The longitudinal propagation of water-quality and ecological impairments in rivers during and after wildfires remain poorly understood. In Northern California, the 2022 McKinney Fire burned 243 km2 of the Klamath National Forest, with 83% of the burned area classified as moderate to high severity. During the active wildfire, a high-intensity monsoonal rain event triggered sediment-laden flooding and runoff-initiated debris flows, causing extreme water-quality impairments and a 95 km fish kill zone along the main-stem Klamath River. This rain-on-wildfire event produced a flood wave that outpaced a sediment pulse, diminishing the dilution effect of the floodwaters. A network of high-frequency water-quality sensors recorded water-quality impairments that propagated 296 km downstream. Impairments at the nearest monitoring station, situated 71 km downstream from the fire perimeter, included dissolved oxygen sags to zero (anoxia) for 5.25 h, turbidity spikes exceeding 1000 FNU, a doubling of specific conductance from 175 to 415 µS/cm (at 25 °C), and pH anomalies of 0.5 units from 7.8 to 7.3. This novel rain-on-wildfire event triggered the first flush of fire-scar material during an active wildfire, resulting in water-quality impairments unprecedented in the historical monitoring data for the river spanning 2012 to 2022. This study provides new insights into the potential role of rain-on-wildfire events in generating extreme downstream water-quality and ecological impairments in a more fire-prone future.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-08179-9","usgsCitation":"Curtis, J., Johnson, G., Cahill, J., Genzoli, L., Dahm, C., Schenk, L.N., and Oberholzer, J., 2025, 2022 McKinney rain-on-wildfire event, dissolved oxygen sags, and a fish kill on the Klamath River, California: Scientific Reports, v. 15, 24668, 14 p., https://doi.org/10.1038/s41598-025-08179-9.","productDescription":"24668, 14 p.","ipdsId":"IP-161626","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":493309,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-08179-9","text":"Publisher Index Page"},{"id":492907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.18203511324225,\n 42.00661546335127\n ],\n [\n -122.63359944859491,\n 42.00661546335127\n ],\n [\n -122.63359944859491,\n 41.86910985623359\n ],\n [\n -122.18203511324225,\n 41.86910985623359\n ],\n [\n -122.18203511324225,\n 42.00661546335127\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Curtis, Jennifer 0000-0001-7766-994X","orcid":"https://orcid.org/0000-0001-7766-994X","contributorId":212727,"corporation":false,"usgs":true,"family":"Curtis","given":"Jennifer","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":943996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Grant 0009-0003-9549-2713","orcid":"https://orcid.org/0009-0003-9549-2713","contributorId":358610,"corporation":false,"usgs":false,"family":"Johnson","given":"Grant","affiliations":[{"id":80103,"text":"Karuk Tribe","active":true,"usgs":false}],"preferred":false,"id":943997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahill, Josh 0009-0008-0811-3305","orcid":"https://orcid.org/0009-0008-0811-3305","contributorId":358613,"corporation":false,"usgs":false,"family":"Cahill","given":"Josh","affiliations":[{"id":38097,"text":"Yurok Tribe","active":true,"usgs":false}],"preferred":false,"id":943998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Genzoli, Laurel 0000-0001-5660-7627","orcid":"https://orcid.org/0000-0001-5660-7627","contributorId":358616,"corporation":false,"usgs":false,"family":"Genzoli","given":"Laurel","affiliations":[{"id":28239,"text":"Univ of Montana","active":true,"usgs":false}],"preferred":false,"id":943999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dahm, Clifford 0000-0003-0191-6830","orcid":"https://orcid.org/0000-0003-0191-6830","contributorId":358619,"corporation":false,"usgs":false,"family":"Dahm","given":"Clifford","affiliations":[{"id":35754,"text":"Univ of New Mexico","active":true,"usgs":false}],"preferred":false,"id":944000,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":944001,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oberholzer, John 0009-0005-9164-0330","orcid":"https://orcid.org/0009-0005-9164-0330","contributorId":358622,"corporation":false,"usgs":false,"family":"Oberholzer","given":"John","affiliations":[{"id":80103,"text":"Karuk Tribe","active":true,"usgs":false}],"preferred":false,"id":944002,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268446,"text":"ofr20251026 - 2025 - Wake Atoll vessel movement biosecurity program efficacy","interactions":[],"lastModifiedDate":"2026-02-03T14:18:39.033143","indexId":"ofr20251026","displayToPublicDate":"2025-07-08T10:21:43","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":"2025-1026","displayTitle":"Wake Atoll Vessel Movement Biosecurity Program Efficacy","title":"Wake Atoll vessel movement biosecurity program efficacy","docAbstract":"The purpose of this Wake Atoll Vessel Movement Biosecurity Program Efficacy document is to provide the United States Air Force (USAF) with an unbiased review of the current (2015; hereafter referred to as the 2015 Biosecurity Plan) biosecurity plan for the military base Wake Island Airfield (WIA) on Wake Atoll (hereafter Wake). Periodic reviews are an integral step for evaluating plan efficacy and updating plans with new information for improving plan effectiveness. The U.S. Geological Survey (USGS) acted as an external expert to provide the first unbiased assessment of the program and observe how it was being implemented. The USAF 2015 Wake Island Biosecurity Management Plan goes beyond sea vessel and container biosecurity; however, those aspects were not included in this evaluation.
We used several methods for a quality assurance evaluation of the 2015 sea vessel and shipping container biosecurity program specified in the Biosecurity Plan. Our evaluation included real-time observations in Hawai`i and at Wake. We surveyed cargo staging areas and empty shipping containers before supply shipment and the containers, barge, and marina at Wake after shipment. We used various detection tools and techniques (for example, visual encounter surveys, glue boards, chew cards, camera traps, and so on). We carried out an insect mortality experiment trial using one of the required shipping container biosecurity tools (dichlorvos impregnated pest strips). We also included a table-top review of documentation (largely the 2015 Biosecurity Plan) with respect to our observations to provide an assessment of how well the Biosecurity Plan protocols were carried out and how well they serve their intended purpose.
We observed biosecurity concerns in each focal area and stage of cargo handling (before and after barge movement) across all surveys of containers, flat racks, break bulk, warehouses, and dock areas. Using visual inspections, we recorded biosecurity concerns for every empty container we inspected before it was to be stuffed with cargo. Most containers had structural integrity issues (such as holes and damaged floorboards) and sanitation concerns, including live animals and plant matter or seeds. About one third of the containers had mold and a few had wet floorboards or standing water. We detected live animals on the break bulk, and flat racks were in poor condition. Next, we inspected cargo staging areas and noted extensive permeability of the building where cargo was staged for the 2018 resupply shipment and the building that had typically been used. We included the adjacent dock area used for staging break bulk, shipping containers and mooring the barge. We detected more than 5,000 individuals of 105 species. We also detected seeds in each location and scattered vegetation in the dock area, including growing in from the area outside separated by a chain link fence.
During surveys at Wake, we observed that 100 percent of the shipping containers, including all containers sent with required biosecurity tools, had live animals. The barge had only one unsecured snap trap for intercepting rodents aboard, we saw areas with fairly deep layers of dirt (or soil; we did not examine it to determine its properties), and there was plant matter with seed heads on the barge gangway that could easily be transported onto the barge. There was also only one snap trap station that was improperly placed on the dock. We also observed piled wood and vegetation nearby that could provide refuge to potential stowaway animals escaping.
Combined, surveys of the containers, staging areas, barges, and receiving area in Hawai`i and at Wake resulted in detection of more than 9,000 individuals of 131 animal species; nearly 4,000 individuals of 62 species were detected in surveys of containers once they had arrived at Wake. None of the species identified are known to be native to Wake. Our preliminary risk analysis of all species detected included eight species that we scored as high risk of potentially negative effects to biodiversity, infrastructure, or human health should they arrive at Wake and become established. Six of these species were only recorded using tools not clearly required by the Biosecurity Plan or being used to implement the plan.
We observed that the required biosecurity tools intended to intercept animals in the cargo staging area did not target the suite nor number of species present. Our analysis also indicated the required biosecurity tools intended to intercept animals in shipping containers were inadequate to handle the volume of organisms that were in the containers. The insect mortality trial experiment showed the pest strips were highly effective for only one of the three species tested, leaving uncertainty about how effective they are across the suite of potential species stowing away in cargo and containers.
Base Operating Support (BOS) did not carry out all Biosecurity Plan actions, but we also noted the document uses terminology such as “recommendation” as opposed to “requirement” which may lead contractors to consider those actions as optional. However, USAF provided evidence of BOS training and follow up; this included detailed identification of specific requirements for some of the biosecurity actions that we did not observe being carried out.
The 2015 Biosecurity Plan contains critical and useful components that seem to be well carried out. However, we also saw discrepancies, weaknesses, or both across methods and protocols currently used for Wake Atoll biosecurity. We observed shortcomings at each stage of our survey as well as in the plan as written, and we suggest general modifications to the Biosecurity Plan for consideration to potentially strengthen biosecurity overall.
