Wildlife surveillance programs often use serological data to monitor exposure to pathogens. Diagnostic sensitivity and specificity of a serological assay quantify the true positive and negative rates of the diagnostic assay, respectively. However, an assay’s accuracy can be affected by wild animals’ pathogen exposure history and quality of the sample collected, requiring separate estimates of an assay’s detection ability for wild-sampled animals where an animal’s true disease status is unknown (referred to hereafter as sampling sensitivity and specificity). We assessed the sampling sensitivity and specificity of a Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) surrogate virus neutralization test (sVNT) and conventional virus neutralization tests (cVNT) to detect antibodies for ancestral and Omicron B.1.1.529 variants of SARS-CoV-2 in wild white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus). We studied the influence of sample collection method using paired blood samples collected in serum separator tubes and on Nobuto strips from the same animal. Mean estimates of sampling sensitivity and specificity ranged from 0.21–0.95 and 0.94–1.00, respectively, varying by sample collection method, host species, and SARS-CoV-2 variant targeted by the assay. Broadly, sampling sensitivity was estimated to be higher for 1) sera collected in tubes, 2) detecting pre-Omicron SARS-CoV-2 variants, and 3) sVNT relative to cVNT assays. Sampling specificity tended to be high for all tests. We augmented our study with SARS-CoV-2 spike protein sequences derived from sampling locations and times coincident with white-tailed deer captures, finding common amino acid mutations relative to the sVNT Omicron antigen variant. The mutations may indicate that the SARS-CoV-2 variants circulating in cervids from 2021 through 2024 may be better adapted to cervid hosts and more closely related to variants that circulated in humans prior to Omicron variants. We conclude our study with an inter-test comparison of sVNT results, revealing that 40 % inhibition is an optimal threshold for test positivity when testing deer sera for responses to Omicron variant B.1.1.529, compared to the 30 % inhibition recommended for ancestral variants.
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These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.
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Moreover, time-modulated stochastic models, which adjust ground motion amplitudes over time, typically use functions that fail to capture multiple strong-motion phases. The February 2023 Turkey (Türkiye) earthquake exhibited diverse recordings, including near-fault and far-field motions with pulse-like and non-pulse-like characteristics, along with single and multiple strong-motion phases. To better represent such a diverse set of recordings, this study enhances a fully non-stationary site-based stochastic model without combining it with a deterministic model. Improvements include a new band-pass filter with upper- and lower-frequency limits, which refines the representation of the low-frequency content. Moreover, a time-modulating function that can represent energy arrival in multiple strong phases is introduced. The reference model’s parameters are identified by fitting to the energy content, zero-level crossings, and cumulative counts of positive-minima and negative-maxima of a target accelerogram. This fitting procedure is modified to address the increased number of parameters. These improvements broaden the reference model’s applicability while preserving its simplicity, a key aspect appealing to engineering practitioners. The improved model’s applicability is demonstrated by simulating a dataset from the February 2023 Türkiye earthquake, and the accuracy is tested using a pulse-like Next Generation Attenuation Relationships for Western United States dataset. Validations are performed based on total energy, zero-level crossings, Fourier amplitude spectrum, elastic response spectra, and peak ground motion parameters. Validations are performed schematically in the time and frequency domains and quantitatively using goodness-of-fit scores, various validation-metrics errors, and inter-period correlations. Overall, the improved stochastic model can effectively simulate a set of diverse ground motion recordings, including near-fault pulse-like records, records with multiple strong phases, and far-field motions across a broad frequency range.
","language":"English","publisher":"Sage Publications","doi":"10.1177/87552930251331981","usgsCitation":"Hussaini, S.M., Karimzadeh, S., Rezaeian, S., and Lourenco, P., 2025, Broadband stochastic simulation of earthquake ground motions with multiple strong phases with an application to the 2023 Kahramanmaraş, Turkey (Türkiye), earthquake: Earthquake Spectra, v. 41, no. 3, p. 2399-2435, https://doi.org/10.1177/87552930251331981.","productDescription":"37 p.","startPage":"2399","endPage":"2435","ipdsId":"IP-174088","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":494180,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/87552930251331981","text":"Publisher Index Page"},{"id":493834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","city":"Kahramanmaraş","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hussaini, S. M. Sajad","contributorId":359418,"corporation":false,"usgs":false,"family":"Hussaini","given":"S.","middleInitial":"M. Sajad","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karimzadeh, Shaghayegh","contributorId":359419,"corporation":false,"usgs":false,"family":"Karimzadeh","given":"Shaghayegh","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":945290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lourenco, Paulo B.","contributorId":359420,"corporation":false,"usgs":false,"family":"Lourenco","given":"Paulo B.","affiliations":[{"id":85799,"text":"University of Minho, Portugal","active":true,"usgs":false}],"preferred":false,"id":945291,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70273016,"text":"70273016 - 2025 - Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","interactions":[],"lastModifiedDate":"2025-12-12T15:31:33.490472","indexId":"70273016","displayToPublicDate":"2025-04-25T09:25:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","title":"Daily survival rate and nest-site selection of Zone-tailed Hawks (Buteo albonotatus) in the Chihuahuan Desert ecoregion of Texas","docAbstract":"The Zone-tailed Hawk (Buteo albonotatus) is one of the least studied raptors in North America and lacks contemporary literature allowing informed management decisions for this species. Zone-tailed Hawks occupy rugged areas in the southwestern region of the United States and are listed as state threatened in Texas. Our objectives were to assess habitat, productivity, and daily survival rate (DSR) of Zone-tailed Hawk nests in riparian zones of the Chihuahuan Desert Ecoregion of Texas. We surveyed for Zone-tailed Hawk nests along ∼30 km of 12 riparian corridors in Brewster, Jeff Davis, and Presidio Counties, Texas. We monitored 11 and 15 Zone-tailed Hawk nests in 2018 and 2019, respectively, and conducted vegetation surveys at the nest tree, nest site (11.3-m radius), and paired random locations. We used nest survival modeling to evaluate the effects of eight habitat variables (nest tree diameter at breast height [DBH], nest tree height, nest height, nest distance to main stem, nest to tree height ratio, mean stand height, number of trees within nest site, and mean nest site DBH) on nest DSR. DSR was positively correlated with nest to tree height ratio and nest tree DBH. Zone-tailed Hawk nests had an estimated 0.991 (standard error [SE] = 0.004, 95% CI = 0.980–0.996) constant DSR and ultimately a 51.4% chance of nest success (SE = 0.0943) across the nesting season. Our results suggest that by selecting larger trees for nesting as well as placing nests higher within the tree, Zone-tailed Hawks may increase their chances of successfully fledging young.
