{"pageNumber":"60","pageRowStart":"1475","pageSize":"25","recordCount":40756,"records":[{"id":70258432,"text":"70258432 - 2024 - Mitigating disparate elevation differences between adjacent topobathymetric data models using binary code","interactions":[],"lastModifiedDate":"2024-09-17T12:14:54.180722","indexId":"70258432","displayToPublicDate":"2024-09-14T07:12:52","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mitigating disparate elevation differences between adjacent topobathymetric data models using binary code","docAbstract":"<div class=\"html-p\">Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.</div><div id=\"html-keywords\"><br></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs16183418","usgsCitation":"Cushing, W.M., and Tyler, D., 2024, Mitigating disparate elevation differences between adjacent topobathymetric data models using binary code: Remote Sensing, v. 16, no. 18, 3418, 27 p., https://doi.org/10.3390/rs16183418.","productDescription":"3418, 27 p.","ipdsId":"IP-165492","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16183418","text":"Publisher Index Page"},{"id":434822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -126.23441162750055,\n              49.57840324954546\n            ],\n            [\n              -126.23441162750055,\n              46.88620217182037\n            ],\n            [\n              -121.13675537750063,\n              46.88620217182037\n            ],\n            [\n              -121.13675537750063,\n              49.57840324954546\n            ],\n            [\n              -126.23441162750055,\n              49.57840324954546\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.88284912750069,\n              42.38665397257583\n            ],\n            [\n              -125.88284912750069,\n              32.05895893871687\n            ],\n            [\n              -116.56644287750072,\n              32.05895893871687\n            ],\n            [\n              -116.56644287750072,\n              42.38665397257583\n            ],\n            [\n              -125.88284912750069,\n              42.38665397257583\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.70591072570998,\n              31.6874931932974\n            ],\n            [\n              -95.70591072570998,\n              28.494264237112944\n            ],\n            [\n              -87.00473885071047,\n              28.494264237112944\n            ],\n            [\n              -87.00473885071047,\n              31.6874931932974\n            ],\n            [\n              -95.70591072570998,\n              31.6874931932974\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83.22544197571013,\n              30.55893298927404\n            ],\n            [\n              -69.33872322571042,\n              30.55893298927404\n            ],\n            [\n              -69.33872322571042,\n              42.6472791589093\n            ],\n            [\n              -83.22544197571013,\n              42.6472791589093\n            ],\n            [\n              -83.22544197571013,\n              30.55893298927404\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"16","issue":"18","noUsgsAuthors":false,"publicationDate":"2024-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Cushing, William M. 0000-0001-5209-6006","orcid":"https://orcid.org/0000-0001-5209-6006","contributorId":341859,"corporation":false,"usgs":true,"family":"Cushing","given":"William","email":"","middleInitial":"M.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":913271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tyler, Dean J.","contributorId":344273,"corporation":false,"usgs":false,"family":"Tyler","given":"Dean J.","affiliations":[{"id":54519,"text":"U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":913272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70258715,"text":"70258715 - 2024 - Informative priors can account for location uncertainty in stop-level analyses of the North American Breeding Bird Survey (BBS), allowing fine-scale ecological analyses","interactions":[],"lastModifiedDate":"2024-12-26T16:44:29.090612","indexId":"70258715","displayToPublicDate":"2024-09-14T06:39:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Informative priors can account for location uncertainty in stop-level analyses of the North American Breeding Bird Survey (BBS), allowing fine-scale ecological analyses","docAbstract":"<p class=\"chapter-para\">Ecological inferences are often based on the locations at which species are present, but many species records have substantial uncertainty in spatial metadata, limiting their utility for fine-scale analyses. This is especially prevalent in historical records such as museum specimens, and in some citizen-science data. For example, the North American Breeding Bird Survey (BBS) has 55+ years of bird data from regular transects (“routes”) across the continent but was not designed to capture the spatial component of point count events, limiting analyses of species-habitat relationships for which it would otherwise be well suited. We present a new methodology for quantifying location uncertainty in BBS records using digitized estimated stop locations, deriving the corresponding environmental covariate uncertainty distributions, and incorporating this information into hierarchical species distribution models using informative Bayesian priors. This approach allows for estimation of species–environment relationships in a way that fully accounts for underlying spatial uncertainty. We quantify stop-location uncertainty in BBS data across the central United States, model bird–land cover relationships in the upper Midwest, and validate our method by comparing posterior land cover estimates to known covariate values for a subset of GPS-digitized stop locations. We provide code for implementing this method in R. Posterior land cover estimates (forest, grass/hay, and developed land cover), based on our informative priors, were highly correlated with known land cover values from GPS-digitized stop locations. Our approach thus makes it possible to responsibly leverage large historic and citizen science databases, such as the BBS, for fine-scale ecological analyses.</p>","language":"English","publisher":"American Ornithological Society","doi":"10.1093/ornithapp/duae041","usgsCitation":"Burner, R.C., Kirschbaum, A., Hostetler, J.A., Ziolkowski, D., Anich, N.M., Turek, D., Striegel, E.D., and Niemuth, N.D., 2024, Informative priors can account for location uncertainty in stop-level analyses of the North American Breeding Bird Survey (BBS), allowing fine-scale ecological analyses: Ornithological Applications, v. 126, no. 4, duae041, 17 p., https://doi.org/10.1093/ornithapp/duae041.","productDescription":"duae041, 17 p.","ipdsId":"IP-165153","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":466929,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae041","text":"Publisher Index Page"},{"id":462236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Burner, Ryan C. 0000-0002-7314-9506","orcid":"https://orcid.org/0000-0002-7314-9506","contributorId":304152,"corporation":false,"usgs":true,"family":"Burner","given":"Ryan","email":"","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":913802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirschbaum, Alan","contributorId":344465,"corporation":false,"usgs":false,"family":"Kirschbaum","given":"Alan","affiliations":[{"id":82351,"text":"U.S. National Park Service (NPS)","active":true,"usgs":false}],"preferred":false,"id":913803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hostetler, J. A. 0000-0003-3669-1758","orcid":"https://orcid.org/0000-0003-3669-1758","contributorId":11319,"corporation":false,"usgs":true,"family":"Hostetler","given":"J.","middleInitial":"A.","affiliations":[],"preferred":true,"id":913804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":913805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anich, Nicholas M.","contributorId":344466,"corporation":false,"usgs":false,"family":"Anich","given":"Nicholas","email":"","middleInitial":"M.","affiliations":[{"id":82352,"text":"Wisconsin Department of Natural Resources (WI DNR)","active":true,"usgs":false}],"preferred":false,"id":913806,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turek, Daniel","contributorId":290437,"corporation":false,"usgs":false,"family":"Turek","given":"Daniel","email":"","affiliations":[{"id":62426,"text":"Dept of Math and Statistics, Williams College","active":true,"usgs":false}],"preferred":false,"id":913807,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Striegel, Eli D.","contributorId":344467,"corporation":false,"usgs":false,"family":"Striegel","given":"Eli","email":"","middleInitial":"D.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":913808,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Niemuth, Neal D. 0009-0006-9637-5588","orcid":"https://orcid.org/0009-0006-9637-5588","contributorId":204334,"corporation":false,"usgs":false,"family":"Niemuth","given":"Neal","email":"","middleInitial":"D.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":913809,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70262253,"text":"70262253 - 2024 - Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability","interactions":[],"lastModifiedDate":"2025-01-21T23:19:46.238354","indexId":"70262253","displayToPublicDate":"2024-09-13T16:12:45","publicationYear":"2024","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":"Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability","docAbstract":"<p>1. Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise.</p><p>2. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie (<i>Pygoscelis adeliae</i>) and emperor penguin (<i>Aptenodytes forsteri</i>) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species.</p><p>3. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%).</p><p>4. rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.</p>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210x.14423","usgsCitation":"Sen, B., Che-Castaldo, C., Lynch, H., Ventura, F., LaRue, M., and Jenouvrier, S., 2024, Detecting stochasticity in population time series using a non-parametric test of intrinsic predictability: Methods in Ecology and Evolution, v. 15, no. 10, p. 1834-1846, https://doi.org/10.1111/2041-210x.14423.","productDescription":"13 p.","startPage":"1834","endPage":"1846","ipdsId":"IP-159742","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481060,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14423","text":"Publisher Index Page"},{"id":480893,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sen, Bilgecan","contributorId":348620,"corporation":false,"usgs":false,"family":"Sen","given":"Bilgecan","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":923658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Che-Castaldo, Christian Joseph 0000-0002-7670-2178","orcid":"https://orcid.org/0000-0002-7670-2178","contributorId":347906,"corporation":false,"usgs":true,"family":"Che-Castaldo","given":"Christian Joseph","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":923659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynch, Heather J.","contributorId":347911,"corporation":false,"usgs":false,"family":"Lynch","given":"Heather J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":923660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ventura, Francesco","contributorId":348623,"corporation":false,"usgs":false,"family":"Ventura","given":"Francesco","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":923661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LaRue, Michelle A.","contributorId":348627,"corporation":false,"usgs":false,"family":"LaRue","given":"Michelle A.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":923662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jenouvrier, Stephanie","contributorId":348629,"corporation":false,"usgs":false,"family":"Jenouvrier","given":"Stephanie","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":923663,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70264617,"text":"70264617 - 2024 - Geodetic monitoring of the recent activity and the dome forming eruption at Nevado del Ruiz (Colombia), 2010–2023","interactions":[],"lastModifiedDate":"2025-03-17T14:42:19.586878","indexId":"70264617","displayToPublicDate":"2024-09-13T09:35:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Geodetic monitoring of the recent activity and the dome forming eruption at Nevado del Ruiz (Colombia), 2010–2023","docAbstract":"<p><span>Nevado del Ruiz (Colombia) is infamous for the catastrophic eruption of 1985 that destroyed the villages of Armero and Chinchiná. However, this was not the volcano’s first destructive event; similar eruptions also occurred in 1595 and 1845. In 1985, the limited geodetic data available failed to provide a clear warning of the impending eruption. Since then, advancement in geodetic monitoring, now incorporating tilt and satellite geodesy, along with improvements in seismic, geochemical, geological and remote sensing monitoring, have enhanced hazards assessment and mitigated the risk during subsequent eruptions in 1989, 2012, and 2015–2019, as well as during periods of unrest over the last 13&nbsp;years. Modeling of deformation data over the past 13&nbsp;years reveals complex interactions between the local, shallow magmatic system beneath Nevado del Ruiz and a deep, regional magmatic system beneath Nevado de Santa Isabel, 9&nbsp;km southwest of Nevado del Ruiz. Before February 2012, the volcano deflated because of the depressurization of the local shallow reservoir. This same reservoir later fueled ash emissions and gas release (2012–2023), and a dome-forming eruption (2015–2019). In contrast, the inflation observed from 2012 to 2023 is linked to the pressurization of the deep reservoir beneath the Nevado de Santa Isabel.