Martian gullies resemble water-carved gullies on Earth, yet their present-day activity cannot be explained by water-driven processes. The sublimation of CO2 has been proposed as an alternative driver for sediment transport, but how this mechanism works remains unknown. Here we combine laboratory experiments of CO2-driven granular flows under Martian atmospheric pressure with 1D climate simulation modelling to unravel how, where, and when CO2 can drive present-day gully activity. Our work shows that sublimation of CO2 ice, under Martian atmospheric conditions can fluidize sediment and creates morphologies similar to those observed on Mars. Furthermore, the modelled climatic and topographic boundary conditions for this process, align with present-day gully activity. These results have implications for the influence of water versus CO2-driven processes in gully formation and for the interpretation of gully landforms on other planets, as their existence is no longer definitive proof for flowing liquids.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-024-01298-7","usgsCitation":"Roelofs, L., Conway, S.J., de Haas, T., Dundas, C., Lewis, S.R., McElwaine, J., Pasquon, K., Raack, J., Sylvest, M., and Patel, M., 2024, How, when and where current mass flows in Martian gullies are driven by CO2 sublimation: Communications Earth and Environment, v. 5, 125, 9 p., https://doi.org/10.1038/s43247-024-01298-7.","productDescription":"125, 9 p.","ipdsId":"IP-143281","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":440137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-024-01298-7","text":"Publisher Index Page"},{"id":433000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Roelofs, Lonneke","contributorId":343523,"corporation":false,"usgs":false,"family":"Roelofs","given":"Lonneke","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":911294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Susan J.","contributorId":203697,"corporation":false,"usgs":false,"family":"Conway","given":"Susan","email":"","middleInitial":"J.","affiliations":[{"id":36693,"text":"University of Nantes","active":true,"usgs":false}],"preferred":false,"id":911295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Haas, Tjalling","contributorId":336830,"corporation":false,"usgs":false,"family":"de Haas","given":"Tjalling","affiliations":[],"preferred":false,"id":911296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":911297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Stephen R.","contributorId":64081,"corporation":false,"usgs":true,"family":"Lewis","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":911298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McElwaine, Jim","contributorId":201623,"corporation":false,"usgs":false,"family":"McElwaine","given":"Jim","affiliations":[{"id":25252,"text":"Durham University","active":true,"usgs":false}],"preferred":false,"id":911299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pasquon, Kelly","contributorId":343526,"corporation":false,"usgs":false,"family":"Pasquon","given":"Kelly","email":"","affiliations":[{"id":82106,"text":"Nantes Universite","active":true,"usgs":false}],"preferred":false,"id":911300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raack, Jan","contributorId":343527,"corporation":false,"usgs":false,"family":"Raack","given":"Jan","email":"","affiliations":[{"id":82107,"text":"Westfalische Wilhelms-Universitat, Innomago GmbH","active":true,"usgs":false}],"preferred":false,"id":911301,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sylvest, Matt","contributorId":343528,"corporation":false,"usgs":false,"family":"Sylvest","given":"Matt","email":"","affiliations":[{"id":47593,"text":"The Open University","active":true,"usgs":false}],"preferred":false,"id":911302,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Patel, Manish","contributorId":343529,"corporation":false,"usgs":false,"family":"Patel","given":"Manish","email":"","affiliations":[{"id":47593,"text":"The Open University","active":true,"usgs":false}],"preferred":false,"id":911303,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259800,"text":"70259800 - 2024 - Arsenic and other geogenic contaminants in global groundwater","interactions":[],"lastModifiedDate":"2024-10-25T15:56:51.495002","indexId":"70259800","displayToPublicDate":"2024-03-12T10:50:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7460,"text":"Nature Reviews Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic and other geogenic contaminants in global groundwater","docAbstract":"Geogenic groundwater contaminants (GGCs) affect drinking-water availability and safety, with up to 60% of groundwater sources in some regions contaminated by more than recommended concentrations. As a result, an estimated 300–500 million people are at risk of severe health impacts and premature mortality. In this Review, we discuss the sources, occurrences and cycling of arsenic, fluoride, selenium and uranium, which are GGCs with widespread distribution and/or high toxicity. The global distribution of GGCs is controlled by basin geology and tectonics, with GGC enrichment in both orogenic systems and cratonic basement rocks. This regional distribution is broadly influenced by climate, geomorphology and hydrogeochemical evolution along groundwater flow paths. GGC distribution is locally heterogeneous and affected by in situ lithology, groundwater flow and water–rock interactions. Local biogeochemical cycling also determines GGC concentrations, as arsenic, selenium and uranium mobilizations are strongly redox-dependent. Increasing groundwater extraction and land-use changes are likely to modify GGC distribution and extent, potentially exacerbating human exposure to GGCs, but the net impact of these activities is unknown. Integration of science, policy, community involvement programmes and technological interventions is needed to manage GGC-enriched groundwater and ensure equitable access to clean water.