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We hypothesized that, in addition to these abiotic influences, mismatch of hosts from their reproductive habitat heightens infection risk via exposure and/or susceptibility mechanisms. We evaluated this “reproductive habitat mismatch hypothesis” by quantifying the interplay of host breeding mode, habitat, and rainfall on Bd infection dynamics using two years of frog survey data—including swab data for 3427 captures representing 44 species—from Brazil’s Atlantic Forest. We modeled infection prevalence, infection intensity, and the number of frogs captured as a function of rainfall, reproductive mode (aquatic or terrestrial), and habitat (aquatic or terrestrial) using hierarchical models. High rainfall was associated with increases in infection prevalence and infection intensity; however, these increases were particularly apparent for species in habitats that were mismatched from the species’ reproductive habitat. Tropical regions experiencing increases in precipitation will likely see higher Bd risk, and our results indicate that such increases in rainfall will be particularly problematic for species that are forced to move from their reproductive habitats by factors such as habitat loss or thermal stress.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2025.e03599","usgsCitation":"Gilbert, N.A., Bell, R.C., Catenazzi, A., Martins, R.A., Buttimer, S., Neely, W.J., Lambertini, C., Saenz Calderon, V., Haddad, C.F., Becker, C.G., and DiRenzo, G.V., 2025, Reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community: Global Ecology and Conservation, v. 60, e03599, 12 p., https://doi.org/10.1016/j.gecco.2025.e03599.","productDescription":"e03599, 12 p.","ipdsId":"IP-171930","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494048,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13TRTVG","text":"USGS data release","linkHelpText":"Code for reproductive habitat mismatch influences chytrid infection dynamics in a tropical amphibian community"},{"id":493796,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2025.e03599","text":"Publisher Index Page"},{"id":493706,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Parque Estadual da Serra do Mar–Núcleo Santa Virgínia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -45.079106081456246,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.236053619691134\n ],\n [\n -45.236468001362624,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.443275890477395\n ],\n [\n -45.079106081456246,\n -23.236053619691134\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Neil A.","contributorId":359068,"corporation":false,"usgs":false,"family":"Gilbert","given":"Neil","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":944942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, Rayna C.","contributorId":359069,"corporation":false,"usgs":false,"family":"Bell","given":"Rayna","middleInitial":"C.","affiliations":[{"id":12937,"text":"California Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":944943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catenazzi, Alessandro","contributorId":359070,"corporation":false,"usgs":false,"family":"Catenazzi","given":"Alessandro","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":944944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martins, Renato A.","contributorId":359071,"corporation":false,"usgs":false,"family":"Martins","given":"Renato","middleInitial":"A.","affiliations":[{"id":85744,"text":"Universidade Federal de São Carlo","active":true,"usgs":false}],"preferred":false,"id":944945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buttimer, Shannon","contributorId":359073,"corporation":false,"usgs":false,"family":"Buttimer","given":"Shannon","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944946,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Neely, Wesley J.","contributorId":359075,"corporation":false,"usgs":false,"family":"Neely","given":"Wesley","middleInitial":"J.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":944947,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lambertini, Carolina","contributorId":359077,"corporation":false,"usgs":false,"family":"Lambertini","given":"Carolina","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944948,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saenz Calderon, Veronica","contributorId":359079,"corporation":false,"usgs":false,"family":"Saenz Calderon","given":"Veronica","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944949,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Haddad, Célio F.B.","contributorId":359081,"corporation":false,"usgs":false,"family":"Haddad","given":"Célio","middleInitial":"F.B.","affiliations":[{"id":48854,"text":"Universidade Estadual Paulista","active":true,"usgs":false}],"preferred":false,"id":944950,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Becker, C. Guilherme","contributorId":359083,"corporation":false,"usgs":false,"family":"Becker","given":"C.","middleInitial":"Guilherme","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":944951,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"DiRenzo, Graziella Vittoria 0000-0001-5264-4762","orcid":"https://orcid.org/0000-0001-5264-4762","contributorId":243404,"corporation":false,"usgs":true,"family":"DiRenzo","given":"Graziella","email":"","middleInitial":"Vittoria","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":944952,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70266162,"text":"70266162 - 2025 - Detection of landslide-generated tsunami by shipborne GNSS precise point positioning","interactions":[],"lastModifiedDate":"2025-04-29T14:12:46.499973","indexId":"70266162","displayToPublicDate":"2025-04-25T09:08:24","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":"Detection of landslide-generated tsunami by shipborne GNSS precise point positioning","docAbstract":"Precise point positioning (PPP) of ships using Global Navigation Satellite System (GNSS) data reveals the precise movements of marine vessels. This method may quantify anomalies in sea surface height with implications for oceanographic monitoring, exploration, and tsunami warning. The GNSS PPP data from the R/V Sikuliaq, a research ship of the University of Alaska Fairbanks, were processed to detect a small local tsunami generated by the Lowell Point landslide, which occurred near Seward, Alaska, on 8 May 2022 (UTC). The GNSS receiver aboard the R/V Sikuliaq recorded the waves generated by the landslide, with a maximum wave amplitude of 6 cm and wave periods between 40 and 50 s. These results are consistent with simulations of the landslide event.