</span></p>","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-72058-y","usgsCitation":"Ordonez, M., Idárraga, J., Adamo, R., and Battaglia, M., 2024, Geodetic monitoring of the recent activity and the dome forming eruption at Nevado del Ruiz (Colombia), 2010–2023: Scientific Reports, v. 14, 21441, 13 p., https://doi.org/10.1038/s41598-024-72058-y.","productDescription":"21441, 13 p.","ipdsId":"IP-164180","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-72058-y","text":"Publisher Index Page"},{"id":483449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Colombia","otherGeospatial":"Nevado del Ruiz","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -75.75,\n              5.25\n            ],\n            [\n              -75.75,\n              4.333\n            ],\n            [\n              -74.667,\n              4.333\n            ],\n            [\n              -74.667,\n              5.25\n            ],\n            [\n              -75.75,\n              5.25\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Ordonez, Milton","contributorId":139632,"corporation":false,"usgs":false,"family":"Ordonez","given":"Milton","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":930965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Idárraga, Juan","contributorId":352362,"corporation":false,"usgs":false,"family":"Idárraga","given":"Juan","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":930966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adamo, Roberta","contributorId":352363,"corporation":false,"usgs":false,"family":"Adamo","given":"Roberta","affiliations":[{"id":18868,"text":"Sapienza - University of Rome","active":true,"usgs":false}],"preferred":false,"id":930967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":930968,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70261016,"text":"70261016 - 2024 - Bay Miwok evening primrose: A new subspecies of Oenothera deltoides (Onagraceae) endemic to California","interactions":[],"lastModifiedDate":"2024-11-20T15:41:23.951915","indexId":"70261016","displayToPublicDate":"2024-09-13T08:36:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2639,"text":"Madroño","active":true,"publicationSubtype":{"id":10}},"title":"Bay Miwok evening primrose: A new subspecies of Oenothera deltoides (Onagraceae) endemic to California","docAbstract":"<p><span>California contains exceptional biodiversity in geography and plant life, including numerous endemic species, some of which are cryptic. The&nbsp;</span><i>Oenothera deltoides</i><span>&nbsp;Torr. &amp; Frém. species complex represents a prime example of cryptic diversity. Here, we recognize a new subspecies of&nbsp;</span><i>Oenothera deltoides</i><span>,&nbsp;</span><i>O. deltoides</i><span>&nbsp;subsp.&nbsp;</span><i>julpunensis</i><span>&nbsp;S.F.Jones, subsp. nov., that is a local endemic of windblown sand deposits on the eastern Antioch Dunes sand sheet in the San Francisco Bay-Delta region of California, USA. With the goal of providing clarity to managers of listed species and better understanding of California's diverse flora, we addressed the puzzle of&nbsp;</span><i>O. deltoides</i><span>&nbsp;in the region by combining range-wide field surveys with modern genomic tools. We describe the proposed subspecies, its ecology and distribution, and discuss its conservation. As a somewhat cryptic local endemic with small population size and disappearing habitat, the proposed subspecies would benefit from conservation and management to persist as a member of the California flora.</span></p>","language":"English","publisher":"BioOne","doi":"10.3120/0024-9637-71.2.84","usgsCitation":"Jones, S., Milano, E.R., O’Dell, R., Ferrell, M., Vandergast, A.G., and Thorne, K., 2024, Bay Miwok evening primrose: A new subspecies of Oenothera deltoides (Onagraceae) endemic to California: Madroño, v. 71, no. 2, p. 84-104, https://doi.org/10.3120/0024-9637-71.2.84.","productDescription":"21 p.","startPage":"84","endPage":"104","ipdsId":"IP-152160","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":464343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Ryan","contributorId":346411,"corporation":false,"usgs":false,"family":"O’Dell","given":"Ryan","email":"","affiliations":[{"id":6696,"text":"BLM","active":true,"usgs":false}],"preferred":false,"id":918934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferrell, Molly","contributorId":346412,"corporation":false,"usgs":false,"family":"Ferrell","given":"Molly","email":"","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":918935,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918936,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":918937,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70263106,"text":"70263106 - 2024 - Identifying Pareto-efficient eradication strategies for invasive populations","interactions":[],"lastModifiedDate":"2025-01-29T15:16:31.262149","indexId":"70263106","displayToPublicDate":"2024-09-13T08:11:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1326,"text":"Conservation Letters","active":true,"publicationSubtype":{"id":10}},"title":"Identifying Pareto-efficient eradication strategies for invasive populations","docAbstract":"<p><span>Invasive species are a major cause of biodiversity loss and are notoriously expensive and challenging to manage. We developed a decision-analytic framework for evaluating invasive species removal strategies, given objectives of maximizing eradication probability and minimizing costs. The framework uses an existing estimation model for spatially referenced removal data—one of the most accessible types of invasive species data—to obtain estimates of population growth rate, movement probability, and detection probability. We use these estimates in simulations to identify Pareto-efficient strategies—strategies where increases in eradication probability cannot be obtained without increases in cost—from a set of proposed strategies. We applied the framework post hoc to a successful eradication of veiled chameleons (</span><i>Chamaeleo calyptratus</i><span>) and identified the potential for substantial improvements in efficiency. Our approach provides managers and policymakers with tools to identify cost-effective strategies for a range of invasive species using only prior knowledge or data from initial physical removals.</span></p>","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/conl.13051","usgsCitation":"Yackel Adams, A.A., Hostetter, N.J., Link, W., and Converse, S.J., 2024, Identifying Pareto-efficient eradication strategies for invasive populations: Conservation Letters, v. 17, no. 5, e13051, 10 p., https://doi.org/10.1111/conl.13051.","productDescription":"e13051, 10 p.","ipdsId":"IP-163878","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":487603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.13051","text":"Publisher Index Page"},{"id":481449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -156.64418198330387,\n              21.042535468371597\n            ],\n            [\n              -156.7100067465097,\n              20.94926990791153\n            ],\n            [\n              -156.5767160394666,\n              20.620019168030595\n            ],\n            [\n              -156.3095497551291,\n              20.54804441612707\n            ],\n            [\n              -155.97692015447151,\n              20.640096885489086\n            ],\n            [\n              -155.96802530273143,\n              20.818214275871142\n            ],\n            [\n              -156.26695266911045,\n              20.967008230356555\n            ],\n            [\n              -156.64418198330387,\n              21.042535468371597\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":925523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":925524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Link, William A.","contributorId":299684,"corporation":false,"usgs":false,"family":"Link","given":"William A.","affiliations":[{"id":36625,"text":"Emeritus","active":true,"usgs":false}],"preferred":false,"id":925525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":925526,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70269047,"text":"70269047 - 2024 - Neotectonic mapping of Puerto Rico","interactions":[],"lastModifiedDate":"2025-07-16T13:11:53.52837","indexId":"70269047","displayToPublicDate":"2024-09-13T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"Neotectonic mapping of Puerto Rico","docAbstract":"Puerto Rico is part of the Puerto Rico-Virgin Islands microplate, along the Caribbean–North American plate boundary between the Puerto Rico trench subduction zone and the Muertos Trough incipient subduction zone. Despite recent seismicity and geodetically constrained deformation of ~3 mm/yr of left-lateral shear across the island, Quaternary fault locations remain largely uncertain. Preservation of recent faulting in the landscape is masked by distributed faulting, high weathering rates of the tropical climate, steep topography, frequent landsliding, and extensive agriculture and urbanization along coastlines and river valleys. We present remote neotectonic mapping of historical aerial imagery from the 1930s-60s and <1-m lidar topography, integrated with field observations, to create an updated active fault map of Puerto Rico. We focus on faults that offset younger geomorphic surfaces, with ages estimated by geologic mapping, OSL and radiocarbon dating, and morphologic interpretations. We present new evidence for Quaternary activity on seven faults, including the South Lajas, Salinas, Punta Montalva, Great Southern Puerto Rico, Cerro Goden, Parguera, and San Marcos faults. We find that active faulting occurs preferentially along the southern and western coasts and does not always spatially coincide with preexisting bedrock faults, possibly reflecting partial reactivation of older faults in the modern strain field.","language":"English","publisher":"PKP Preservation Network","doi":"10.26443/seismica.v3i1.1102","usgsCitation":"Jobe, J.A., Briggs, R.W., Hughes, K.S., Joyce, J., Gold, R.D., Mahan, S.A., Gray, H., and Strickland, L.E., 2024, Neotectonic mapping of Puerto Rico: Seismica, v. 3, no. 1, p. 1-34, https://doi.org/10.26443/seismica.v3i1.1102.","productDescription":"34 p.","startPage":"1","endPage":"34","ipdsId":"IP-160573","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":492498,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v3i1.1102","text":"Publisher Index Page"},{"id":492798,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13KZZAZ","text":"USGS data release","linkHelpText":"Datasets documenting neotectonic mapping of Puerto Rico"},{"id":492244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -67.32486577983607,\n              18.61158323852655\n            ],\n            [\n              -67.32486577983607,\n              17.880230085724676\n            ],\n            [\n              -65.61724418272189,\n              17.880230085724676\n            ],\n            [\n              -65.61724418272189,\n              18.61158323852655\n            ],\n            [\n              -67.32486577983607,\n              18.61158323852655\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"3","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Richard W. 0000-0001-8108-0046 rbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-8108-0046","contributorId":4136,"corporation":false,"usgs":true,"family":"Briggs","given":"Richard","email":"rbriggs@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hughes, K. Stephen","contributorId":218339,"corporation":false,"usgs":false,"family":"Hughes","given":"K.","email":"","middleInitial":"Stephen","affiliations":[{"id":16585,"text":"University of Puerto Rico - Mayaguez","active":true,"usgs":false}],"preferred":false,"id":943082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joyce, J.","contributorId":341912,"corporation":false,"usgs":false,"family":"Joyce","given":"J.","affiliations":[{"id":34129,"text":"University of Puerto Rico Mayaguez","active":true,"usgs":false}],"preferred":false,"id":943083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":943085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":943086,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Strickland, Laura E. 0000-0002-1958-7273 lstrickland@usgs.gov","orcid":"https://orcid.org/0000-0002-1958-7273","contributorId":4682,"corporation":false,"usgs":true,"family":"Strickland","given":"Laura","email":"lstrickland@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":943087,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70258367,"text":"70258367 - 2024 - Efficacy of non-lead ammunition distribution programs to offset fatalities of golden eagles in southeast Wyoming","interactions":[],"lastModifiedDate":"2024-10-07T16:29:43.517516","indexId":"70258367","displayToPublicDate":"2024-09-12T08:49:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of non-lead ammunition distribution programs to offset fatalities of golden eagles in southeast Wyoming","docAbstract":"<p><span>Golden eagles (</span><i>Aquila chrysaetos</i><span>) face many anthropogenic risks including illegal shooting, electrocution, collision with wind turbines and vehicles, and lead poisoning. Minimizing or offsetting eagle deaths resulting from human-caused sources is often viewed as an important management objective. Despite understanding the leading anthropogenic sources of eagle fatalities, existing scientific research supports few practical solutions to mitigate these causes of death. We implemented a non-lead ammunition distribution program in southeast Wyoming, USA, and evaluated its effectiveness as a compensatory mitigation action to offset incidental take (i.e., fatalities) of golden eagles at wind energy facilities. In 2020 and 2022, we distributed non-lead ammunition to 699 hunters with big-game tags specific to our &gt;400,000-ha study area. These hunters harvested 296 pronghorn (</span><i>Antilocapra americana</i><span>), 14 deer (</span><i>Odocoileus</i><span>&nbsp;spp.), and 33 elk (</span><i>Cervus canadensis</i><span>) in the study area, which accounted for 6.9% and 6.5% of the harvest in these hunt units in 2020 and 2022, respectively. We used road surveys in 2020 to estimate a density of 0.036 (95% CI = 0.018–0.058) golden eagles/km</span><sup>2</sup><span>&nbsp;during the big game hunting season in our study area. Model output suggests that our non-lead ammunition distribution program offset the fatality of 3.84 (95% CI = 1.06–23.72) eagles over the course of these 2 hunting seasons. Our work illustrates the potential usefulness of non-lead ammunition distribution programs as an action to mitigate eagle fatalities caused by wind facilities or other anthropogenic causes of death.