Objective
Yellow Perch Perca flavescens was first encountered in Lake Cascade, Idaho, in 1957. Since its introduction, the abundance of Yellow Perch in Lake Cascade has been highly variable. Historically, declines in Yellow Perch abundance were attributed to predation by Northern Pikeminnow Ptychocheilus oregonensis. In the 1990s, Smallmouth Bass Micropterus dolomieu became established in Lake Cascade. Although Yellow Perch abundance has been high and currently supports a world-class fishery, the Idaho Department of Fish and Game has documented poor Yellow Perch recruitment in recent years, concerning regional managers. An investigation into the food habits of Yellow Perch, Smallmouth Bass, and Northern Pikeminnow was conducted to better understand the importance of juvenile Yellow Perch to predator diets.
Methods
In Lake Cascade, gill nets were deployed monthly from April 2022 to May 2023. Ageing structures and stomach contents were removed from Yellow Perch, Smallmouth Bass, and Northern Pikeminnow. Diet composition was quantified and then summarized by species, cohort, and season. Bioenergetics modeling, coupled with estimates of predator abundance, was used to evaluate overall consumption of Yellow Perch.
Results
In total, stomach contents were extracted from 1,099 Yellow Perch, 440 Smallmouth Bass, and 980 Northern Pikeminnow. Food habits varied by season and predator length, but Yellow Perch was a primary prey item of all study species. The energetic contribution of Yellow Perch to Yellow Perch varied from 10% to 80% across seasons. Smallmouth Bass consumed fewer Yellow Perch than either Yellow Perch or Northern Pikeminnow. The highest average per capita consumption of Yellow Perch (i.e., across seasons) was observed for 500–600-mm Northern Pikeminnow (five Yellow Perch/Northern Pikeminnow). Energetic contribution of Yellow Perch to Northern Pikeminnow diets was highest in spring 2023, yet Northern Pikeminnow consumed the highest average number of Yellow Perch in July. Bioenergetics modeling estimated that per-capita annual consumption of Yellow Perch was 6.0 kg for Yellow Perch, 3.4 kg for Smallmouth Bass, and 9.9 kg for Northern Pikeminnow. In total, estimated consumption of all study species was as high as 37% of the estimated total number age-0 Yellow Perch produced.
Conclusions
Yellow Perch was an important prey item for all three species, particularly Yellow Perch and Northern Pikeminnow. Results from this study provide important information on the predation of Yellow Perch in Lake Cascade. Further, this work will bolster a growing body of research on the food habits of and interactions between native and nonnative predators in western reservoir systems.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/najfmt/vqaf062","usgsCitation":"Marciniak, B., Thomas, M., Messner, J., Corsi, M., Quist, M., 2025, Food habits of Yellow Perch, Smallmouth Bass, and Northern Pikeminnow in Lake Cascade, Idaho: North American Journal of Fisheries Management, v. 45, no. 5, p. 795-812, https://doi.org/10.1093/najfmt/vqaf062.","productDescription":"18 p.","startPage":"795","endPage":"812","ipdsId":"IP-173300","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Cascade","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.20393006754492,\n 44.763754267268894\n ],\n [\n -116.20393006754492,\n 44.4319010241793\n ],\n [\n -115.97067657023298,\n 44.4319010241793\n ],\n [\n -115.97067657023298,\n 44.763754267268894\n ],\n [\n -116.20393006754492,\n 44.763754267268894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","issue":"5","noUsgsAuthors":false,"plainLanguageSummary":"The participants of the 12th International Atomic Energy Agency (IAEA) meeting on stable isotope reference materials reached a consensus, acknowledging the existence and use of two carbon isotope delta scales: the VPDB (Vienna Peedee belemnite) scale and the VPDB-LSVEC (LSVEC - lithium carbonate prepared by H. J. Svec). Conversion models between the two scales can be established and used but introduce uncertainty. A format for isotope delta scale definition was agreed upon and was used to define the two carbon isotope delta scales and the two main oxygen isotope delta scales, VSMOW-SLAP (Vienna Standard Mean Ocean Water–Standard Light Antarctic Precipitation) and VPDB. Confirmation or identification of a second-scale–defining point is still necessary for the nitrogen and sulfur isotope delta scales.
Efforts are encouraged to improve consistency among laboratories in the isotopic analysis of “non-exchangeable hydrogen” in bulk organic materials and oxygen in carbonates using the phosphoric acid reaction. Additional topics discussed include (1) need for improvement in reference materials for accurate greenhouse gas isotopic analyses; (2) reference materials under production by the IAEA, the US Geological Survey (USGS), and the US National Institute of Standards and Technology (NIST); (3) methods for value and uncertainty assignment of reference materials; and (4) calculation of carbon-13 isotope delta and oxygen-18 isotope delta of CO2 measured by dual-inlet isotope ratio mass spectrometry.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.10018","usgsCitation":"Camin, F., Besic, D., Brewer, P.J., Allison, C.E., Coplen, T.B., Dunn, P.J., Gehre, M., Gröning, M., Meijer, H.A., Hélie, J., Iacumin, P., Kraft, R., Krajnc, B., Kümmel, S., Lee, S., Meija, J., Mester, Z., Mohn, J., Moossen, H., Qi, H., Skrzypek, G., Sperlich, P., Viallon, J., Wassenaar, L.I., and Wielgosz, R.I., 2025, Stable isotope reference materials and scale definitions – Outcomes of the 2024 IAEA experts meeting: Rapid Communications in Mass Spectrometry, v. 39, no. 14, e10018, 11 p., https://doi.org/10.1002/rcm.10018.","productDescription":"e10018, 11 p.","ipdsId":"IP-167836","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":496933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rcm.10018","text":"Publisher Index 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Peter","contributorId":363037,"corporation":false,"usgs":false,"family":"Sperlich","given":"Peter","affiliations":[{"id":86591,"text":"National Institute of Water & Atmospheric Research Ltd, Wellington New Zealand","active":true,"usgs":false}],"preferred":false,"id":950944,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Viallon, Joelle","contributorId":363038,"corporation":false,"usgs":false,"family":"Viallon","given":"Joelle","affiliations":[{"id":86592,"text":"Bureau International des Poids et Mesures, Sevres, France","active":true,"usgs":false}],"preferred":false,"id":950945,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Wassenaar, Leonard I.","contributorId":202666,"corporation":false,"usgs":false,"family":"Wassenaar","given":"Leonard","middleInitial":"I.","affiliations":[{"id":36516,"text":"International Atomic Energy Agency, Water Resources Section, PO Box 100, Vienna. 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Certain mineral-derived elements act as catalysts and reaction intermediaries, facilitating early-stage hydrocarbon formation in potential source rocks. This study investigated the role of sulfur contributed from pyrite as an accelerant in thermal reaction, focusing on its effects on early maturation and consequent hydrocarbon generation from gilsonite (low-sulfur solid petroleum). Hydrous pyrolysis (HP) experiments were conducted on mixtures of gilsonite and pyrite in varying ratios (1:0.1, 1:0.5, 1:1, 1:2, and 1:10 w/w gilsonite:pyrite) at 320, 350, and 370 °C for 72 h. Untreated and thermally altered residues were analyzed using solid bitumen reflectance (BRo, %), total organic carbon (TOC) content, programmed temperature pyrolysis, scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) to evaluate the potential accelerant role of pyritic sulfur in hydrocarbon formation. The results show HP residues at 320 and 350 °C with greater pyrite concentrations had higher BRo, while reflectance values were similar in the 370 °C residues, regardless of pyrite concentration, suggesting enhanced reaction at lower thermal conditions. Increasing pyrite content systematically decreased hydrogen index (HI) values while increasing the transformation ratio (TR) and production index (PI), indicating enhanced conversion of organic matter to hydrocarbons with increasing pyrite concentrations. Gas yields increased with pyrite addition, particularly at 350 °C, confirming secondary cracking effects. However, gas production stabilized or declined at higher pyrite loadings (1:10), suggesting alternative reaction pathways such as coke formation. Our data indicate the presence of pyrite lowers the activation energy for thermal cracking, shifting peak experimental hydrocarbon generation temperatures downward by 20–30 °C, with the most pronounced accelerant effects observed at moderate pyrite concentrations (1:0.5 and 1:1). The thermodynamic framework reveals that pyrite stability is influenced by experimental conditions, with pyrrhotite formation favored in the presence of gilsonite due to reduced oxygen fugacity. Pyrite transformation to pyrrhotite, as observed through XRD, SEM-EDS, and predicted by thermodynamic data, further supports the accelerant role of S, as pyrrhotite exhibits a higher hydrogen transfer potential, promoting early oil generation. These findings highlight the importance of pyrite in modulating hydrocarbon generation pathways in organic-rich systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaap.2025.107307","usgsCitation":"Adsul, T., Hackley, P.C., Hatcherian, J.J., McAleer, R.J., Green, C.J., Burnham, A.K., Ghosh, S., Werne, J.P., and Varma, A.K., 2025, Reaction kinetics and accelerant effects of sulfides in early mature hydrocarbon generation using hydrous pyrolysis: Journal of Analytical and Applied Pyrolysis, v. 192, 