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL112472","usgsCitation":"Manaster, A., Sheehan, A.F., Goldberg, D.E., Barnhart, K.R., and Roth, E., 2025, Detection of landslide-generated tsunami by shipborne GNSS precise point positioning: Geophysical Research Letters, v. 52, e2024GL112472, 9 p., https://doi.org/10.1029/2024GL112472.","productDescription":"e2024GL112472, 9 p.","ipdsId":"IP-169782","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487831,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl112472","text":"Publisher Index Page"},{"id":485128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Seward","otherGeospatial":"Resurrection Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -149.60008399844827,\n 60.18626730817803\n ],\n [\n -149.60008399844827,\n 59.864306105004715\n ],\n [\n -149.1691851387203,\n 59.864306105004715\n ],\n [\n -149.1691851387203,\n 60.18626730817803\n ],\n [\n -149.60008399844827,\n 60.18626730817803\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"52","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Manaster, Adam E. 0000-0001-8183-4274","orcid":"https://orcid.org/0000-0001-8183-4274","contributorId":215663,"corporation":false,"usgs":true,"family":"Manaster","given":"Adam E.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheehan, Anne F 0000-0002-9629-1687","orcid":"https://orcid.org/0000-0002-9629-1687","contributorId":224234,"corporation":false,"usgs":false,"family":"Sheehan","given":"Anne","email":"","middleInitial":"F","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":934769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldberg, Dara Elyse 0000-0002-0923-3180","orcid":"https://orcid.org/0000-0002-0923-3180","contributorId":289891,"corporation":false,"usgs":true,"family":"Goldberg","given":"Dara","email":"","middleInitial":"Elyse","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":934770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":934771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roth, Ethan F.","contributorId":353914,"corporation":false,"usgs":false,"family":"Roth","given":"Ethan F.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":934772,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268068,"text":"70268068 - 2025 - Destruction and transformation: Lessons learned from the 2015 Gorkha, Nepal, earthquake","interactions":[],"lastModifiedDate":"2025-12-01T16:08:57.389863","indexId":"70268068","displayToPublicDate":"2025-04-25T09:07:06","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21821,"text":"Temblor","active":true,"publicationSubtype":{"id":10}},"title":"Destruction and transformation: Lessons learned from the 2015 Gorkha, Nepal, earthquake","docAbstract":"No abstract available.
","language":"English","publisher":"Temblor, Inc.","doi":"10.32858/temblor.363","usgsCitation":"Shrestha, S., and Hough, S., 2025, Destruction and transformation: Lessons learned from the 2015 Gorkha, Nepal, earthquake: Temblor, HTML Document, https://doi.org/10.32858/temblor.363.","productDescription":"HTML Document","ipdsId":"IP-176968","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":490507,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","otherGeospatial":"Gorkha district west of Kathmandu Valley","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[88.12044,27.87654],[88.04313,27.44582],[88.1748,26.81041],[88.06024,26.41462],[87.22747,26.3979],[86.02439,26.63098],[85.25178,26.7262],[84.67502,27.2349],[83.30425,27.36451],[81.99999,27.92548],[81.0572,28.4161],[80.08842,28.79447],[80.47672,29.72987],[81.11126,30.18348],[81.5258,30.42272],[82.32751,30.11527],[83.33712,29.46373],[83.89899,29.32023],[84.23458,28.83989],[85.01164,28.64277],[85.82332,28.20358],[86.95452,27.97426],[88.12044,27.87654]]]},\"properties\":{\"name\":\"Nepal\"}}]}","noUsgsAuthors":false,"publicationDate":"2025-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrestha, Surya Narayan","contributorId":356806,"corporation":false,"usgs":false,"family":"Shrestha","given":"Surya Narayan","affiliations":[{"id":37310,"text":"National Society for Earthquake Technology, Nepal","active":true,"usgs":false}],"preferred":false,"id":940115,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":350979,"corporation":false,"usgs":true,"family":"Hough","given":"Susan E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":940116,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70273501,"text":"70273501 - 2025 - Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery","interactions":[],"lastModifiedDate":"2026-01-20T15:43:08.696041","indexId":"70273501","displayToPublicDate":"2025-04-25T08:38:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery","docAbstract":"Mapping the land area used for mining in the past is essential for guiding the remediation of affected landscapes and assessing the resource potential of related waste products. Despite significant recent progress delineating footprints of active and inactive mining globally, the known inventory of such mine lands remains incomplete. Here, I describe a new map dataset of footprints of land surface disturbance and waste at sites of past mining in Alaska (USA) based on visual interpretation of satellite imagery. This dataset maps 6–14 times the area of previous regional and global mine footprint maps in Alaska and is the first in the region to explicitly delineate mine waste landforms (e.g., tailings piles). The data are publicly available from the U.S. Geological Survey under a “no rights reserved” Creative Commons (CC0) license agreement.