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22647","usgsCitation":"Slabe, V.S., Crandall, R.H., Katzner, T., Duerr, A.E., and Miller, T.A., 2024, Efficacy of non-lead ammunition distribution programs to offset fatalities of golden eagles in southeast Wyoming: Journal of Wildlife Management, v. 88, no. 8, e22647, 10 p., https://doi.org/10.1002/jwmg.22647.","productDescription":"e22647, 10 p.","ipdsId":"IP-161994","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":439167,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22647","text":"Publisher Index Page"},{"id":434761,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -107.92931952980723,\n              43.24589994296005\n            ],\n            [\n              -107.92931952980723,\n              41.159953154947516\n            ],\n            [\n              -105.14921780613884,\n              41.159953154947516\n            ],\n            [\n              -105.14921780613884,\n              43.24589994296005\n            ],\n            [\n              -107.92931952980723,\n              43.24589994296005\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"88","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Slabe, Vincent S.","contributorId":344176,"corporation":false,"usgs":false,"family":"Slabe","given":"Vincent","email":"","middleInitial":"S.","affiliations":[{"id":63970,"text":"Conservation Science Global","active":true,"usgs":false}],"preferred":false,"id":913075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crandall, Ross H.","contributorId":198926,"corporation":false,"usgs":false,"family":"Crandall","given":"Ross","email":"","middleInitial":"H.","affiliations":[{"id":6657,"text":"Craighead Beringia South","active":true,"usgs":false}],"preferred":false,"id":913076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":913077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":913078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":913079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70259248,"text":"70259248 - 2024 - Reexamining the Honolulu Volcanics: Hawai‘i's classic case of rejuvenation volcanism","interactions":[],"lastModifiedDate":"2024-10-03T15:55:03.375339","indexId":"70259248","displayToPublicDate":"2024-09-11T09:12:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Reexamining the Honolulu Volcanics: Hawai‘i's classic case of rejuvenation volcanism","docAbstract":"<p><span>Rejuvenated volcanism is a worldwide phenomenon occurring on many oceanic islands in all of the major ocean basins. This plume-related volcanism follows the main edifice-building stage after a hiatus of variable duration (e.g. 0.6–2 Myrs in Hawai'i). The Honolulu Volcanics (HV), the classic case of rejuvenated volcanism, involved monogenetic eruptions from at least 48 vent areas. Previous studies inferred these vents were aligned along 3 to 11 rifts oriented orthogonal to the propagation direction of the Hawaiian plume. HV basalts are known for having high MgO contents (greater than 10 wt %) and upper mantle xenoliths. Thus, HV magmas are assumed to be relatively primitive and to have ascended rapidly (less than 1 day) through the crust. However, new analyses of olivine cores in basalts from 24 HV vents are mostly too low in forsterite content (74–86 mol %) to be in equilibrium with mantle melts. Olivine and clinopyroxene in HV basalts commonly show reverse zoning indicating magma mixing prior to eruption. These results are inconsistent with the rapid ascent of HV magmas directly from their mantle source. Many of the HV magmas underwent storage (probably in the lower crust or uppermost mantle), crystal fractionation and magma mixing prior to eruption. New </span><sup>40</sup><span>Ar/</span><sup>39</sup><span>Ar dates were determined for 11&nbsp;HV lavas to evaluate their eruptive history. These ages, 80 to 685&nbsp;ka, combined with our previous and other&nbsp;</span><sup>40</sup><span>Ar/</span><sup>39</sup><span>Ar ages for HV lavas reveal long gaps (greater than 50 kyr) between some eruptions. Our comprehensive, whole-rock major and trace element database (63 XRF analyses, 57 ICPMS analyses) of basalts from 37 vents show remarkable compositional diversity with no obvious spatial pattern or temporal trends. The two most recent eruptive sequences have the greatest diversity (basanite and melilitite compositions). HV basanites show systematic trace element trends that may reflect mixing of multiple source components. The nephelinites and melilitites require a complex source history that may have involved residual accessory minerals during mantle melting and a metasomatic component that was not carbonatitic. The new ages and geochemical data show eruptions along most of the previously proposed rift systems were unrelated (except for the Koko Rift). Therefore, geodynamic models that relate HV volcanism to these rift systems are invalid. Lava volumes for two HV eruptions were estimated at 0.11 and 0.23 km</span><sup>3</sup><span>&nbsp;using surface mapping and water well data. Similar size, recent monogenetic eruptions in Auckland, New Zealand, were inferred to have lasted several months. Thus, if another HV eruption were to occur, which is possible given the long hiatus between eruptions, it would be extremely disruptive for the nearly 1 million residents of Honolulu. None of the existing geodynamic models fully explain the age duration, volumes and the locations of Hawai'i's rejuvenated volcanism. Thus, the cause of this secondary volcanism remains enigmatic.</span></p>","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egae093","usgsCitation":"Garcia, M.O., Norman, M.D., Jicha, B., Lynn, K.J., and Jiang, P., 2024, Reexamining the Honolulu Volcanics: Hawai‘i's classic case of rejuvenation volcanism: Journal of Petrology, v. 65, no. 9, egae093, 24 p., https://doi.org/10.1093/petrology/egae093.","productDescription":"egae093, 24 p.","ipdsId":"IP-161257","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":498025,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egae093","text":"Publisher Index Page"},{"id":462482,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Honolulu Volcanics, Oahu","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -157.95228727057142,\n              21.303437544076758\n            ],\n            [\n              -157.7876854268278,\n              21.244288947214542\n            ],\n            [\n              -157.68978498381696,\n              21.25531845437243\n            ],\n            [\n              -157.6413726768336,\n              21.309451323199127\n            ],\n            [\n              -157.6446001639659,\n              21.336510281621585\n            ],\n            [\n              -157.72098402609515,\n              21.470728941249874\n            ],\n            [\n              -157.7780029654312,\n              21.470728941249874\n            ],\n            [\n              -157.98671379998177,\n              21.336510281621585\n            ],\n            [\n              -157.95228727057142,\n              21.303437544076758\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"65","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia, Michael O.","contributorId":225524,"corporation":false,"usgs":false,"family":"Garcia","given":"Michael","email":"","middleInitial":"O.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":914550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norman, Marc D.","contributorId":344700,"corporation":false,"usgs":false,"family":"Norman","given":"Marc","email":"","middleInitial":"D.","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":914551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jicha, Brian","contributorId":213920,"corporation":false,"usgs":false,"family":"Jicha","given":"Brian","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":914552,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":914553,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jiang, Peng","contributorId":344701,"corporation":false,"usgs":false,"family":"Jiang","given":"Peng","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":914554,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70258256,"text":"sir20245075 - 2024 - Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020","interactions":[],"lastModifiedDate":"2025-12-23T19:34:36.395091","indexId":"sir20245075","displayToPublicDate":"2024-09-11T08:55:00","publicationYear":"2024","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":"2024-5075","displayTitle":"Low-Flow Statistics Computed for Streamflow Gages and Methods for Estimating Selected Low-Flow Statistics for Ungaged Stream Locations in Ohio, Water Years 1975–2020","title":"Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020","docAbstract":"<p>A study was conducted by the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority and the Ohio Environmental Protection Agency, to compute low-flow frequency, flow-duration, and harmonic mean flow statistics for long-term streamflow gages and to develop regression equations to estimate those statistics at unregulated, ungaged stream locations in Ohio. The flow statistics were computed with data collected after the 1974 water year because upward trends and statistically significant step changes (occurring after the late 1960s but before 1975) in annual flow statistics were detected at many candidate gages in Ohio. A total of 180 continuous-record gages in Ohio and bordering States were identified as having at least 10 years of daily flow records during the analytical period (water years 1975–2020). Also identified were five low-flow partial-record gages in Ohio that had instantaneous low flows that correlated strongly with daily streamflows at one of the continuous-record gages (also referred to as index gages). For continuous-record gages, the following flow statistics were computed: annual and seasonal minimum 1-, 7-, 30-, and 90-day flows with 2-, 5-, 10-, 20-, and 50-year recurrence intervals; annual and seasonal 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 20-, and 10-percent duration flows; and the harmonic mean flow. For partial-record gages, estimates were made for annual and seasonal minimum 1-, 7-, 30-, and 90-day low flows with 2-, 10-, and 20-year recurrence intervals and annual and seasonal 98-, 95-, 90-, 85-, and 80-percent duration flows.</p><p>The drainage basin of each gage was inspected for anthropogenic or karst features that could appreciably affect or regulate low flows. That inspection resulted in data from 53 of the 180 continuous-record gages and the 5 low-flow partial-record gages being categorized as “unregulated” and subsequently used in regression analyses to develop equations for estimating low-flow statistics. Two hundred and sixty potential explanatory variables were tested for this study. In most cases, a streamflow-variability index (SVI) was chosen as the sole explanatory variable for the regression analyses to predict the harmonic mean and annual and seasonal low-flow yields. The exceptions were for one of the September–November low-flow yield statistics and all the December–February yield statistics. Drainage area, decimal longitude, and usually SVI were chosen as the explanatory variables for those exceptions and to predict the 80-percent duration flows. The SVI values used in the model were estimated from a geospatial grid of SVI values developed for this study by using an empirical Bayesian kriging regression prediction. Observations for continuous-record gages used in the regression analyses were weighted as a function of their record length. Weights for partial-record gages were estimated based on the weights determined for their index gages.</p><p>Equations for low-flow yields were developed by using censored regressions with a censoring level of 0.00001 cubic foot per second per square mile. Numerical constraints were placed on the yield equations if they could compute yields less than the yield censoring level or if the yields did not monotonically decrease with increasing SVI. Logistic-regression equations were developed, with SVI and drainage area as explanatory variables, to estimate the probability that the low-flow statistics were greater than the flow censoring level (0.01 cubic foot per second).</p><p>The regression equations presented in this report were developed for implementation in the Ohio StreamStats application. The equations are applicable to unregulated streams in Ohio and are not applicable to streams with karst drainage features, diversions, regulation, or other anthropogenic activities that can appreciably affect low flow. The equations were developed by using observations with a range of SVI values from 0.41 to 1.23 log10 cubic foot per second and a range of drainage areas from 0.21 to 540 square miles. The applicability of the equations outside these ranges is not known.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245075","collaboration":"Prepared in cooperation with the Ohio Water Development Authority and the Ohio Environmental Protection Agency","usgsCitation":"VonIns, B.L., and Koltun, G.F., 2024, Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020 (ver. 1.1, October 2024): U.S. Geological Survey Scientific Investigations Report 2024–5075, 37 p., https://doi.org/10.3133/sir20245075.","productDescription":"Report: v, 37 p.; Data Release: 2 Tables","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-155169","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":462619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5075/coverthb2.jpg"},{"id":462944,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075.pdf","text":"Report","size":"3.