Prevention is the most efficient and cost-effective biosecurity measure. Based on our findings, we see possible solutions to improve existing preventative biosecurity efforts and reduce potential incursion at Wake. These potential solutions include creating and implementing the following:
Management of invasive species enhances capability to protect human health and the environment as well as to advance mission accomplishment. Biosecurity plans are an integral component for addressing invasive species. Periodic evaluation of the efficacy of these plans is useful for identifying elements that are working well and for illuminating those that can be improved. Evaluations encourage consideration of new tools and adaptation of processes to achieve better outcomes and accommodate potential future threats more efficiently and more cost effectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251026","collaboration":"Prepared in cooperation with the U.S. Air Force","programNote":"Ecosystems Mission Area—Biological Threats and Invasive Species Research Program","usgsCitation":"Hathaway, S.A., Molden, J.C., Peck, R., Rex, K.R., Brehme, C.S., Black, T., and Fisher, R.N., 2025, Wake Atoll vessel movement biosecurity program efficacy: U.S. Geological Survey Open-File Report 2025–1026, 130 p., https://doi.org/10.3133/ofr20251026","productDescription":"x, 130 p.","onlineOnly":"Y","ipdsId":"IP-155305","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":491323,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1026/ofr20251026.pdf","text":"Report","size":"22.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1026"},{"id":491324,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1026"},{"id":491326,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1026/ofr20251026.XML"},{"id":491325,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1026/images"},{"id":491322,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1026/coverthb.jpg"}],"otherGeospatial":"Wake Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 166.58609930546095,\n 19.335566334904826\n ],\n [\n 166.58609930546095,\n 19.259871066135005\n ],\n [\n 166.6712110656557,\n 19.259871066135005\n ],\n [\n 166.6712110656557,\n 19.335566334904826\n ],\n [\n 166.58609930546095,\n 19.335566334904826\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Background
Proteins encoded by the canonical transient receptor potential (Trpc) gene family form transmembrane channels involved in diverse signal-transduction pathways. Trpc4 has been shown necessary for the induction of nonshivering thermogenesis (NST) in mice, a key component of which is thermogenic brown adipose tissue (BAT). In bats, Trpc4 exhibited diversifying selection within exons encoding regulatory binding sites of TRPC4.
Methods
To assess whether diversification of these regulatory sequences mirrors the diversification of mammalian thermoregulatory strategies, the ratio of nonsynonymous to synonymous substitutions (ω) was estimated for multiple tetrapod outgroups and eutherian orders. Four questions were addressed: (1) Did the ancestral eutherian Trpc4 diverge under positive selection from nonplacental mammals that lack BAT? (2) Did Trpc4 subsequently become more constrained in descendant eutherian clades? (3) In eutherian clades that subsequently lost BAT by inactivation of the thermogenin gene Ucp1, did Trpc4 become less constrained? (4) Does the evolutionary rate of Trpc4 differ between quantitatively more heterothermic mammal orders (bats and rodents) relative to quantitatively less heterothermic outgroups (carnivores, artiodactylids, and primates)?
Results
Coincident with the advent of BAT, Trpc4 evolutionary rate increased significantly in ancestral eutheria after their divergence from nonplacental mammals but a branch-site model did not support a rate class ω > 1 along that branch. In descendant eutherian mammals, Trpc4 became far more constrained, with an evolutionary rate less than half that of tetrapod clades lacking NST, a pattern was not seen in other Trp channel genes. Intensifying selection in descendent eutherian mammals was further supported with the RELAX program, which also indicated reduced constraint on Trpc4 in clades that have secondarily lost BAT. However, no consistent pattern was identified within mammalian orders with strong variation in heterothermy: evidence of increased evolutionary rate was again found in bats for Trpc4 as well as homologs it directly binds in heteromeric membrane channels (Trpc5 and Trpc1), yet all rodent Trpc genes had low evolutionary rates. Evolutionary rates of Trpc4 and Trpc1 in bats were consistent with relaxed constraint whereas bat Trpc5 experienced diversifying selection. Most variation among tetrapod TRPC4 sequences lies within an 85 amino-acid window that is functionally uncharacterized. Sequence alignments demonstrated that the TRPC4 β isoform, which lacks a portion of the C-terminal regulatory region, originated in basal eutherians but appears to be lost in many tip lineages. Collectively, the data indicate that the C-terminal region of TRPC4 has responded to selection on NST thermoregulation during the diversification of eutherian mammals. The drivers of increased diversification of Trpc4 and interacting genes in bats remain to be determined.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.19697","usgsCitation":"Cornman, R.S., 2025, Molecular evolution of TRPC4 regulatory sequences supports a role in mammalian thermoregulatory adaptation: PeerJ, v. 13, e19697, 25 p., https://doi.org/10.7717/peerj.19697.","productDescription":"e19697, 25 p.","ipdsId":"IP-175755","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":492081,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.19697","text":"Publisher Index Page"},{"id":491897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2025-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":942469,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70271514,"text":"70271514 - 2025 - The structural and functional impacts of invasive Psidium cattleianum in forests on the Island of Hawai’i","interactions":[],"lastModifiedDate":"2025-09-18T15:47:04.577037","indexId":"70271514","displayToPublicDate":"2025-07-07T10:36:22","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The structural and functional impacts of invasive Psidium cattleianum in forests on the Island of Hawai’i","title":"The structural and functional impacts of invasive Psidium cattleianum in forests on the Island of Hawai’i","docAbstract":"During the past century, the proliferation of invasive species has contributed to loss of biodiversity and ecosystem degradation. In forests, invasive tree species can alter ecosystem function, but the underlying mechanisms of these changes are not fully understood. We use the ongoing invasion of P. cattleianum on the Island of Hawai’i to test the hypotheses that invasive structural changes drive changes to forest evapotranspiration (ET). The aim of our study is first to quantify the structural changes to native ‘ōhi‘a -dominated forest impacted by a gradient of P. cattleianum invasion. Our results suggest that invasive P. cattleianum causes significant changes to the vegetation density and structure of native forest on the Island of Hawai’i, including increased vegetation area index, decreased mean leaf height, and decreased structural heterogeneity. Second, we strove to understand the functional implications of structural changes through a biophysical modeling simulation, testing the sensitivity of ET to canopy structure under contrasting scenarios. Modeling the functional impact of structural change, we found that plots with P. cattleianum invasion importance value (IVinv) above 0.35 have a higher likelihood to increase ET compared to plots with P. cattleianum invasion less than 0.35 IVinv. Modeled increases in ET due to invasion ranged from 19 and 123% relative to native transects. The large variation in ET increases is caused by structural variation because the modeling scenarios did not include potential species differences in leaf physiology. Diagnostic scenario modeling shows the effect size of increased leaf area on modeled ET is constrained by the structural arrangement, that is vertical distribution, of the increased vegetation. Thus, invasion structure that increases vegetation density in taller, more sunlit forest strata will lead to a greater increase in ET compared to invasion structure that increases vegetation density in the shaded forest understory. Overall, we conclude the vertical distribution of vegetation is an important factor shaping the impact of invasive P. cattleianum on the forest water balance.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-025-00974-9","usgsCitation":"Seely, T., Fortini, L., Liang, Y., and Battles, J.J., 2025, The structural and functional impacts of invasive Psidium cattleianum in forests on the Island of Hawai’i: Ecosystems, v. 28, 39, 17 p., https://doi.org/10.1007/s10021-025-00974-9.","productDescription":"39, 17 p.","ipdsId":"IP-166688","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":495749,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10021-025-00974-9","text":"Publisher Index Page"},{"id":495716,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n 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The objective of this study was to compare YCS estimates derived from both approaches applied to catch-at-age data for the lake trout (Salvelinus namaycush) population in the main basin of Lake Huron, one of the Laurentian Great Lakes of North America. YCS was reconstructed for both hatchery-stocked and wild lake trout. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to compare 14 linear mixed-effects models for longitudinal analysis of catch-at-age data, and three linear mixed-effects models for catch-curve regression. From the best models based on AIC or BIC comparisons, YCS estimates with year-class as a fixed effect were consistent with those estimated with year-class as a random effect. Patterns and trends in the YCS estimates were also the same or similar between the longitudinal analysis of catch-at-age data approach and the catch-curve regression approach, suggesting that both modeling approaches are applicable to a variety of fish populations. indicating that both approaches provide robust measures of YCS. Potential bias in using the approach of catch-curve regression could be caused by abrupt changes in adult mortality. It is also critical to recognize multiple recruitment origins for using the approach of longitudinal analysis of catch-at-age data.","language":"English","publisher":"MDPI","doi":"10.3390/fishes10070332","usgsCitation":"He, J.X., and Madenjian, C.P., 2025, Comparing year-class strength indices from longitudinal analysis of catch-at-age data with those from catch-curve regression: Application to Lake Huron lake trout: Fishes, v. 10, no. 7, 332, 15 p., https://doi.org/10.3390/fishes10070332.","productDescription":"332, 15 p.","ipdsId":"IP-180112","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":492085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes10070332","text":"Publisher Index Page"},{"id":491902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.81244272435032,\n 46.219483545610046\n ],\n [\n -84.45502123393308,\n 45.72128532835587\n ],\n [\n -83.53367997338103,\n 45.26517590645393\n ],\n [\n -83.41430066653513,\n 44.44170419606339\n ],\n [\n -84.10774211212554,\n 43.61709237133303\n ],\n [\n -83.62831234986241,\n 43.568154757819165\n ],\n [\n -82.8360145811905,\n 44.05230260646631\n ],\n [\n -82.51388267584665,\n 43.01830574568019\n ],\n [\n -81.70146487579785,\n 43.13447283374384\n ],\n [\n -81.19143655270658,\n 44.558403858438155\n ],\n [\n -81.968013163843,\n 45.696943546171696\n ],\n [\n -84.81244272435032,\n 46.219483545610046\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"He, Ji X.","contributorId":181528,"corporation":false,"usgs":false,"family":"He","given":"Ji","email":"","middleInitial":"X.","affiliations":[],"preferred":false,"id":942466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":942467,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70268843,"text":"70268843 - 2025 - Relating surface water dynamics in wetlands and lakes to spatial variability in hydrologic signatures","interactions":[],"lastModifiedDate":"2025-07-08T15:05:18.801102","indexId":"70268843","displayToPublicDate":"2025-07-05T08:01:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21989,"text":"Wetland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Relating surface water dynamics in wetlands and lakes to spatial variability in hydrologic signatures","docAbstract":"The retention of surface water in wetlands and lakes can modify the timing, duration, and magnitude of river discharge. However, efforts to characterize the influence of surface water on discharge regimes have been generally limited to small, wetland-dense watersheds. We developed random forest models to explain spatial variability in six hydrologic signatures, reflecting flashiness, high, and low flow conditions, at 72 gaged watersheds with variable water storage capacity across the conterminous United States. In addition to variables representing meteorology and landscape characteristics, we also tested the inclusion of surface water dynamics, derived from Sentinel-1 and Sentinel-2. Models for all six signatures improved with the addition of catchment characteristics, including surface water dynamics, relative to models with only climate variables. Percent improvement in model adjusted R2, mean square error, and Akaike information criterion ranged from 4.00 to 14.33%, 5.00 to 20.30%, and 2.75–8.14, respectively. Automated variable selection can be indicative of the relative importance of certain variables over others. Using a forward selection process, five of the six signature models selected remotely sensed inundation or wetland variables (p < 0.05). For example, the variable semi-permanent and permanent (SP + P) floodplain inundation (i.e., lakes along rivers) was associated with lower annual flashiness. Further, SP + P non-floodplain waters and geographically isolated wetlands significantly contributed to explaining variability in the low flow signatures. Our findings underscore the capacity of wetlands to stabilize and maintain flows during dry periods. Improved understanding of how surface water dynamics influence hydrologic signatures can inform wetland restoration efforts and facilitate improved resilience to extreme flow conditions.