","language":"English","doi":"10.1038/s41597-025-05039-z","usgsCitation":"Bender, A., 2025, Footprints of past mining in Alaska (USA) derived from high-resolution satellite imagery: Scientific Data, v. 12, 699, 11 p., https://doi.org/10.1038/s41597-025-05039-z.","productDescription":"699, 11 p.","ipdsId":"IP-170627","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":498983,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05039-z","text":"Publisher Index Page"},{"id":498776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1011","displayTitle":"Genetic Structure and Diversity in Wild Populations of the Light-Footed Ridgway’s Rail Reflect 20 Years of Augmentation Through Captive Breeding and Release","title":"Genetic structure and diversity in wild populations of the Light-footed Ridgway’s Rail reflect 20 years of augmentation through captive breeding and release","docAbstract":"Captive breeding and release programs aimed at recovery of rare species can be informed by genetic data to help select high-diversity source populations, make pairing decisions to minimize inbreeding, and manage release strategies. We developed a set of 54 microsatellite loci to assess genetic structure and diversity across the United States range of the Light-footed Ridgway’s Rail (Rallus obsoletus levipes), a federally endangered marsh bird for which populations have been augmented by a captive breeding program annually since 2001. We identified three regional genetic clusters, with the highest genetic diversity reported in the central cluster, which included all sampled wetlands in north San Diego County. Recent (2019–24) captive-breeding adults all clustered within the northernmost cluster (Orange and Ventura Counties), which was expected given that this cluster included the source wetland for the captive breeding program. Gene flow rates, which approximate the proportions of individuals in a population originating from other populations, were relatively high among clusters (4–24 percent) and may have been enhanced through the release of captive-bred rails. Based on the genetic data analyzed in a genetic rescue decision framework, sourcing new breeding birds from the north San Diego County cluster could provide the greatest genetic diversity benefits. The northernmost cluster, which included Mugu Lagoon and all sampled Orange County wetlands, was considered the most in need of genetic rescue. Recent breeding pairs in the captive breeding program have comparatively low diversity and high interrelatedness. Sourcing birds from wetlands with high genetic diversity and population sizes, assessing genetic relatedness before pairing, and focusing releases in areas that have low estimates of genetic diversity could improve the distribution of genetic diversity across wild populations in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251011","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, Carlsbad Fish and Wildlife Office","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Vandergast, A.G., Smith, J.G., Mitelberg, A., Wood, D.A., Sawyer, K.A., and Conway, C.J., 2025, Genetic structure and diversity in wild populations of the Light-footed Ridgway’s Rail reflect 20 years of augmentation through captive breeding and release: U.S. Geological Survey Open-File Report 2025–1011, 24 p., https://doi.org/10.3133/ofr20251011.","productDescription":"Report: viii, 24 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-169671","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":485012,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1011/coverthb.jpg"},{"id":485013,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1011/ofr20251011.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1011"},{"id":485015,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14CYDJC","text":"USGS data release","description":"USGS data release","linkHelpText":"Microsatellite genotypes for light-footed Ridgway's rail (Rallus obsoletus levipes) sampled in southern California"},{"id":485017,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1011/ofr20251011.XML"},{"id":485014,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1011"},{"id":485016,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1011/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.10331263380175,\n 34.10687099022617\n ],\n [\n -118.03807861620402,\n 33.46101273577156\n ],\n [\n -117.15926055168606,\n 32.54485080859107\n ],\n [\n -116.83812382579282,\n 32.56519890177364\n ],\n [\n -117.45219990652568,\n 33.65951802680661\n ],\n [\n -118.60202603728568,\n 34.09630540323134\n ],\n [\n -119.10331263380175,\n 34.10687099022617\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
Multispecies management can contribute to meeting growing challenges of preserving biodiversity, yet current game and threatened species management often focuses on individual species. Satellite imagery available at high spatial and temporal resolution provides a potential tool to overcome the challenge posed by multispecies management of linking patterns of habitat use among species. We sought to determine whether satellite imagery could be used to describe patterns of species occupancy and inform multispecies management in pine savannas in Georgia, USA. We conducted point-count surveys at 7 sites in 2022 for 3 bird species: Colinus virginianus (Northern Bobwhite), Dryobates borealis (Red-cockaded Woodpecker), and Peucaea aestivalis (Bachman’s Sparrow). We built single-season occupancy models comparing a set of models using covariates collected from field vegetation surveys and another set using covariates extracted from Sentinel-2 satellite imagery. We then used a multi-objective optimization algorithm to identify quasi-optimal management solutions (i.e., sets of covariate values from top satellite imagery metric models). We found that models created using satellite imagery performed well at predicting occupancy of all 3 species as measured by the area under the receiver operating characteristic curve (AUC > 0.8) and had higher AUC scores than field-derived habitat covariate-based models. We found combinations of metrics that could result in high rates of predicted probability of occupancy for all species (within 86% of highest possible occupancy probability), but these combinations did not exist at any of the sites. Our results demonstrate that (1) satellite imagery can allow users to build reliable occupancy models without intensive field-based vegetation surveys; and (2) C. virginianus, D. borealis, and P. aestivalis in pine savanna ecosystems could be simultaneously managed through more frequent burning, changes in canopy cover or by producing suitable heterogeneity of habitats after identifying an appropriate scale of management.