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5075 PDF"},{"id":497941,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117489.htm","linkFileType":{"id":5,"text":"html"}},{"id":462953,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92GD1WL","text":"USGS data release","linkHelpText":"Supporting data for low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020 (ver. 1.1, October 2024)"},{"id":462951,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075_app2_table2.1.csv","text":"Appendix 2 Table 2.1","size":"4.54 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":462950,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075_app2_table2.1.xlsx","text":"Appendix 2 Table 2.1","size":"24.1 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Table 2.1. Selected streamflow duration and low-frequency statistics for partial-record streamflow gages in Ohio"},{"id":462948,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075_app1_table1.1.csv","text":"Appendix 1 Table 1.1","size":"115 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":462949,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5075/sir20245075_app1_table1.1.xlsx","text":"Appendix 1 Table 1.1","size":"243 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Table 1.1. 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 \"}}]}","edition":"Version 1.0: September 2024; Version 1.1: October 2024","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/oki-water\" data-mce-href=\"https://www.usgs.gov/centers/oki-water\">Ohio-Kentucky-Indiana Water Science Center</a><br>U.S. Geological Survey<br>6460 Busch Blvd, Suite 100<br>Columbus, OH 43229</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods for Computing Low-Flow Statistics</li><li>Determination and Selection of Explanatory Variables</li><li>Equations for Estimating Low-Flow Statistics</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1. Low-Flow, Flow Duration, and Harmonic Mean Flow Statistics for Continuous-Record Streamflow Gages in Ohio, 1975–2020</li><li>Appendix 2. Low-Flow, Flow Duration, and Harmonic Mean Flow Statistics for Partial-Record Streamflow Gages in Ohio, 1975–2020</li><li>Appendix 3. Basin Characteristics Tested for Use in Low-Flow Regression Analyses in Ohio</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-09-11","revisedDate":"2024-10-22","noUsgsAuthors":false,"publicationDate":"2024-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"VonIns, Branden L. 0000-0001-6185-2854 blvonins@usgs.gov","orcid":"https://orcid.org/0000-0001-6185-2854","contributorId":191787,"corporation":false,"usgs":true,"family":"VonIns","given":"Branden","email":"blvonins@usgs.gov","middleInitial":"L.","affiliations":[{"id":513,"text":"Ohio Water Science Center","active":true,"usgs":true}],"preferred":false,"id":912763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koltun, G. F. 0000-0003-0255-2960 gfkoltun@usgs.gov","orcid":"https://orcid.org/0000-0003-0255-2960","contributorId":140048,"corporation":false,"usgs":true,"family":"Koltun","given":"G.","email":"gfkoltun@usgs.gov","middleInitial":"F.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912764,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70258443,"text":"70258443 - 2024 - Parsimonious high-resolution landslide susceptibility modeling at continental scales","interactions":[],"lastModifiedDate":"2024-09-17T11:55:26.177981","indexId":"70258443","displayToPublicDate":"2024-09-11T06:54:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7751,"text":"AGU Advances","active":true,"publicationSubtype":{"id":10}},"title":"Parsimonious high-resolution landslide susceptibility modeling at continental scales","docAbstract":"<div class=\"article-section__content en main\"><p>Landslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental-scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under-represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database (<i>N</i>&nbsp;=&nbsp;613,724), a high-resolution digital elevation model (10-m), and high-performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split-sample calibration. We down-sample our results to a 90-m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (<i>N</i>&nbsp;=&nbsp;172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide-prone terrain within each down-sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.</p></div>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024AV001214","usgsCitation":"Mirus, B., Belair, G.M., Wood, N.J., Jones, J.M., and Martinez, S.N., 2024, Parsimonious high-resolution landslide susceptibility modeling at continental scales: AGU Advances, v. 5, no. 5, e2024AV001214, 25 p., https://doi.org/10.1029/2024AV001214.","productDescription":"e2024AV001214, 25 p.","ipdsId":"IP-162877","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":439169,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024av001214","text":"Publisher Index Page"},{"id":434904,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13KAGU3","text":"USGS data release","linkHelpText":"Slope-Relief Threshold Landslide Susceptibility Models for the United States and Puerto Rico"},{"id":434818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":913297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belair, Gina Marie 0000-0003-4995-684X","orcid":"https://orcid.org/0000-0003-4995-684X","contributorId":290411,"corporation":false,"usgs":true,"family":"Belair","given":"Gina","email":"","middleInitial":"Marie","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":913298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":913299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Jeanne M. 0000-0001-7549-9270 jmjones@usgs.gov","orcid":"https://orcid.org/0000-0001-7549-9270","contributorId":4676,"corporation":false,"usgs":true,"family":"Jones","given":"Jeanne","email":"jmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":913300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martinez, Sabrina N. 0000-0002-1812-5990","orcid":"https://orcid.org/0000-0002-1812-5990","contributorId":237051,"corporation":false,"usgs":true,"family":"Martinez","given":"Sabrina","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":913301,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70258412,"text":"70258412 - 2024 - Lead isotopes constrain Precambrian crustal architecture, thermal history, and lithospheric foundering in Laurentia","interactions":[],"lastModifiedDate":"2025-03-25T15:43:15.380363","indexId":"70258412","displayToPublicDate":"2024-09-10T07:02:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3531,"text":"Terra Nova","active":true,"publicationSubtype":{"id":10}},"title":"Lead isotopes constrain Precambrian crustal architecture, thermal history, and lithospheric foundering in Laurentia","docAbstract":"<div class=\"abstract-group \"><div class=\"article-section__content en main\"><p>Laurentia (ancestral North America) records nearly 4 billion years of crustal evolution. Here, a newly compiled continental-scale Pb isotopic database is used to evaluate the Precambrian crustal evolution of Laurentia. Pb model ages yield a 2.7 Ga peak, a 2.5–1.8 Ga minimum and 1.8–0.9 Ga continuum. Pb model ages yield thermochronometric data and track crustal growth via arc-related magmatism and accretionary orogenesis. Model<span>&nbsp;</span><sup>232</sup>Th/<sup>204</sup>Pb and<span>&nbsp;</span><sup>238</sup>U/<sup>204</sup>Pb broadly correlate with mapped crustal domains. More homogeneous and less radiogenic<span>&nbsp;</span><sup>238</sup>U/<sup>204</sup>Pb and<span>&nbsp;</span><sup>232</sup>Th/<sup>238</sup>U after 2.7 Ga suggests a shift to more juvenile sources, loss of early isotopic reservoirs and greater crustal reworking. U and Th are fractionated from Pb in Proterozoic orogens with abundant ferroan and anorthosite–mangerite–charnockite–granite(AMCG)-suite magmatism. This fractionation suggests the removal of Pb-rich lower crust, supporting petrogenetic models involving lithospheric foundering and magmatic underplating. Lithospheric thinning and associated magmatism may have contributed to high middle Proterozoic geothermal gradients.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/ter.12748","usgsCitation":"Hillenbrand, I.W., 2024, Lead isotopes constrain Precambrian crustal architecture, thermal history, and lithospheric foundering in Laurentia: Terra Nova, v. 37, no. 2, p. 65-76, https://doi.org/10.1111/ter.12748.","productDescription":"12 p.","startPage":"65","endPage":"76","ipdsId":"IP-164853","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":434820,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Hillenbrand, Ian William 0000-0003-2801-3674","orcid":"https://orcid.org/0000-0003-2801-3674","contributorId":299032,"corporation":false,"usgs":true,"family":"Hillenbrand","given":"Ian","email":"","middleInitial":"William","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":913234,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70259537,"text":"70259537 - 2024 - Hookworm prevalence in ocelots in Costa Rica is inconsistent with spillover from domestic dogs despite high overlap","interactions":[],"lastModifiedDate":"2024-10-11T12:03:23.186227","indexId":"70259537","displayToPublicDate":"2024-09-10T06:58:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Hookworm prevalence in ocelots in Costa Rica is inconsistent with spillover from domestic dogs despite high overlap","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>Spatial overlap between wildlife and related domestic animals can lead to disease transmission, with substantial evidence for viral and bacterial spillover. Domestic and wild animals can also share potentially harmful helminth parasites, many of which have environmental transmission stages that do not require direct contact between hosts. We used camera traps, fecal sampling, and mathematical modeling to evaluate the potential for hookworm parasites to spillover from domestic dogs to wild cats in the Osa Peninsula, Costa Rica. Traditional microscopy was found to be more sensitive than DNA-based diagnostics for parasites, though the methods were complementary. We found high hookworm (<i>Ancylostoma</i><span>&nbsp;</span>spp.) prevalence in domestic dogs (74.2%, 95% CI: 67.0%–80.7%,<span>&nbsp;</span><i>N</i> = 155), and considerable spatial overlap with ocelots (<i>Leopardus pardalis</i>) and pumas (<i>Puma concolor</i>), particularly on trails and dirt roads. Pumas had hookworm prevalence of 36.4% (18.6%–57.2%,<span>&nbsp;</span><i>N</i> = 22), and ocelots had 27.3% (7.6%–56.5%,<span>&nbsp;</span><i>N</i> = 11); however, molecular identification of these parasites was inconclusive. We developed a macroparasite transmission model to infer the likelihood of spillover, compared with separate parasite cycles, or different parasite species in each host. According to the model, spillover of hookworm from dogs would lead to a prevalence of less than 10% in wild hosts. Low presumed compatibility between wild hosts and parasites adapted to domestic species limits the prevalence that could be reached in wild species, even under potentially higher overlap. The prevalence observed was more consistent with a model that assumes hookworms in wild cats in the Osa are a cat-specific parasite. The combination of parasitology, molecular diagnostics, and mathematical modeling used here could complement wildlife disease monitoring programs worldwide to shed light on understudied helminth–host dynamics at the domestic–wild animal interface.</p></div></div>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4947","usgsCitation":"Soto, J.S., Gstic, K.M., Melnycky, N.A., Johnson, J.G., Dobson, A.P., Coates, P.S., Standley, C., and Molnar, P.K., 2024, Hookworm prevalence in ocelots in Costa Rica is inconsistent with spillover from domestic dogs despite high overlap: Ecosphere, v. 15, no. 9, https://doi.org/10.1002/ecs2.4947.","productDescription":"e4947, 15 p.","startPage":"e4947","ipdsId":"IP-162871","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":466932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4947","text":"Publisher Index Page"},{"id":462822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -83.9697003561128,\n              8.903941886144324\n            ],\n            [\n              -83.9697003561128,\n              8.205662501482308\n            ],\n            [\n              -83.09045939675416,\n              8.205662501482308\n            ],\n            [\n              -83.09045939675416,\n              8.903941886144324\n            ],\n            [\n              -83.9697003561128,\n              8.903941886144324\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Soto, Juan S. Vargas","contributorId":345100,"corporation":false,"usgs":false,"family":"Soto","given":"Juan","email":"","middleInitial":"S. Vargas","affiliations":[{"id":82492,"text":"University of Ontario","active":true,"usgs":false}],"preferred":false,"id":915647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gstic, Katelyn M.","contributorId":345101,"corporation":false,"usgs":false,"family":"Gstic","given":"Katelyn","email":"","middleInitial":"M.","affiliations":[{"id":81620,"text":"Princeton","active":true,"usgs":false}],"preferred":false,"id":915648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Melnycky, Natalka A.","contributorId":345102,"corporation":false,"usgs":false,"family":"Melnycky","given":"Natalka","email":"","middleInitial":"A.","affiliations":[{"id":81620,"text":"Princeton","active":true,"usgs":false}],"preferred":false,"id":915649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Julianna G.","contributorId":345103,"corporation":false,"usgs":false,"family":"Johnson","given":"Julianna","email":"","middleInitial":"G.","affiliations":[{"id":81620,"text":"Princeton","active":true,"usgs":false}],"preferred":false,"id":915650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobson, Andrew P.","contributorId":298529,"corporation":false,"usgs":false,"family":"Dobson","given":"Andrew","email":"","middleInitial":"P.","affiliations":[{"id":64608,"text":"Department of Ecology and Evolutionary Biology, Princeton University,117 Eno Hall, Princeton, NJ 08544, USA","active":true,"usgs":false}],"preferred":false,"id":915651,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":915652,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Standley, Claire J.","contributorId":345104,"corporation":false,"usgs":false,"family":"Standley","given":"Claire J.","affiliations":[{"id":7146,"text":"Georgetown University","active":true,"usgs":false}],"preferred":false,"id":915653,"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":915654,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70259183,"text":"70259183 - 2024 - Precariously balanced rocks in northern New York and Vermont, U.S.A.: Ground-motion constraints and implications for fault