107307, https://doi.org/10.1016/j.jaap.2025.107307.","productDescription":"107307","ipdsId":"IP-176736","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":496496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"192","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adsul, Tushar","contributorId":330815,"corporation":false,"usgs":false,"family":"Adsul","given":"Tushar","email":"","affiliations":[{"id":79028,"text":"Indian Institute of Technology (Indian School of Mines), India","active":true,"usgs":false}],"preferred":false,"id":949842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":949845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Green, Carlin J. 0000-0002-6557-6268 cjgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6557-6268","contributorId":193013,"corporation":false,"usgs":true,"family":"Green","given":"Carlin","email":"cjgreen@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":949846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burnham, Alan K.","contributorId":362030,"corporation":false,"usgs":false,"family":"Burnham","given":"Alan","middleInitial":"K.","affiliations":[{"id":86422,"text":"Stratify//MH Chew Associates, 7633 Southfront Rd #170, Livermore, California 94551, USA","active":true,"usgs":false}],"preferred":false,"id":949847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ghosh, Santanu","contributorId":330824,"corporation":false,"usgs":false,"family":"Ghosh","given":"Santanu","email":"","affiliations":[{"id":79037,"text":"Mizoram University, India","active":true,"usgs":false}],"preferred":false,"id":949848,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Werne, Josef P.","contributorId":362031,"corporation":false,"usgs":false,"family":"Werne","given":"Josef","middleInitial":"P.","affiliations":[{"id":86423,"text":"Organic and Stable Isotope Biogeochemistry Laboratory, Department of Geology and Environmental Science, University of Pittsburgh, 200 Space Research Coordination Center, 4107 O’Hara Street, Pittsburgh, PA 15260, USA","active":true,"usgs":false}],"preferred":false,"id":949849,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Varma, Atul K.","contributorId":290219,"corporation":false,"usgs":false,"family":"Varma","given":"Atul","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":949850,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70275146,"text":"70275146 - 2025 - Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River","interactions":[],"lastModifiedDate":"2026-04-20T15:35:48.147791","indexId":"70275146","displayToPublicDate":"2025-07-29T00:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River","title":"Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River","docAbstract":"Information on the favorable conditions for invasive species as well as potential constraints to their distribution can be valuable for management efforts. We used a niche modeling approach to analyze the patterns of species distributions along gradients of hypothesized influential environmental variables. Many ecological datasets may have incomplete coverage across the environmental gradients, infrequent sampling under some conditions, insufficient time for an invasive species to occupy all sites, and complex interactions among environmental variables (measured or unmeasured) that may result in species response curves that are difficult to interpret and may be ecologically misleading. To ensure the model and species response curves aligned with ecological niche theory, shape constraints were imposed to guarantee relationships follow a unimodal distribution to reflect the fundamental niche (where a species could occur). We compared a shape-constrained model to an unconstrained model and interpreted the species response curves from the constrained model to better characterize the ecological niche of reed canarygrass in floodplain forests of the Upper Mississippi River, USA. We found the probability of reed canarygrass occurrence decreases with increasing tree canopy cover, tree species richness, distance from forest edge, distance from invaded wet meadows, and island isolation. Probability of reed canarygrass presence exhibited bell-shaped curves in response to hydrology (inundation depth, frequency, and duration) and forest stress metrics indicating an optimum with less favorable conditions on either end of the ecological gradients. This information could be used to prioritize restoration efforts and enhance landcover change research in forested floodplains.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s13157-025-01956-2","usgsCitation":"Delaney, J.T., Van Appledorn, M., De Jager, N.R., Bouska, K.L., and Rohweder, J.J., 2025, Characterizing the niche of Phalaris arundinacea (reed canarygrass) in floodplain forests of the Upper Mississippi River: Wetlands, v. 45, 86, 12 p., https://doi.org/10.1007/s13157-025-01956-2.","productDescription":"86, 12 p.","ipdsId":"IP-164703","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":504066,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1NPO8BT","text":"USGS data release","linkHelpText":"Analysis data for reed canarygrass (Phalaris arundinacea) niche modeling in Upper Mississippi River floodplain forest understories"},{"id":503213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.44020500286925,\n 45.20810051838578\n ],\n [\n -91.09888752607472,\n 41.81975291615561\n ],\n [\n -91.79588050539074,\n 40.07343528894683\n ],\n [\n -90.14935731160756,\n 36.647803520755886\n ],\n [\n -88.93474584520565,\n 37.11378048828301\n ],\n [\n -90.74845311041965,\n 39.84831202944948\n ],\n [\n -89.84649625728352,\n 41.9195697474386\n ],\n [\n -92.01609711969336,\n 45.28516191702806\n ],\n [\n -93.44020500286925,\n 45.20810051838578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2025-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, John T. 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":959655,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269987,"text":"70269987 - 2025 - Climate and land use drivers of freshwater fish biodiversity in the northeastern United States","interactions":[],"lastModifiedDate":"2025-08-07T15:19:03.836084","indexId":"70269987","displayToPublicDate":"2025-07-28T10:10:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Climate and land use drivers of freshwater fish biodiversity in the northeastern United States","docAbstract":"Freshwater habitats can sustain high biodiversity, but habitat degradation, species invasion, and overexploitation have imperiled freshwater species. The multiple threats to freshwater habitats and changing stream characteristics due to climate change make it challenging to identify the drivers of fish vulnerability, especially given that the importance of drivers may vary by the biodiversity endpoint. The goals of this study were to 1) describe freshwater (lotic) fish biodiversity across northeastern states in the United States, and 2) identify geographic, climate, and land use drivers of fish biodiversity, toward improving freshwater fish conservation. We predicted habitat suitability for 53 fish species using survey data and corresponding geographic, climate, and land use data. After model fitting, we grouped species using a traditional approach based on traits and a new approach using modeled streamflow and stream temperature to create clusters. We found that climate and land use vulnerable groups did not always have similar geographic patterns or relationships to the climate or land use variables, suggesting that biota vulnerable to changing conditions may be overlooked by reliance on predefined guilds to set conservation goals. Biodiversity groups that were vulnerable to climate variables were also related to land use variables that can be used to identify restoration opportunities. This approach may be useful for managers interested in holistic freshwater management under changing stream conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2025.111337","usgsCitation":"Rogers, J.B., DiRenzo, G.V., Quiñones, R.M., Richards, T., and Roy, A.H., 2025, Climate and land use drivers of freshwater fish biodiversity in the northeastern United States: Biological Conservation, v. 310, 111337, 18 p., https://doi.org/10.1016/j.biocon.2025.111337.","productDescription":"111337, 18 p.","ipdsId":"IP-172448","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, 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This premise served as motivation to collect sediment cores during the summer of 2022 at four sites on the Canadian continental shelf and Alaskan upper continental slope to reconstruct changes in the marine and terrestrial environments to provide a comprehensive picture of the ocean environment during the preindustrial period before anthropogenic influences. We dated the sediments based on the 137Cs radioactivity of bulk sediments and the 14C concentrations of mollusk shells. The 137Cs radioactivity shows a distinct onset corresponding to 1950 Common Era (CE) and the most prominent peak corresponding to 1963 CE. Multiple peaks appeared above the most prominent one, coinciding with nuclear power plant accidents in 1986 and 2011. Inventories of excess 210Pb in all cores exceed the estimated supply of excess 210Pb from atmospheric deposition, likely due to the scavenging supply of excess 210Pb. By comparing 137Cs and radiocarbon conventional ages, we estimated the local radiocarbon reservoir age value of each site. Using these local radiocarbon reservoir age and the conventional ages of mollusk shell samples, we established the age-depth models by the Bayesian method. The optimal ΔR values were 598, 511, 65, and –60 years at the MT1, MT2, BC2, and BC2-2 sites, respectively. The cores consist of clayey silts continuously deposited with uniquely high sedimentation rates of 0.17 to 0.74 cm y−1. Variation in the Ca/Ti ratio indicates ~ 20, ~ 30, 50–60, 100–125, and 300-year cycles, likely attributed to the variation in the Aleutian Low that controls the Bering Strait inflow of Pacific waters influencing our core sites. These sediments will be used for further high-resolution, multi-proxy studies with forthcoming results.