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0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":944955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266101,"text":"fs20253008 - 2025 - U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs","interactions":[],"lastModifiedDate":"2025-04-29T18:25:49.532193","indexId":"fs20253008","displayToPublicDate":"2025-04-24T18:10:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3008","displayTitle":"U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing Cutting-Edge Science to Adapt to Extreme Weather Events and Stakeholder Needs","title":"U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs","docAbstract":"The Rocky Mountains and the Colorado River Basin in the Western United States are complex, interconnected systems that sustain a large variety of species, including tens of millions of humans. These regions face risks from drought, wildfires, invasive plant and animal species, and habitat reduction. Working with many stakeholders, scientists can help to characterize these risks by providing data and analytical tools to inform land and water resource management decisions.
The U.S. Geological Survey 2024 Rocky Mountain Region (Region 7) Science Exchange Workshop, held in April 2024, focused on cutting-edge science techniques, evaluating complex interconnected risks, and coproducing science with science partners and stakeholders. These science topics and communication strategies can be used for developing data, interpretations, and decision support tools needed to provide science that resource managers and other stakeholders can use to better understand complex, dynamic natural systems and develop management strategies to plan for and adapt to risks that threaten human communities and natural ecosystems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253008","usgsCitation":"Andrews, W.J., Titus, T.N., Eng, L.E., Zellman, K.L., Anderson, P.J., and Havens, J.C., 2025, U.S. Geological Survey 2024 Rocky Mountain Region Science Exchange—Showcasing cutting-edge science to adapt to extreme weather events and stakeholder needs: U.S. Geological Survey Fact Sheet 2025-3008, 4 p., https://doi.org/10.3133/fs20253008.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-167985","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":485159,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253008/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3008"},{"id":484983,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3008/coverthb.jpg"},{"id":484985,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3008/fs20253008.pdf","text":"Report","size":"1.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3008"},{"id":485041,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3008/images"},{"id":485042,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3008/fs20253008.xml"}],"contact":"Director, Rocky Mountain Region
U.S. Geological Survey
Box 25046, MS-911
Denver, CO 80225-0046
Atmospheric rivers (ARs) drive most riverine floods on the United States (U.S.) West Coast. However, estimating flood risk based solely on AR intensity and duration is challenging because precipitation phase, antecedent conditions, and physical watershed characteristics (e.g., slope and soil depth) can influence the magnitude of floods. Here, we analyze how antecedent soil moisture (ASM) conditions contribute to variability in streamflow during AR events and how that changes across climatic regimes and physiography in 122 U.S. West Coast watersheds. We identify a robust non-linear relationship between streamflow and ASM during ARs in 89% of watersheds. The inflection point in this relationship represents a watershed-specific critical ASM threshold, above which event maximum streamflow is, on average, two to four and a half times larger. Wet ASM conditions amplify the hydrologic impacts of more frequent but weaker, lower moisture transport AR events, while dry ASM conditions attenuate the hydrologic impacts that stronger, higher moisture transport AR events could otherwise cause. Our research shows that watersheds prone to ASM-amplified streamflows have higher evaporation ratios, lower cold-season precipitation, lower snow-to-rain ratios, and shallower, clay-rich soils. Higher evaporation and lower precipitation lead to greater ASM variability during the cold season, increasing streamflow during wet periods and buffering streamflow during dry periods. Lower snow fraction and shallower soils limit the antecedent water storage capacity of a watershed, contributing to greater sensitivity of streamflow peaks to ASM variability. Incorporating ASM thresholds into hydrologic models in these regions prone to AR-amplified streamflow could improve forecasts and decrease uncertainty.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-24-0078.1","collaboration":"Desert Research Institute, Reno, NV","usgsCitation":"Webb, M., Albano, C., Harpold, A., Wagner, D.M., and Wilson, A.M., 2025, Wet antecedent soil moisture increases atmospheric river streamflow magnitudes non-linearly: Journal of Hydrometeorology, v. 26, no. 6, p. 741-758, https://doi.org/10.1175/JHM-D-24-0078.1.","productDescription":"18 p.","startPage":"741","endPage":"758","ipdsId":"IP-166108","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":485998,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.92121191786663,\n 33.407810452030205\n ],\n [\n -116.523784768193,\n 36.41502072713284\n ],\n [\n -119.64994516515054,\n 39.88994270690637\n ],\n [\n -120.51138519071847,\n 42.20041323206641\n ],\n [\n -119.3720758190301,\n 48.777534854733574\n ],\n [\n -122.9287960859929,\n 48.977595241145025\n ],\n [\n -123.36076938971661,\n 48.210450784064506\n ],\n [\n -125.09530509112804,\n 48.5114622057049\n ],\n [\n -124.0899142321606,\n 45.77238715755897\n ],\n [\n -124.63673786495912,\n 42.844084204189784\n ],\n [\n -124.31096484013645,\n 41.342509106103535\n ],\n [\n -124.81776995325123,\n 40.42865225014583\n ],\n [\n -123.57485679616491,\n 