","language":"English","publisher":"Nature","doi":"10.1038/s43017-024-00519-z","usgsCitation":"Mukherjee, A., Coomar, P., Sarkar, S., Johannesson, K., Fryar, A., Schreiber, M., Ahmed, K.M., Alam, M.A., Bhattacharya, P., Bundschuh, J., Burgess, W., Chakraborty, M., Coyte, R., Farooqi, A., Guo, H., Ijumulana, J., Jeelani, G., Mondal, D., Nordstrom, D.K., Podgorski, J., Polya, D., Scanlon, B.R., Shamsudduha, M., Tapia, J., and Vengosh, A., 2024, Arsenic and other geogenic contaminants in global groundwater: Nature Reviews Earth & Environment, v. 5, p. 312-328, https://doi.org/10.1038/s43017-024-00519-z.","productDescription":"17 p.","startPage":"312","endPage":"328","ipdsId":"IP-162090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467025,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.dora.lib4ri.ch/eawag/islandora/object/eawag%3A32679","text":"External Repository"},{"id":463197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2024-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mukherjee, Abhijit","contributorId":213833,"corporation":false,"usgs":false,"family":"Mukherjee","given":"Abhijit","email":"","affiliations":[],"preferred":false,"id":916735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coomar, Poulomee","contributorId":345478,"corporation":false,"usgs":false,"family":"Coomar","given":"Poulomee","email":"","affiliations":[{"id":82595,"text":"Indian Institute of Technology Kharagpur","active":true,"usgs":false}],"preferred":false,"id":916736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sarkar, Soumyajit","contributorId":345479,"corporation":false,"usgs":false,"family":"Sarkar","given":"Soumyajit","email":"","affiliations":[{"id":82595,"text":"Indian Institute of Technology Kharagpur","active":true,"usgs":false}],"preferred":false,"id":916737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johannesson, Karen H.","contributorId":150171,"corporation":false,"usgs":false,"family":"Johannesson","given":"Karen H.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":916749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fryar, Alan","contributorId":345484,"corporation":false,"usgs":false,"family":"Fryar","given":"Alan","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":916745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schreiber, Madeline","contributorId":248255,"corporation":false,"usgs":false,"family":"Schreiber","given":"Madeline","affiliations":[{"id":49841,"text":"Virginia Tech, Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":916755,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ahmed, Kazi M.","contributorId":345480,"corporation":false,"usgs":false,"family":"Ahmed","given":"Kazi","email":"","middleInitial":"M.","affiliations":[{"id":65425,"text":"University of Dhaka","active":true,"usgs":false}],"preferred":false,"id":916738,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Alam, Mohd. 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Pickle Research Campus, Bldg. 130, 10100 Burnet Rd., Austin, TX 78758-4445","active":true,"usgs":false}],"preferred":false,"id":916754,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Shamsudduha, Mohd.","contributorId":345488,"corporation":false,"usgs":false,"family":"Shamsudduha","given":"Mohd.","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":916756,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Tapia, Joseline","contributorId":345489,"corporation":false,"usgs":false,"family":"Tapia","given":"Joseline","email":"","affiliations":[{"id":82602,"text":"Universidad Católica Del Norte, Antofagasta, Chile","active":true,"usgs":false}],"preferred":false,"id":916757,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Vengosh, Avner","contributorId":208460,"corporation":false,"usgs":false,"family":"Vengosh","given":"Avner","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":916758,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70252861,"text":"70252861 - 2024 - Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","interactions":[],"lastModifiedDate":"2024-09-23T15:25:30.720271","indexId":"70252861","displayToPublicDate":"2024-03-12T07:06:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","docAbstract":"Less than a year after the 2018 Kīlauea caldera collapse and eruption, water appeared in newly deepened Halemaʻumaʻu crater. The lake—unprecedented in the written record—grew to a depth of ∼50 m before lava from the December 2020 eruption boiled it away. Surface water heightened concerns of potential phreatic or phreatomagmatic explosions but also offered a new means of possibly identifying eruption precursors. The U.S. Geological Survey Hawaiian Volcano Observatory (HVO) monitored the lake via direct visual observation, webcams, thermal imaging, colorimetry, and laser rangefinders. HVO also employed uncrewed aircraft systems to sample the water and measure near-lake gas composition. The lake's δD and δ18O indicate a groundwater source with substantial evaporation. The initial sample had a salinity (total dissolved solids concentration) of 71,000 mg/L and was rich in sulfate (∼53,000 mg/L), iron (∼500 mg/L), and magnesium (∼10,000 mg/L). Subsequent samples were slightly more dilute. The water's pH (∼4), δ34S (+4.3‰), and surface temperatures (up to 85°C) suggest, rather than significant scrubbing of magmatic volatiles, leaching of basalt and reactions with sulfate minerals resulted in high concentrations of sulfate and other solutes. Thermodynamic modeling and precipitate mineralogy indicate that water composition was controlled by iron oxidation and sulfate dissolution. Although the lake exhibited no detectable precursors before the next eruption, and phreatic or phreatomagmatic explosions did not materialize, our multi-parameter approach to monitoring yielded an enhanced understanding of the hydrologic, geologic, and magmatic conditions that led to the formation of the unique and short-lived lake.
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.
The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of “social” and “solitary” inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae, Order Carnivora) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species’ context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation.
","language":"English","publisher":"National Academy of Sciences of the United States of America","doi":"10.1073/pnas.2312252121","usgsCitation":"Twining, J., Sutherland, C., Zalewski, A., Cove, M., Birks, J., Wearn, O.R., Haysom, J., Wereszczuk, A., Manzo, E., Bartolommei, P., Mortelliti, A., Evans, B., Gerber, B., McGreevy, T., Ganoe, L.S., Masseloux, J., Mayer, A.E., Wierzbowska, I., Loch, J., Akins, J., Drummey, D., McShea, W., Manke, S., Pardo, L., Boyce, A., Li, S., Binti Ragai, R., Sukmasuang, R., Villafane Trujillo, A.J., Lopez-Gonzalez, C., Lara-Diaz, N.E., Cosby, O., Waggershauser, C.N., Bamber, J., Stewart, F., Fisher, J., Fuller, A.K., Perkins, K., and Powell, R.A., 2024, Using global remote camera data of a solitary species complex to evaluate the drivers of group formation: PNAS, v. 121, no. 12, e2312252121, 8 p., https://doi.org/10.1073/pnas.2312252121.","productDescription":"e2312252121, 8 p.","ipdsId":"IP-151912","costCenters":[{"id":199,"text":"Coop Res Unit 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,{"id":70257405,"text":"70257405 - 2024 - Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes","interactions":[],"lastModifiedDate":"2024-08-30T15:53:54.80827","indexId":"70257405","displayToPublicDate":"2024-03-11T08:39:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat amount and edge effects, not perch proximity, nest exposure, or vegetation diversity affect cowbird parasitism in agricultural landscapes","docAbstract":"Prior research documented relationships between brown-headed cowbird (Molothrus ater) brood parasitism and edge effects, proximity of perches, and nest exposure. Those relationships have not been evaluated in agroecosystems containing extremes of fragmentation and vegetation diversity.
