email":"","affiliations":[],"preferred":false,"id":878327,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Carnero-Bravo, Vladislav","contributorId":304655,"corporation":false,"usgs":false,"family":"Carnero-Bravo","given":"Vladislav","email":"","affiliations":[],"preferred":false,"id":878328,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hillaire-Marcel, Claude","contributorId":304656,"corporation":false,"usgs":false,"family":"Hillaire-Marcel","given":"Claude","email":"","affiliations":[],"preferred":false,"id":878329,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pearce, Christof","contributorId":197126,"corporation":false,"usgs":false,"family":"Pearce","given":"Christof","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":878330,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Archambault, Philippe","contributorId":304657,"corporation":false,"usgs":false,"family":"Archambault","given":"Philippe","email":"","affiliations":[],"preferred":false,"id":878331,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70245174,"text":"ofr20231049 - 2023 - Age-0 sablefish size and growth indices from seabird diets at Middleton Island, Gulf of Alaska","interactions":[],"lastModifiedDate":"2023-09-18T19:48:30.761903","indexId":"ofr20231049","displayToPublicDate":"2023-06-21T07:48:16","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1049","displayTitle":"Age-0 Sablefish Size and Growth Indices from Seabird Diets at Middleton Island, Gulf of Alaska","title":"Age-0 sablefish size and growth indices from seabird diets at Middleton Island, Gulf of Alaska","docAbstract":"Sablefish (Anoplopoma fimbria) is a commercially valuable groundfish species in Alaska, with the population assessed annually by the National Oceanic and Atmospheric Administration Alaska Fisheries Science Center. Sablefish recruit into the commercially fished population at 2 years old and are poorly sampled by most surveys before that age. However, information on the abundance, distribution, and size of pre-recruitment age fish is valuable as an ecosystem indicator for older fish. Size and an index of growth rate of age-0 sablefish were quantified using samples from seabird diets at Middleton Island, Alaska, an island in the northern Gulf of Alaska. Age-0 sablefish information may serve as an indicator for potential recruitment into older age populations. This report (1) provides information on the data collection for age-0 sablefish from seabird diets at Middleton Island, Alaska from 1978 to 2022, (2) describes a method for quantifying age-0 sablefish size and growth rate, and (3) describes the size and growth rate of sablefish sampled over time. An annual release of age-0 sablefish size and growth data by U.S. Geological Survey based on continued collections on Middleton Island, Alaska, may be used to assess ecosystem status and as a recruitment indicator for sablefish.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231049","collaboration":"Prepared in cooperation with National Oceanic and Atmospheric Administration Alaska Fisheries Science Center","usgsCitation":"Arimitsu, M.L., and Hatch, S.A., 2023, Age-0 sablefish size and growth indices from seabird diets at Middleton Island, Gulf of Alaska: U.S. Geological Survey Open-File Report 2023–1049, 4 p., https://doi.org/10.3133/ofr20231049.","productDescription":"Report: v, 4 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-151412","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":418265,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94KVH9X","text":"USGS data release","description":"USGS data release","linkHelpText":"Age-0 Sablefish size and growth indices from seabird diets at Middleton Island, Alaska"},{"id":418263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1049/ofr20231049.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1049"},{"id":418262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1049/coverthb.jpg"},{"id":418267,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1049/ofr20231049.XML"},{"id":418266,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1049/images"},{"id":418264,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231049/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1049"}],"country":"United States","state":"Alaska","otherGeospatial":"Middleton Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.10005001165774,\n 59.77785521975687\n ],\n [\n -147.10005001165774,\n 58.9779941146154\n ],\n [\n -145.39789946473627,\n 58.9779941146154\n ],\n [\n -145.39789946473627,\n 59.77785521975687\n ],\n [\n -147.10005001165774,\n 59.77785521975687\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Climate warming is rapidly altering Arctic ecosystems. Polar bears (Ursus maritimus) need sea ice as a platform from which to hunt seals, but increased sea-ice loss is lengthening periods when bears are without access to primary hunting habitat. During periods of food scarcity, survival depends on the energy that a bear has stored in body reserves, termed storage energy, making this a key metric in predictive models assessing climate change impacts on polar bears. Here, we developed a body composition model for polar bears that estimates storage energy while accounting for changes in storage tissue composition. We used data of dissected polar bears (n = 31) to link routinely collected field measures of total body mass and straight-line body length to the body composition of individual bears, described in terms of structural mass and two storage compartments, adipose and muscle. We then estimated the masses of metabolizable proteins and lipids within these storage compartments, giving total storage energy. We tested this multi-storage model by using it to predict changes in the lipid stores from an independent dataset of wild polar bears (n = 36) that were recaptured 8–200 days later. Using length and mass measurements, our model successfully predicted direct measurements of lipid changes via isotopic dilutions (root mean squared error of 14.5 kg). Separating storage into two compartments, and allowing the molecular composition of storage to vary, provides new avenues for quantifying energy stores of individuals across their life cycle. The multi-storage body composition model thus provides a basis for further exploring energetic costs of physiological processes that contribute to individual survival and reproductive success. Given bioenergetic models are increasingly used as a tool to predict individual fitness and population dynamics, our approach for estimating individual energy stores could be applicable to a wide range of species.