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The Neoproterozoic Timanian, Paleozoic Caledonian and Uralian, and late Mesozoic Verkhoyansk–Kolyma, Chukotkan and Brookian orogenies formed several large fold-and-thrust belts (FTBs). The FTBs are exposed across vast areas of continents and continue offshore to form a complex tectonic basement for thick sedimentary basins, playing an important role in the history of accumulation and deformation of younger unmetamorphosed sedimentary successions that are the subject of this volume. Recognition of the importance of FTBs in the Arctic geological history and their role as a controlling factor of development of Arctic sedimentary basins resulted in this chapter, in which we review the current state of knowledge about Arctic FTBs and highlight questions that remain to be addressed. Enclosure D, a map showing boundaries of the FTB and their internal first-order structural fabric, is a part of the overview.
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Understanding how U is taken up by organisms can aid in evaluating the potential for bioaccumulation and toxicity. Although the importance of aqueous geochemical speciation is well recognized for U bioavailability after dissolved exposures, far less is known about the processes controlling U bioavailability after dietary exposures. This study characterizes the biogeochemical drivers of dietary U uptake in the freshwater snail Lymnaea stagnalis in laboratory experiments. Solids tested included benthic diatoms pre-exposed to dissolved U(VI), soils from contaminated U mine sites, and colloidal hydrous ferric oxide (HFO) synthesized in the presence of dissolved U(VI) or with U complexed by natural organic matter (NOM). Results showed that U was bioavailable from all solids. Uranium assimilation efficiency (AE), a proxy for dietary U bioavailability, varied among solids. AE was lowest for the U-contaminated soils (25 ± 17%) and highest for the U-laden diatoms (71 ± 13%). AE varied slightly among HFO preparations, suggesting modest influences of NOM and iron on U bioavailability. Increases in dietary U exposures reduced feeding rates, and the extent of feeding inhibition appeared inversely related to U bioavailability. The high U assimilation and range of bioavailability have implications for toxicity risks inferred without considering dietary uptake.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.5c05140","usgsCitation":"Croteau, M.N., Fuller, C.C., Cain, D.J., and Campbell, K.M., 2025, Dietary bioavailability of uranium to a model freshwater invertebrate: Environmental Science and Technology, v. 59, no. 31, p. 16641-16651, https://doi.org/10.1021/acs.est.5c05140.","productDescription":"11 p.","startPage":"16641","endPage":"16651","ipdsId":"IP-172293","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":493091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"31","noUsgsAuthors":false,"publicationDate":"2025-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":944277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, Daniel J. 0000-0002-3443-0493 djcain@usgs.gov","orcid":"https://orcid.org/0000-0002-3443-0493","contributorId":1784,"corporation":false,"usgs":true,"family":"Cain","given":"Daniel","email":"djcain@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":944279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":944280,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269556,"text":"ofr20251022 - 2025 - Contributions of erosion, deposition, and human activities to a change in sand storage in the bed of San Francisco Bay, California, 1980s to 2010s","interactions":[],"lastModifiedDate":"2026-02-03T14:38:01.331405","indexId":"ofr20251022","displayToPublicDate":"2025-07-28T08:39:19","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1022","displayTitle":"Contributions of Erosion, Deposition, and Human Activities to a Change in Sand Storage in the Bed of San Francisco Bay, California, 1980s to 2010s","title":"Contributions of erosion, deposition, and human activities to a change in sand storage in the bed of San Francisco Bay, California, 1980s to 2010s","docAbstract":"This study by the U.S. Geological Survey (USGS) provides estimates of the change in sand storage in bed sediments from the 1980s to 2010s in the San Francisco Bay area, California. The study is part of a larger project called “Research to Understand Impacts of Bay Sand Mining on Sand Transport in San Francisco Bay and the Outer Coast” that has the goal of providing information for the California Coastal Conservancy to inform decision making regarding sand mining activities. Information from this study will contribute to the sand budget for the San Francisco Bay system by accounting for sand made available by erosion of bay sediment and sequestered by deposition in the bay.
Sediment budgets for estuaries typically account for change in sediment storage in the bed without discriminating for sediment size. However, the physics of mud and sand erosion, deposition, and transport differ. Sediment budgets that treat mud and sand separately give a more complete understanding of the system, including how human activities related to sediment size, such as sand mining, affect the system. We used bathymetric change analysis in combination with a three-dimensional model to generate estimates of net change in sand storage within the San Francisco Bay floor. We document sediment volume change from a 1980s bathymetric surface to a 2010s bathymetric surface, in combination with information on the sand content of the bed sediment derived from sediment cores and surface samples from six different sediment studies, to estimate the net change in sand volume in the bed of San Francisco Bay. This analysis includes areas heavily affected by human activities (such as sand mining, dredging, and sediment disposal) as well as regions more representative of natural transport processes.
Overall, the sediment bed of San Francisco Bay is losing sand. Across the total area surveyed in San Francisco Bay, including areas affected by natural processes, oyster shell beds, and human activities, a net loss of about 17 million cubic meters (Mm3) of sand from the sediment bed occurred from the 1980s to 2010s, at a rate of about 0.8 Mm3 per year. For the period of this study, sand loss from bed level changes in permitted sand-lease mining areas (about 11 Mm3) accounts for about two-thirds of the total sand loss throughout the study area. It is important to consider potential uncertainty bounds when interpreting these findings. A key part of the report is an assessment of the uncertainties in our estimates of sand volumes. We estimate that variability in modeled sand content values of Bay floor sediments could result in an uncertainty of approximately 25 percent of the net sand volume change. Even larger uncertainty amounts may be associated with uncertainty in the systematic errors in the bathymetric surveys. Further refining estimates of uncertainty in bathymetric change is important in guiding the use of this study. The results presented here can fill a critical gap that may enable the creation of the first comprehensive sand budget of San Francisco Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251022","collaboration":"Prepared in cooperation with the San Francisco Estuary Institute","usgsCitation":"Fregoso, T.A., Jaffe, B.E., Foxgrover, A.C., Woodrow, D.L., Kharrazi, B., and Orzech, K., 2025, Contributions of erosion, deposition, and human activities to a change in sand storage in the bed of San Francisco Bay, California, 1980s to 2010s: U.S. Geological Survey Open-File Report 2025–1022, 30 p., https://doi.org/10.3133/ofr20251022.","productDescription":"Report: vii, 30 p.; 4 Data Releases","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-154253","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":492954,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XG9PB0","text":"USGS data release","description":"Woodrow, D.L., Chin, J.L., Wong, F.L., Fregoso, T.A., Jaffe, B.E., 2017, Gravity cores from San Pablo Bay and Carquinez Strait, San Francisco Bay, California: U.S. Geological Survey data release, https://doi.org/10.5066/F7XG9PB0.","linkHelpText":"Gravity cores from San Pablo Bay and Carquinez Strait, San Francisco Bay, California"},{"id":492953,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BDEB3K","text":"USGS data release","description":"Takesue, R.K., McGann, M.L., Lorenson, T.D., and Watt, J.T., 2021, Geophysical properties, geochronologic, and geochemical data of sediment cores collected from San Pablo Bay, California, October 17–20, 2016: U.S. Geological Survey data release, https://doi.org/10.5066/P9BDEB3K.","linkHelpText":"Geophysical properties, geochronologic, and geochemical data of sediment cores collected from San Pablo Bay, California, October 17–20, 2016"},{"id":492950,"rank":5,"type":{"id":34,"text":"Image 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2885 Mission St.