38.65999426544576\n ],\n [\n -122.45718830306802,\n 37.1653556881036\n ],\n [\n -121.77165200602349,\n 35.90307547409293\n ],\n [\n -120.64970139084915,\n 34.560810038256434\n ],\n [\n -117.92121191786663,\n 33.407810452030205\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Mariana J. 0000-0003-0331-2635","orcid":"https://orcid.org/0000-0003-0331-2635","contributorId":353576,"corporation":false,"usgs":false,"family":"Webb","given":"Mariana J.","affiliations":[{"id":84438,"text":"Division of Hydrological Sciences, Desert Research Institute, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":933999,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albano, Christine M.","contributorId":17681,"corporation":false,"usgs":true,"family":"Albano","given":"Christine M.","affiliations":[],"preferred":false,"id":934000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harpold, Adrian A. 0000-0002-2566-9574","orcid":"https://orcid.org/0000-0002-2566-9574","contributorId":353577,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian A.","affiliations":[{"id":84439,"text":"Dept. of Natural Resources and Environmental Science, Univ. of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":934001,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Daniel M. 0000-0002-0432-450X dwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-0432-450X","contributorId":4531,"corporation":false,"usgs":true,"family":"Wagner","given":"Daniel","email":"dwagner@usgs.gov","middleInitial":"M.","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":934002,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wilson, Anna M.","contributorId":211536,"corporation":false,"usgs":false,"family":"Wilson","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":934003,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266282,"text":"70266282 - 2025 - Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","interactions":[],"lastModifiedDate":"2025-05-02T14:54:35.492386","indexId":"70266282","displayToPublicDate":"2025-04-24T09:53:58","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning","docAbstract":"Tracking the extent of seasonal snow on glaciers over time is critical for assessing glacier vulnerability and the response of glacierized watersheds to climate change. Existing snow cover products do not reliably distinguish seasonal snow from glacier ice and firn, preventing their use for glacier snow cover detection. Despite previous efforts to classify glacier surface facies using machine learning on local scales, currently there is no published comparison of machine learning models for classifying glacier snow cover across different satellite image products. We present an automated snow detection workflow for mountain glaciers using supervised machine-learning-based image classifiers and Landsat 8 and 9, Sentinel-2, and PlanetScope satellite imagery. We develop the image classifiers by testing numerous machine learning algorithms with training and validation data from the U.S. Geological Survey Benchmark Glacier Project glaciers. The workflow produces daily to twice monthly time series of several glacier mass balance and snowmelt indicators (snow-covered area, accumulation area ratio, and seasonal snow line) from 2013 to present. Workflow performance is assessed by comparing automatically classified images and snow lines to manual interpretations at each glacier site. The image classifiers exhibit overall accuracies of 92%–98%, K scores of 84%–96%, and F scores of 93%–98% for all image products. The median difference between automatically and manually delineated median snow line altitudes is 31m (IQR of 73to0m)across all image products. The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and f irn the most reliably. Although they are less accurate, the Landsat- and PlanetScope-derived estimates greatly enhance the temporal coverage of observations. The transient accumulation area ratio produces the least noisy time series, making it the most reliable indicator for characterizing seasonal snow trends. The temporally detailed accumulation area ratio time series reveal that the timing of minimum snow cover conditions varies by up to a month between Arctic (63°N) and midlatitude (48°N) sites, underscoring the potential for bias when estimating glacier minimum snow cover conditions from a single late-summer image. Widespread application of our automated snow detection workflow has the potential to improve regional assessments of glacier mass balance, land ice representations within Earth system models, water resources, and the impacts of climate change on snow cover across broad spatial scales.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/tc-19-1675-2025","usgsCitation":"Aberle, R., Enderlin, E., O'Neel, S., Florentine, C., Sass, L., Dickson, A., Marshall, H., and Flores, A., 2025, Automated snow cover detection on mountain glaciers usingspaceborne imagery and machine learning: The Cryosphere, v. 19, p. 1675-1693, https://doi.org/10.5194/tc-19-1675-2025.","productDescription":"19 p.","startPage":"1675","endPage":"1693","ipdsId":"IP-161789","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":487924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-19-1675-2025","text":"Publisher Index Page"},{"id":485326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Unite States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.76360091465392,\n 47.06180837633883\n ],\n [\n -121.3176201884703,\n 48.805343460206615\n ],\n [\n -120.0015482436147,\n 50.335300241584264\n ],\n [\n -130.45623413084917,\n 62.431155673423405\n ],\n [\n -144.1767604606605,\n 64.77803371822662\n ],\n [\n -156.74065293077143,\n 62.94965967944643\n ],\n [\n -153.58716512013862,\n 58.34813914012679\n ],\n [\n -151.25294354192766,\n 56.670611414741046\n ],\n [\n -147.2303378681512,\n 59.785680900702204\n ],\n [\n -139.72127967128716,\n 59.315677918279675\n ],\n [\n -134.82069406634898,\n 55.48786621236388\n ],\n [\n -131.59648302534626,\n 51.752911462025\n ],\n [\n -123.76360091465392,\n 