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coad043","usgsCitation":"Penk, S.R., Sadana, P., Archer, L.C., Pagano, A.M., Cattet, M.R., Lunn, N.J., Thiemann, G.W., and Molnar, P.K., 2023, A body composition model with multiple storage compartments for polar bears (Ursus maritimus), v. 11, no. 1, coad043, 20 p., https://doi.org/10.1093/conphys/coad043.","productDescription":"coad043, 20 p.","ipdsId":"IP-142122","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443007,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coad043","text":"Publisher Index Page"},{"id":418459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Penk, Stephanie R. 0000-0002-8027-4372","orcid":"https://orcid.org/0000-0002-8027-4372","contributorId":312472,"corporation":false,"usgs":false,"family":"Penk","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":67687,"text":"University of Toronto Scarborough","active":true,"usgs":false}],"preferred":false,"id":876207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sadana, Pranav","contributorId":312473,"corporation":false,"usgs":false,"family":"Sadana","given":"Pranav","email":"","affiliations":[{"id":16930,"text":"University of Winnipeg","active":true,"usgs":false}],"preferred":false,"id":876208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archer, Louise C. 0000-0002-1983-3825","orcid":"https://orcid.org/0000-0002-1983-3825","contributorId":312474,"corporation":false,"usgs":false,"family":"Archer","given":"Louise","email":"","middleInitial":"C.","affiliations":[{"id":67687,"text":"University of Toronto Scarborough","active":true,"usgs":false}],"preferred":false,"id":876209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":876210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cattet, Marc R. L. 0000-0002-2318-1452","orcid":"https://orcid.org/0000-0002-2318-1452","contributorId":312475,"corporation":false,"usgs":false,"family":"Cattet","given":"Marc","email":"","middleInitial":"R. L.","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":876211,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lunn, Nicholas J. 0000-0003-0189-5494","orcid":"https://orcid.org/0000-0003-0189-5494","contributorId":312476,"corporation":false,"usgs":false,"family":"Lunn","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":876212,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thiemann, Gregory W.","contributorId":83023,"corporation":false,"usgs":false,"family":"Thiemann","given":"Gregory","email":"","middleInitial":"W.","affiliations":[{"id":27291,"text":"York University, Toronto, ON","active":true,"usgs":false}],"preferred":false,"id":876213,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Molnar, Peter K. 0000-0001-7260-2674","orcid":"https://orcid.org/0000-0001-7260-2674","contributorId":312477,"corporation":false,"usgs":false,"family":"Molnar","given":"Peter","email":"","middleInitial":"K.","affiliations":[{"id":67687,"text":"University of Toronto Scarborough","active":true,"usgs":false}],"preferred":false,"id":876214,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70245183,"text":"70245183 - 2023 - A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data","interactions":[],"lastModifiedDate":"2023-08-08T14:19:03.582056","indexId":"70245183","displayToPublicDate":"2023-06-19T07:06:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A hierarchical modelling framework for estimating individual- and population-level reproductive success from movement data","docAbstract":"Genetic diversity is theorized to decrease in populations closer to a species' range edge, where habitat may be suboptimal. Generalist species capable of long-range dispersal may maintain sufficient gene flow to counteract this, though the presence of significant barriers to dispersal (e.g., large water bodies, human-dominated landscapes) may still lead to, and exacerbate, the edge effect. We used microsatellite data for 2421 gray wolves (Canis lupus) from 24 subpopulations (groups) to model how allelic richness and expected heterozygosity varied with mainland–island position and two measures of range edge (latitude and distance from range center) across >7.3 million km2 of northern North America. We expected low genetic diversity both at high latitudes, due to harsh environmental conditions, and on islands, but no change in diversity with distance to the range center due to the species' exceptional dispersal ability and favorable conditions in far eastern and western habitats. We found that allelic richness and expected heterozygosity of island groups were measurably less than that of mainland groups, and that these differences increased with the island's distance to the species' range center in the study area. Our results demonstrate how multiple axes of geographic isolation (distance from range center and island habitation) can act synergistically to erode the genetic diversity of wide-ranging terrestrial vertebrate populations despite the counteracting influence of long-range dispersal ability. These findings emphasize how geographic isolation is a potential threat to the genetic diversity and viability of terrestrial vertebrate populations even among species capable of long-range dispersal.