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Recreation demand analysis has relied on mail and internet surveys to collect information on individual recreators. However, conducting these surveys is costly and time-consuming. Alternative sources that report aggregate visitation may go unused due to a lack of information about trip starting points. We set up and solve a system of equations that predict reservoir visits and the home locations of recreational anglers. Using mode-level effort statistics from Nebraska creel surveys, we separate the effects of travel cost and site attributes between bank and boat anglers, which allows us to measure heterogenous values for public reservoir access.
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\"}}]}","volume":"4","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Boehm, Luke","contributorId":360175,"corporation":false,"usgs":false,"family":"Boehm","given":"Luke","affiliations":[{"id":33535,"text":"Loyola University Chicago","active":true,"usgs":false}],"preferred":false,"id":946877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Melstrom, Richard T.","contributorId":360179,"corporation":false,"usgs":false,"family":"Melstrom","given":"Richard","middleInitial":"T.","affiliations":[{"id":33535,"text":"Loyola University Chicago","active":true,"usgs":false}],"preferred":false,"id":946878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":946879,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269752,"text":"70269752 - 2025 - The conundrum of taxonomic uniformitarianism in planktic foraminifera","interactions":[],"lastModifiedDate":"2025-07-31T14:23:30.840961","indexId":"70269752","displayToPublicDate":"2025-07-27T09:18:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2998,"text":"Palaeontology","active":true,"publicationSubtype":{"id":10}},"title":"The conundrum of taxonomic uniformitarianism in planktic foraminifera","docAbstract":"Planktic foraminiferal species distributions in the modern ocean track environmental features like sea surface temperature (SST). Species shift their distributions as the marine environment changes, providing an analogue for past behaviour. Stationarity of species' ecological tolerances is therefore a first-order assumption of all palaeoenvironmental reconstructions based upon modern analogue methods. In this paper we test the hypothesis that planktic foraminifer species temperature preferences did not change between the Late Pliocene and present, using a dataset which contains faunal abundance data and alkenone palaeotemperature data from the same samples. Our dataset includes 463 samples from 29 localities. Pliocene relative abundances of four taxa (Globigerina bulloides, Globigerinita glutinata, Neogloboquadrina pachyderma and Neogloboquadrina incompta) are compared to SST estimates of the same age derived using the alkenone unsaturation ratio (UK'37) palaeothermometer. Core-top abundances of the same taxa were compared to pre-industrial SST. Our Pliocene data are generally concordant with previous work. Pliocene SST responses and those of the pre-industrial are similar, supporting the hypothesis that temperature preferences of planktic foraminifera have been relatively stable since the Late Pliocene. This documentation of stationarity of planktic foraminiferal species' temperature tolerances is helpful in identifying situations where environmental variables other than temperature (e.g. salinity or productivity) exhibit a first-order control on faunal diversity. Our results support the notion of taxonomic uniformitarianism and therefore provide additional confidence in using planktic foraminifera to evaluate both regional and global climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/pala.70018","usgsCitation":"Dowsett, H., Robinson, M.M., Foley, K.M., and Spivey, W., 2025, The conundrum of taxonomic uniformitarianism in planktic foraminifera: Palaeontology, v. 68, no. 4, e70018, 10 p., https://doi.org/10.1111/pala.70018.","productDescription":"e70018, 10 p.","ipdsId":"IP-172361","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":493297,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/pala.70018","text":"Publisher Index Page"},{"id":493238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Dowsett, Harry J. 0000-0003-1983-7524","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":261665,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":944564,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":332062,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":944565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Foley, Kevin M. 0000-0003-1013-462X kfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-1013-462X","contributorId":2543,"corporation":false,"usgs":true,"family":"Foley","given":"Kevin","email":"kfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":944566,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":944567,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270212,"text":"70270212 - 2025 - Real-time oil spill concentration assessment through fluorescence imaging and deep learning","interactions":[],"lastModifiedDate":"2025-08-18T15:29:16.381499","indexId":"70270212","displayToPublicDate":"2025-07-27T09:15:09","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Real-time oil spill concentration assessment through fluorescence imaging and deep learning","docAbstract":"Oil spills may pose severe ecological and socioeconomic threats, necessitating rapid and accurate environmental assessment. Traditional assessment methods used to determine the extent of a spill including gas chromatography-mass spectrometry, satellite imaging, and visual surveys, are often time-consuming, expensive, and limited by weather conditions or sampling constraints. Furthermore, these methods frequently struggle to provide real-time data crucial for prompt decision-making during spill emergencies. This study addresses these limitations by combining fluorescence imaging, deep learning, a mobile application, and a data management system for automated and real-time oil spill assessment. Our approach leverages a convolutional neural network architecture for feature extraction coupled with a custom regression model, trained and evaluated on a self-curated comprehensive dataset of 1,530 fluorescence images from two distinct oil types, a napthalenic crude oil and an aromatic-napthalenic crude oil, at concentrations ranging from 0 to 500 mg/L. The proposed approach demonstrates superior performance compared to both traditional machine learning models and more complex deep learning architectures, achieving an R² score of 0.9958 and RMSE of 9.28. The application enables rapid, cost-effective field measurements with robust data tracking and analysis capabilities. This research advances oil spill monitoring technology with a scalable solution that balances accuracy, speed, and accessibility for real-time environmental assessment and emergency response.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2025.139374","usgsCitation":"Poudel, B., Xie, J., Guo, C., Watt, O., Pulster, E.L., Patel, R.J., Steevens, J.A., and Xu, D., 2025, Real-time oil spill concentration assessment through fluorescence imaging and deep learning: Journal of Hazardous Materials, v. 496, 139374, 10 p., https://doi.org/10.1016/j.jhazmat.2025.139374.","productDescription":"139374, 10 p.","ipdsId":"IP-177565","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":494021,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"496","noUsgsAuthors":false,"publicationDate":"2025-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Poudel, Biplab","contributorId":359506,"corporation":false,"usgs":false,"family":"Poudel","given":"Biplab","affiliations":[{"id":39687,"text":"University of Missouri, Columbia","active":true,"usgs":false}],"preferred":false,"id":945730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Jiacheng","contributorId":331598,"corporation":false,"usgs":false,"family":"Xie","given":"Jiacheng","email":"","affiliations":[],"preferred":false,"id":945731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guo, Congyu","contributorId":359509,"corporation":false,"usgs":false,"family":"Guo","given":"Congyu","affiliations":[{"id":39687,"text":"University of Missouri, Columbia","active":true,"usgs":false}],"preferred":false,"id":945732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Watt, Olivia","contributorId":359518,"corporation":false,"usgs":false,"family":"Watt","given":"Olivia","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":945733,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pulster, Erin L. 0000-0003-4574-8613","orcid":"https://orcid.org/0000-0003-4574-8613","contributorId":300266,"corporation":false,"usgs":true,"family":"Pulster","given":"Erin","email":"","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":945734,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Patel, Rishi J.","contributorId":359520,"corporation":false,"usgs":false,"family":"Patel","given":"Rishi","middleInitial":"J.","affiliations":[{"id":16806,"text":"Missouri State University","active":true,"usgs":false}],"preferred":false,"id":945735,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":945736,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Xu, Dong","contributorId":305418,"corporation":false,"usgs":false,"family":"Xu","given":"Dong","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":945737,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70269604,"text":"70269604 - 2025 - Reflections on a trio of North American earthquakes in 1925","interactions":[],"lastModifiedDate":"2025-12-15T16:24:36.85827","indexId":"70269604","displayToPublicDate":"2025-07-25T08:49:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Reflections on a trio of North American earthquakes in 1925","docAbstract":"In 1925, three moderately large damaging earthquakes occurred in North America over four months: the 28 February (local time; LT) M 6.2 