We compared three existing hypotheses on how cowbirds locate host nests with two new hypotheses regarding habitat amount and vegetation diversity to determine how the configuration and location of agricultural conservation practices affect grassland bird nest parasitism rates and predicted rates for eight common conservation practices.
We assessed cowbird parasitism of grassland bird nests on corn and soybean farms in Iowa, USA, and measured perch proximity, nest exposure, edge effects, habitat amount, and vegetation diversity for each nest. We fit a global generalized linear mixed-effects model and compared importance of model parameters using odds ratios. We predicted parasitism likelihood for every subset model and averaged predictions to explore individual effects.
The variables that most influenced parasitism rates included main effects for nest initiation day-of-season (OR = 0.71, CI95 = 0.60–0.84) and the landscape variables of distance to nearest crop edge (0.63, 0.51–0.76) and proportion of grass land cover within 660 m (0.75, 0.57–1.00). We found little support that perch proximity, nest exposure, or native vegetation diversity affected parasitism. We also assessed parasitism likelihood by conservation practice and found no significant differences.
Our results provide evidence to support the edge effect and habitat amount hypotheses, but not the nest exposure, vegetation diversity, or perch proximity hypotheses.
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University","active":true,"usgs":false}],"preferred":false,"id":910261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaver, Robert W. 0000-0002-3263-9701 bklaver@usgs.gov","orcid":"https://orcid.org/0000-0002-3263-9701","contributorId":3285,"corporation":false,"usgs":true,"family":"Klaver","given":"Robert","email":"bklaver@usgs.gov","middleInitial":"W.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910262,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70251898,"text":"ofr20241013 - 2024 - Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report","interactions":[],"lastModifiedDate":"2024-12-04T14:29:22.266119","indexId":"ofr20241013","displayToPublicDate":"2024-03-11T08:24:27","publicationYear":"2024","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":"2024-1013","displayTitle":"Growth, Survival, and Cohort Formation of Juvenile Lost River (Deltistes luxatus) and Shortnose Suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 Monitoring Report","title":"Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report","docAbstract":"The work reported in this publication provides updated data and interpretation for sampling years 2015 and 2022 of the juvenile monitoring project. The study objectives, background, study area, species description, and methods remained the same or similar throughout the years, while the executive summary, results, and discussion were updated each year. Therefore much of this paper was originally presented in previous reports (Bart and others 2020a, b; Bart and others, 2021; Burdick and others, 2016; Burdick and others, 2018; Martin and others, 2022) and is repeated here for the reader’s convenience.
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only because of adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth; (2) better understand when juvenile sucker mortality is greatest; and (3) identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey (USGS) monitoring program, begun in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker production, juvenile sucker apparent survival, growth, species composition, and health.
Upper Klamath Lake indices of year-class strength suggest that the 2022 age-0 cohort is the lowest since standardized monitoring began. The 2021 cohort, like most cohorts, had moderately low catch rates their first year of life, with a steep drop off during the second year. Although the 2020 cohort persisted through the September 2022 sampling, this cohort was sparsely represented after the first year with no representatives from this cohort captured from July 2021 through July 2022. Despite apparently low fall through spring apparent survival, the relatively large 2019 cohort persisted in our 2020–21 samples, but has not been detected since June 2021. Klamath largescale (Catostomus snyderi) and shortnose suckers were only differentiated from each other starting in 2020. Shortnose suckers dominated the age-1 catch in 2020 and 2022, whereas age-1 Klamath largescale suckers were slightly more prevalent in 2021. Although there were occasionally age-2 and older suckers captured, none of these fish were Lost River suckers. Except for 2015, 2017, and 2021, there were more age-0 Lost River suckers than presumed shortnose suckers in Upper Klamath Lake. However, in all years sampled, there were more age-1 presumed shortnose suckers than Lost River suckers.
Age distribution of suckers captured in Clear Lake indicates greater juvenile survival than in Upper Klamath Lake. Most juvenile suckers captured throughout the years were from the 2016 and 2017 cohorts; however, by 2022 most of these fish were no longer susceptible to standard trap nets and were not as prevalent in 2022 juvenile catches, and these suckers presumedly recruited to the adult population. As the 2016 and 2017 cohorts catches declined, so did the catch in overall numbers of suckers. Excluding age-0 catches, the 2016 cohort catches peaked at age-3 and the 2017 catches peaked at age-2. In 2022, the majority of the catch was composed of age-3 to age-5 suckers. The majority of suckers captured in Clear Lake during this multiyear project were classified as the combination of Klamath largescale suckers and shortnose suckers from the Lost River Basin, from the 2016 and 2017 cohorts. The few suckers identified as Lost River or definitive shortnose suckers were from the 2016 and 2017 cohorts. A lack of age-0 suckers captured in Clear Lake during years with low spawning tributary inflow or lake levels suggested that low water prevented spawning and year class formation. However, recent data indicate that some cohorts with Klamath largescale and shortnose sucker genetics that were not captured as age-0 suckers were detected in later years at age-1 or age-2. This finding indicates that juvenile suckers in Clear Lake may spend one or more years in the tributaries and that these cohorts may primarily be represented by Klamath largescale suckers.