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For grizzly (Ursus arctos) and polar bears (U. maritimus), den visits are impractical because of safety and logistical considerations. Reproduction is typically documented through direct observation, which can be difficult, costly, and often occurs long after den departure. Reproduction could be documented remotely, however, from post-denning movement data if discernable differences exist between females with and without cubs.
We trained support vector machines (SVMs) with eight variables derived from telemetry data of female grizzly (2000–2022) and polar bears (1985–2016) with or without cubs during seven periods with lengths ranging from 5 to 60 days starting at den departure. We assessed SVM classification accuracy by withholding two samples (one cub-present, one cub-absent), training SVMs with the remaining data, predicting classification of the withheld samples, and repeating this process for each sample combination. Additionally, we evaluated how classification accuracy for grizzly bears was influenced by sample size, length of the post-departure period, and frequency of standardized location estimates.
Accuracy of predicting cub presence or absence was 87% for grizzly bears with only 5 days of post-departure data and increased to a maximum of 92% with 20 days of data. For polar bears, accuracy was 86% at 5 days post-departure and increased to a maximum of 93% at 50 days. Classification accuracy for grizzly bears increased from 76 to 90% when sample size increased from 10 to 30 bears while holding period length constant (30 days) but did not increase at larger sample sizes. When sample size was held constant, increasing the length of the post-departure period did not affect classification accuracy markedly.
Presence or absence of grizzly and polar bear cubs can be identified with high accuracy even when SVM models are trained with limited data. Detecting cub presence or absence remotely could improve estimates of reproductive success and litter survival, enhancing our understanding of factors affecting cub recruitment.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-025-00577-y","usgsCitation":"Andersen, E., Clapp, J., Vinks, M., Atwood, T.C., Bjornlie, D., Costello, C., Gustine, D., Haroldson, M.A., Roberts, L.L., Rode, K.D., van Manen, F.T., and Wilson, R.H., 2025, Identifying presence or absence of grizzly and polar bear cubs from the movements of adult females with machine learning: Movement Ecology, v. 13, 48, 13 p., https://doi.org/10.1186/s40462-025-00577-y.","productDescription":"48, 13 p.","ipdsId":"IP-171094","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":492088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-025-00577-y","text":"Publisher Index 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Arctic-nesting goose across a half-century","docAbstract":"Joint estimation of demographic rates and population size has become an essential tool in ecology because it enables evaluating mechanisms for population change and testing hypotheses about drivers of demography in a single modeling framework. This approach provides a comprehensive perspective on population dynamics and how animal populations will respond to global pressures in future years. However, long-term data for such analyses are often limited in quantity and quality. We developed an integrated population model combining data on demography and population size from nine different sources to understand the population ecology of the lesser snow goose (Anser caerulescens caerulescens) in the Pacific Flyway in North America from 1970 to 2022. We divided the flyway population into Wrangel Island and Western Arctic subpopulations and assessed demographic mechanisms for population change and environmental and anthropogenic drivers that influenced demography. During 1970–2022, the estimated spring population of snow geese in the Pacific Flyway increased from ~300,000 to ~2,300,000. Short-term changes in population growth rate were primarily driven by changes in productivity in the Western Arctic and productivity and immigration in Wrangel Island. Changes in hunting and natural mortality had less influence on short-term but likely contributed to the pronounced long-term population growth. Early snowmelt positively influenced per capita productivity in both regions, and warm, rainy weather during the non-breeding season was associated with high per capita productivity in the Western Arctic. In the Western Arctic, per capita productivity was negatively associated with population size, and adult natural mortality was positively associated with population size, indicating density-dependent regulation in this subpopulation. In Wrangel Island, warm weather in early fall decreased juvenile natural mortality. Our results demonstrate that per capita productivity and immigration, rather than adult survival, were the primary mechanisms of short-term population change in this long-lived species. Our results also indicate that environmental conditions and density-dependent effects can impact population dynamics more than harvest, even for a long-lived, commonly harvested species. We demonstrate that a warming climate can have multiple effects on demography, emphasizing the importance of assessing a variety of spatial and temporal factors when predicting how populations might respond to large-scale environmental changes. This emphasizes the importance of conservation plans that consider these environmental drivers, although this may complicate direct management of such populations.