Tanaga Island in the Central Aleutian Islands includes four stratovolcanoes: Sajaka, Tanaga, and East Tanaga in the northwest, and Takawangha in the central part of the island. Of these volcanoes, only Tanaga has a confirmed record of historical eruptive activity. We use double-difference methods to relocate Tanaga Island earthquakes from the period 2003–2017 to gain insight into volcanic and tectonic activity in the region. High precision relative relocations show subsurface structures in the Tanaga region related to volcanism and crustal tectonics. In 2005, a swarm of ∼600 volcano-tectonic events located below the NW portion of the island culminated with an episode of volcanic tremor. Although there was no known eruption associated with this swarm, we suggest that this activity is associated with fluids beneath Takawangha volcano. Overall, relatively little seismicity appears to be associated with volcanism: of 5,680 earthquakes relocated in this study, only ∼700 took place within 10 km of the volcanoes themselves. Other regions appear to experience primarily, if not exclusively, tectonic seismicity. Two regions on Tanaga Island became seismically active following a M6.6 tectonic earthquake east of the island on 2 May 2008, suggesting the triggering of tectonic earthquakes by the M6.6. Seismic activity recorded offshore and below the southern part of the island is interpreted as due to the clockwise rotation of the Delarof Block in the forearc, and bookshelf faulting along the volcanic arc. This suggests a complex pattern of earthquake hypocenters governed by both volcanic and tectonic processes surrounding Tanaga and Takawangha volcanoes.
Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.
Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska.
This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm.
We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems.
Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish (Carassius auratus) and the New Zealand mudsnail (Potamopyrgus antipodarum). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (Salvelinus fontinalis), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions.
Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).
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Biology WTEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":917528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, Aaron C.","contributorId":210583,"corporation":false,"usgs":false,"family":"Martin","given":"Aaron C.","affiliations":[{"id":38096,"text":"U.S. Fish and Wildlife Service, Alaska Regional Office","active":true,"usgs":false}],"preferred":false,"id":917529,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Davis, Tammy","contributorId":345800,"corporation":false,"usgs":false,"family":"Davis","given":"Tammy","email":"","affiliations":[{"id":56688,"text":"adfg","active":true,"usgs":false}],"preferred":false,"id":917530,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kelty, Rachel","contributorId":345801,"corporation":false,"usgs":false,"family":"Kelty","given":"Rachel","email":"","affiliations":[{"id":82717,"text":"UAA","active":true,"usgs":false}],"preferred":false,"id":917531,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243075,"text":"70243075 - 2023 - Geochronology and mapping constraints on the time-space evolution of the igneous and hydrothermal systems in the Taurus Cu-Mo district, eastern Alaska","interactions":[],"lastModifiedDate":"2023-10-12T15:23:34.75519","indexId":"70243075","displayToPublicDate":"2023-06-01T06:36:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Geochronology and mapping constraints on the time-space evolution of the igneous and hydrothermal systems in the Taurus Cu-Mo district, eastern Alaska","docAbstract":"The Taurus porphyry Cu-Mo district contains four mineralized porphyry centers in the eastern interior of Alaska. All four centers were emplaced during a magmatic episode that spanned from ca. 72 to 67 Ma, with seven distinct igneous suites. Each igneous suite resulted in hydrothermal alteration and mineralization, with younger pulses overprinting older pulses. Each magmatic-hydrothermal system is not present at all four mineralized centers. Apart from the Dennison occurrence, each mineralized center records pulses of repeated intermediate-silicic magmatism and associated alteration and mineralization.