47.06180837633883\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Aberle, Rainey","contributorId":354302,"corporation":false,"usgs":false,"family":"Aberle","given":"Rainey","affiliations":[],"preferred":false,"id":935376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":935377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":935378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":935381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickson, Adam","contributorId":354305,"corporation":false,"usgs":false,"family":"Dickson","given":"Adam","affiliations":[],"preferred":false,"id":935383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marshall, Hans-Peter","contributorId":330964,"corporation":false,"usgs":false,"family":"Marshall","given":"Hans-Peter","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":935379,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":935382,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70273766,"text":"70273766 - 2025 - Movement patterns of invasive red swamp crayfish vary with sex and environmental factors","interactions":[],"lastModifiedDate":"2026-01-28T16:36:02.719179","indexId":"70273766","displayToPublicDate":"2025-04-24T09:27:43","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":"Movement patterns of invasive red swamp crayfish vary with sex and environmental factors","docAbstract":"Invasive species disproportionately invade freshwater ecosystems, threatening biodiversity. Defining when, where, and why aquatic invasive species move can help inform management strategies, yet the movement ecology of some of the most pervasive invasive species remains unknown. Red swamp crayfish (Procambarus clarkii; RSC) are the most widespread invasive crayfish and negatively affect ecosystems worldwide. We employed high-dimensional acoustic telemetry to investigate the movement patterns of 24 individual RSC across three months in an invaded water body. We assessed the effects of various extrinsic factors, such as time of day, temperature, precipitation, and proximity to the water’s edge, along with intrinsic factors, such as sex, reproductive form, and size, at different scales, including movement steps, range distribution, and behavioral states. We found that movement patterns across all scales were overwhelmingly driven by sex and reproductive form. Furthermore, RSC showed increased overall activity at night and near the water’s edge. By establishing baseline movement patterns and identifying key contributing factors, these findings provide a foundation for the development of adaptive management strategies for controlling invasive RSC populations.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-025-96379-8","usgsCitation":"Raboin, M.J., Roth, B.M., Sullivan, A., Allert, A.L., Stoeckel, J.A., Nathan, L.R., Quebedeaux, K.B., Sholtis, M.D., Smerud, J.R., Erickson, R.A., and Cupp, A.R., 2025, Movement patterns of invasive red swamp crayfish vary with sex and environmental factors: Scientific Reports, v. 15, 14312, 12 p., https://doi.org/10.1038/s41598-025-96379-8.","productDescription":"14312, 12 p.","ipdsId":"IP-172876","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":499329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-96379-8","text":"Publisher Index Page"},{"id":499178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","city":"Novi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.54165103908441,\n 42.52197691173481\n ],\n [\n -83.54165103908441,\n 42.44570736959304\n ],\n [\n -83.43173535865259,\n 42.44570736959304\n ],\n [\n -83.43173535865259,\n 42.52197691173481\n ],\n [\n -83.54165103908441,\n 42.52197691173481\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Raboin, Maggie Jo 0000-0002-1475-7253","orcid":"https://orcid.org/0000-0002-1475-7253","contributorId":317839,"corporation":false,"usgs":true,"family":"Raboin","given":"Maggie","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":954679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roth, Brian 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B.","contributorId":365734,"corporation":false,"usgs":false,"family":"Quebedeaux","given":"Kathleen","middleInitial":"B.","affiliations":[{"id":50471,"text":"Michigan Department of Natural Resources, Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":954685,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sholtis, Matthew D. 0000-0003-1904-8250","orcid":"https://orcid.org/0000-0003-1904-8250","contributorId":317840,"corporation":false,"usgs":true,"family":"Sholtis","given":"Matthew","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":954686,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smerud, Justin R. 0000-0003-4385-7437 jrsmerud@usgs.gov","orcid":"https://orcid.org/0000-0003-4385-7437","contributorId":5031,"corporation":false,"usgs":true,"family":"Smerud","given":"Justin","email":"jrsmerud@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":954687,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":954688,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":954689,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70266821,"text":"70266821 - 2025 - Linking age and social status of cooperative breeders to vulnerability throughout the harvest season","interactions":[],"lastModifiedDate":"2025-06-23T15:22:37.563269","indexId":"70266821","displayToPublicDate":"2025-04-24T09:21:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Linking age and social status of cooperative breeders to vulnerability throughout the harvest season","docAbstract":"Individual behaviors are influenced by environmental, genetic, and demographic factors. Some animals choose to live in groups and cooperatively breed, and their behaviors can change depending on dynamic factors such as group size and composition that affect group persistence. In Idaho, USA, gray wolves (Canis lupus) are harvested annually, providing an opportunity to investigate the effects of harvest and seasonal behaviors on a population of cooperative breeders. These