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Todd","contributorId":357733,"corporation":false,"usgs":false,"family":"Sanders","given":"Todd","affiliations":[{"id":85545,"text":"U.S. Fish and Wildlife Service, Division of Migratory Bird Management","active":true,"usgs":false}],"preferred":false,"id":942384,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Weegman, Mitch D.","contributorId":207459,"corporation":false,"usgs":false,"family":"Weegman","given":"Mitch D.","affiliations":[],"preferred":false,"id":942385,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70270037,"text":"70270037 - 2025 - Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA","interactions":[],"lastModifiedDate":"2025-08-08T15:33:12.382534","indexId":"70270037","displayToPublicDate":"2025-07-03T10:29:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9346,"text":"Science of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA","docAbstract":"Lidar and structure from motion-derived digital elevation and surface models have widespread application. Consideration of a topographic model's vertical root mean squared error (RMSEz) and systematic directional bias is important for many of these applications, particularly landscape change detection and measurement. Due to logistic, resource, and time constraints, wide area remotely sensed topographic surveys are not always accompanied by an in situ checkpoint network for validating and characterizing survey error. Here we describe and test a method for automatically generating synthetic elevation checkpoints in bulk across hundreds of kilometers using a publicly available lidar-derived DEM time-series, road vector network, and landcover classification map. Our method produced 6000–10,000 synthetic checkpoints across the developed barrier island coastline of North Carolina. These checkpoints characterized vertical error metrics in a statistically similar way as in situ checkpoints when assessing the vertical accuracy of a contemporary lidar-derived DEM and produced RMSEz metrics an average of 0.018 m from the RMSEz of historical lidar DEMs published with tested accuracy metrics. This new method has the potential to A) lower the cost and time required to validate new remotely sensed topographic surveys by reducing or eliminating the field work associated with in situ checkpoint surveys, B) provide a means of retroactively assessing the absolute vertical accuracy and systematic bias of historical topographic datasets that were not published with tested accuracy metrics, and C) generate reference networks to assess and correct spatially variable patterns of vertical bias in topographic datasets.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.srs.2025.100252","usgsCitation":"Seymour, A.C., Kranenburg, C.J., and Doran, K., 2025, Automated generation of an urban synthetic elevation checkpoint network across the North Carolina coastline, USA: Science of Remote Sensing, v. 12, 100252, 16 p., https://doi.org/10.1016/j.srs.2025.100252.","productDescription":"100252, 16 p.","ipdsId":"IP-160786","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":494186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.srs.2025.100252","text":"Publisher Index Page"},{"id":493850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.25473663967196,\n 36.645619157350026\n ],\n [\n -76.25473663967196,\n 35.102166390295025\n ],\n [\n -75.37748509576826,\n 35.102166390295025\n ],\n [\n -75.37748509576826,\n 36.645619157350026\n ],\n [\n -76.25473663967196,\n 36.645619157350026\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Seymour, Alexander C. 0000-0002-7680-6102","orcid":"https://orcid.org/0000-0002-7680-6102","contributorId":238616,"corporation":false,"usgs":true,"family":"Seymour","given":"Alexander","email":"","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kranenburg, Christine J. 0000-0002-2955-0167 ckranenburg@usgs.gov","orcid":"https://orcid.org/0000-0002-2955-0167","contributorId":169234,"corporation":false,"usgs":true,"family":"Kranenburg","given":"Christine","email":"ckranenburg@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":292448,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":945219,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273905,"text":"70273905 - 2025 - Contaminated stormwater sediment source tracking for polychlorinated biphenyls in an urban watershed of the Chesapeake Bay, United States","interactions":[],"lastModifiedDate":"2026-02-13T15:35:19.238788","indexId":"70273905","displayToPublicDate":"2025-07-03T08:26:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2233,"text":"Journal of Contaminant Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Contaminated stormwater sediment source tracking for polychlorinated biphenyls in an urban watershed of the Chesapeake Bay, United States","docAbstract":"Fine-grained sediment in stormwater acts as a vector for persistent organic pollutants, like polychlorinated biphenyls (PCBs), through mobilization from sources within drainage areas of impacted urban watersheds. This study implemented a novel approach to identify the relative contributions of various landscape and stream sources of sediment from the Back River watershed in eastern Baltimore, Maryland, and investigated the applicability of using trace PCBs found in an urban environment as discriminants between each source type. Trace PCBs were found to be poor discriminants when identifying the relative sediment contributions of watershed-scale land use categories. When excluding PCBs in the development of a sediment fingerprinting model and instead utilizing trace elements and carbon only, sediment fingerprint modeling successfully differentiated green spaces and eroding streambanks as the most significant contributors to stormwaters sediment (37.1 % and 44.0 %, respectively) of the total sediment contributions of all considered source categories. In all samples collected from various landscape sources, storms, and cores detectable concentrations of PCBs were measured. The results of this study indicate that sediment fingerprinting may not be an effective method in predicting where PCBs may be found within an impacted watershed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jconhyd.2025.104657","usgsCitation":"Foss, E.P., Clifton, Z.J., Majcher, E.H., Needham, T.P., and Psoras, A.W., 2025, Contaminated stormwater sediment source tracking for polychlorinated biphenyls in an urban watershed of the Chesapeake Bay, United States: Journal of Contaminant Hydrology, v. 274, 104657, 16 p., https://doi.org/10.1016/j.jconhyd.2025.104657.","productDescription":"104657, 16 p.","ipdsId":"IP-177312","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":500086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","city":"Baltimore","otherGeospatial":"Back River watershed, Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.50954075138807,\n 39.32799166380633\n ],\n [\n -76.50954075138807,\n 39.256713104432606\n ],\n [\n -76.43110757990168,\n 39.256713104432606\n ],\n [\n -76.43110757990168,\n 39.32799166380633\n ],\n [\n -76.50954075138807,\n 39.32799166380633\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"274","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Foss, Ellie P. 0000-0001-9090-4617","orcid":"https://orcid.org/0000-0001-9090-4617","contributorId":290902,"corporation":false,"usgs":true,"family":"Foss","given":"Ellie","middleInitial":"P.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clifton, Zachary J. 0000-0002-8148-5454","orcid":"https://orcid.org/0000-0002-8148-5454","contributorId":220551,"corporation":false,"usgs":true,"family":"Clifton","given":"Zachary","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Majcher, Emily H. 0000-0001-7144-6809","orcid":"https://orcid.org/0000-0001-7144-6809","contributorId":203335,"corporation":false,"usgs":true,"family":"Majcher","given":"Emily","middleInitial":"H.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Needham, Trevor P. 0000-0001-9356-4216","orcid":"https://orcid.org/0000-0001-9356-4216","contributorId":245024,"corporation":false,"usgs":true,"family":"Needham","given":"Trevor","email":"","middleInitial":"P.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Psoras, Andrew W. 0000-0002-1779-5079","orcid":"https://orcid.org/0000-0002-1779-5079","contributorId":347166,"corporation":false,"usgs":true,"family":"Psoras","given":"Andrew","middleInitial":"W.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":955725,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268798,"text":"70268798 - 2025 - Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center","interactions":[],"lastModifiedDate":"2025-11-18T16:54:14.031875","indexId":"70268798","displayToPublicDate":"2025-07-02T10:14:18","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":"Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center","docAbstract":"The U.S. Geologic Survey National Earthquake Information Center (NEIC) monitors global seismicity, producing a catalog of earthquake source parameters in near-real-time to provide information that can help mitigate the societal impact of earthquakes. The NEIC commonly relies on teleseismic observations to constrain earthquake source parameters (e.g., location, depth, magnitude, and mechanism) due to a lack of local and regional observations. For these ‘teleseismic’ events, depth phase (i.e., pP, sP) arrival time observations provide the best estimate on source depth. However, depth phases are often difficult to accurately identify and/or pick. Therefore, NEIC relies on waveform modeling, such as those determined from W-phase (Mww), body wave (Mwb), and regional (Mwr) moment tensor estimations, to provide constraints on source depth. While depth estimates from these approaches are informative, higher frequency observations provide more precise estimates because depth phases are more prominently observed at higher frequencies. Here, we present NEIC’s relatively high-frequency (~0.04 to 1 Hz) teleseismic waveform modeling approach, termed Synthetic Depth Phase Modeling (SynDepth), for determining source depth. SynDepth was developed to provide NEIC with a tool that enables rapid, accurate, and quantifiable estimates of earthquake source depth in cases where locator depths are not reliable. This relatively simple and fast procedure searches over 1 km-incremented source depths and an expanding triangular source-time function to find the best-fitting solution. We compare automatic SynDepth solutions for a dataset of 1,216 earthquakes (M5.5-M7.6) between 2017 and 2021 to NEIC-derived depth estimates from other methods. Our approach provides a robust depth estimate for earthquakes lacking local arrival time data, and it minimizes the need for analyst review of depth-phase picks (pP, sP) or using predefined ‘fixed’ depths.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0220240372","usgsCitation":"Yeck, W.L., Herrmann, R., Patton, J., Barnhart, W.D., and Benz, H.M., 2025, Estimating earthquake source depth using teleseismic broadband waveform modeling at the USGS National Earthquake Information Center: Seismological Research Letters, v. 96, no. 6, p. 3643-3655, https://doi.org/10.1785/0220240372.","productDescription":"13 p.","startPage":"3643","endPage":"3655","ipdsId":"IP-167539","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":491836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"6","noUsgsAuthors":false,"publicationDate":"2025-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942024,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, Robert B.","contributorId":80255,"corporation":false,"usgs":false,"family":"Herrmann","given":"Robert B.","affiliations":[],"preferred":false,"id":942025,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patton, John 0000-0003-0142-5118","orcid":"https://orcid.org/0000-0003-0142-5118","contributorId":218681,"corporation":false,"usgs":true,"family":"Patton","given":"John","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, William D. 0000-0003-0498-1697 wbarnhart@usgs.gov","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":294678,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":942027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942028,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268796,"text":"70268796 - 2025 - 2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska","interactions":[],"lastModifiedDate":"2025-07-08T16:16:15.639994","indexId":"70268796","displayToPublicDate":"2025-07-02T09:12:05","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":"2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska","docAbstract":"Alaska's coastal communities face growing landslide hazards owing to glacier retreat and extreme weather intensified by the warming climate, yet hazard monitoring remains challenging. As part of ongoing experimental monitoring in Prince William Sound, we detected three large landslides (0.5–2.3 M m3) at Surprise Inlet on 20 September 2024, within the span of an hour. These events were identified in near real-time through seismic data and later confirmed using satellite imagery, tidal records, and infrasound. The landslides generated a modest tsunami, and a 4 cm wave was recorded by a tide gauge 18 km away, marking the first recorded landslide to reach water since monitoring began in this region in 2021. Here, we examine the detection and interpretation of these landslides using multiple data sources and modeling. We demonstrate the effectiveness of this regional seismic monitoring system and show how complementary instrumentation, where available, can enhance detection capabilities.