Laser ablation-inductively coupled plasma-mass spectrometry U-Pb zircon crystallization ages indicate that an early quartz porphyry dike swarm ranges in age from ca. 71 to 70 Ma and is associated with potassic, sericitic, and propylitic alteration. Quartz latite intrusions were emplaced at ca. 69 Ma and exhibit early sodiccalcic alteration overprinted by potassic, sericitic, and propylitic alteration. The Taurus monzonite suite is cut by quartz latite but yielded an ca. 70 Ma emplacement age and exhibits the largest footprint of potassic and sericitic alteration. Feldspar porphyry dikes were emplaced ca. 69 Ma and have significant tourmaline-bearing potassic and sericitic alteration. This suite was followed by development of an igneous breccia with a monzonitic igneous matrix. Sodic-calcic alteration was associated with the igneous brecciation. A small stock of monzonite was emplaced at ca. 68 Ma causing locally pervasive sericite-tourmaline-pyrite alteration. The youngest suite of magmatism dated in the district is a series of granodiorite porphyry dikes with weak sodic-calcic and propylitic alteration that truncates earlier alteration assemblages.
Mineralization in the district consists of chalcopyrite and molybdenite associated with sugary quartz veins with potassium feldspar and biotite alteration envelopes (A veins). Less common banded quartz-molybdenite veins (B veins) occur with potassium feldspar envelopes. Gold occurs throughout the district and is strongly correlated with copper grade. Sericitic alteration contains lower copper contents and is predominantly associated with quartz-pyrite veins with sericite envelopes (D veins). Pyrrhotite and local arsenopyrite are present in sericitic assemblages. Pyrrhotite also occurs as inclusions in pyrite within D veins.
Magmas across the district exhibit oxidized characteristics, evidenced by the presence of abundant magnetite, rare titanite, and elevated Eu/Eu* and Ce/Ce* in zircon. Zircon Th/U and Yb/Gd compositions suggest a fractionation path controlled by apatite, titanite, and hornblende. Zircon rare earth element ratios and trace element data indicate two distinct batches of magma evolved from mafic parental compositions to monzonite and granodioritic compositions via fractional crystallization. In the early pulse of magma (ca. 72–69 Ma), fractional crystallization was key to ore formation. Earlier, better mineralized suites evolve to less negative Eu anomalies (Eu/Eu* > 0.7), indicating more oxidized and higher-water-pressure conditions evidenced by the suppression of plagioclase crystallization, compared to later, more poorly mineralized suites.
The temporal and spatial evolution of the district was determined from mapping and U-Pb and Re-Os geochronology. Mapping of igneous and hydrothermal assemblages indicates that the locus of the intrusive suites and hydrothermal systems shifted spatially over time, based on the presence of high-temperature (K-silicate–dominant) alteration, which is coincident with the highest Cu and Au grades. The earliest hydrothermal system was centered at Bluff and East Taurus and transitioned to West Taurus during emplacement of the second magmatic suite. Emplacement of the third magmatic suite was centered back at East Taurus, and the fourth and fifth suites were centered at West Taurus. The latest suites were widespread without a core of high-temperature alteration marking a central locus. East Taurus contains the overlap of six of the seven magmatic and hydrothermal suites and has the highest intersected grades and tonnages in the district. The Bluff and Dennison occurrences exhibit fewer igneous suites and hydrothermal assemblages with weak mineralization. Sodic-calcic alteration, common on the deep and distal flanks of porphyry systems, is only present at West Taurus and is indicative of a localized source of high-salinity nonmagmatic fluids.