The first 7 years of this monitoring program indicated different patterns in recruitment and survival of juvenile suckers between Upper Klamath and Clear Lakes. Since the monitoring program began in 2015, age-0 sucker catch rates, interpreted as indices of year-class strength, were greatest in Upper Klamath Lake in 2016 and 2019. In those years, Lost River suckers made up the majority of age-0 sucker catches. However, in 2017 and 2020, the age-1 sucker catches from these cohorts were mainly composed of shortnose suckers or suckers with genetic markers of both Klamath largescale and shortnose suckers, indicating a low first year survival for Lost River suckers even when age-0 catches were high. Age-0 suckers do not fully recruit to our sampling gear in Upper Klamath Lake until August, experience high mortality by September, and are almost undetectable in subsequent years. In Clear Lake, suckers are often not captured until age-1 or age-2 and juvenile annual survival appears much greater; however, there does appear to be a drop-off in catch rates as the suckers age and become less susceptible to the fishing gear.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241013","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Martin, B.A., Caldwell, J.M., Krause, J.R., and Harris, A.C., 2024, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report: U.S. Geological Survey Open-File Report 2024–1013, 39 p., https://doi.org/10.3133/ofr20241013.","productDescription":"Report: vi, 39 p.; 1 Data Release","onlineOnly":"Y","ipdsId":"IP-159115","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":426337,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.XML"},{"id":426335,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93KYGEG","text":"USGS data release","description":"USGS data release","linkHelpText":"Upper Klamath Lake and Clear Lake sampling for suckers from 2015 through 2022. Reston, Virginia: U.S. Geological Survey, Klamath Falls Field Station, Klamath Falls, Oregon"},{"id":426334,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241013/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1013"},{"id":426333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.pdf","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1013"},{"id":426332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.jpg"},{"id":426336,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1013/images"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.01266997658502,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.93494622821743\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78179025160858,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.649405814487636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Harvest in walleye Sander vitreus fisheries is size-selective and could influence phenotypic traits of spawners; however, contributions of individual spawners to recruitment are unknown. We used parentage analyses using single nucleotide polymorphisms to test whether parental traits were related to the probability of offspring survival in Escanaba Lake, Wisconsin. From 2017 to 2020, 1339 adults and 1138 juveniles were genotyped and 66% of the offspring were assigned to at least one parent. Logistic regression indicated the probability of reproductive success (survival of age-0 to first fall) was positively (but weakly) related to total length and growth rate in females, but not age. No traits analyzed were related to reproductive success for males. Our analysis identified the model with the predictors' growth rate and year for females and the models with year and age and year for males as the most likely models to explain variation in reproductive success. Our findings indicate that interannual variation (i.e., environmental conditions) likely plays a key role in determining the probability of reproductive success in this population and provide limited support that female age, length, and growth rate influence recruitment.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.13665","usgsCitation":"Davis, R.P., Simmons, L.M., Shaw, S., Sass, G., Sard, N., Isermann, D.A., Larson, W.A., and Homola, J.J., 2024, Demographic patterns of walleye (Sander vitreus) reproductive success in a Wisconsin population: Evolutionary Applications, v. 17, no. 3, e13665, 16 p., https://doi.org/10.1111/eva.13665.","productDescription":"e13665, 16 p.","ipdsId":"IP-156437","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440160,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/eva.13665","text":"External Repository"},{"id":433563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Vilas County","otherGeospatial":"Escanaba Lake","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.9879,46.0971],[-88.9329,46.0746],[-88.9332,45.9822],[-89.0478,45.9822],[-89.0477,45.8953],[-89.1091,45.8973],[-89.1752,45.8993],[-89.1754,45.859],[-89.3008,45.8606],[-89.3007,45.9014],[-89.3628,45.8987],[-89.4256,45.8987],[-89.5498,45.8988],[-89.6741,45.8987],[-89.7571,45.8985],[-89.797,45.898],[-89.8199,45.8984],[-89.9212,45.8981],[-89.9846,45.8974],[-90.0428,45.8972],[-90.0442,45.9823],[-90.0134,45.9824],[-89.9853,45.9821],[-89.9289,45.9818],[-89.9282,46.0693],[-89.9288,46.1558],[-89.9287,46.2428],[-89.929,46.3],[-89.7599,46.268],[-89.7368,46.2636],[-89.5829,46.2347],[-89.5331,46.2252],[-89.5133,46.2215],[-89.4272,46.2048],[-89.3759,46.1949],[-89.2666,46.1737],[-89.2302,46.1662],[-89.0854,46.1365],[-88.9879,46.0971]]]},\"properties\":{\"name\":\"Vilas\",\"state\":\"WI\"}}]}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Robert P.","contributorId":342846,"corporation":false,"usgs":false,"family":"Davis","given":"Robert","email":"","middleInitial":"P.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910442,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simmons, Levi M.","contributorId":342849,"corporation":false,"usgs":false,"family":"Simmons","given":"Levi","email":"","middleInitial":"M.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910443,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaw, Stephanie L.","contributorId":342852,"corporation":false,"usgs":false,"family":"Shaw","given":"Stephanie L.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sass, Greg G.","contributorId":342855,"corporation":false,"usgs":false,"family":"Sass","given":"Greg G.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sard, Nicholas M.","contributorId":342858,"corporation":false,"usgs":false,"family":"Sard","given":"Nicholas M.","affiliations":[{"id":81942,"text":"State University of New York-Oswego","active":true,"usgs":false}],"preferred":false,"id":910446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910447,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larson, Wesley A.","contributorId":342859,"corporation":false,"usgs":false,"family":"Larson","given":"Wesley","email":"","middleInitial":"A.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":910448,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Homola, Jared Joseph 0000-0003-3821-7224","orcid":"https://orcid.org/0000-0003-3821-7224","contributorId":303741,"corporation":false,"usgs":true,"family":"Homola","given":"Jared","email":"","middleInitial":"Joseph","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910449,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254289,"text":"70254289 - 2024 - Modern coral range expansion off southeast Florida falls short of Late Holocene baseline","interactions":[],"lastModifiedDate":"2024-05-17T12:12:12.214655","indexId":"70254289","displayToPublicDate":"2024-03-09T07:08:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13795,"text":"Nature Communications Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Modern coral range expansion off southeast Florida falls short of Late Holocene baseline","docAbstract":"As thermal stress and disease outbreaks decimate coral reefs throughout the tropics, there is growing evidence that higher latitude marine environments may provide crucial refuges for many at-risk, temperature-sensitive coral species. However, our understanding of how coral populations expand into new areas and sustain themselves over time is constrained by the limited scope of modern observations. Here, we provide geological insights into coral range expansions by reconstructing the composition of a Late Holocene-aged subfossil coral death assemblage on the southeast Florida reef tract and comparing it to modern reefs throughout the region. Our findings show that the Late Holocene coral assemblages were dominated by now critically endangered Acropora species between ~3500 and 1800 years before present, mirroring classic zonation patterns characteristic of healthy pre-1970s Caribbean reefs. In contrast, the modern reefs off southeast Florida are becoming increasingly dominated by stress-tolerant species like Porites astreoides and Siderastrea siderea despite modest expansions of Acropora cervicornis over the past several decades. Our results suggest that ongoing anthropogenic stressors, not present during the Late Holocene, are likely limiting the ability of modern higher latitude reefs in Florida to function as long-term climate refugia.