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2025GL115911","usgsCitation":"Karasozen, E., West, M.E., Barnhart, K.R., Lyons, J.J., Nichols, T., Schaefer, L.N., Bahng, B., Ohlendorf, S., Staley, D.M., and Wolken, G.J., 2025, 2024 Surprise Inlet landslides: Insights from a prototype landslide‐triggered tsunami monitoring system in Prince William Sound, Alaska: Geophysical Research Letters, v. 52, no. 13, e2025GL115911, 11 p., https://doi.org/10.1029/2025GL115911.","productDescription":"e2025GL115911, 11 p.","ipdsId":"IP-176964","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":492061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2025gl115911","text":"Publisher Index Page"},{"id":491813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.69339399398933,\n 61.18214394792622\n ],\n [\n -148.69339399398933,\n 59.91280847695441\n ],\n [\n -145.64673816140657,\n 59.91280847695441\n ],\n [\n -145.64673816140657,\n 61.18214394792622\n ],\n [\n -148.69339399398933,\n 61.18214394792622\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","issue":"13","noUsgsAuthors":false,"publicationDate":"2025-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Karasozen, Ezgi 0000-0003-1140-1427","orcid":"https://orcid.org/0000-0003-1140-1427","contributorId":244223,"corporation":false,"usgs":false,"family":"Karasozen","given":"Ezgi","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":942014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"West, Michael E.","contributorId":147407,"corporation":false,"usgs":false,"family":"West","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":942015,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942016,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":942017,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, Terry","contributorId":357616,"corporation":false,"usgs":false,"family":"Nichols","given":"Terry","affiliations":[{"id":85473,"text":"National Tsunami Warning Center, Palmer, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":942018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942019,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bahng, Bohyun","contributorId":357617,"corporation":false,"usgs":false,"family":"Bahng","given":"Bohyun","affiliations":[{"id":85473,"text":"National Tsunami Warning Center, Palmer, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":942020,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ohlendorf, Summer","contributorId":357618,"corporation":false,"usgs":false,"family":"Ohlendorf","given":"Summer","affiliations":[{"id":85473,"text":"National Tsunami Warning Center, Palmer, Alaska, USA","active":true,"usgs":false}],"preferred":false,"id":942021,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":942022,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wolken, Gabriel J.","contributorId":221149,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","email":"","middleInitial":"J.","affiliations":[{"id":40336,"text":"Alaska Department of Natural Resources: Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":942023,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70269945,"text":"70269945 - 2025 - Estimating mortality of Lake Sturgeon in the Lake Winnebago system using traditional age-based approaches and capture–recapture models","interactions":[],"lastModifiedDate":"2025-08-18T15:25:53.813171","indexId":"70269945","displayToPublicDate":"2025-07-02T08:29:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating mortality of Lake Sturgeon in the Lake Winnebago system using traditional age-based approaches and capture–recapture models","docAbstract":"Objective
The Lake Winnebago system in Wisconsin supports a popular winter spear fishery for Lake Sturgeon Acipenser fulvescens. Setting harvest caps for this fishery relies on estimating instantaneous natural mortality rate (M), which can be done using age-based approaches or capture–recapture models that incorporate recoveries of fish with passive integrated transponder (PIT) tags or detections of fish with acoustic transmitters. Our objectives were to determine (1) if recent estimates of exploitation (u) have exceeded the 5% harvest cap, (2) if M and total mortality rates are similar among estimation methods that rely on age estimates or capture–recapture methods, and (3) if potential differences in mortality estimates would affect harvest caps.
Methods
Harvest of PIT-tagged fish was used to evaluate u from 2010 to 2019. Catch curves incorporating corrected fin ray ages were used to estimate total mortality and M for fish collected from 2010 to 2019. Capture–recapture models were used to estimate annual survival and M from detections of fish with acoustic transmitters from 2007 to 2019 and recoveries of PIT-tagged fish from 1999 to 2020. Mortality estimates were used to calculate and compare sex-specific harvest caps among estimation methods.
Results
Observed u did not exceed 5% for either sex between 2010 and 2019. Estimates of M varied among methods (males: M = 0.001–0.134; females: M = 0.001–0.131), with PIT-based models consistently providing the lowest and telemetry-based models providing the highest estimates. Simulations indicated that female u has limited potential to exceed 5% if M from fin ray ages or telemetry is used to set harvest caps, while PIT-based simulations showed no indication of cap exceedance.
Conclusions
Harvest management practices in the Lake Winnebago system appear to have kept Lake Sturgeon exploitation below the 5% harvest cap from 2010 to 2019. Capture–recapture models relying on PIT tags appear to provide the most precise approach for setting harvest caps for this fishery.
","language":"English","publisher":"American Fisheries Society","doi":"10.1093/najfmt/vqaf044","usgsCitation":"Shrovnal, J., Stadig, M., Raabe, J., and Isermann, D.A., 2025, Estimating mortality of Lake Sturgeon in the Lake Winnebago system using traditional age-based approaches and capture–recapture models: North American Journal of Fisheries Management, v. 45, no. 4, p. 616-632, https://doi.org/10.1093/najfmt/vqaf044.","productDescription":"17 p.","startPage":"616","endPage":"632","ipdsId":"IP-171106","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":493716,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Lake Winnebago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.55626587032395,\n 44.217649685789354\n ],\n [\n -88.55626587032395,\n 43.78463531825764\n ],\n [\n -88.25352305766155,\n 43.78463531825764\n ],\n [\n -88.25352305766155,\n 44.217649685789354\n ],\n [\n -88.55626587032395,\n 44.217649685789354\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrovnal, Jeremiah S.","contributorId":359167,"corporation":false,"usgs":false,"family":"Shrovnal","given":"Jeremiah S.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":945008,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stadig, Margaret H.","contributorId":359168,"corporation":false,"usgs":false,"family":"Stadig","given":"Margaret H.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":945009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Raabe, Joshua K.","contributorId":348735,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":945010,"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":945011,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70268143,"text":"sir20235101 - 2025 - Hydraulic conductivity and transmissivity estimates from slug tests in wells within the Mississippi Alluvial Plain, Arkansas and Mississippi, 2020","interactions":[],"lastModifiedDate":"2026-01-26T19:15:04.764709","indexId":"sir20235101","displayToPublicDate":"2025-07-02T07:37:05","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5101","displayTitle":"Hydraulic Conductivity and Transmissivity Estimates from Slug Tests in Wells Within the Mississippi Alluvial Plain, Arkansas and Mississippi, 2020","title":"Hydraulic conductivity and transmissivity estimates from slug tests in wells within the Mississippi Alluvial Plain, Arkansas and Mississippi, 2020","docAbstract":"During the spring and summer of 2020, the U.S. Geological Survey conducted single-well slug tests on selected observation wells within the Mississippi Alluvial Plain in Arkansas and Mississippi to estimate hydraulic conductivity and transmissivity values for the Mississippi River Valley alluvial and middle Claiborne aquifers. Well and aquifer data were collected from field measurements, well-construction reports, and published aquifer-thickness information. A total of 324 slug-in and slug-out tests were conducted on 48 wells by using mechanical slugs to displace the water column and submersible pressure transducers to record changes in water levels in the wells. Hydraulic conductivity of the aquifers in which the wells are screened was estimated by curve fitting the water-level-change data using aquifer test analysis software. Estimates of aquifer transmissivity were made by multiplying the estimated hydraulic conductivity value by the aquifer thickness at well locations. Mean hydraulic conductivity estimates for 44 observation wells screened in the Mississippi River Valley alluvial aquifer range from 3 to 401 feet per day, and mean transmissivity estimates range from 285 to 80,559 feet squared per day. Mean hydraulic conductivity estimates for four observation wells screened in units of the middle Claiborne aquifer range from 0.14 to 183 feet per day, and mean transmissivity estimates range from 55 to 67,913 feet squared per day. The results from these tests can be used to improve the understanding of water availability and groundwater migration, to refine groundwater models, and to ultimately provide stakeholders and decisionmakers better information for management of the groundwater resources within the Mississippi Alluvial Plain.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"Following the recovery of Peregrine Falcons (Falco peregrinus), the US Fish and Wildlife Service began a process to allow “take” (capture) of wild peregrines for falconry in the United States. Recently, that effort involved generating updated estimates of the collective abundance of the three North American peregrine subspecies: F. p. anatum, F. p. tundrius, and F. p. pealei (Peale's Peregrine Falcon). Because of the more limited distribution of F. p. pealei, we conducted an analysis specific to its geographic range. We analyzed data from a long-term banding and resighting program on three beaches on the southern coast of Washington, USA, to estimate the annual abundance of migrating and overwintering F. p. pealei, using the capture histories of 250 Peregrine Falcons, nearly all of which were captured during 1277 vehicle surveys between 1995 and 2024. Because we studied an open population of migratory individuals, we used a zero-inflated Poisson log-normal mark-resight model to estimate annual abundance. For the analyses, we partitioned our survey data into sighting periods, each of which extended from 1 September of one year to 31 May of the next. We anticipated that first-year F. p. pealei would be identified for falconry take, and our annual abundance estimates for first-year birds of this subspecies ranged from a high of 24.8 ± 6.1 (SE) individuals in the 2014–2015 sighting period to a low of 1.9 ± 1.4 individuals in the 2023–2024 sighting period. Peregrine Falcon abundance varied annually and appeared to decline during the last two sighting periods. Our sighting rate of marked peregrines was negatively associated with Bald Eagle (Haliaeetus leucocephalus) encounter rate. There was a lesser relationship to human activity, and we suspect the change in sighting rate was a behavioral response by Peregrine Falcons to the threat of kleptoparasitism by Bald Eagles. We currently lack comprehensive information about the natal origin of the individual peregrines in our study area, which prevented us from assessing the degree to which falconry take from the pool of falcons migrating to or through Washington might potentially impact local or regional abundances. Although a better understanding of natal origins is needed, our data add clarity to the migration and overwinter abundance of F. p. pealei on the Washington coast and may inform decisions about the take of this subspecies for falconry.