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.4999","usgsCitation":"Kreiner, D.C., Holm-Denoma, C., Pianowski, L., Flood, Z., Stevenson, D.J., Graham, G.E., Vazquez, J.A., and Creaser, R.A., 2023, Geochronology and mapping constraints on the time-space evolution of the igneous and hydrothermal systems in the Taurus Cu-Mo district, eastern Alaska: Economic Geology, v. 118, no. 4, p. 745-778, https://doi.org/10.5382/econgeo.4999.","productDescription":"34 p.","startPage":"745","endPage":"778","ipdsId":"IP-136126","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":435302,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RANVXY","text":"USGS data release","linkHelpText":"U-Pb zircon data for igneous units related to mineralization in the eastern 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jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":870930,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Creaser, Robert A 0000-0002-7672-035X","orcid":"https://orcid.org/0000-0002-7672-035X","contributorId":304556,"corporation":false,"usgs":false,"family":"Creaser","given":"Robert","email":"","middleInitial":"A","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":870931,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243994,"text":"70243994 - 2023 - Climate change and coastal wetland salinization: Physiological and ecological consequences for Arctic waterfowl","interactions":[],"lastModifiedDate":"2023-07-11T16:07:02.8137","indexId":"70243994","displayToPublicDate":"2023-05-27T07:04:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and coastal wetland salinization: Physiological and ecological consequences for Arctic waterfowl","docAbstract":"Redox-active functional groups in dissolved organic matter (DOM) are crucial for microbial electron transfer and methane emissions. However, the extent of aquatic DOM redox properties across northern high-latitude lakes and their relationships with DOM composition have not been thoroughly described. We quantified electron donating capacity (EDC) and electron accepting capacity (EAC) in lake DOM from Canada to Alaska and assessed their relationships with parameters from absorbance, fluorescence, and ultrahigh resolution mass spectrometry (FT-ICR MS) analyses. EDC and EAC are strongly tied to aromaticity and negatively related to aliphaticity and protein-like content. Redox-active formulae spanned a range of aromaticity, including highly unsaturated phenolic formulae, and correlated negatively with many aliphatic N and S-containing formulae. This distribution illustrates the compositional diversity of redox-sensitive functional groups and their sensitivity to ecosystem properties such as local hydrology and residence time. Finally, we developed a reducing index (RI) to predict EDC in aquatic DOM from FT-ICR MS spectra and assessed its robustness using riverine DOM. As the hydrology of the northern high-latitudes continues to change, we expect differences in the quantity and partitioning of EDC and EAC within these lakes, which have implications for local water quality and methane emissions.
We evaluated wildlife population health from the perspective of statistical means vs. variances. We outlined the choices necessary to provide the framework for our study. These consisted of spatial and temporal boundaries (e.g., choice of sentinel species, populations, time frame), measurement techniques (molecular to population level), and appropriate statistical analyses. We chose to assess the health of 19 sea otter populations, located in the north Pacific from the Aleutian Islands, AK, to Santa Barbara, CA, and varying in population growth rates and length of occupancy. Our focal metric was gene expression (i.e., mRNA transcripts) data that we had previously generated across sea otter populations as a measure of population health. We used statistical methods with different approaches (i.e., means vs. variances) and examined the subsequent interpretive outcomes and how these influence our assessment of “health.” Interpretations based on analyses using variances versus means overlapped to some degree. In general, sea otter populations with low variation in gene expression were limited by food resources and at or near carrying capacity. In populations where the variation in gene expression was moderate or high, four out of five populations were increasing in abundance, or had been recently increasing. Where we had additional information on sources of stressors at the level of the population, we were able to draw inferences from those stressors to specific gene expression results. For example, gene expression patterns of sea otters from Western Prince William Sound were consistent with long term exposure to petroleum hydrocarbons, whereas in Kachemak Bay, patterns were consistent with exposure to algal toxins. Ultimately, determination of population or ecosystem health will be most informative when multiple metrics are examined across disciplines in the context of specific scenarios and goals.