sources","interactions":[],"lastModifiedDate":"2024-12-10T15:18:14.075142","indexId":"70259183","displayToPublicDate":"2024-09-10T06:53:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Precariously balanced rocks in northern New York and Vermont, U.S.A.: Ground-motion constraints and implications for fault sources","docAbstract":"<div class=\"\"><div id=\"146571874\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>Precariously balanced rocks (PBRs) and other fragile geologic features have the potential to constrain the maximum intensity of earthquake ground shaking over millennia. Such constraints may be particularly useful in the eastern United States (U.S.), where few earthquake‐source faults are reliably identified, and moderate earthquakes can be felt at great distances due to low seismic attenuation. We describe five PBRs in northern New York and Vermont—a region of elevated seismic hazard associated with historical seismicity. These boulders appear to be among the most fragile PBRs in the region, based on reports from hobbyists. The PBRs are glacial erratics, best evidenced by glacial striations on bedrock pedestals. The pedestals themselves are locally high knobs, often situated on regionally high topography; this setting limits soil development and indicates that any outwash deposits were likely ephemeral. As a result, PBR ages can be reliably established by the retreat of the last continental ice sheet, ∼15–13 ka. To quantify the fragility of the PBRs, we surveyed them with ground‐based light detection and ranging and calculated geometric parameters from the point clouds, field observations, and seismic responses. Preliminary validation of the 2023 time‐independent U.S. National Seismic Hazard Model (NSHM) shows that the existence of PBRs is generally consistent with the median site‐specific hazard curves. Only the Blue Ridge Road site suggests a modest reduction in hazard. To visualize the ensemble of data, we mapped the minimum permissible distance to potential source faults around each PBR site as a function of source magnitude by using the ground‐motion models from the 2023 NSHM. Viewed in this manner, our data are consistent with potential<span>&nbsp;</span><strong>M</strong>∼6.5 earthquake‐source faults in many parts of the Lake Champlain Valley and northern Adirondack Mountains. Our work illustrates a potential pathway for better constraining earthquake‐source faults in regions of cryptic faults.</p></div></div>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120240069","usgsCitation":"McPhillips, D., and Pratt, T.L., 2024, Precariously balanced rocks in northern New York and Vermont, U.S.A.: Ground-motion constraints and implications for fault sources: Bulletin of the Seismological Society of America, v. 114, no. 6, p. 3171-3182, https://doi.org/10.1785/0120240069.","productDescription":"12 p.","startPage":"3171","endPage":"3182","ipdsId":"IP-164823","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":462433,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-09-10","publicationStatus":"PW","contributors":{"authors":[{"text":"McPhillips, Devin 0000-0003-1987-9249","orcid":"https://orcid.org/0000-0003-1987-9249","contributorId":217362,"corporation":false,"usgs":true,"family":"McPhillips","given":"Devin","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":914418,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":914419,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70258255,"text":"sir20245085 - 2024 - Suspended sediment and trace element transport in the Big River downstream from the Old Lead Belt in southeastern Missouri, 2018–21","interactions":[],"lastModifiedDate":"2026-02-03T19:53:25.900966","indexId":"sir20245085","displayToPublicDate":"2024-09-09T11:58:04","publicationYear":"2024","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":"2024-5085","displayTitle":"Suspended Sediment and Trace Element Transport in the Big River Downstream from the Old Lead Belt in Southeastern Missouri, 2018–21","title":"Suspended sediment and trace element transport in the Big River downstream from the Old Lead Belt in southeastern Missouri, 2018–21","docAbstract":"<p>Lead Belt, an area of major lead mining from the 1860s until 1972 where more than 8.5 million tons of lead were mined. After active mining ceased, the effects of mining activities persisted in the Big River system because of large mine waste pile erosion, and floodplain sediment and streambank contamination along several tributaries and the main stem of the Big River. Lead-contaminated streambed and floodplain sediments extend more than 90 miles from the Old Lead Belt to the confluence of the Big River with the Meramec River. The waste piles and mine-waste contaminated streambed and floodplain sediments have been sources of high concentrations of several trace elements, primarily cadmium, lead, and zinc. The U.S. Environmental Protection Agency Region 7 has made several efforts to prevent further erosion of contaminated sediments into the Big River including the capping of major mine waste piles, reclaiming sediment deposits along the floodplains, and monitoring soil conditions of croplands and residential properties.</p><p>A cooperative effort began in 2011 between the U.S. Geological Survey and the U.S. Environmental Protection Agency Region 7 to characterize suspended sediment quantity and quality in the Big River downstream from the Old Lead Belt as reclamation activities in the drainage basin progressed. The study was completed in two phases, and each phase included continuous stage, turbidity, and water temperature monitoring at the Big River below Bonne Terre, Missouri, streamgage and sampling station. Periodic suspended sediment samples also were collected manually (discrete samples) during base flow and selected stormflow events. Continuous streamflow, turbidity, and discrete suspended sediment data were used to develop regression models to compute daily suspended sediment concentrations and loads. During both phases, the discrete stormflow event samples were also evaluated to determine particle size distribution and concentrations of select trace elements. Phase one was completed from October 2011 through September 2013, and phase two, which is the primary focus of this report, was completed from October 2018 through September 2021. Phase two also included time-integrated suspended sediment samples collected using passive samplers. Discrete samples (collected during stormflow events) and passive samples were analyzed for concentrations of barium, cadmium, lead, and zinc in two sediment size fractions (when possible) to estimate trace element loads. Suspended sediment concentrations and loads and select trace element concentration results computed during phase one were compared to those computed during phase two to identify trends in the Big River Basin during the full study period.</p><p>The concentrations of cadmium, lead, and zinc in nearly all discrete stormflow event suspended sediment samples and passive suspended sediment samples exceeded the threshold effect concentrations and the probable effect concentrations, which are two sediment quality guidelines. Most samples also exceeded the toxic effect threshold, the level at which sediment is considered to be heavily contaminated and problematic for sediment-dwelling organisms. Bulk cadmium concentrations (median of 7.90 milligrams per kilogram [mg/kg]) exceeded the toxic effect threshold (3.0 mg/kg) in 17 discrete stormflow event samples, and bulk lead concentrations (median of 1,070 mg/kg) exceeded the toxic effect threshold (170 mg/kg) in all 18 discrete stormflow event samples. Bulk zinc concentrations (median of 500 mg/kg) exceeded the toxic effect threshold (540 mg/kg) in eight discrete stormflow event samples. Bulk concentrations of these trace elements in passive suspended sediment samples were slightly greater, with concentrations of cadmium (median of 14.0 mg/kg) and lead (median of 1,860 mg/kg) exceeding the toxic effect threshold in all 18 samples. Bulk concentrations of zinc (median of 733 mg/kg) exceeded the toxic effect threshold in 15 passive samples. Compared to phase one (water years 2012–13), phase two (water years 2019–21) concentrations of lead and cadmium in the fine fraction of discrete suspended sediment samples collected at Big River below Bonne Terre were statistically similar; concentrations of barium and zinc were statistically smaller in samples collected during phase two (water years 2018–21).</p><p>Sediment quality data from passive samples and daily mean suspended sediment loads from the regression model were used to calculate annual oads of barium, cadmium, lead, and zinc at the Bonne Terre streamgage. Water year 2019 had the largest loads of barium, cadmium, lead, and zinc (58.6, 1.43, 194, and 76.5 tons, respectively). The total loads of barium, cadmium, lead, and zinc for phase two (water years 2019–21) were 149, 4.00, 520, and 213 tons, respectively. Less than 5 percent of the total lead load calculated for the study period was transported when daily mean streamflow was less than 455 cubic feet per second, which is the approximate flow at which the passive samplers were inundated and began sampling. This highlights that most of the lead load is transported during stormflow events and the effectiveness of using passive samplers for ongoing monitoring of the Big River.</p><p>Annual suspended sediment loads at the Bonne Terre streamgage computed using the regression model were 113,000 tons in water year 2019, 83,400 tons in water year 2020, and 96,500 tons in water year 2021. The event-based suspended sediment loads for the eight sampled stormflow events ranged from 45.3 to 32,500 tons. Although only a portion of all stormflow events during phase two were sampled, the loads accounted for during these eight stormflow events represented approximately 30.9 percent of the total suspended sediment load calculated for the study period, confirming that a large part of suspended sediments continue to be transported in the Big River during stormflow events. Event-based loads of barium, cadmium, lead, and zinc were greatest during the stormflow events sampled in January 2020 (event 4) and March 2021 (event 8). Event-based loads calculated for event 4 for barium, cadmium, lead, and zinc were 17.1, 0.206, 27.2, and 14.5 tons, respectively. During event 8, an estimated 15.6 tons of barium, 0.239 tons of cadmium, 34.0 tons of lead, and 13.6 tons of zinc were transported in suspended sediments. The continued high concentrations of lead in suspended sediments in the Big River, despite reclamation activities, is likely because of the continual transport from streambed and stream banks of lead-enriched sediment, which remain in the system from historical mining activities.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245085","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Markland, K.M., and Buckley, C.E., 2024, Suspended sediment and trace element transport in the Big River downstream from the Old Lead Belt in southeastern Missouri, 2018–21: U.S. Geological Survey Scientific Investigations Report 2024–5085, 45 p., https://doi.org/10.3133/sir20245085.","productDescription":"Report: ix, 45 p.; 2 Appendixes; Data Release; Dataset","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-153957","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":433616,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1B8YS78","text":"USGS data release","linkHelpText":"Geochemical analyses of water, mine tailings, fluvial suspended sediments, fluvial bed sediments, and fluvial flood deposit sediments from the Big River and Meramec River drainage basins, Missouri"},{"id":433610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5085/coverthb.jpg"},{"id":433611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5085/sir20245085.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5085"},{"id":433612,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5085/sir20245085.XML"},{"id":433613,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5085/downloads/","text":"Appendixes 1–2","linkHelpText":"- Model Archives Summaries for Regression Models"},{"id":433614,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5085/images/"},{"id":499482,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117311.htm","linkFileType":{"id":5,"text":"html"}},{"id":433617,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"UGSS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":433615,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245085/full"}],"country":"United States","state":"Missouri","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.28892432173701,\n              38.3\n            ],\n            [\n              -91.28892432173701,\n              37.29246138824874\n            ],\n            [\n              -89.90464697798724,\n              37.29246138824874\n            ],\n            [\n              -89.90464697798724,\n              38.3\n            ],\n            [\n              -91.28892432173701,\n              38.3\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/cm-water\" data-mce-href=\"https://www.usgs.gov/centers/cm-water\">Central Midwest Water Science Center</a><br>U.S. Geological Survey<br>400 South Clinton Street, Suite 269<br>Iowa City, IA 52240</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Surface-Water Quality</li><li>Suspended Sediment Transport</li><li>Trace Element Transport</li><li>Summary</li><li>References Cited</li><li>Appendixes 1–2. Model Archives Summaries for Regression Models</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2024-09-09","noUsgsAuthors":false,"publicationDate":"2024-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Markland, Kendra M. 0000-0002-0276-8684 kmarkland@usgs.gov","orcid":"https://orcid.org/0000-0002-0276-8684","contributorId":306212,"corporation":false,"usgs":true,"family":"Markland","given":"Kendra","email":"kmarkland@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":912745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buckley, Camille E. 0000-0002-1692-5644","orcid":"https://orcid.org/0000-0002-1692-5644","contributorId":289852,"corporation":false,"usgs":false,"family":"Buckley","given":"Camille","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":912746,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70262256,"text":"70262256 - 2024 - Evaluating habitat use and relative abundance of Iowa's river otter with harvest data","interactions":[],"lastModifiedDate":"2025-01-22T16:57:13.643782","indexId":"70262256","displayToPublicDate":"2024-09-09T10:53:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating habitat use and relative abundance of Iowa's river otter with harvest data","docAbstract":"<p><span>The North American river otter (</span><i>Lontra canadensis</i><span>) was extirpated from much of the United States in the early 20th century due to habitat loss, pollution of waterways, and overharvesting. The Iowa Department of Natural Resources began a river otter reintroduction effort in 1985, which placed otters in 14 sites across the state. Otters have since been known to occur in every county in Iowa and appear to have successfully repopulated their former range throughout the state. Our objective was to relate land cover characteristics and otter abundance using harvest data. We used data collected by agency staff to map the locations of otter harvest in Iowa from 2006 to 2016. We mapped otter harvest locations at the subwatershed level (also called 12-digit Hydrologic Unit Code or HUC-12). We related otter harvest to land cover variables and predicted otter abundance by land cover type. We found that roads, forests, larger waterways, and Ictaluridae (catfish) presence were negatively correlated with otter harvest. Variables positively correlated with otter harvest were areas with greater land cover diversity, wetland patch density, average stream density, and waterway and wetland areas. The land cover model predicted otters in equal or greater numbers than the harvest data in 62.8% of HUC-12s. The areas of greatest otter abundance estimates were located near recreation areas and urban areas, indicating the underutilization of these heavy-trafficked areas by trappers. Areas of fewer predicted otters were not concentrated in a single area of the state but occurred along the Interstate 80 corridor.