","language":"English","publisher":"BioOne","doi":"10.3356/jrr2482","usgsCitation":"Daniel E. Varland, Joseph B. Buchanan, Guthrie S. Zimmerman, Bauder, J.M., Tracy L. Fleming, Brian A. Millsap, 2025, Estimated annual abundance of migratory Peale's Peregrine Falcons in coastal Washington, USA: Journal of Raptor Research, v. 59, no. 3, p. 1-16, https://doi.org/10.3356/jrr2482.","productDescription":"16 p.","startPage":"1","endPage":"16","ipdsId":"IP-177337","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr2482","text":"Publisher Index Page"},{"id":500426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.72822793496042,\n 47.20172472210811\n ],\n [\n -124.72822793496042,\n 46.15365987938665\n ],\n [\n -123.42708843918685,\n 46.15365987938665\n ],\n [\n -123.42708843918685,\n 47.20172472210811\n ],\n [\n -124.72822793496042,\n 47.20172472210811\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel E. Varland","contributorId":366594,"corporation":false,"usgs":false,"family":"Daniel E. Varland","affiliations":[{"id":37634,"text":"Coastal Raptors","active":true,"usgs":false}],"preferred":false,"id":956075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Joseph B. Buchanan","contributorId":366595,"corporation":false,"usgs":false,"family":"Joseph B. Buchanan","affiliations":[{"id":7113,"text":"private citizen","active":true,"usgs":false}],"preferred":false,"id":956076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guthrie S. Zimmerman","contributorId":366596,"corporation":false,"usgs":false,"family":"Guthrie S. Zimmerman","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":956077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tracy L. Fleming","contributorId":366597,"corporation":false,"usgs":false,"family":"Tracy L. Fleming","affiliations":[{"id":7113,"text":"private citizen","active":true,"usgs":false}],"preferred":false,"id":956079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brian A. Millsap","contributorId":366598,"corporation":false,"usgs":false,"family":"Brian A. Millsap","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":956080,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70268807,"text":"70268807 - 2025 - Arctic speleothems reveal nearly permafrost-free Northern Hemisphere in the Late Miocene","interactions":[],"lastModifiedDate":"2025-07-07T15:54:50.530354","indexId":"70268807","displayToPublicDate":"2025-07-01T10:46:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Arctic speleothems reveal nearly permafrost-free Northern Hemisphere in the Late Miocene","docAbstract":"Arctic warming is happening at nearly four times the global average rate. Long-term trends of permafrost dynamics cannot be estimated directly from monitoring of present-day thaw processes, requiring paleoclimate-proxy information. Here we use cave carbonates (speleothems) from a northern Siberian cave to determine when the Northern Hemisphere was mostly permafrost-free. At present, thick continuous permafrost in this region prevents speleothem growth. In a series of partially eroded caves, speleothems grew during the late Tortonian stage (8.68 ± 0.09 Ma), a time when the geographic position of this site was already similar to today. Paleotemperatures reconstructed from speleothems show that mean annual air temperatures (MAAT) in the region were + 6.6°C to + 11.1°C, when contemporary global MAAT were ~ 4.5 °C higher than modern. Our findings provide direct evidence that warming to Tortonian-like temperatures would leave most of the Northern Hemisphere permafrost-free. This may release up to ~ 130 petagrams of carbon, enhancing further warming.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-025-60381-5","usgsCitation":"Vaks, A., Mason, A., Breitenbach, S., Giesche, A., Osinzev, A., Adrian, I., Kononov, A., Umbo, S., Lechleitner, F., Rosensaft, M., and Henderson, G., 2025, Arctic speleothems reveal nearly permafrost-free Northern Hemisphere in the Late Miocene: Nature Communications, v. 16, 5483, 13 p., https://doi.org/10.1038/s41467-025-60381-5.","productDescription":"5483, 13 p.","ipdsId":"IP-165979","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":492044,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-025-60381-5","text":"Publisher Index Page"},{"id":491740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China, Mongolia, Russia","otherGeospatial":"Siberia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 140,\n 75\n ],\n [\n 90,\n 75\n ],\n [\n 90,\n 45\n ],\n [\n 140,\n 45\n ],\n [\n 140,\n 75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Vaks, Anton","contributorId":357620,"corporation":false,"usgs":false,"family":"Vaks","given":"Anton","affiliations":[{"id":85474,"text":"Geochemistry and Environmental Geology Division, Geological Survey of Israel, Jerusalem, 9692100, Israel","active":true,"usgs":false}],"preferred":false,"id":942040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mason, Andrew","contributorId":357621,"corporation":false,"usgs":false,"family":"Mason","given":"Andrew","affiliations":[{"id":85476,"text":"Department of Earth Sciences, Oxford University, Oxford, OX1 3AN United Kingdom","active":true,"usgs":false}],"preferred":false,"id":942041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breitenbach, Sebastian F.M.","contributorId":357622,"corporation":false,"usgs":false,"family":"Breitenbach","given":"Sebastian F.M.","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":942042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giesche, Alena Maria 0000-0003-3673-7269","orcid":"https://orcid.org/0000-0003-3673-7269","contributorId":344659,"corporation":false,"usgs":true,"family":"Giesche","given":"Alena Maria","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":942043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osinzev, Alexander","contributorId":357623,"corporation":false,"usgs":false,"family":"Osinzev","given":"Alexander","affiliations":[{"id":84245,"text":"Speleoclub Arabika, St. Mamina-Sibiryaka 6a, 664058 Irkutsk, Russia","active":true,"usgs":false}],"preferred":false,"id":942044,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adrian, Irina","contributorId":357624,"corporation":false,"usgs":false,"family":"Adrian","given":"Irina","affiliations":[{"id":85477,"text":"Lena Delta Wildlife Reserve, Tiksi, Sakha Republic, 678400 Russia","active":true,"usgs":false}],"preferred":false,"id":942045,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kononov, Aleksandr","contributorId":357625,"corporation":false,"usgs":false,"family":"Kononov","given":"Aleksandr","affiliations":[{"id":85478,"text":"Irkutsk National Research Technical University, Irkutsk, 664074, Russia; Institute of the Earth's Crust, Russian Academy of Sciences, Siberian Branch, Irkutsk, 664033, Russia","active":true,"usgs":false}],"preferred":false,"id":942046,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Umbo, Stuart","contributorId":357626,"corporation":false,"usgs":false,"family":"Umbo","given":"Stuart","affiliations":[{"id":84240,"text":"Department of Earth and Environmental Sciences, Northumbria University, Newcastle-Upon-Tyne, NE1 8ST, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":942047,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lechleitner, Franziska A.","contributorId":357627,"corporation":false,"usgs":false,"family":"Lechleitner","given":"Franziska A.","affiliations":[{"id":85479,"text":"Department of Chemistry, Biochemistry and Pharmaceutical Sciences & Oeschger Centre for Climate Change Research, Bern, 2012, Switzerland","active":true,"usgs":false}],"preferred":false,"id":942048,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rosensaft, Marcelo","contributorId":357628,"corporation":false,"usgs":false,"family":"Rosensaft","given":"Marcelo","affiliations":[{"id":85480,"text":"Geological Mapping Division, Geological Survey of Israel, Jerusalem, 9692100, Israel","active":true,"usgs":false}],"preferred":false,"id":942049,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Henderson, Gideon M.","contributorId":357629,"corporation":false,"usgs":false,"family":"Henderson","given":"Gideon M.","affiliations":[{"id":85476,"text":"Department of Earth Sciences, Oxford University, Oxford, OX1 3AN United Kingdom","active":true,"usgs":false}],"preferred":false,"id":942050,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70268753,"text":"70268753 - 2025 - Hydrologic response of groundwater and streamflow to natural and anthropogenic drivers of change in headwaters of the upper Colorado River basin during recent wet (1982–1999) and drought (2000–2022) conditions","interactions":[],"lastModifiedDate":"2025-07-08T16:36:55.393587","indexId":"70268753","displayToPublicDate":"2025-07-01T09:29:50","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic response of groundwater and streamflow to natural and anthropogenic drivers of change in headwaters of the upper Colorado River basin during recent wet (1982–1999) and drought (2000–2022) conditions","docAbstract":"Study region: Headwaters of the upper Colorado River basin (UCOL), USA
Study focus: Surface-water and groundwater numerical models incorporating water-use information were used to investigate changes in climate, water use, and simulated hydrologic responses of snow processes, evapotranspiration, groundwater, and streamflow during recent wet (1982–1999) and drought (2000–2022) periods in the headwater subregions of the upper Colorado River basin.