Charlevoix, 27 June (LT) M 6.6 Montana, and 29 June M 6.5 Santa Barbara earthquakes. The centennial anniversaries of these events motivated this retrospective consideration focused on the ground motions generated by the three events, including a reconsideration of early intensity assignments for the Montana earthquake. At the time, these three earthquakes appeared to support the arguments of some geologists who downplayed the severity of seismic hazard in southern California relative to other parts of the country. Some of the arguments advanced at that time, for example that Los Angeles “has the least to fear from ‘Acts of God’ of any city under the American flag,” (Hill, 1928) sound naïve if not laughable now, but a comparison of well‐constrained shaking distributions for the three earthquakes reveals the dramatic difference in wave propagation efficiency in western versus eastern North America (ENAM), which leads to moderate ENAM events being felt to much larger distances. At M 6.2, the 1925 Charlevoix earthquake was a notably large event in ENAM. This earthquake was the largest event in eastern Canada since 1870 and caused damage in the epicentral region in addition to towns as far away as 200 km, with felt shaking extending over 1000 km. In contrast, felt shaking from the Santa Barbara earthquake barely extended beyond ∼200 km. Compiling published intensity distributions for larger ENAM earthquakes, we show that perceptible earthquake shaking is not uncommon in ENAM over century time scales, but experience with weakly felt shaking may incline people to downplay potential earthquake risk.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220250149","usgsCitation":"Hough, S., Lamontagne, M., Ebel, J.E., and Baise, L., 2025, Reflections on a trio of North American earthquakes in 1925: Seismological Research Letters, v. 97, no. 1, p. 548-563, https://doi.org/10.1785/0220250149.","productDescription":"16 p.","startPage":"548","endPage":"563","ipdsId":"IP-177706","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":492992,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":493792,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0220250149","text":"Publisher Index Page"}],"country":"Canada, United States","state":"California, Montana, Quebec","city":"Charlevoix, Santa Barabara","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -59.53926709002974,\n 53.87081636094325\n ],\n [\n -83.28222707125245,\n 53.87081636094325\n ],\n [\n -83.28222707125245,\n 38.33111191824375\n ],\n [\n -59.53926709002974,\n 38.33111191824375\n ],\n [\n -59.53926709002974,\n 53.87081636094325\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.68351578110878,\n 50.1420000300991\n ],\n [\n -118.68351578110878,\n 41.07790483235203\n ],\n [\n -105.88082499225033,\n 41.07790483235203\n ],\n [\n -105.88082499225033,\n 50.1420000300991\n ],\n [\n -118.68351578110878,\n 50.1420000300991\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.32391559009457,\n 34.119859058363375\n ],\n [\n -116.7888148930596,\n 34.119859058363375\n ],\n [\n -116.7888148930596,\n 37.37154662231801\n ],\n [\n -122.49989970320146,\n 37.667148547520824\n ],\n [\n -120.32391559009457,\n 34.119859058363375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"97","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":350979,"corporation":false,"usgs":true,"family":"Hough","given":"Susan E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":944166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamontagne, Maurice","contributorId":358790,"corporation":false,"usgs":false,"family":"Lamontagne","given":"Maurice","affiliations":[{"id":85683,"text":"Canadian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":944167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebel, John E.","contributorId":198671,"corporation":false,"usgs":false,"family":"Ebel","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":944168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baise, L.","contributorId":358791,"corporation":false,"usgs":false,"family":"Baise","given":"L.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":944169,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270106,"text":"70270106 - 2025 - The impact of the May 1921 superstorm on American telecommunication systems","interactions":[],"lastModifiedDate":"2025-08-11T15:09:51.182641","indexId":"70270106","displayToPublicDate":"2025-07-25T08:03:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"The impact of the May 1921 superstorm on American telecommunication systems","docAbstract":"A compilation is presented of impacts (interference and damage) realized on long-line telegraph\nand telephone systems across North America during the 13-16 May 1921 magnetic storm. Impacts\noccurred primarily during local nighttime, after the third of four sudden commencements, and\nduring the storm’s most-prominent main phase. Impacts are attributed to rapid and high-amplitude\ngeomagnetic field variation generated by substorms. This induced potential di erences and\nbetween the grounding points of communication networks that were su cient to cause system\ninterference and damage. In the United States, impacts were concentrated in the Midwest and in\nthe East, regions characterized by high electromagnetic surface impedance. Given technological\nchanges, modern telecommunication systems are less exposed to storms like that of May 1921,\nwhile power-grid systems are now more exposed to them.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025SW004563","usgsCitation":"Love, J.J., Lucas, G.M., Kelbert, A., Schnepf, N.R., Bedrosian, P.A., and McBride, S., 2025, The impact of the May 1921 superstorm on American telecommunication systems: Space Weather, v. 23, no. 7, e2025SW004563, 7 p., https://doi.org/10.1029/2025SW004563.","productDescription":"e2025SW004563, 7 p.","ipdsId":"IP-176044","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025sw004563","text":"Publisher Index Page"},{"id":493931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.04591235883407,\n 48.55929857106818\n ],\n [\n -113.11562382583244,\n 44.989521883693286\n ],\n [\n -97.39942377885234,\n 45.62679588654639\n ],\n [\n -94.09937380848437,\n 36.67842728651029\n ],\n [\n -73.77940090499652,\n 39.39897652697945\n ],\n [\n -62.59060976277681,\n 46.438434374285634\n ],\n [\n -112.04591235883407,\n 48.55929857106818\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M.","contributorId":359448,"corporation":false,"usgs":false,"family":"Lucas","given":"Greg","middleInitial":"M.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":945468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelbert, Anna","contributorId":359449,"corporation":false,"usgs":false,"family":"Kelbert","given":"Anna","affiliations":[{"id":85814,"text":"Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, 02138, USA","active":true,"usgs":false}],"preferred":false,"id":945469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schnepf, Neesha R.","contributorId":359450,"corporation":false,"usgs":false,"family":"Schnepf","given":"Neesha","middleInitial":"R.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":945470,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":945471,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":945472,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70275785,"text":"70275785 - 2025 - A flexible framework for N-mixture occupancy models: Applications to breeding bird surveys","interactions":[],"lastModifiedDate":"2026-05-19T15:12:10.723215","indexId":"70275785","displayToPublicDate":"2025-07-24T10:09:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1039,"text":"Biometrics","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A flexible framework for N-mixture occupancy models: Applications to breeding bird surveys","title":"A flexible framework for N-mixture occupancy models: Applications to breeding bird surveys","docAbstract":"Estimating species abundance under imperfect detection is a key challenge in biodiversity conservation. The N-mixture model, widely recognized for its ability to distinguish between abundance and individual detection probability without marking individuals, is constrained by its stringent closure assumption, which leads to biased estimates when violated in real-world settings. To address this limitation, we propose an extended framework based on a development of the mixed Gamma-Poisson model, incorporating a community parameter that represents the proportion of individuals consistently present throughout the survey period. This flexible framework generalizes both the zero-inflated type occupancy model and the standard N-mixture model as special cases, corresponding to community parameter values of 0 and 1, respectively. The model’s effectiveness is validated through simulations and applications to real-world datasets, specifically with 5 species from the North American Breeding Bird Survey and 46 species from the Swiss Breeding Bird Survey, demonstrating its improved accuracy and adaptability in settings where strict closure may not hold.