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Heather","contributorId":258849,"corporation":false,"usgs":false,"family":"Coletti","given":"Heather","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":873753,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ballachey, Brenda E. 0000-0003-1855-9171 bballachey@usgs.gov","orcid":"https://orcid.org/0000-0003-1855-9171","contributorId":2966,"corporation":false,"usgs":true,"family":"Ballachey","given":"Brenda","email":"bballachey@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":873754,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":873755,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miles, A. Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873756,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243706,"text":"70243706 - 2023 - Barrier island reconfiguration leads to rapid erosion and relocation of a rural Alaska community","interactions":[],"lastModifiedDate":"2023-07-11T16:04:06.943392","indexId":"70243706","displayToPublicDate":"2023-05-18T07:47:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Barrier island reconfiguration leads to rapid erosion and relocation of a rural Alaska community","docAbstract":"Coastal erosion is one of the foremost hazards that circumpolar communities face. Climate change and warming temperatures are anticipated to accelerate coastal change, increasing risk to coastal communities. Most erosion hazard studies for Alaska communities only consider linear erosion and do not anticipate coastal morphologic changes. This study showcases the possibility and consequence of accelerated erosion by examining a shift from stability to rapid erosion that forced the rural Alaska Native village of Meshik (now Port Heiden) to abandon the original town site and relocate inland. A combination of remote sensing, coastal surveys, and community-based monitoring are used to map coastal morphologic changes and identify erosion drivers. The community’s shoreline was stable until a protective barrier island eroded away. The exposure to open ocean waves, coupled with unconsolidated, low-density sediments, led to rapid erosion rates averaging of 5.8 ± 0.6 m/y from the 1970s to 2020s. The sudden and rapid erosion put great stress on Meshik and resulted in the loss of homes, erosion of a safe boat harbor, and pollution of the beach and bay. Erosion of the barrier island coincided with a period of greater storm activity and sea ice decline, but the exact cause for its erosion could not be determined. Many polar communities are built on or behind barriers and are on easily erodible soils such as sands and thawing permafrost. This study highlights the need to study, monitor, and predict morphologic change and regime shifts that can bring catastrophic impacts to coastal communities.","language":"English","publisher":"Allen Press","doi":"10.2112/JCOASTRES-D-22-00093.1","usgsCitation":"Buzard, R., Kinsman, N.E., Maio, C.V., Erikson, L.H., Jones, B.M., Anderson, S.K., Glenn, R., and Overbeck, J.R., 2023, Barrier island reconfiguration leads to rapid erosion and relocation of a rural Alaska community: Journal of Coastal Research, v. 39, no. 4, p. 625-642, https://doi.org/10.2112/JCOASTRES-D-22-00093.1.","productDescription":"18 p.","startPage":"625","endPage":"642","ipdsId":"IP-147862","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":417202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Meshik, Port Heiden","otherGeospatial":"Aniakchak Volcano, Bristol Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.82365753904958,\n 56.95251936361021\n ],\n [\n -158.82365753904958,\n 56.879736242479424\n ],\n [\n -158.64890766892418,\n 56.879736242479424\n ],\n [\n -158.64890766892418,\n 56.95251936361021\n ],\n [\n -158.82365753904958,\n 56.95251936361021\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buzard, Richard M.","contributorId":208627,"corporation":false,"usgs":false,"family":"Buzard","given":"Richard M.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":873006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinsman, Nicole E.M.","contributorId":305383,"corporation":false,"usgs":false,"family":"Kinsman","given":"Nicole","email":"","middleInitial":"E.M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":873007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maio, Christopher V.","contributorId":208635,"corporation":false,"usgs":false,"family":"Maio","given":"Christopher","email":"","middleInitial":"V.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":873008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":873150,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Scott K.","contributorId":71748,"corporation":false,"usgs":false,"family":"Anderson","given":"Scott","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":873011,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Glenn, Roberta","contributorId":305500,"corporation":false,"usgs":false,"family":"Glenn","given":"Roberta","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":873012,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Overbeck, Jacquelyn R.","contributorId":181813,"corporation":false,"usgs":false,"family":"Overbeck","given":"Jacquelyn","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":873013,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70244197,"text":"70244197 - 2023 - Environmental antimicrobial resistance gene detection from wild bird habitats using two methods: A commercially available culture-independent qPCR assay and culture of indicator bacteria followed by whole-genome sequencing","interactions":[],"lastModifiedDate":"2023-06-07T12:05:21.407844","indexId":"70244197","displayToPublicDate":"2023-05-18T06:58:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7759,"text":"Journal of Global Antimicrobial Resistance","active":true,"publicationSubtype":{"id":10}},"title":"Environmental antimicrobial resistance gene detection from wild bird habitats using two methods: A commercially available culture-independent qPCR assay and culture of indicator bacteria followed by whole-genome sequencing","docAbstract":"A variety of methods have been developed to detect antimicrobial resistance (AMR) in different environments to better understand the evolution and dissemination of this public health threat. Comparisons of results generated using different AMR detection methods, such as quantitative PCR (qPCR) and whole-genome sequencing (WGS), are often imperfect, and few studies have analysed samples in parallel to evaluate differences. In this study, we compared bacterial culture and WGS to a culture-independent commercially available qPCR assay to evaluate the concordance between methods and the utility of each in answering research questions regarding the presence and epidemiology of AMR in wild bird habitats.
We first assessed AMR gene detection using qPCR in 45 bacterial isolates from which we had existing WGS data. We then analysed 52 wild bird faecal samples and 9 spatiotemporally collected water samples using culture-independent qPCR and WGS of phenotypically resistant indicator bacterial isolates.