New hydrologic insights for the region: Decreases in average streamflow between wet and drought periods ranged from 20 % in the Colorado River headwaters subregion to 23 % in the Gunnison River headwaters subregion. Like streamflow, average surface runoff was statistically less during the drought than the wet period, with decreases from 24–31 % in the headwaters. On a volume basis, runoff decreases were greater than streamflow decreases in both the Colorado River and Gunnison River headwaters. Although the amount of water-year groundwater discharge to streams remained nearly the same between the wet and drought periods, groundwater as a percentage of streamflow increased between the wet and drought periods, highlighting the importance of groundwater in sustaining streamflow during drought conditions. Multiple linear regression analyses revealed that snowmelt-only models were better than the best precipitation and temperature models at explaining streamflow variability from all headwater subregions for both the wet and drought periods.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2025.102554","usgsCitation":"Tillman, F.D., Masbruch, M.D., Knight, J., Engott, J.A., Lopez, S.F., Jones, C.J., Dickinson, J.E., and Miller, M., 2025, Hydrologic response of groundwater and streamflow to natural and anthropogenic drivers of change in headwaters of the upper Colorado River basin during recent wet (1982–1999) and drought (2000–2022) conditions: Journal of Hydrology: Regional Studies, v. 60, 102554, 19 p., https://doi.org/10.1016/j.ejrh.2025.102554.","productDescription":"102554, 19 p.","ipdsId":"IP-176624","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":492063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2025.102554","text":"Publisher Index Page"},{"id":491819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"upper Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.83855791620346,\n 42.22226218528715\n ],\n [\n -110.83855791620346,\n 35.70206223466056\n ],\n [\n -106.7875698491801,\n 35.70206223466056\n ],\n [\n -106.7875698491801,\n 42.22226218528715\n ],\n [\n -110.83855791620346,\n 42.22226218528715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Masbruch, Melissa D. 0000-0001-6568-160X mmasbruch@usgs.gov","orcid":"https://orcid.org/0000-0001-6568-160X","contributorId":1902,"corporation":false,"usgs":true,"family":"Masbruch","given":"Melissa","email":"mmasbruch@usgs.gov","middleInitial":"D.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knight, Jacob E. 0000-0003-0271-9011","orcid":"https://orcid.org/0000-0003-0271-9011","contributorId":204140,"corporation":false,"usgs":true,"family":"Knight","given":"Jacob E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engott, John A. 0000-0003-1889-4519 jaengott@usgs.gov","orcid":"https://orcid.org/0000-0003-1889-4519","contributorId":1142,"corporation":false,"usgs":true,"family":"Engott","given":"John","email":"jaengott@usgs.gov","middleInitial":"A.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941863,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lopez, Samuel Francisco 0000-0002-3544-7465","orcid":"https://orcid.org/0000-0002-3544-7465","contributorId":344607,"corporation":false,"usgs":true,"family":"Lopez","given":"Samuel","email":"","middleInitial":"Francisco","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941864,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Casey J.R. 0000-0002-6991-8026","orcid":"https://orcid.org/0000-0002-6991-8026","contributorId":223364,"corporation":false,"usgs":true,"family":"Jones","given":"Casey","email":"","middleInitial":"J.R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941865,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dickinson, Jesse E. 0000-0002-0048-0839 jdickins@usgs.gov","orcid":"https://orcid.org/0000-0002-0048-0839","contributorId":152545,"corporation":false,"usgs":true,"family":"Dickinson","given":"Jesse","email":"jdickins@usgs.gov","middleInitial":"E.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941866,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - 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The 2025 survey included 230 trawls in the main lake and embayments and sampled depths from 5.5 to 245 m (15 – 810 ft). The survey captured 504,541 fish from 33 species with a total weight of 7,301 kg (16,095 lbs). Alewife were 85% of the total catch numerically, while Yellow Perch Perca flavescens, Round Goby Neogobius melanostomus, Deepwater Sculpin Myoxocephalus thompsonii, and Rainbow Smelt Osmerus mordax, comprised 5%, 4%, 3%, and 1% of the catch, respectively.
The Alewife biomass index decreased from 2024 to 2025 (83 to 78 kg·ha-1) however due to an abundant 2024 Alewife year class the density index increased from 3,727 to 9,182 fish per ha-1. The Age-1 biomass (2024 year class) was 27.5 kg·ha-1, which was the greatest value estimated in the modern time series (since 1997). The abundance estimate for the 2024 Alewife year class (13.8 billion) was more than three times the number of all other Alewife combined (3.6 billion). Adult Alewife abundance decreased in 2025 which was consistent with predictions from 2024. Those predictive models suggested that adult Alewife biomass is likely to increase in 2026 and 2027, as the 2024 year class matures. Alewife condition declined in 2025, which was expected given the relatively high Alewife density. Acoustic-based prey fish densities were greater than previous years acoustic estimates especially at depths from 180 – 220 m (591 – 722 ft), however acoustic based densities continue to be substantially lower than trawl-based densities.
The 2025 biomass index was similar to 2024 for Emerald Shiner Notropis atherinoides and Threespine Stickleback Gasterosteus aculeatus, but was lower for Rainbow Smelt, and higher for Cisco Coregonus artedi. Three purported Bloater Coregonus hoyi were caught in the 2025 survey. Analysis of archived tissue identified five Bloater captured in previous surveys which increased the total number caught in Lake Ontario bottom trawl surveys to n = 24, since restoration stocking began in 2012. Whole lake density estimates of Lake Whitefish Coregonus clupeaformis increased in 2025 relative to 2024. Those density increases were due to increased catches in Canadian waters, as density in U.S. waters has remained low. The density index for wild or naturally reproduced juvenile Lake Trout Salvelinus namaycush increased in 2025 relative to 2024, with the most frequent catches occurring in waters around the Niagara River.
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0009-0002-0248-4523","orcid":"https://orcid.org/0009-0002-0248-4523","contributorId":339870,"corporation":false,"usgs":true,"family":"Stahl","given":"Scott","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":947166,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchinson, Olivia Margaret 0009-0002-7999-1160","orcid":"https://orcid.org/0009-0002-7999-1160","contributorId":339869,"corporation":false,"usgs":true,"family":"Mitchinson","given":"Olivia","email":"","middleInitial":"Margaret","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":947167,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Malley, Brian 0000-0001-5035-3080 bomalley@usgs.gov","orcid":"https://orcid.org/0000-0001-5035-3080","contributorId":216560,"corporation":false,"usgs":true,"family":"O’Malley","given":"Brian","email":"bomalley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":947168,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Berry, Nicole Lynn 0000-0002-7889-197X","orcid":"https://orcid.org/0000-0002-7889-197X","contributorId":347450,"corporation":false,"usgs":true,"family":"Berry","given":"Nicole Lynn","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":947169,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anweiler, Katie Victoria 0000-0002-9344-0691","orcid":"https://orcid.org/0000-0002-9344-0691","contributorId":334260,"corporation":false,"usgs":true,"family":"Anweiler","given":"Katie","email":"","middleInitial":"Victoria","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":947170,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ackiss, Amanda Susanne 0000-0002-8726-7423","orcid":"https://orcid.org/0000-0002-8726-7423","contributorId":272165,"corporation":false,"usgs":true,"family":"Ackiss","given":"Amanda","email":"","middleInitial":"Susanne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":947171,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70270114,"text":"70270114 - 2025 - Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses","interactions":[],"lastModifiedDate":"2025-08-11T15:22:04.615437","indexId":"70270114","displayToPublicDate":"2025-07-01T08:15:23","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses","docAbstract":"Seawater intrusion threatens groundwater resources in coastal regions, including southern Baldwin County, Alabama, where the freshwater-saltwater interface dynamics remain poorly understood. To address this gap, this study uses combined physics-based and machine-learning models to quantify seawater intrusion caused by natural (storm surges) and anthropogenic (human activities) perturbations. The long short-term memory network and wavelet analysis were used to assess vertical aquifer vulnerabilities, revealing that the shallow part of the Coastal lowlands aquifer system (CL1) in the southern Baldwin County region is more susceptible to sea level rise and groundwater extraction than deeper aquifers. Based on these findings, a cross-sectional numerical model (physics approach) for the CL1 aquifer was developed to evaluate tidal and storm surge effects, using Tropical Storm Claudette (June 2021) as a case study. Results showed that tidal fluctuations had a minimal impact on the saltwater-freshwater interface location, whereas storm surges caused substantial inland movement, with effects lasting for nine months. The steady-state version of the three-dimensional (3D) physical model predicted seawater intrusion across the entire area, and convolutional neural network-based modeling further validated the model results. The 3D physical model was also applied to a smaller area to assess human impact on the saltwater interface due to two groundwater pumping scenarios (± 50% of the baseline pumping rate). Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-06613-6","usgsCitation":"Gholizadeh, H., Clement, T., Green, C., Tick, G.R., Plattner, A., and Zhang, Y., 2025, Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses: Scientific Reports, v. 15, 21699, 18 p., https://doi.org/10.1038/s41598-025-06613-6.","productDescription":"21699, 18 p.","ipdsId":"IP-175082","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":494188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-06613-6","text":"Publisher Index Page"},{"id":493932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","county":"Baldwin County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.91513509957319,\n 30.59435431566419\n ],\n [\n -87.91513509957319,\n 30.204167726022206\n ],\n [\n -87.32356119618261,\n 30.204167726022206\n ],\n [\n -87.32356119618261,\n 30.59435431566419\n ],\n [\n -87.91513509957319,\n 30.59435431566419\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gholizadeh, Hossein","contributorId":352234,"corporation":false,"usgs":false,"family":"Gholizadeh","given":"Hossein","affiliations":[{"id":84136,"text":"Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":945513,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clement, T. 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Since 2014, multiple large-scale seabird mortality events have occurred in Alaska waters, with STXs detected in some carcasses. To investigate the sublethal behavioral and ecological effects of STX on seabirds, we conducted captive dosing trials with common murres (Uria aalge). We gavaged purified STX (dehydrated STX dihydrocholoride, STX-diHCl) or an Alexandrium catenella culture extract into murres, monitored behavioral responses and recovery times, and assessed tissue concentrations in individuals that died or were euthanized. Using a modified up-and-down dose-finding scheme, we estimated a median effective dose (ED50) of 89 µg STX-equivalents (eq) kg-1 for STX-diHCl and 366 µg STX-eq kg-1 for the A. catenella extract based on ecologically relevant behavior. Differences between the ED50 estimates could reflect uncertainties in toxin equivalency factors for PST congeners, which are based on studies using purified toxins in mice and may vary across taxa or toxin matrices. Post-dosing concentrations of STX varied by tissue type across individuals, with quantifiable levels ranging from 3 to 379 µg STX-eq 100g-1. Evidence of biotransformation of STX in A. catenella extract-dosed birds was observed. We also measured the chronic effects of dosing with sublethal levels of STX-diHCl over seven-days, which resulted in lower fish intake among treatment birds compared to controls (-187 g day-1). This investigation improves our understanding of the ecological effects of PSTs on seabird health.