Remote sensing observations of Searles Lake following the 2019 moment magnitude 7.1 Ridgecrest, California, earthquake reveal an area where surface ejecta is arranged in a repeating hexagonal pattern that is collocated with a solution-mining operation. By analyzing geologic and geotechnical data, here we show that the hexagonal surface ejecta is likely not a result of liquefaction. Instead, we propose dissolution cavity collapse (DCC) as an alternative driving mechanism. We support this theory with pre-event Interferometric Synthetic Aperture Radar data, which reveals differential subsidence patterns and the creation of subsurface void space. We also find that DCC is likely triggered at a lower shaking threshold than classical liquefaction. This and other unknown mechanisms can masquerade as liquefaction, introducing bias into liquefaction prediction models that rely on liquefaction inventories. This paper also highlights the opportunities and drawbacks of using remote sensing data to disentangle the complex factors that influence earthquake-triggered ground failure.
The amplitude and frequency content of background seismic noise is highly variable with geographic location. Understanding the characteristics and behavior of background seismic noise as a function of location can inform approaches to improve network performance and in turn increase earthquake detection capabilities. Here, we calculate power spectral density estimates in one‐hour windows for over 15 yr of vertical‐component data from the nine‐station Caribbean network (CU) and look at background noise within the 0.05–300 s period range. We describe the most visually apparent features observed at the CU stations. One of the most prominent features occurs in the 0.75–3 s band for which power levels are systematically elevated and decay as a function of proximity to the coastline. Further examination of this band on 1679 contiguous USArray Transportable Array stations reveals the same relationship. Such a relationship with coastal distance is not observed in the 4–8 s range more typical of globally observed secondary microseisms. A simple surface‐wave amplitude decay model fits the observed decay well with geometric spreading as the most important factor for stations near the coast (<∼50 km). The model indicates that power levels are strongly influenced by proximity to coastline at 0.75–3 s. This may be because power from nearshore wave action at 0.75–3 s overwhelms more distant and spatially distributed secondary microseism generation. Application of this basic model indicates that a power reduction of ∼25 dB can be achieved by simply installing the seismometer 25 km away from the coastline. This finding may help to inform future site locations and array design thereby improving network performance and data quality, and subsequently earthquake detection capabilities.
The unprecedented 2018 summit collapse at Kīlauea and subsequent 2020–2021 eruption within the newly deepened Halema‘uma‘u Crater provide an unparalleled opportunity to understand how collapse events impact a volcano’s shallow reservoir system and magmatic processes. Glass and olivine from tephra ejected by lava fountains and several explosions on 20–21 December, within a few hours of the 2020 eruption onset, yield information about pre-eruptive magma storage and transport. The olivine population is bimodal with zoned and non-zoned phenocrysts. Normally zoned olivine crystals with core compositions around Fo88 have 30–50 μm wide Fo82 overgrowth rims that have skeletal textures. Two skeletal xenocrysts (cores Fo74 and Fo81) are also reversely zoned up to Fo82 rims. The crystal cores have trace element records of at least two cycles of growth and dissolution prior to the formation of the overgrowth rims. These rims and a separate population of non-zoned Fo82 crystals are in Fe–Mg equilibrium with their host glass (average MgO of 6.9 ± 0.4 wt% (1σ), Mg# [Mg / (Mg + Fe2+)] of 0.57), which suggests undercooling after intrusion of magma to shallow levels in the plumbing system. In the years prior to the 2018 collapse, non-zoned Fo81 olivine and slightly lower MgO glasses (6.8 wt%) reflected continuous mixing and compositional buffering of magma recharge into several km3 of stored magma in the Halema‘uma‘u reservoir (1–2 km depth). The 2020 olivine crystals lack evidence of an intrusion mixing with resident shallow magma, indicating that magma transport occurred in a disrupted system, and/or it may not have significantly mixed with stored magma remaining in the Halema‘uma‘u reservoir after the events of 2018. Diffusion modeling of Fe–Mg exchange in the zoned 2020 olivine crystals yield timescales that are mostly 60 days prior to the eruption or less, which aligns well with 22–24 October 2020 and subsequent seismic swarms at Nāmakani Paio ~ 5 km west of Kīlauea’s summit caldera. This correlation indicates that magma intruding beneath the summit (volume accommodation, recorded by the olivine crystals) was expressed by tectonic earthquakes along the Ka‘ōiki fault zone (stress accommodation). The absence of precursory SO2 within minutes prior to eruption also indicates that the 2020 December magma may have risen from 1 to 2 km depth to the surface in as little as 10 min.
The deep sea is the largest biome on earth, but one of the least studied despite its critical role in global carbon cycling and climate buffering. Deep-sea organisms largely rely on particulate organic matter from the surface ocean for energy – these organisms in turn play critical roles in energy transport, transformation, storage, and sequestration of carbon. Within the deep sea, submarine canyons are amongst the most complex and dynamic environments in our oceans, where varied morphology, powerful currents, and variable nutrient conditions influence the distribution of species and transport of organic material throughout the water column and the seafloor. Significant habitat heterogeneity provides ideal substrates for cold-water corals, making submarine canyons of interest to conservation and management. However, how these and other topographic features in the deep ocean influence energy flow and trophic pathways is poorly known. Thus, submarine canyons serve as model systems to track variability in organic material flux and consequential utilization and assimilation by the benthos. In this study, we used an extensive stable isotope dataset to examine food-web structure in Baltimore and Norfolk submarine canyons and compared them to their adjacent slopes located along the U.S. Atlantic margin. Linear models were used to construct geospatially-explicit consumer isoscapes that predicted variation in carbon and nitrogen isotopes across the canyon-slope seascape, providing a predictive map from which to test hypotheses on the distribution and flow of energy resources, relevant to understanding whole community function. Communities were composed of isotopically diverse feeding groups with photosynthetically-derived organic carbon providing the basal food resource. Canyon communities were distinct from the slope, with canyon consumers significantly 13C-depleted, indicating a greater supply and/or utilization of fresh organic matter compared to the slope. Isoscapes for benthic and suspension feeders were distinct, possibly due to the consumption of different quality organic matter sources (fresh = suspension feeders, old = benthic feeders), each with distinct isotope composition. To our knowledge, our modeled isoscapes represent the first spatially extensive isotopic maps of deep-sea consumers, providing insights into regional-scale variation in stable carbon and nitrogen isotopes for different consumer groups. They provide a baseline for tracking climate-change induced fluctuations in the quality and availability of surface primary production and the consequential impact to benthic communities, which play critical roles in carbon cycling in our world’s oceans.
Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 μg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c07576","usgsCitation":"Tesoriero, A.J., Wherry, S., Dupuy, D., and Johnson, T., 2024, Predicting redox conditions in groundwater at a national scale using random forest classification: Environmental Science and Technology, v. 58, no. 11, p. 5079-5092, https://doi.org/10.1021/acs.est.3c07576.","productDescription":"14 p.","startPage":"5079","endPage":"5092","ipdsId":"IP-154897","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":440191,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c07576","text":"Publisher Index Page"},{"id":435024,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DVPJIX","text":"USGS data release","linkHelpText":"Input and results from a random forest 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]\n}","volume":"58","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dupuy, Danielle 0000-0001-9007-641X","orcid":"https://orcid.org/0000-0001-9007-641X","contributorId":222277,"corporation":false,"usgs":true,"family":"Dupuy","given":"Danielle","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896394,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254184,"text":"70254184 - 2024 - Modeled coastal-ocean pathways of land-sourced contaminants in the aftermath of Hurricane Florence","interactions":[],"lastModifiedDate":"2024-05-13T11:57:09.891301","indexId":"70254184","displayToPublicDate":"2024-03-07T06:53:04","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2321,"text":"Journal of Geophysical Research: Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Modeled coastal-ocean pathways of land-sourced contaminants in the aftermath of Hurricane Florence","docAbstract":"Extreme precipitation during Hurricane Florence, which made landfall in North Carolina in September 2018, led to breaches of hog waste lagoons, coal ash pits, and wastewater facilities. In the weeks following the storm, freshwater discharge carried pollutants, sediment, organic matter, and debris to the coastal ocean, contributing to beach closures, algae blooms, hypoxia, and other ecosystem impacts. Here, the ocean pathways of land-sourced contaminants following Hurricane Florence are investigated using the Regional Ocean Modeling System (ROMS) with a river point source with fixed water properties from a hydrologic model (WRF-Hydro) of the Cape Fear River Basin, North Carolina's largest watershed. Patterns of contaminant transport in the coastal ocean are quantified with a finite duration tracer release based on observed flooding of agricultural and industrial facilities. A suite of synthetic events also was simulated to investigate the sensitivity of the river plume transport pathways to river discharge and wind direction. The simulated Hurricane Florence discharge event led to westward (downcoast) transport of contaminants in a coastal current, along with intermittent storage and release of material in an offshore (bulge) or eastward (upcoast) region near the river mouth, modulated by alternating upwelling and downwelling winds. The river plume patterns led to a delayed onset and long duration of contaminants affecting beaches 100 km to the west, days to weeks after the storm. Maps of the onset and duration of hypothetical water quality hazards for a range of weather conditions may provide guidance to managers on the timing of swimming/shellfishing advisories and water quality sampling.
Temperature can influence mosquito-borne diseases like dengue. These effects are expected to vary geographically and over time in both magnitude and direction and may interact with other environmental variables, making it difficult to anticipate changes in response to climate change. Here, we investigate global variation in temperature–dengue relationship by analyzing published correlations between temperature and dengue and matching them with remotely sensed climatic and socioeconomic data. We found that the correlation between temperature and dengue was most positive at intermediate (near 24°C) temperatures, as predicted from an independent mechanistic model. Positive temperature–dengue associations were strongest when temperature variation and population density were high and decreased with infection burden and rainfall mean and variation, suggesting alternative limiting factors on transmission. Our results show that while climate effects on diseases are context-dependent they are also predictable from the thermal biology of transmission and its environmental and social mediators.
Oyster reefs across North America have declined precipitously over the past 140 years. In Washington State, Olympia oyster Ostrea lurida reefs historically provided water filtration and nearshore structural habitat for fishes and invertebrates, but this species is now functionally extinct across its historical range. In place of these naturally occurring reefs, shellfish farms consisting mainly of nonnative Pacific oysters Magallana gigas now occupy patches of nearshore habitat across Washington. These farms modify intertidal substrate by adding structural habitat via suspended oyster grow bags, predator exclusion nets, loose oyster beds, and other shellfish grow-out gear. As interest and investment in shellfish aquaculture have expanded both locally and globally, so has interest in how these farms modify intertidal habitat and whether the complex structure created by the shellfish and shellfish growing gear provides ecosystem services that are comparable to those of unfarmed areas, such as mudflats and eelgrass meadows.
In this study, we sought to quantify how shellfish farms are used as foraging habitat for several common nearshore species of fish and crabs in Puget Sound, Washington. We used direct observations of species-specific behaviors from underwater video to model how habitat type affected observed foraging rates.
We obtained a total of 393 crab observations, 431 demersal fish observations, and 1856 pelagic fish observations across all seven farm sites. Several common species of pelagic fish (e.g., surfperch [Embiotocidae]) used aquaculture-growing gear more frequently than unfarmed areas as foraging habitat, but Metacarcinus spp. crabs displayed higher foraging frequency in unfarmed mudflats. Species groups such as sculpins (Cottidae) and small flatfish (Pleuronectidae) clearly used specific aquaculture-growing gear and mudflats in roughly equal proportion.