Overall concordance was strong between qPCR and WGS of bacterial isolates, although concordance differed among antibiotic classes. Analysis of wild bird faecal and water samples revealed that more samples were determined to be positive for AMR via qPCR than via culture and WGS of bacterial isolates, although qPCR did not detect AMR genes in two samples from which phenotypically resistant isolates were found.
Both qPCR and culture followed by sequencing may be effective approaches for characterising AMR genes harboured by wild birds, although data streams produced using these different tools may have advantages and disadvantages that should be considered given the application and sample matrix.
Carbapenem-resistant Enterobacteriaceae (CRE) are a global threat to human health and are increasingly being isolated from nonclinical settings. OXA-48-producing Escherichia coli sequence type 38 (ST38) is the most frequently reported CRE type in wild birds and has been detected in gulls or storks in North America, Europe, Asia, and Africa. The epidemiology and evolution of CRE in wildlife and human niches, however, remains unclear. We compared wild bird origin E. coli ST38 genome sequences generated by our research group and publicly available genomic data derived from other hosts and environments to (i) understand the frequency of intercontinental dispersal of E. coli ST38 clones isolated from wild birds, (ii) more thoroughly measure the genomic relatedness of carbapenem-resistant isolates from gulls sampled in Turkey and Alaska, USA, using long-read whole-genome sequencing and assess the spatial dissemination of this clone among different hosts, and (iii) determine whether ST38 isolates from humans, environmental water, and wild birds have different core or accessory genomes (e.g., antimicrobial resistance genes, virulence genes, plasmids) which might elucidate bacterial or gene exchange among niches. Our results suggest that E. coli ST38 strains, including those resistant to carbapenems, are exchanged between humans and wild birds, rather than separately maintained populations within each niche. Furthermore, despite close genetic similarity among OXA-48-producing E. coli ST38 clones from gulls in Alaska and Turkey, intercontinental dispersal of ST38 clones among wild birds is uncommon. Interventions to mitigate the dissemination of antimicrobial resistance throughout the environment (e.g., as exemplified by the acquisition of carbapenem resistance by birds) may be warranted.
","language":"English","publisher":"ASM Journals","doi":"10.1128/aem.00319-23","usgsCitation":"Ahlstrom, C., Woksepp, H., Sandegren, L., Ramey, A.M., and Bonnedahl, J., 2023, Exchange of carbapenem-resistant Escherichia coli Sequence Type 38 intercontinentally and among wild bird, human, and environmental niches: Applied and Environmental Microbiology, v. 89, no. 6, e0031923, https://doi.org/10.1128/aem.00319-23.","productDescription":"e0031923","ipdsId":"IP-149692","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443503,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10304903","text":"External Repository"},{"id":419396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":879254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woksepp, Hanna","contributorId":207263,"corporation":false,"usgs":false,"family":"Woksepp","given":"Hanna","email":"","affiliations":[],"preferred":false,"id":879255,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sandegren, Linus","contributorId":279688,"corporation":false,"usgs":false,"family":"Sandegren","given":"Linus","email":"","affiliations":[{"id":57339,"text":"Department of Medical Biochemistry and Microbiology, Infection biology, antimicrobial resistance and immunology, Uppsala University","active":true,"usgs":false}],"preferred":false,"id":879256,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":879257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":879258,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243553,"text":"sir20235039 - 2023 - 2019 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory","interactions":[],"lastModifiedDate":"2026-03-06T21:29:38.662584","indexId":"sir20235039","displayToPublicDate":"2023-05-12T12:37:20","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":"2023-5039","displayTitle":"2019 Volcanic Activity in Alaska—Summary of Events and Response of the Alaska Volcano Observatory","title":"2019 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory","docAbstract":"The Alaska Volcano Observatory responded to eruptions, volcanic unrest or suspected unrest, increased seismicity, and other significant activity at 17 volcanic centers in Alaska in 2019. The most notable volcanic activity was an eruption of Shishaldin Volcano, featuring eruptive activity that produced lava flows, lahars, and ash. Weak explosive activity also took place at Great Sitkin Volcano and Semisopochnoi Island. Mount Cleveland had one small ash-producing eruption followed by dome growth in early January but was quiet thereafter, and flank activity at Shrub mud volcano produced new mud deposits. Other activity documented in 2019 consists of declining unrest at Mount Veniaminof after its 2018 eruption; large ice and rock avalanches at Iliamna Volcano and Mount Spurr; anomalous seismicity and an increase in degassing at Pavlof Volcano; long-term inflation at Westdahl volcano, Akutan Volcano, and Mount Okmok; steam plumes and anomalous seismicity at Makushin Volcano; elevated seismicity at Mount Martin; and resuspended ash from the 1912 Novarupta-Katmai eruption deposits.