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biomtc/ujaf087","usgsCitation":"Huynh, H., Royle, J., and Hwang, W., 2025, A flexible framework for N-mixture occupancy models: Applications to breeding bird surveys: Biometrics, v. 81, no. 3, ujaf087, https://doi.org/10.1093/biomtc/ujaf087.","productDescription":"ujaf087","ipdsId":"IP-170038","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":504529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Huynh, Huu-Dinh","contributorId":371389,"corporation":false,"usgs":false,"family":"Huynh","given":"Huu-Dinh","affiliations":[{"id":88130,"text":"Industrial University of Ho Chi Minh City, Vietnam","active":true,"usgs":false}],"preferred":false,"id":961762,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":80808,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":961763,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hwang, Wen-Han","contributorId":371390,"corporation":false,"usgs":false,"family":"Hwang","given":"Wen-Han","affiliations":[{"id":88131,"text":"National Tsing Hua University, Taiwan","active":true,"usgs":false}],"preferred":false,"id":961764,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270366,"text":"70270366 - 2025 - Benchmarking shoreline prediction models over multi-decadal timescales","interactions":[],"lastModifiedDate":"2025-08-18T14:37:00.840674","indexId":"70270366","displayToPublicDate":"2025-07-24T09:14:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Benchmarking shoreline prediction models over multi-decadal timescales","docAbstract":"Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-025-02550-4","usgsCitation":"Mao, Y., Coco, G., Vitousek, S., Antolinez, J.A., Azorakos, G., Banno, M., Bouvier, C., Bryan, K., Cagigal, L., Calcraft, K., Castelle, B., Chen, X., D'Anna, M., de Freitas Pereira, L., de Santiago, I., Deshmukh, A., Dong, B., Elghandour, A., Gohari, A., Gomez-de la Peña, E., Harley, M.D., Ibrahim, M., Idier, D., Jaramillo Cardona, C., Lim, C., Mingo, I., O'Grady, J., Pais, D., Repina, O., Robinet, A., Roelvink, D., Simmons, J., Sogut, E., Wilson, K., and Splinter, K., 2025, Benchmarking shoreline prediction models over multi-decadal timescales: Communications Earth & Environment, v. 6, 581, 15 p., https://doi.org/10.1038/s43247-025-02550-4.","productDescription":"581, 15 p.","ipdsId":"IP-176655","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494452,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-025-02550-4","text":"Publisher Index Page"},{"id":494255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","state":"New South Wales","otherGeospatial":"Curl Curl Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 151.28764961958893,\n -33.740052047115455\n ],\n [\n 151.28764961958893,\n -33.78320924309755\n ],\n [\n 151.33874708018328,\n -33.78320924309755\n ],\n [\n 151.33874708018328,\n -33.740052047115455\n ],\n [\n 151.28764961958893,\n -33.740052047115455\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Mao, Yongjing","contributorId":359742,"corporation":false,"usgs":false,"family":"Mao","given":"Yongjing","affiliations":[{"id":80179,"text":"UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":946204,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coco, Giovanni","contributorId":359744,"corporation":false,"usgs":false,"family":"Coco","given":"Giovanni","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":946206,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":946207,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Antolinez, Jose A. 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reliable estimates of demographic parameters is critical to effective wildlife conservation and management. Densities of Mojave Desert Tortoises (Gopherus agassizii) were historically derived from capture–mark–recapture surveys on small, often strategically placed demography plots, or demographic study areas, that also provided information on demographic composition and vital rates. After protection was afforded to Desert Tortoises under the US Endangered Species Act in 1990, monitoring shifted mostly to line-distance sampling across broad areas for estimating densities of primarily adult tortoises to inform long-term population trends. However, that approach is incapable of providing data about other demographic characteristics important to population growth and viability. We surveyed a previously unsampled demography plot in the western Mojave Desert, California, USA, during 2022 and applied spatial capture–recapture (SCR) models to estimate spatially explicit Desert Tortoise density and sex-by-size class compositions. Directly accounting for spatiotemporally varying survey effort in SCR models via hazard-based adjustment reduced the estimated detection rate by 91% and increased the estimated density by 17%. Estimated spatial mean Desert Tortoise density across a 2.53-km2 area was 18.53 tortoises/km2 (95% confidence interval [CI] = 12.36–27.77). The SCR model–estimated size class ratio was skewed toward prereproductive tortoises (64% prereproductive; 36% adults), whereas the adult sex ratio was female biased (61% females; 39% males). Those ratios corresponded to densities of 11.86 prereproductive tortoises/km2 (95% CI = 7.91–17.77), 4.08 adult female tortoises/km2 (95% CI = 2.72–6.11), and 2.59 adult male tortoises/km2 (95% CI = 1.73–3.89). Estimated tortoise density and demographic composition collectively support a high potential for population growth. Our study provides an illustrative example of using SCR models to directly estimate spatially explicit local Desert Tortoise densities and demographic composition that can be used for long-term monitoring and comparisons with other demography plots to inform conservation.
","language":"English","publisher":"Allen Press","doi":"10.1655/Herpetologica-D-24-00059","usgsCitation":"Doyle, S., Murphy, S.M., Drake, K.K., Hendrix, J., and Esque, T.C., 2025, Spatially explicit demographics of Mojave Desert Tortoises on a demography plot in California, USA: Herpetologica, v. 81, no. 3, p. 215-223, https://doi.org/10.1655/Herpetologica-D-24-00059.","productDescription":"9 p.","startPage":"215","endPage":"223","ipdsId":"IP-173099","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":494090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"western Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.015278,\n 35.569444\n ],\n [\n -117.015278,\n 35.544444\n ],\n [\n -116.991667,\n 35.544444\n ],\n [\n -116.991667,\n 35.569444\n ],\n [\n -117.015278,\n 35.569444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"81","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Doyle, Sarah 0009-0003-7699-139X","orcid":"https://orcid.org/0009-0003-7699-139X","contributorId":346966,"corporation":false,"usgs":true,"family":"Doyle","given":"Sarah","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":945987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murphy, Sean M. 0000-0002-9404-8878","orcid":"https://orcid.org/0000-0002-9404-8878","contributorId":346967,"corporation":false,"usgs":true,"family":"Murphy","given":"Sean","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":945988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, K. 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The State of Louisiana’s Coastal Protection and Restoration Authority (CPRA) and the RESTORE Act Center of Excellence for Louisiana (LA-COE) have adopted a co-production of science framework to help ensure that scientific research funded through the LA-COE supports the research needs of CPRA and the Louisiana Coastal Master Plan (CMP), a large-scale coastal restoration plan that outlines activities needed to restore and protect Louisiana’s coast. In this paper, we describe the co-production of science framework established between the LA-COE, CPRA, and research projects funded by the LA-COE. Through an iterative process, the Louisiana CMP is revised every 6 years with improved model and scientific information. The LA-COE leverages the cyclical nature of the CMP by working with CPRA to identify research needs for each Request for Proposals solicited by the LA-COE. The LA-COE proposal review process is structured to include anonymous CPRA review of projects to promote the production of actionable science, and once funded, multiple mechanisms are in place to support collaboration between researchers and CPRA staff. We highlight these mechanisms and provide examples of four LA-COE-funded projects where co-produced science is being used by CPRA to inform the CMP and its implementation. Finally, we discuss challenges of co-production within the confines of funding requirements and review restrictions, highlighting how the LA-COE continues to adapt to navigate these challenges and enhance the implementation of co-production throughout the research funding lifecycle.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-025-01584-3","usgsCitation":"Oster, J., Henkel, J., Dausman, A., Windhoffer, E., Liu, B., Baustian, M.M., Reed, D., Langlois, S., and Lindquist, D., 2025, Advancing the implementation of coastal restoration in Louisiana through a co-production of science framework: Estuaries and Coasts, v. 48, no. 6, 148, 13 p., https://doi.org/10.1007/s12237-025-01584-3.","productDescription":"148, 13 p.","ipdsId":"IP-173895","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":493790,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-025-01584-3","text":"Publisher Index Page"},{"id":493409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.87899927144403,\n 30.400700935125144\n ],\n [\n -93.87899927144403,\n 28.558100428162405\n ],\n [\n -89.02763423210459,\n 28.558100428162405\n ],\n [\n -89.02763423210459,\n 30.400700935125144\n ],\n [\n -93.87899927144403,\n 30.400700935125144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Oster, Jacob M.","contributorId":358974,"corporation":false,"usgs":false,"family":"Oster","given":"Jacob M.","affiliations":[{"id":34838,"text":"Texas A&M Corpus Christi","active":true,"usgs":false}],"preferred":false,"id":944671,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henkel, Jessica R.","contributorId":358976,"corporation":false,"usgs":false,"family":"Henkel","given":"Jessica R.","affiliations":[{"id":81504,"text":"The Water Institute","active":true,"usgs":false}],"preferred":false,"id":944672,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dausman, Alyssa","contributorId":223766,"corporation":false,"usgs":false,"family":"Dausman","given":"Alyssa","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":944673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Windhoffer, Eva D.","contributorId":358978,"corporation":false,"usgs":false,"family":"Windhoffer","given":"Eva D.","affiliations":[{"id":81504,"text":"The Water