Our results indicate that shellfish farms within a larger nearshore habitat mosaic of eelgrass meadows, mudflats, bivalve aquaculture gear, and edge habitat can provide foraging habitat for several species of nearshore fish.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10282","usgsCitation":"Veggerby, K., Scheuerell, M.D., Sanderson, B., Kiffney, P., and Ferriss, B., 2024, Shellfish aquaculture farms as foraging habitat for nearshore fishes and crabs: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 16, no. 2, e10282, 14 p., https://doi.org/10.1002/mcf2.10282.","productDescription":"e10282, 14 p.","ipdsId":"IP-158997","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":440203,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10282","text":"Publisher Index Page"},{"id":430280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.227401475166,\n 48.993797248377604\n ],\n [\n -123.17273320541344,\n 48.993797248377604\n ],\n [\n -123.17273320541344,\n 46.919549204528664\n ],\n [\n -122.227401475166,\n 46.919549204528664\n ],\n [\n -122.227401475166,\n 48.993797248377604\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Veggerby, Karl","contributorId":338024,"corporation":false,"usgs":false,"family":"Veggerby","given":"Karl","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":903251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanderson, Beth","contributorId":338027,"corporation":false,"usgs":false,"family":"Sanderson","given":"Beth","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kiffney, Peter","contributorId":242881,"corporation":false,"usgs":false,"family":"Kiffney","given":"Peter","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ferriss, Bridget","contributorId":338414,"corporation":false,"usgs":false,"family":"Ferriss","given":"Bridget","email":"","affiliations":[{"id":53980,"text":"NMFS","active":true,"usgs":false}],"preferred":false,"id":903255,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263414,"text":"70263414 - 2024 - Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence","interactions":[],"lastModifiedDate":"2025-02-10T16:28:46.482152","indexId":"70263414","displayToPublicDate":"2024-03-06T09:21:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence","docAbstract":"The Mendocino triple junction, the intersection of the Pacific, North American, and Gorda plates, activates a collection of disparate faults that reconcile Cascadia subduction with San Andreas transform motion. The December 20, 2022, Mw 6.4 Ferndale, California earthquake occurred within this complex zone as strike-slip faulting within the subducting Gorda slab. Here, we analyze the seismic and geodetic signatures of the mainshock and aftershock sequence to illuminate its role within complex tectonic surroundings. We find aftershocks on varied fault structures within the uppermost Gorda slab, yet seismicity on the subduction interface itself was notably absent. Nevertheless, we identify small but coherent postseismic deformation that is well modeled by aseismic slip on this interface, likely triggered by stresses generated at the updip limit of coseismic rupture. This sequence demonstrates the potential for interactions between intra-slab earthquakes and slip on the subduction megathrust, highlighting the need to consider this and other subduction zones as coupled systems of interacting faults.","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.adl1226","usgsCitation":"Shelly, D.R., Goldberg, D.E., Materna, K.Z., Skoumal, R.J., Hardebeck, J.L., Yoon, C., Yeck, W.L., and Earle, P.S., 2024, Subduction intraslab-interface fault interactions in the 2022 Mw 6.4 Ferndale, California earthquake sequence: Science Advances, v. 10, no. 10, eadl1226, 10 p., https://doi.org/10.1126/sciadv.adl1226.","productDescription":"eadl1226, 10 p.","ipdsId":"IP-157945","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":489932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adl1226","text":"Publisher Index 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wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","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":926901,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":926902,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70252162,"text":"70252162 - 2024 - When to target control efforts? Using novel GPS telemetry to quantify drivers of invasive Argentine black and white tegu (Salvator merianae) movement","interactions":[],"lastModifiedDate":"2024-05-20T15:27:10.978583","indexId":"70252162","displayToPublicDate":"2024-03-06T06:19:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"displayTitle":"When to target control efforts? Using novel GPS telemetry to quantify drivers of invasive Argentine black and white tegu (Salvator merianae) movement","title":"When to target control efforts? Using novel GPS telemetry to quantify drivers of invasive Argentine black and white tegu (Salvator merianae) movement","docAbstract":"In South Florida, the Argentine black and white tegu (Salvator merianae), a large, omnivorous lizard, has become a recent threat to the Everglades ecosystem. The increase in tegu observations, especially near ecologically sensitive areas such as Everglades National Park, makes informed management critical to contain the tegu population. Using Very High Frequency (VHF) and Global Positioning System (GPS) telemetry, we tracked 24 tegus in the Southern Glades Wildlife Management Area and Redland Agricultural Area in Homestead, Florida from March 2016 to November 2018 and March 2021 to January 2022. We used generalized additive models to determine factors that drive tegu movement to inform managers when traps and surveillance plots are most likely to be effective. Our top model included temporal (time of day and time of year), environmental (air temperature, relative humidity, rain, and wind speed from closest weather station), and biological (sex) variables. This model explained 34.6% of the deviance in tegu minimum rate of movement. We determined that tegus were most active between mid-March and mid-April, and tegu minimum rate of movement positively correlates with air temperature (i.e., highest activity during the hottest part of the day in the early afternoon). We observed a slight positive trend between tegu minimum rate of movement and relative humidity, and no clear trend between rate of movement and either rainfall or wind. Our results can inform natural resource management actions to target tegu removal and surveillance during high activity periods to maximize resource use.
Earthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people’s protective action decision-making in earthquakes by leveraging explainable machine learning and video data. Specifically, this study first collected real-world CCTV footage and video postings from social media platforms, and then identified and annotated changes in the environment and people’s behavioral responses during the M7.1 2018 Anchorage earthquake. By using the fully annotated video data, we applied XGBoost, a widely-used machine learning method, to model and forecast people’s protective actions (e.g., drop and cover, hold on, and evacuate) during the earthquake. Then, explainable machine learning techniques were used to reveal the complex, nonlinear relationships between different factors and people’s choices of protective actions. Modeling results confirm that social and environmental cues played critical roles in affecting the probability of different protective actions. Certain factors, such as the earthquake shaking intensity and number of people shown in the environment, displayed evident nonlinear relationships with the probability of choosing to evacuate. These findings can help emergency managers and policymakers design more effective protective action recommendations during earthquakes.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-55584-7","usgsCitation":"Zhang, X., Zhao, X., Baldwin, D., McBride, S., Bellizzi, J., Cochran, E.S., Luco, N., Wood, M., and Cova, T.J., 2024, Modeling protective action decision-making in earthquakes by using explainable machine learning and video data: Scientific Reports, v. 14, 5480, 13 p., https://doi.org/10.1038/s41598-024-55584-7.","productDescription":"5480, 13 p.","ipdsId":"IP-162087","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":440208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-55584-7","text":"Publisher Index Page"},{"id":432865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Xiaojian","contributorId":214967,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaojian","email":"","affiliations":[{"id":39141,"text":"Department of Basic Science, College of Veterinary Medicine, Mississippi State University, 9 Mississippi, United States;","active":true,"usgs":false}],"preferred":false,"id":910521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Xilei","contributorId":342942,"corporation":false,"usgs":false,"family":"Zhao","given":"Xilei","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":910522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldwin, Dare","contributorId":269660,"corporation":false,"usgs":false,"family":"Baldwin","given":"Dare","email":"","affiliations":[],"preferred":false,"id":910523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bellizzi, Josephine","contributorId":342943,"corporation":false,"usgs":false,"family":"Bellizzi","given":"Josephine","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":910525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luco, Nicolas 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":140191,"corporation":false,"usgs":true,"family":"Luco","given":"Nicolas","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":910527,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wood, Matthew","contributorId":342944,"corporation":false,"usgs":false,"family":"Wood","given":"Matthew","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":910528,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cova, Thomas J.","contributorId":342946,"corporation":false,"usgs":false,"family":"Cova","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":910529,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70273191,"text":"70273191 - 2024 - California community Earth Models for Seismic Hazard Assessments workshop report","interactions":[],"lastModifiedDate":"2025-12-18T15:39:21.401578","indexId":"70273191","displayToPublicDate":"2024-03-05T09:33:06","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"California community Earth Models for Seismic Hazard Assessments workshop report","docAbstract":"The California Community Earth Models for Seismic Hazard Assessments Workshop (https://www.scec.org/workshops/2024/california-community-models) was held online March 4–5, 2024, with more than 200 participants over the two days. In this report, we provide a summary of the key points from the presentations and discussions. We highlight three use cases that drive the development of community Earth models, present an inventory of existing community Earth models in California, summarize a few techniques for integrating and merging models, discuss potential connections with the Cascadia Region Earthquake Science Center, and discuss what “community” means in community Earth models.","language":"English","publisher":"Statewide California Earthquake Center","usgsCitation":"Aagaard, B.T., Marshall, S., Minson, S.E., Boyd, D., Denolle, M.A., Fielding, E.J., Gabriel, A., Goulet, C.A., Graymer, R., Hardebeck, J.L., Hatem, A.E., Hirakawa, E.T., Huynh, T., Hwang, L., Luttrell, K., Materna, K.Z., Montesi, L., Oskin, M., Rodgers, A., Pitarka, A., and Zachariasen, J., 2024, California community Earth Models for Seismic Hazard Assessments workshop report, 67 p.","productDescription":"67 p.","ipdsId":"IP-173281","costCenters":[{"id":237,"text":"Earthquake Science 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Precipitation and streamflow data for both watersheds were examined from water year (WY) 1952 through 2022. Temporal changes in hydrologic metrics were identified by calculating trends in annual precipitation, annual peak flow, mean daily flow, minimum daily flow, stream flashiness, and the runoff ratio. The impact of climate and urbanization on watershed hydrology was assessed by computing trends on both raw and precipitation-adjusted data. Despite increasing precipitation in both watersheds, increasing monotonic trends in most hydrologic metrics were observed only in 4MR. At 4MR, the long-term trend in annual peak flow was non-linear, thus trends were calculated on separate periods. Annual peak flow increased from WY 1952 through 1968, coinciding with a period of rapid urbanization. During WY 1969 through 1981, annual peak flows decreased, coinciding with construction of a flood channelization project. Trends for both periods were robust to precipitation adjustment. From WY 1982 through 2022, no change in the precipitation-adjusted annual peak flows occurred, suggesting annual peak flows increased due to climate factors during this period. Comparison of area-normalized hydrologic metrics between the two watersheds revealed higher flows in 4MR than SFQ across all flows, not just high flows. Runoff ratio and stream flashiness also were higher in 4MR. Differences in hydrologic metrics between the two watersheds were driven primarily by differences in land use, land cover, and modifications to the water balance related to urbanization. Climate change has altered watershed hydrology at both sites, but extensive urbanization in 4MR has altered the hydrology more than that of SFQ. We conclude that urban watersheds are likely at greater risk of increased flooding than less developed areas as the climate intensifies.
Both fine- and coarse-scale physicochemical conditions affect the quantity and quality of nursery habitats within riverine ecosystems. Nursery habitats in large, braided, and sandbed streams such as the lower Red River of Oklahoma, Texas, and Arkansas are not well described and likely vary among species. Identification of nursery habitats is important for developing proper conservation and management actions. We used an occupancy model framework to determine how hierarchical habitat factors related to the occupancy of 38 juvenile fish species. Our findings indicate that large river nursery habitats can generally be defined by reaches with off-channel slackwater habitat, having deep pools but shallow thalweg depths, typically located further away from dams, and with low percentages of limestone lithology. Species within the same genera often exhibited variable relationships with river slope, amount of large woody debris, channel shape, discharge, and position of reaches within the stream network. Our results indicate important species-specific relationships that define nursery habitats, indicating an important context dependency of nursery habitats even within fishes that are taxonomically similar. If the goal is to improve recruitment by native fishes, then consideration of the important species-specific differences would be beneficial if improvements are made to nursery habitats. Moreover, careful consideration of the effects of dam operations will help maintain proper connectivity to off-channel habitats important in downriver portions of the river network.
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