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U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
Timing of seasonal bird migrations is broadly determined by internal biological clocks, which are synchronized by photoperiod, but individuals often refine their migratory timing decisions in response to external factors. Using 11 years of satellite telemetry data, we show that Pacific Barrow’s Goldeneye (Bucephala islandica), at higher latitudes, initiated spring and molt migrations later and fall migration earlier than individuals at lower latitudes. We further show that individuals refined migratory timing in response to interannual variation in environmental conditions. Individual Barrow’s Goldeneye initiated spring migration earlier in years with warmer springs at their overwintering locations and concluded spring migration earlier in years with earlier annual snow melt on their breeding grounds. Because individuals respond to conditions both where they initiate and where they conclude spring migration, our results suggest that Barrow’s Goldeneye update their migratory decisions en route. For all three migrations in their annual cycle, birds delayed initiating migration if they had been captured and tagged prior to that migration. Birds that initiated migration late for their latitude were less likely to include a stopover and completed that migration faster, partially compensating for delayed departures. Our results are consistent with the hypothesis that Barrow’s Goldeneye use a combination of endogenous cues and environmental cues in migratory decision making. Sensitivity to environmental cues suggests that Barrow’s Goldeneye may have behavioural plasticity that is adaptive when faced with ongoing climate change.
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Sean","contributorId":199405,"corporation":false,"usgs":false,"family":"Boyd","given":"W.","email":"","middleInitial":"Sean","affiliations":[{"id":35539,"text":"Science and Technology Branch, Environment and Climate Change Canada, Delta, BC, Canada","active":true,"usgs":false}],"preferred":false,"id":874834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forstner, Tesia M.","contributorId":306140,"corporation":false,"usgs":false,"family":"Forstner","given":"Tesia","email":"","middleInitial":"M.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":874835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":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},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":874836,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bowman, Timothy D.","contributorId":80779,"corporation":false,"usgs":false,"family":"Bowman","given":"Timothy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":874837,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":874838,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hogan, Danica H.","contributorId":241001,"corporation":false,"usgs":false,"family":"Hogan","given":"Danica","email":"","middleInitial":"H.","affiliations":[{"id":48188,"text":"Environment Canada","active":true,"usgs":false}],"preferred":false,"id":874839,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McAdie, Malcolm","contributorId":14757,"corporation":false,"usgs":false,"family":"McAdie","given":"Malcolm","email":"","affiliations":[],"preferred":false,"id":874840,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, Jonathan","contributorId":222570,"corporation":false,"usgs":false,"family":"Thompson","given":"Jonathan","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":874841,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Willie, Megan","contributorId":199404,"corporation":false,"usgs":false,"family":"Willie","given":"Megan","email":"","affiliations":[{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":false,"id":874842,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Green, David","contributorId":167789,"corporation":false,"usgs":false,"family":"Green","given":"David","affiliations":[],"preferred":false,"id":874843,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70249368,"text":"70249368 - 2023 - The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","interactions":[],"lastModifiedDate":"2023-10-05T12:06:20.98252","indexId":"70249368","displayToPublicDate":"2023-05-09T07:05:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","docAbstract":"Accurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo-referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.
Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts.
This chapter describes the geomorphology, hydrology, chemistry, biodiversity, and ecology of rivers in the North American Arctic. The history, physiography, climate, and land use of the Arctic regions are also described. The chapter includes details on the Kobuk and Colville rivers in Alaska, the Thelon and Kazan rivers in the central Canadian Arctic, Koroc River and Nakvak Brook in the eastern Canadian low Arctic, Thomsen River on Banks Island in the western Canadian Arctic Archipelago, and Ruggles River on Ellesmere Island in the Canadian high Arctic. The rivers are characteristic of the major ecoregions of the North American Arctic, covering a range of geomorphological and physiographic conditions. The history of use of the rivers by Inuit and Dene First Nations Peoples of the north provides the foundation to understand the social, cultural, and economic importance of the river systems, and potential threats to the rivers from climate change are outlined.