Institute","active":true,"usgs":false}],"preferred":false,"id":944674,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liu, Bingqing","contributorId":304014,"corporation":false,"usgs":false,"family":"Liu","given":"Bingqing","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":944675,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":944676,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":944677,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Langlois, Summer","contributorId":223756,"corporation":false,"usgs":false,"family":"Langlois","given":"Summer","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":944678,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lindquist, David C.","contributorId":358980,"corporation":false,"usgs":false,"family":"Lindquist","given":"David C.","affiliations":[{"id":13608,"text":"Louisiana Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":944679,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70269547,"text":"70269547 - 2025 - The pre-maria geologic history of the Imbrium basin preserved by remnant highlands massifs","interactions":[],"lastModifiedDate":"2025-07-25T13:37:32.725623","indexId":"70269547","displayToPublicDate":"2025-07-24T08:32:55","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"The pre-maria geologic history of the Imbrium basin preserved by remnant highlands massifs","docAbstract":"The Imbrium basin is one of the largest and youngest impact basins on the Moon. It has experienced multiple phases of volcanism that filled the basin with basaltic lavas, obscuring most evidence of geologic activity prior to the emplacement of mare basalts. Elevated basin ring massifs, however, can retain some of that history due to their higher topographic elevation compared to the maria. In this work, we use thermal infrared and radar data sets in conjunction with compositional data sets to establish the presence of external material that has been deposited on top of several remnant basin massifs of Imbrium. These massifs originally formed as part of the Imbrium basin ring structure, but their material properties indicate that they have since experienced modification from outside sources. In southwest Imbrium, we present evidence that Mons Vinogradov was mantled by rock-poor, glassy pyroclastic material prior to the deposition of Eratosthenian-era basalts immediately surrounding the mons. In northern Imbrium, we find that Montes Recti and Montes Teneriffe were not affected by pyroclastic volcanism but rather were mantled by rock- and glass-poor ejecta materials likely related to the Iridum basin impact. At Mons Piton in eastern Imbrium, we see weaker glass signatures than those found at Mons Vinogradov, which we suggest could be due to a thin layer of reworked or partially buried glassy pyroclastic material. These results indicate that basin ring massifs provide a mechanism for studying the geologic history of lunar impact basins.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JE008646","usgsCitation":"Byron, B., Elder, C., Pigue, L.M., and Williams, J., 2025, The pre-maria geologic history of the Imbrium basin preserved by remnant highlands massifs: JGR Planets, v. 130, no. 7, e2024JE008646, 19 p., https://doi.org/10.1029/2024JE008646.","productDescription":"e2024JE008646, 19 p.","ipdsId":"IP-160135","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":492901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Imbrium basin, Moon","volume":"130","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Byron, Ben D. 0000-0003-4435-0347","orcid":"https://orcid.org/0000-0003-4435-0347","contributorId":358634,"corporation":false,"usgs":false,"family":"Byron","given":"Ben D.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":944013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elder, Catherine M. 0000-0002-9993-8861","orcid":"https://orcid.org/0000-0002-9993-8861","contributorId":358637,"corporation":false,"usgs":false,"family":"Elder","given":"Catherine M.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":944014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigue, Lori M. 0000-0002-6675-6877","orcid":"https://orcid.org/0000-0002-6675-6877","contributorId":330994,"corporation":false,"usgs":true,"family":"Pigue","given":"Lori","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":944015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Jean-Pierre","contributorId":358640,"corporation":false,"usgs":false,"family":"Williams","given":"Jean-Pierre","affiliations":[{"id":85660,"text":"Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":944016,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269627,"text":"70269627 - 2025 - Rupture process of the Mw7.0 December 5, 2024 Offshore Cape Mendocino earthquake","interactions":[],"lastModifiedDate":"2025-07-28T13:36:59.810848","indexId":"70269627","displayToPublicDate":"2025-07-24T08:32:47","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Rupture process of the Mw7.0 December 5, 2024 Offshore Cape Mendocino earthquake","docAbstract":"The Mw7.0 December 5, 2024 Offshore Cape Mendocino earthquake ruptured a km long portion of the east-west trending Mendocino fault zone (MFZ). In order to clarify the rupture process, we assemble three-component seismograms from regional seismic stations, horizontal coseismic displacement vectors derived from Global Navigation Satellite System (GNSS) time series, and a Sentinel-1 ascending interferogram. These data are interpreted with a model of slip distributed on two vertical fault planes representative of the eastern MFZ and spanning the ~70 km length of the aftershock zone. Assuming right-lateral strike slip, we find that the rupture initiates in the oceanic mantle at 20-30 km depth and proceeds unilaterally updip and toward the east. Early aftershocks locate adjacent to the peak slip areas, tracking the coseismic rupture propagation from oceanic mantle to shallower depth and implying a significant role of static stress transfer in driving aftershocks in an ocean plate environment.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115613","usgsCitation":"Pollitz, F., Guns, K., and Yoon, C., 2025, Rupture process of the Mw7.0 December 5, 2024 Offshore Cape Mendocino earthquake: Geophysical Research Letters, v. 52, no. 14, e2025GL115613, 10 p., https://doi.org/10.1029/2025GL115613.","productDescription":"e2025GL115613, 10 p.","ipdsId":"IP-176306","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":493311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115613","text":"Publisher Index Page"},{"id":492990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Offshore Cape Mendocino","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -126,\n 42\n ],\n [\n -126,\n 38\n ],\n [\n -121,\n 38\n ],\n [\n -121,\n 42\n ],\n [\n -126,\n 42\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"14","noUsgsAuthors":false,"publicationDate":"2025-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":944214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guns, Katherine Anna 0000-0002-2956-1536","orcid":"https://orcid.org/0000-0002-2956-1536","contributorId":358824,"corporation":false,"usgs":true,"family":"Guns","given":"Katherine Anna","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":944215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yoon, Clara 0000-0003-4521-3889","orcid":"https://orcid.org/0000-0003-4521-3889","contributorId":222019,"corporation":false,"usgs":true,"family":"Yoon","given":"Clara","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":944216,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70270343,"text":"70270343 - 2025 - Mapping global coral vulnerability to stony coral tissue loss disease: Implications for biosecurity and conservation","interactions":[],"lastModifiedDate":"2025-08-15T14:58:46.564785","indexId":"70270343","displayToPublicDate":"2025-07-24T07:53:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Mapping global coral vulnerability to stony coral tissue loss disease: Implications for biosecurity and conservation","docAbstract":"Stony Coral Tissue Loss Disease (SCTLD) has devastated Caribbean coral reefs since 2014, but its potential for global impact remains uncertain. We developed predictive models to assess the worldwide vulnerability of coral reefs to SCTLD under different origin and spread hypotheses. Using random forest regression models incorporating coral taxonomy and zooxanthellae clade associations from 52 taxa, we projected SCTLD susceptibility and mortality patterns globally using six indices: Mean susceptibility per genus per location, Summed susceptibilities across genera per location, Summed susceptibilities across genera per realm, Mean mortality per genus per location, Summed mortalities across genera per location, and Summed mortalities across genera per realm. Models demonstrated strong predictive performance (R² = 0.57 for susceptibility; R² = 0.73 for mortality) and revealed that about 7% of coral genera per location are potentially susceptible to SCTLD. While mean susceptibility and mortality per genus were highest in the Tropical Atlantic, the summed susceptibility and mortality across genera were much higher in the biodiverse Central Indo-Pacific. Natural barriers could limit SCTLD’s spread, including the mid-Atlantic gap and the low diversity of the Tropical Eastern Pacific, supporting the contained disease hypothesis. However, the widespread distribution of susceptible genera across coral reef realms indicates significant vulnerability should SCTLD circumvent these barriers through human-mediated transport, particularly via ballast water or the aquarium trade. If SCTLD is an invasive pathogen originating in the Pacific, as shipping patterns for the aquarium trade suggest, mortality in its native range would likely be lower than our projections. These findings point to targeted intervention strategies, including enhanced monitoring at key locations, assessment of biosecurity needs in high-risk areas, and prioritized conservation efforts in vulnerable high-diversity regions to prevent SCTLD from spreading globally.