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1543","usgsCitation":"Nixon, B., Evelsizer, V., and Klaver, R.W., 2024, Evaluating habitat use and relative abundance of Iowa's river otter with harvest data: Wildlife Society Bulletin, v. 48, no. 3, e1543, 13 p., https://doi.org/10.1002/wsb.1543.","productDescription":"e1543, 13 p.","ipdsId":"IP-155037","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wsb.1543","text":"Publisher Index Page"},{"id":480935,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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 \"}}]}","volume":"48","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Nixon, Bridget A.","contributorId":348634,"corporation":false,"usgs":false,"family":"Nixon","given":"Bridget A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evelsizer, Vince","contributorId":348636,"corporation":false,"usgs":false,"family":"Evelsizer","given":"Vince","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":923666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70258621,"text":"70258621 - 2024 - Mantle melting in regions of thick continental lithosphere: Examples from Late Cretaceous and younger volcanic rocks, Southern Rocky Mountains, Colorado (USA)","interactions":[],"lastModifiedDate":"2024-10-07T16:37:46.120127","indexId":"70258621","displayToPublicDate":"2024-09-09T07:03:58","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Mantle melting in regions of thick continental lithosphere: Examples from Late Cretaceous and younger volcanic rocks, Southern Rocky Mountains, Colorado (USA)","docAbstract":"<div id=\"146575567\" class=\"article-section-wrapper js-article-section js-content-section  \" data-section-parent-id=\"0\"><p>Major- and trace-element data together with Nd and Sr isotopic compositions and<span>&nbsp;</span><sup>40</sup>Ar/<sup>39</sup>Ar age determinations were obtained for Late Cretaceous and younger volcanic rocks from north-central Colorado, USA, in the Southern Rocky Mountains to assess the sources of mantle-derived melts in a region underlain by thick (≥150 km) continental lithosphere. Trachybasalt to trachyandesite lava flows and volcanic cobbles of the Upper Cretaceous Windy Gap Volcanic Member of the Middle Park Formation have low εNd(t) values from −3.4 to −13,<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr(t) from ~0.705 to ~0.707, high large ion lithophile element/high field strength element ratios, and low Ta/Th (≤0.2) values. These characteristics are consistent with the production of mafic melts during the Late Cretaceous to early Cenozoic Laramide orogeny through flux melting of asthenosphere above shallowly subducting and dehydrating oceanic lithosphere of the Farallon plate, followed by the interaction of these melts with preexisting, low εNd(t), continental lithospheric mantle during ascent. This scenario requires that asthenospheric melting occurred beneath continental lithosphere as thick as 200 km, in accordance with mantle xenoliths entrained in localized Devonian-age kimberlites. Such depths are consistent with the abundances of heavy rare earth elements (Yb, Sc) in the Laramide volcanic rocks, which require parental melts derived from garnet-bearing mantle source rocks. New<span>&nbsp;</span><sup>40</sup>Ar/<sup>39</sup>Ar ages from the Rabbit Ears and Elkhead Mountains volcanic fields confirm that mafic magmatism was reestablished in this region ca. 28 Ma after a hiatus of over 30 m.y. and that the locus of volcanism migrated to the west through time. These rocks have εNd(t) and<span>&nbsp;</span><sup>87</sup>Sr/<sup>86</sup>Sr(t) values equivalent to their older counterparts (−3.5 to −13 and 0.7038–0.7060, respectively), but they have higher average chondrite-normalized La/Yb values (~22 vs. ~10), and, for the Rabbit Ears volcanic field, higher and more variable Ta/Th values (0.29–0.43). The latter are general characteristics of all other post– 40 Ma volcanic rocks in north-central Colorado for which literature data are available. Transitions from low to intermediate Ta/Th mafic volcanism occurred diachronously across southwest North America and are interpreted to have been a consequence of melting of continental lithospheric mantle previously metasomatized by aqueous fluids derived from the underthrusted Farallon plate. Melting occurred as remnants of the Farallon plate were removed and the continental lithospheric mantle was conductively heated by upwelling asthenosphere. A similar model can be applied to post–40 Ma magmatism in north-central Colorado, with periodic, east to west, removal of stranded remnants of the Farallon plate from the base of the continental lithospheric mantle accounting for the production, and western migration, of volcanism. The estimated depth of the lithosphere-asthenosphere boundary in north-central Colorado (~150 km) indicates that the lithosphere remains too thick to allow widespread melting of upwelling asthenosphere even after lithospheric thinning in the Cenozoic. The preservation of thick continental lithospheric mantle may account for the absence of oceanic-island basalt–like basaltic volcanism (high Ta/Th values of ~1 and εNd[t] &gt; 0), in contrast to areas of southwest North America that experienced larger-magnitude extension and lithosphere thinning, where oceanic-island basalt–like late Cenozoic basalts are common.</p></div>","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02749.1","usgsCitation":"Farmer, L., Morgan, L.E., Cosca, M., Mize, J., Bailey, T., Turner, K.J., Mercer, C.M., Ellison, E.T., and Bell, A., 2024, Mantle melting in regions of thick continental lithosphere: Examples from Late Cretaceous and younger volcanic rocks, Southern Rocky Mountains, Colorado (USA): Geosphere, v. 20, no. 5, p. 1411-1440, https://doi.org/10.1130/GES02749.1.","productDescription":"30 p.","startPage":"1411","endPage":"1440","ipdsId":"IP-161877","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":439172,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02749.1","text":"Publisher Index Page"},{"id":439130,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-09-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, Lang","contributorId":344306,"corporation":false,"usgs":false,"family":"Farmer","given":"Lang","affiliations":[{"id":82327,"text":"U of Colorado","active":true,"usgs":false}],"preferred":false,"id":913415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":913416,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cosca, M. 0000-0002-0600-7663","orcid":"https://orcid.org/0000-0002-0600-7663","contributorId":107417,"corporation":false,"usgs":true,"family":"Cosca","given":"M.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":913417,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mize, James","contributorId":344307,"corporation":false,"usgs":false,"family":"Mize","given":"James","email":"","affiliations":[{"id":82327,"text":"U of Colorado","active":true,"usgs":false}],"preferred":false,"id":913418,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bailey, Treasure","contributorId":344308,"corporation":false,"usgs":false,"family":"Bailey","given":"Treasure","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":913419,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Turner, Kenzie J. 0000-0002-4940-3981 kturner@usgs.gov","orcid":"https://orcid.org/0000-0002-4940-3981","contributorId":496,"corporation":false,"usgs":true,"family":"Turner","given":"Kenzie","email":"kturner@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":913420,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mercer, Cameron Mark 0000-0003-0534-848X","orcid":"https://orcid.org/0000-0003-0534-848X","contributorId":301880,"corporation":false,"usgs":true,"family":"Mercer","given":"Cameron","email":"","middleInitial":"Mark","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":913421,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ellison, Eric T 0000-0002-6761-1397","orcid":"https://orcid.org/0000-0002-6761-1397","contributorId":302853,"corporation":false,"usgs":false,"family":"Ellison","given":"Eric","email":"","middleInitial":"T","affiliations":[{"id":52978,"text":"Department of Geological Sciences, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":913422,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bell, Aaron","contributorId":344309,"corporation":false,"usgs":false,"family":"Bell","given":"Aaron","email":"","affiliations":[{"id":82327,"text":"U of Colorado","active":true,"usgs":false}],"preferred":false,"id":913423,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70259506,"text":"70259506 - 2024 - Using parentage-based tagging to estimate survival of Chinook salmon fry in a large storage reservoir","interactions":[],"lastModifiedDate":"2024-10-10T11:52:36.956629","indexId":"70259506","displayToPublicDate":"2024-09-09T06:50:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Using parentage-based tagging to estimate survival of Chinook salmon fry in a large storage reservoir","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Research efforts focusing on salmonid populations have highlighted the need to better understand demographic parameters for the fry and parr life stages. Monitoring these small fish presents a challenge because negative effects from handling and tagging can bias subsequent parameter estimates. Removal models and associated sampling designs represent one class of mark-recapture models with potential to be applied to very small juvenile salmon, yet existing methods associated with removal studies are not well-suited for all study environments. For example, populations residing in large storage reservoirs may yield low capture probabilities when subjected to removal sampling, making unbiased estimation of survival using traditional removal models difficult. To address this limitation, we developed a sampling design and associated model using parentage-based tagging in hatchery-raised juvenile Chinook salmon (<i>Oncorhynchus tshawytscha</i>) to estimate survival over a 2-year study period in a large storage reservoir in western Oregon, USA. Individual fish were identified to family groups, serving as replicate batch marks in a robust design removal model framework. Results from a simulation suggested that parameter estimates were unbiased even at very low capture probabilities, although the use of model constraints (i.e., covariates or constant parameter values) was necessary to achieve this. Model fitting to field data supported a trend in survival over time, with survival increasing with time since release in the first study year but decreasing in the second.</p></div></div>","language":"English","publisher":"Springer Nature","doi":"10.1007/s10641-024-01564-9","usgsCitation":"Pope, A., Kock, T.J., Perry, R., Cogliati, K.M., O'Malley, K., Murphy, C.A., Hance, D., and Fielding, S.D., 2024, Using parentage-based tagging to estimate survival of Chinook salmon fry in a large storage reservoir: Environmental Biology of Fishes, v. 107, p. 735-754, https://doi.org/10.1007/s10641-024-01564-9.","productDescription":"20 p.","startPage":"735","endPage":"754","ipdsId":"IP-153939","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":462780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","noUsgsAuthors":false,"publicationDate":"2024-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kock, Tobias J. 0000-0001-8976-0230","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":214550,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220177,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cogliati, Karen M.","contributorId":200086,"corporation":false,"usgs":false,"family":"Cogliati","given":"Karen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":915534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Malley, Kathleen G.","contributorId":345070,"corporation":false,"usgs":false,"family":"O'Malley","given":"Kathleen G.","affiliations":[{"id":82479,"text":"Department of Fisheries, Wildlife, and Conservation Sciences, Coastal Oregon Marine Experiment Station, Hatfield Marine Science Center, Oregon State University, Newport, Oregon, USA.","active":true,"usgs":false}],"preferred":false,"id":915535,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Christina Amy 0000-0002-3467-6610","orcid":"https://orcid.org/0000-0002-3467-6610","contributorId":335232,"corporation":false,"usgs":true,"family":"Murphy","given":"Christina","email":"","middleInitial":"Amy","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":915536,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915537,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fielding, Scott D.","contributorId":345071,"corporation":false,"usgs":false,"family":"Fielding","given":"Scott","email":"","middleInitial":"D.