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With dam removals, many biological outcomes remain understudied due to a lack of pre-impact data and complex ecosystem recovery timeframes. To avoid this, we created the KRRP molecular library, an environmental specimen bank, for long-term curation of environmental nucleic acids collected from the restoration project. We used these initial samples, environmental DNA metabarcoding, and generalized linear mixed-effects models to evaluate patterns of pre-dam removal fish richness and diversity. Demonstrating the suitability to resolve biological differences, the baseline shows that tributary and mainstem streams had greater native fish diversity and 2.3–10.7 times greater native fish species richness than reservoirs. These and future sampling efforts should, at a minimum, allow tracking of fish community response to ecosystem restoration. Anticipating the acceleration of omics innovation, we preserved samples for long-term storage and identified requisite phases for sustained function and adaptation of the molecular library: securing a physical storage facility for genetic material, establishing a governance structure, and confirming support for archive management.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-07042-1","usgsCitation":"Keel, D.J., Karpenko, K., Blankenship, S.M., Schumer, G., O’Rourke, O., Ostberg, C.O., Chase, D.A., and Duda, J.J., 2025, A molecular specimen bank for contemporary and future study captures landscape-scale biodiversity baselines before Klamath River dam removal: Scientific Reports, v. 15, 20679, 16 p., https://doi.org/10.1038/s41598-025-07042-1.","productDescription":"20679, 16 p.","ipdsId":"IP-173262","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":502171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.187198933266,\n 42.294953678476304\n ],\n [\n -122.187198933266,\n 41.842920701832696\n ],\n [\n -121.52967806160167,\n 41.842920701832696\n ],\n [\n -121.52967806160167,\n 42.294953678476304\n ],\n [\n -122.187198933266,\n 42.294953678476304\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Keel, Dylan J. 0009-0006-8445-7033","orcid":"https://orcid.org/0009-0006-8445-7033","contributorId":369238,"corporation":false,"usgs":false,"family":"Keel","given":"Dylan","middleInitial":"J.","affiliations":[{"id":87745,"text":"Resource Environmental Solutions, LLC. 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Workshop participants agreed that several of the remote sensing datasets have potential for wildfire model evaluation. However, participants also identified several barriers and complications to performing a model evaluation including key gaps in wildfire datasets; uncertainties related to model fire-atmosphere reinitiation; lack of ground truthing and atmospheric correction of remotely sensed datasets; and differences in spatial, geolocation, radiometric, and temporal resolutions between the datasets and models. Further, the absence of standardized methodologies for image interpretation, poor understanding of sensor capabilities and limitations, and a lack of automation also hinder model evaluation efforts. Based on feedback from this workshop, USGS fire modelers are considering a project to address the uncertainties related to fire model reinitiation and encouraging fire practitioners to collaborate with remote sensing experts on wildland fires to improve data collection for a broader community of practice. Additionally, multiagency efforts are in development for a comprehensive cross-sensor validation and ground-truth campaign to test spatial, spectral, and geolocation sensor capabilities, determine limitations, and identify observational gaps for future sensor development and acquisition.
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\"}}]}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Seawater intrusion (SWI) affects coastal landscapes worldwide. Here we describe the hydrologic pathways through which SWI occurs - over land via storm surge or tidal flooding, under land via groundwater transport, and through watersheds via natural and artificial surface water channels—and how human modifications to those pathways alter patterns of SWI. We present an approach to advance understanding of spatiotemporal patterns of salinization that integrates these hydrologic pathways, their interactions, and how humans modify them. We use examples across the East Coast of the United States that exemplify mechanisms of salinization that have been reported around the planet to illustrate how hydrologic connectivity and human modifications alter patterns of SWI. Finally, we suggest a path for advancing SWI science that includes (a) deploying standardized and well-distributed sensor networks at local to global scales that intentionally track SWI fronts, (b) employing remote sensing and geospatial imaging techniques targeted at integrating above and belowground patterns of SWI, and (c) continuing to develop data analysis and model-data fusion techniques to measure the extent, understand the effects, and predict the future of coastal salinization.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038720","usgsCitation":"Helton, A., Dennedy-Frank, J., Emanuel, R., Neubauer, S.C., Adams, K., Ardon, M., Band, L., Befus, K.A., Borstlap, H., Duberstein, J., Gold, A., Kominoski John, Manda, A., Michael, H.A., Moysey, S., Myers-Pigg, A., Neville, J.A., Noe, G.E., Panthi, J., Pezeshki, E., Sirianni, M., and Ward.Nicolas, 2025, Over, under, and through: Hydrologic connectivity and the future of coastal landscape salinization: Water Resources Research, v. 61, no. 7, e2024WR038720, 8 p., https://doi.org/10.1029/2024WR038720.","productDescription":"e2024WR038720, 8 p.","ipdsId":"IP-167925","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":492056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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The likelihood of ruptures propagating across fault discontinuities is thought to be partly controlled by fault geometries, rupture direction, and the history of strain release. However, these parameters vary in space and time over multiple earthquake cycles, making it difficult to forecast the likelihood that an earthquake on one fault will trigger rupture on a nearby fault. Here we use tectono-geomorphic mapping of a geometrically complex fault zone in Panamint Valley, southeastern California, to assess spatiotemporal variations of paleo-rupture patterns and geometries of fault discontinuities over multiple earthquake cycles. First, we identify ten generations of late Pleistocene to Holocene alluvium using geomorphic parameters and luminescence dating to constrain ages of alluvium and bracket late Holocene earthquake timing. Then, we quantify slip kinematics using high-resolution structure from motion digital surface models. We find the Panamint Valley transtensional relay (PVTR) hosted four late Holocene earthquakes, bracketed to ~5.8–3.4 ka, ~3.8–2.2 ka, ~2.4–0.6 ka, and ~0.64–0.16 ka, with ~0.6–1.1 m of slip per event, correlative to Mw ≈ 6.7–6.9 earthquakes. Additionally, we find similarities in earthquake timing on the Ash Hill, PVTR, and Panamint Valley faults and similarities in the slip magnitude and slip kinematics between the Ash Hill and PVTR faults, implying that the PVTR may co-rupture with nearby faults. Paleo-rupture patterns indicate that seismogenic strain transfer may occur through the PVTR, along different combinations of fault segments and jump distances, over multiple earthquake cycles. These data highlight the utility of tectono-geomorphic mapping in evaluating paleo-rupture patterns and suggest that the PVTR may act to propagate and/or arrest rupture between the Ash Hill and Panamint Valley faults.
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In particular, the framework facilitates the development of scenarios and models that can help guide change processes toward desirable futures for nature and people. This paper assesses 31 studies that have engaged with the NFF since its introduction in 2020, aiming to identify which research areas have been addressed, and where development needs remain. The applications exhibit a large diversity in terms of locations, spatial scales, methods, outputs, and stakeholder involvement. The most common use of the framework has been in developing visions and scenarios. Nearly all studies engaged with diverse values of nature through the framework’s fundamental value perspectives: ‘Nature for Society’, ‘Nature for Nature’, and ‘Nature as Culture/One with Nature’. While the framework is generally perceived as useful, challenges remain in integrating the NFF across multiple scales and fully incorporating plural values, particularly in measuring relational aspects and avoiding Western-centric biases. Future research priorities include developing integrated, quantitative studies and exploring transformative pathways to enhance the framework's effectiveness in driving sustainable outcomes. 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