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2025.1608622","usgsCitation":"Lafferty, K.D., and Strona, G., 2025, Mapping global coral vulnerability to stony coral tissue loss disease: Implications for biosecurity and conservation: Frontiers in Marine Science, v. 12, 1608622, 9 p., https://doi.org/10.3389/fmars.2025.1608622.","productDescription":"1608622, 9 p.","ipdsId":"IP-179698","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":494212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Caribbean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.73686836669313,\n 22.715963357915257\n ],\n [\n -88.84154748135387,\n 15.928210740185918\n ],\n [\n -67.48839769068788,\n 11.17109320051204\n ],\n [\n -58.01459835409102,\n 11.345220067643226\n ],\n [\n -60.694682618485345,\n 19.12651464613971\n ],\n [\n -78.60310763267037,\n 27.026321525608623\n ],\n [\n -86.73686836669313,\n 22.715963357915257\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":946152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strona, Giovanni","contributorId":237089,"corporation":false,"usgs":false,"family":"Strona","given":"Giovanni","affiliations":[{"id":47601,"text":"University of Helsinki, Research Centre for Ecological Change, Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":946153,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70269477,"text":"70269477 - 2025 - Bright spots for advancing ecological understanding and conservation decision-making","interactions":[],"lastModifiedDate":"2025-12-01T16:23:56.503019","indexId":"70269477","displayToPublicDate":"2025-07-23T09:32:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Bright spots for advancing ecological understanding and conservation decision-making","docAbstract":"A lot can be learned by studying bright spots—defined as unexpected positive outcomes. In fields like public health, education, and oncology, identifying factors behind bright spots reveals previously unknown drivers of success that can be replicated elsewhere. This concept is being applied in conservation but is hampered by variations in definitions of bright spots and confusion with hotspots—sites with high absolute values of a metric. We developed a framework to clearly define and distinguish between hotspots (e.g., a wetland with high plant diversity) and bright spots (e.g., a biodiverse wetland in a housing development), which outperform conservation expectations. The framework is an iterative cycle, consisting of setting expectations for relative comparisons, classifying systems into bright, dark, hot, and cold categories, and digging deeper to reveal hidden mechanisms and opportunities for intervention. We drew on examples from diverse fields to demonstrate how our framework can generate new knowledge, identify potential interventions, and inform management priorities. Defining conservation and management expectations, often through predictive models, is essential to understanding drivers of success and fosters hypotheses about overlooked factors. Our framework can enhance ecological understanding, guide interventions, and help prioritize actions in conservation and natural resource management.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.70109","usgsCitation":"Embke, H., Feiner, Z.S., Hansen, G., Isermann, D.A., Jensen, O., Rounds, C., Smith, Q., and Vander Zanden, M., 2025, Bright spots for advancing ecological understanding and conservation decision-making: Conservation Biology, v. 39, no. 6, e70109, 13 p., https://doi.org/10.1111/cobi.70109.","productDescription":"e70109, 13 p.","ipdsId":"IP-164862","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":493307,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.70109","text":"Publisher Index Page"},{"id":492829,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":358337,"corporation":false,"usgs":true,"family":"Embke","given":"Holly Susan","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":943845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feiner, Zachary S.","contributorId":342575,"corporation":false,"usgs":false,"family":"Feiner","given":"Zachary","email":"","middleInitial":"S.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":943846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Gretchen","contributorId":174810,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":943847,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":943848,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jensen, Olaf P.","contributorId":348884,"corporation":false,"usgs":false,"family":"Jensen","given":"Olaf P.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":943849,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rounds, Christopher I.","contributorId":349471,"corporation":false,"usgs":false,"family":"Rounds","given":"Christopher I.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":943850,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Quinn","contributorId":358490,"corporation":false,"usgs":false,"family":"Smith","given":"Quinn","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":943851,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vander Zanden, M. 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However, the traditional methods, such as linear interpolation and the continuous-time correlated random walk model, are often inadequate to capture the complexity of animal movements. Here, we develop a deep learning approach to animal trajectory imputation by a conditional diffusion model. Unlike the traditional methods, our deep learning method uses observed data and external covariates to impute missing positions along an animal trajectory, capturing periodic patterns and the influence of covariates, which leads to more accurate imputations. In a case study of imputing deer trajectories, our method not only provides more accurate deterministic imputations than existing approaches but also achieves uncertainty quantification through probabilistic imputation.
","language":"English","publisher":"Wiley","doi":"10.1002/env.70027","usgsCitation":"Yao, K., McGahan, I.P., Zhu, J., Storm, D.J., Walsh, D.P., 2025, Animal trajectory imputation and uncertainty quantification via deep learning: Environmetrics, v. 36, no. 6, e70027, 15 p., https://doi.org/10.1002/env.70027.","productDescription":"e70027, 15 p.","ipdsId":"IP-172985","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500575,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/env.70027","text":"Publisher Index Page"},{"id":500342,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Yao, Kehui","contributorId":339161,"corporation":false,"usgs":false,"family":"Yao","given":"Kehui","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":956327,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGahan, Ian P.","contributorId":366857,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian","middleInitial":"P.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":956328,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Jun","contributorId":354695,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":956329,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":366860,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel","middleInitial":"J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":956330,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":956331,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70269402,"text":"cir1553 - 2025 - The 3D National Topography Model Call for Action—Part 2: The Next Generation 3D Elevation Program","interactions":[],"lastModifiedDate":"2026-02-03T14:31:31.794807","indexId":"cir1553","displayToPublicDate":"2025-07-23T09:15:00","publicationYear":"2025","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":"1553","displayTitle":"The 3D National Topography Model Call for Action—Part 2: The Next Generation 3D Elevation Program","title":"The 3D National Topography Model Call for Action—Part 2: The Next Generation 3D Elevation Program","docAbstract":"The three-dimensional (3D) National Topography Model initiative to integrate elevation and hydrography data includes the next generation of hydrography data from the 3D Hydrography Program and the next generation of elevation data from the 3D Elevation Program (3DEP). The first-ever collection of light detection and ranging (lidar) data for the nation (IfSAR for Alaska) provides a critical baseline reference, and the addition of multiple repeat elevation mapping projects as part of the next generation of 3DEP would substantially expand analysis capabilities. As the U.S. Geological Survey (USGS) is closing in on our goal of complete coverage with 3DEP data available or in progress for 98.3 percent of the Nation at the end of fiscal year 2024, the USGS is already transitioning to the next generation of 3DEP.
Based on the 3D Nation Study results and input from a broad range of stakeholders, the USGS National Geospatial Program has finalized a new design for 3DEP that provides increased lidar quality levels and refresh rates. The new program is designed with more flexibility to meet changing user needs and take advantage of improvements in mapping technologies. The program will aim to expand the level of interagency coordination for topobathymetric lidar acquisition for inland rivers. The next generation of 3DEP will also aim to emphasize research, including advancing program design, products, and services and engaging and leveraging the evolving 3D industry. Research goals also include becoming more flexible in meeting user needs and taking advantage of evolving remote-sensing technologies. The program also plans to move from focusing on producing standard products to producing a concept of a 3D Nation Ecosystem with a variety of inputs, products, and services.
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U.S. Geological Survey
Mail Stop 511
12201 Sunrise Valley Drive
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"