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":915538,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70261944,"text":"70261944 - 2024 - Estimating the benefits of floodplain restoration to juvenile Chinook salmon in the upper San Francisco Estuary, United States, under future climate scenarios","interactions":[],"lastModifiedDate":"2025-01-06T15:17:07.651183","indexId":"70261944","displayToPublicDate":"2024-09-09T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the benefits of floodplain restoration to juvenile Chinook salmon in the upper San Francisco Estuary, United States, under future climate scenarios","docAbstract":"<p>Many river systems within the Central Valley of California have been disconnected from their floodplains, hypothesized to be partially responsible for declining Chinook salmon populations (<i>Oncorhynchus tshawytscha</i>). The primary floodplain of the system, Yolo By-Pass (known regionally as “Yolo Bypass”), offered an opportunity to examine whether improved connectivity between the floodplain and river could limit negative climate change effects on salmon populations. Specifically, the top of the floodplain (Fremont Weir) is being modified to provide Sacramento River Chinook salmon better access to floodplain rearing habitat. We estimated restoration effects on the Yolo By-Pass flood regime now and under future climate scenarios using flow rating curves. Additionally, we used temperature and flow-specific effects on Chinook salmon population dynamics within the Yolo By-Pass and Sacramento River complex to describe how the restoration project and climate change may interact to affect juvenile Chinook salmon biomass production. Our results indicate that the Fremont Weir restoration project will extend the frequency, timing, and duration of Yolo By-Pass flooding. Our production model indicates that the modification will result in greater salmon entrainment rates into the Yolo By-Pass, where salmon growth rates, survival rates, and biomass production were higher when compared to the Sacramento River main stem. The project appears to benefit all regional runs of Chinook salmon, which should help support life history diversity. Our results suggest that the weir modification should benefit native fish from the Central Valley that use floodplain habitat and that these benefits may be resilient to challenges created by a changing climate.</p>","language":"English","publisher":"Wiley","doi":"10.1111/rec.14238","usgsCitation":"Huntsman, B., Wulff, M.L., Knowles, N., Sommer, T., Feyrer, F.V., and Brown, L., 2024, Estimating the benefits of floodplain restoration to juvenile Chinook salmon in the upper San Francisco Estuary, United States, under future climate scenarios: Restoration Ecology, v. 32, no. 7, e14238, 15 p., https://doi.org/10.1111/rec.14238.","productDescription":"e14238, 15 p.","ipdsId":"IP-158338","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":466934,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/rec.14238","text":"Publisher Index Page"},{"id":465670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta of the San Francisco Estuary","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.13752201242781,\n              39.188983094023286\n            ],\n            [\n              -122.13752201242781,\n              37.97745123814863\n            ],\n            [\n              -120.86565837551663,\n              37.97745123814863\n            ],\n            [\n              -120.86565837551663,\n              39.188983094023286\n            ],\n            [\n              -122.13752201242781,\n              39.188983094023286\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"32","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock M.","contributorId":288215,"corporation":false,"usgs":false,"family":"Huntsman","given":"Brock M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":922366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulff, Marissa L. 0000-0003-0121-9066","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":229534,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knowles, Noah 0000-0001-5652-1049","orcid":"https://orcid.org/0000-0001-5652-1049","contributorId":206338,"corporation":false,"usgs":true,"family":"Knowles","given":"Noah","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":922368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sommer, Ted","contributorId":256830,"corporation":false,"usgs":false,"family":"Sommer","given":"Ted","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":922369,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922370,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Larry R. 0000-0003-2272-554X","orcid":"https://orcid.org/0000-0003-2272-554X","contributorId":303111,"corporation":false,"usgs":false,"family":"Brown","given":"Larry R.","affiliations":[{"id":65665,"text":"USGS - deceased","active":true,"usgs":false}],"preferred":false,"id":922371,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70261211,"text":"70261211 - 2024 - Terrebonne Basin northern Gulf of Mexico, 30 July-28 September 2023","interactions":[],"lastModifiedDate":"2024-12-02T15:48:34.350034","indexId":"70261211","displayToPublicDate":"2024-09-08T09:45:28","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":19843,"text":"Preliminary Report","active":true,"publicationSubtype":{"id":1}},"title":"Terrebonne Basin northern Gulf of Mexico, 30 July-28 September 2023","docAbstract":"<p>In the summer and fall of 2023, the Gulf of Mexico Deepwater Hydrate Coring Expedition (UT-GOM2-2) drilled, cored, made downhole measurements, and analyzed samples from the seafloor to the base of the gas hydrate stability zone in one location (Site H, WR313) in the Terrebonne basin, deepwater Gulf of Mexico. </p><p>Analyses of data and samples from the expedition will inform biological, geochemical, and geomechanical models to constrain the role of gas hydrates in the carbon cycle and the potential for gas hydrates as an energy resource. Pressure and conventional cores were collected continuously to a depth of 155.1 meters below the seafloor (mbsf). At deeper depths, cores were taken periodically from hydrate-bearing sands and their bounding muds to a total depth of 861.3 mbsf. 162.6 m of conventional core and 54.8 m of pressure core were obtained. </p><p>Twelve temperature measurements were made between 27.1 and 144.5 mbsf to determine the geothermal gradient. At the seafloor, more than 4 m of sandy silt of unknown origin was encountered. Beneath this sand, to a depth of ~200 mbsf, the section was composed of interbedded mud and biogenic carbonate ooze. The biogenic ooze correlated to low density and high porosity intervals observed in the previously acquired logging while drilling (LWD) data and as measured. Calcareous nannofossil biostratigraphy constrains the entire record to the Pleistocene (&lt; 0.91 million years) with a pronounced increase in sedimentation rate with depth. Beneath 200 mbsf, the section was predominantly composed of mud with two thicker, hydrate-bearing coarse-grained intervals, which are commonly known as the Blue and Orange sands. </p><p>The dissolved gas concentration was quantified from pressure cores. In the shallow section, dissolved methane concentration increased below the sulfate-methane transition zone (SMTZ) and reaches saturation (the limit of solubility for methane) at 147 mbsf. Gas expansion was very common in conventional and depressurized pressure (conventionalized) cores below the SMTZ. </p><p>At deeper depths, the methane concentration within muds bounding the Blue and Orange reservoirs was generally found to be less than saturation. The dissolved and hydrate gas composition is consistent with a microbial source, containing greater than 99.99% methane and only trace concentrations of ethane, propane, and butane. The methane to ethane ratio (C<sub>1</sub>/C<sub>2</sub>) and the methane to ethane plus propane (C<sub>1</sub>/(C<sub>2</sub>+C<sub>3</sub>)) decrease with depth down to at least 678 mbsf, mainly driven by the increase in ethane with depth. It is unclear if this trend continues through the Orange sand interval. The δ<sup>13</sup>C isotopic signature of methane ranges between -69.9 and -78.5 ‰ Vienna Pee Dee Belemnite (VPDB). </p><p>Pressure core recovery of all sandy intervals was poor. However, pressure core logs of the Orange sand show intervals of low density and high velocity, which are indicative of high hydrate saturation. One core from within the Orange sand was composed of interbedded graded sandy silt and mud. The sandy silts from this core are composed of mainly quartz and feldspar with some lithics. Most of the recovered pressure core samples are maintained at near in-situ pressure and temperature (within the hydrate stability field) at the University of Texas Pressure Core Center awaiting analysis. </p><p>In the shallow section, samples will be used to determine the flux of organic carbon through the basin system, find the rate at which that carbon was consumed, and understand the microbial population responsible for these processes. In the deeper section, samples from in and around the hydrate reservoirs will be used to determine the petrophysical properties of the reservoir and bounding seals in these systems.</p>","language":"English","publisher":"U.S. Department of Energy","doi":"10.2172/2439982","usgsCitation":"Flemings, P.B., Thomas, C., Phillips, S.C., Collett, T., Cook, A.E., Solomon, E.S., Colwell, F.S., Johnson, J.E., Awwiller, D., Aylward, I., Bhandari, A., Brooks, D., Cardona, A., Casso, M., Coyte, R., Darrah, T., Davis, M., Dugan, B., Duncan, D., Germaine, J.T., Holland, M., Houghton, J., Mills, N.T., Mimitz, M., Minarich, D., Morono, Y., Murphy, Z., O’Connell, J., Petrou, E., Pettigrew, T., Pohlman, J., Portnov, A., Purkey Phillips, M., Redd, T., Sawyer, D.E., Schultheiss, P., Shannon, K., Sullivan, C., Small, C., Tozier, K., Tsang, M., Van Der Maal, C., Waite, W., and Walton, T., 2024, Terrebonne Basin northern Gulf of Mexico, 30 July-28 September 2023: Preliminary Report, 95 p., https://doi.org/10.2172/2439982.","productDescription":"95 p.","ipdsId":"IP-167042","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466935,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2439982","text":"External Repository"},{"id":464630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Mexico, Terrebonne Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -92,\n              27\n            ],\n            [\n              -92,\n              26.5\n            ],\n            [\n              -91.45,\n              26.5\n            ],\n            [\n              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,{"id":70258259,"text":"70258259 - 2024 - Birdwatching preferences reveal synergies and tradeoffs among recreation, carbon, and fisheries ecosystem services in Pacific Northwest estuaries, USA","interactions":[],"lastModifiedDate":"2024-09-10T13:57:39.79472","indexId":"70258259","displayToPublicDate":"2024-09-07T08:47:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Birdwatching preferences reveal synergies and tradeoffs among recreation, carbon, and fisheries ecosystem services in Pacific Northwest estuaries, USA","docAbstract":"<p><span>Coastal ecosystems provide multiple ecosystem services that are valued in diverse ways. The Nisqually River Delta (the Delta), an estuary in Puget Sound, Washington, U.S.A., is co-managed by the Nisqually Indian Tribe and the Billy Frank Jr. Nisqually National Wildlife Refuge. In an ecosystem services assessment, we used different service-appropriate methods including citizen science, statistical and geospatial models, and scenario analysis to evaluate three ecosystem services – recreational birdwatching, soil carbon accumulation and fishery production – indicated as priorities for the Refuge, Nisqually Indian Tribe, and surrounding communities. We developed a generalized additive mixed model set based on eBird mobile application birdwatching observations to understand the biological and landscape features that influence birdwatching and to project birdwatching visitation based on scenarios of Delta habitat change. We evaluated ecosystem service synergies and tradeoffs associated with habitat change for three coastal habitat types using scenario outputs from the birdwatching model and published results on Delta soil carbon accumulation and fishery production. The highest-ranked birdwatching models explained 88&nbsp;% of the deviance and showed that visitation was greatest in winter months when distance to major cities was approximately 20&nbsp;km. Recreational birdwatching increased with increasing area of forested wetland, emergent wetland, aquatic vegetation bed, open access, and total estuary. With increasing forested and emergent wetland area, recreational birdwatching, out-migrating juvenile Chinook salmon weight and soil carbon accumulation all increased. With increasing aquatic vegetation bed (resulting from sea level rise), recreational birdwatching increased, but salmon weight and soil carbon accumulation decreased. We identified practical ways in which ecosystem services may be incorporated into adaptive management frameworks that support climate adaptation decision making. This study illustrated how use of ecosystem services can help managers make decisions that have greater benefit for wildlife and people, communicate the societal value of decisions and increase local support and participation.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2024.101656","usgsCitation":"Byrd, K.B., Woo, I., Hall, L.A., Pindilli, E., Moritsch, M., Good, A., De La Cruz, S.E., Davis, M.J., and Nakai, G., 2024, Birdwatching preferences reveal synergies and tradeoffs among recreation, carbon, and fisheries ecosystem services in Pacific Northwest estuaries, USA: Ecosystem Services, v. 69, 101656, 14 p., https://doi.org/10.1016/j.ecoser.2024.101656.","productDescription":"101656, 14 p.","ipdsId":"IP-157727","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":486791,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2024.101656","text":"Publisher Index Page"},{"id":433660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.74299559212136,\n              47.116807423542184\n            ],\n            [\n              -122.74299559212136,\n              47.02915653165158\n            ],\n            [\n              -122.65931630942087,\n              47.02915653165158\n            ],\n            [\n              -122.65931630942087,\n              47.116807423542184\n            ],\n            [\n              -122.74299559212136,\n              47.116807423542184\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"69","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":912767,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":912768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hall, Laurie 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Monica","contributorId":344065,"corporation":false,"usgs":false,"family":"Moritsch","given":"Monica","affiliations":[{"id":65966,"text":"EDF","active":true,"usgs":false}],"preferred":false,"id":912771,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Good, Anthony","contributorId":344067,"corporation":false,"usgs":false,"family":"Good","given":"Anthony","affiliations":[{"id":82282,"text":"Prevedere, Inc.","active":true,"usgs":false}],"preferred":false,"id":912772,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":912773,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Melanie J. 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,{"id":70259409,"text":"70259409 - 2024 - Event classification, seismicity, and eruption forecasting at Great Sitkin Volcano, Alaska: 1999–2023","interactions":[],"lastModifiedDate":"2024-10-07T12:13:29.888651","indexId":"70259409","displayToPublicDate":"2024-09-07T07:05:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Event classification, seismicity, and eruption forecasting at Great Sitkin Volcano, Alaska: 1999–2023","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><div id=\"sp0110\" class=\"u-margin-s-bottom\">The frequency content of volcanogenic seismicity is often used to classify events and their spatial and temporal progression is then used to map subsurface volcanic processes. The progression of volcano-seismic events and associated source processes also plays a critical role in eruption forecasting. Here we develop and evaluate a computerized methodology for characterizing volcano-seismic event types using Frequency Index and Average Peak Frequency. We apply and test this technique at Great Sitkin Volcano, Alaska, classifying over 9000 hypocenters between 1999 and 2023. This 24-year time span covers periods of seismic quiescence, earthquake activity on nearby tectonic (bookshelf) faults, precursory unrest from 2016 to 2021, and the explosive onset in May 2021 of the ongoing effusive eruption. We use the spatial and temporal evolution of classified event types to map the active volcanic and tectonic processes, develop a conceptual model of the subsurface magmatic system, and perform a retrospective analysis of eruption forecasts at Great Sitkin Volcano between 2016 and the present. The classification and progression of hypocenters suggests the subsurface Great Sitkin Volcano magmatic system consists of a mid- to lower- crustal source zone between 10 and 40&nbsp;km depth and an upper crustal magma storage area between −1 and 10&nbsp;km depth (hypocenter depth is referenced to sea level and negative depths reflect height above sea level). The earliest precursors occurred in July 2016 and consisted of deep long-period and volcano-tectonic earthquakes at mid-crustal depths suggesting the subsequent unrest and eruption were triggered by a deeper intrusion of magma. This mid-crustal seismic activity was immediately followed by the onset upper-crustal long-period events and volcano-tectonic earthquakes VTs suggesting a strong linkage between the shallow and deeper portions of the magmatic system. The upper crustal area was likely capped by the 1974 lava dome until the magmatic explosion on May 26, 2021.</div></div></div></div><div id=\"reading-assistant-main-body-section\"><br></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2024.108182","usgsCitation":"Power, J., and Diana Roman, 2024, Event classification, seismicity, and eruption forecasting at Great Sitkin Volcano, Alaska: 1999–2023: Journal of Volcanology and Geothermal Research, v. 454, 108182, 20 p., https://doi.org/10.1016/j.jvolgeores.2024.108182.","productDescription":"108182, 20 p.","ipdsId":"IP-167165","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":466936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2024.108182","text":"Publisher Index Page"},{"id":462659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Great Sitkin Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -177.73135885820756,\n              51.218602427845354\n            ],\n            [\n              -175.89308412702204,\n              51.218602427845354\n            ],\n            [\n              -175.89308412702204,\n              52.23819554116983\n            ],\n            [\n              -177.73135885820756,\n              52.23819554116983\n            ],\n            [\n              -177.73135885820756,\n              51.218602427845354\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"454","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diana Roman","contributorId":344975,"corporation":false,"usgs":false,"family":"Diana Roman","affiliations":[{"id":30217,"text":"Carnegie Institution for Science","active":true,"usgs":false}],"preferred":false,"id":915201,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70258108,"text":"cir1526 - 2024 - U.S. Geological Survey climate science plan—Future research directions","interactions":[],"lastModifiedDate":"2024-09-16T18:24:41.049557","indexId":"cir1526","displayToPublicDate":"2024-09-06T08:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1526","displayTitle":"U.S. Geological Survey Climate Science Plan—Future Research Directions","title":"U.S. Geological Survey climate science plan—Future research directions","docAbstract":"<h1>Executive Summary&nbsp;</h1><p>Climate is the primary driver of environmental change and is a key consideration in defining science priorities conducted across all mission areas in the U.S. Geological Survey (USGS). Recognizing the importance of climate change to its future research agenda, the USGS’s Climate Science Steering Committee requested the development of a Climate Science Plan to identify future research directions. Subject matter experts from across the Bureau formed the USGS Climate Science Plan Writing Team, which convened in September 2022 to identify and outline the major climate science topics of future concern and develop an integrated approach to conducting climate science in support of the USGS and U.S. Department of the Interior missions.<br><br>The resulting USGS Climate Science Plan identifies three major priorities under which USGS climate science proceeds: (1) characterize climate change and associated impacts, (2) assess climate change risks and develop approaches to mitigate climate change, and (3) provide climate science tools and support. The Climate Science Plan identifies 12 specific goals to achieve the outcomes of the three priorities.</p><ol><li>Conduct long-term, broad-scale, and multidisciplinary measurements and monitoring and research activities to define, quantify, and predict the impacts of climate change on natural and human systems;</li><li>Provide leadership to standardize measuring, monitoring, reporting, and verifying greenhouse gas emissions, lateral carbon fluxes, and carbon sinks across lands managed by the U.S. Department of the Interior (DOI);</li><li>Provide science capacity, training, tools, and infrastructure to Tribal partners; support Tribal-led science initiatives;</li><li>Conduct climate change research in partnership with the broader climate science community;</li><li>Develop improved data synthesis methods through collaborative and open science across mission areas and between the USGS and agency partners;</li><li>Translate climate change impacts into risk assessments in support of risk management strategies;</li><li>Develop new and improved risk assessments, models, and approaches for mitigating climate change, adapting to its impacts, and reducing uncertainties; design early warning systems for risk mitigation;</li><li>Investigate climate change mitigation strategies and create decision science support tools to inform climate change mitigation and adaptation;</li><li>Provide a framework that facilitates knowledge co-production needed to inform policy decisions;</li><li>Provide access to USGS data and information through novel integration and visualization approaches;</li><li>Build capacity within USGS and DOI through development of scientific training curricula; and</li><li>Coordinate science and capacity building efforts broadly across the Federal Government.</li></ol><p>To achieve these goals, the USGS Climate Science Plan also outlines climate science guidelines—key elements for conducting climate-based research—as well as emerging opportunities to support successful climate science. The USGS Climate Science Plan provided in this circular will guide future research priorities and science-support investments, as well as continued development of the climate workforce for decades to come, ensuring that the USGS continues to serve as one of the Nation’s leading climate science agencies.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1526","usgsCitation":"Wilson, T., Boyles, R.P., DeCrappeo, N., Drexler, J.Z., Kroeger, K.D., Loehman, R.A., Pearce, J.M., Waldrop, M.P., Warwick, P.D., Wein, A.M., Zeigler, S.L., and Beard, T.D., Jr., 2024, U.S. Geological Survey climate science plan—Future research directions: U.S. Geological Survey Circular 1526, 30 p., https://doi.org/10.3133/cir1526.","productDescription":"iv, 30 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163273","costCenters":[{"id":114,"text":"Alaska Science 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Current Climate Science Activities in the U.S. Geological Survey</li><li>Appendix 2. 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,{"id":70256082,"text":"70256082 - 2024 - Science target prioritization framework for remote sensing","interactions":[],"lastModifiedDate":"2026-03-27T18:42:29.813683","indexId":"70256082","displayToPublicDate":"2024-09-05T13:37:35","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Science target prioritization framework for remote sensing","docAbstract":"<p><span>Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. 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,{"id":70258364,"text":"70258364 - 2024 - Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer","interactions":[],"lastModifiedDate":"2024-09-13T14:19:14.432182","indexId":"70258364","displayToPublicDate":"2024-09-05T09:18:15","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer","docAbstract":"<p><span>The goal of the U.S. Geological Survey’s (USGS) 3D Elevation Program (3DEP) is to facilitate the acquisition of nationwide lidar data. Although data meet USGS lidar specifications, some point cloud tiles include noisy and incorrectly classified points. The enhanced accuracy of classified point clouds can improve support for many downstream applications such as hydrologic analysis, urban planning, and forest management. Despite noisy and incorrectly classified points, the current 3DEP classification specifications result in data that can be useful for Digital Terrain Model (DTM) extraction; however, the quality of the classification application can be improved to match state-of-the-art capabilities. Deep Learning (DL)-based approaches have been developed with outstanding performance for point cloud classification. This study will utilize the proven DL technologies to prepare for developing a user-friendly open-source toolkit that would automate classification to refine and enrich the results of existing and future 3DEP data.</span></p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2024 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 7-12, 2024","conferenceLocation":"Athens, Greece","language":"English","publisher":"The Institute of Electrical and Electronics Engineers (IEEE)","doi":"10.1109/IGARSS53475.2024.10641055","usgsCitation":"Liu, J., Qin, R., and Song, S., 2024, Automated deep learning-based point cloud classification on USGS 3DEP lidar data using transformer, <i>in</i> Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Athens, Greece, July 7-12, 2024, p. 8518-8521, https://doi.org/10.1109/IGARSS53475.2024.10641055.","productDescription":"4 p.","startPage":"8518","endPage":"8521","ipdsId":"IP-159942","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":439175,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/poster/Automated_Deep_Learning-based_Point_Cloud_Classification_on_USGS_3DEP_LiDAR_Data_Using_a_Transformer/26169397","text":"External Repository"},{"id":434765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Jung-Kuan 0000-0001-8461-8200","orcid":"https://orcid.org/0000-0001-8461-8200","contributorId":333940,"corporation":false,"usgs":true,"family":"Liu","given":"Jung-Kuan","email":"","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":913062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qin, Rongjun","contributorId":333939,"corporation":false,"usgs":false,"family":"Qin","given":"Rongjun","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":913063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Song, Shuang","contributorId":344174,"corporation":false,"usgs":false,"family":"Song","given":"Shuang","email":"","affiliations":[{"id":18155,"text":"The Ohio State University","active":true,"usgs":false}],"preferred":false,"id":913064,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
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