annual hunting and trapping seasons overlap with the dispersal and breeding periods for wolves and we do not know how harvest affects the vulnerability of different sex and age classes during these important biological periods. We applied 9 years (2009–2018) of genetic, age, and harvest data from harvested wolves to investigate how behaviors (dispersal and breeding) and biological drivers might influence the vulnerability of wolves to harvest. We created pedigrees from genotypes of non-invasively collected scats to estimate the expected proportion of the wolf population composed of 3 different age classes (pup, yearling, and sexually mature or ≥2 years old) and compared them to the observed number of each age class harvested during biologically significant periods (i.e., dispersal and breeding). We found that pups were more vulnerable to harvest in December when wolf harvest transitioned largely to trapping (accounts for 66% of harvest), and found evidence that adults were more vulnerable to harvest during their breeding season in January and February. In contrast, we found no difference in the expected versus observed number of wolves ≥2 years old in the harvest during peak dispersal season (December), or in the expected versus observed number of yearlings in the harvest during September and October when pups are mobile and groups of wolves abandon the use of pup-rearing sites. Some age classes were disproportionally harvested during certain periods for specific years, but this was not consistent across all years, suggesting there is more to learn about the vulnerability of different age classes to harvest. We found harvest can disproportionally affect some demographic classes of individuals depending on year, biological period, and harvest type. With wolves continuing to recolonize historical ranges, our approach can benefit managers and future studies with the goal of identifying how interannual harvest affects groups of wolves.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70024","usgsCitation":"Rebholz, P., Bassing, S., Waits, L., and Ausband, D.E., 2025, Linking age and social status of cooperative breeders to vulnerability throughout the harvest season: Journal of Wildlife Management, v. 89, no. 5, e70024, 14 p., https://doi.org/10.1002/jwmg.70024.","productDescription":"e70024, 14 p.","ipdsId":"IP-158746","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485834,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Characteristics of volcanic ash reveal changes in fragmentation and eruption dynamics at Poás volcano, Costa Rica, 2016–2019","docAbstract":"Determining whether fresh magma has reached the surface during a volcanic eruption can provide important information for forecasts of future activity, especially in the early stages of an eruption. However, identifying fresh, juvenile pyroclasts in tephra fall deposits can be challenging and inconclusive. We studied the products of explosions at Poás volcano, Costa Rica, in 2016–2019, a period during which the volcano transitioned from a pressurized, hydrothermally sealed state to an open conduit with increased degassing to the atmosphere. The activity consisted of semi-continuous explosions producing <500-m-high plumes, with the exception of explosions on April 14 and 22, 2017, that produced 4-km-high plumes. We analyzed the grain size distribution, componentry, and particle density of the products of twenty explosions, and collected groundmass glass composition on juvenile particles for three of them. Our work demonstrates varying degrees of magma involvement with the hydrothermal system through time, with juvenile material representing a wide range of abundance (~10–70 vol.%) in deposits of individual explosions. Before early April 2017, we infer that small phreatomagmatic explosions were triggered by contact between magmatic fluids and/or magma and the hydrothermal system, based on the presence of abundant hydrothermal fragments and minor juvenile magma. Concurrent with decreasing hydrothermal component in the deposits, explosions in April–May 2017 eroded the walls of the shallow plumbing system, evidenced by an increase in wall-rock lithics in the deposits. These changes coincided with drying of the crater lake, leading to several magmatic explosions in April-September 2017, whose juvenile-rich deposits are consistent with primary fragmentation of fresh magma in the conduit. The eruptive activity changed after this magmatic phase, and in 2019, small explosions mostly recycled the heterogeneous deposits of previous events, producing fine material with high proportions of recycled particles. All explosions from 2016–2019 remobilized already-emplaced magma from the 1953–1955 eruptive period, although distinct glass compositions between explosions suggest difference in crystallization or they tapped different portions of this magma body. Our work sheds light on the eruption dynamics and shallow plumbing system of this persistently active volcano and provides a case study for understanding the variable efficiency of phreatomagmatic fragmentation.","language":"English","publisher":"Springer Nature","doi":"10.1007/s00445-025-01820-5","usgsCitation":"Cascante, M., Giachetti, T., Wright, H.M., Van Eaton, A.R., and Avard, G., 2025, Characteristics of volcanic ash reveal changes in fragmentation and eruption dynamics at Poás volcano, Costa Rica, 2016–2019: Bulletin of Volcanology, v. 87, 36, 24 p., https://doi.org/10.1007/s00445-025-01820-5.","productDescription":"36, 24 p.","ipdsId":"IP-164818","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488131,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-025-01820-5","text":"Publisher Index Page"},{"id":485455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Poás volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.27139914148874,\n 10.25040774483874\n ],\n [\n -84.27139914148874,\n 10.196657085474186\n ],\n [\n -84.19880426467607,\n 10.196657085474186\n ],\n [\n -84.19880426467607,\n 10.25040774483874\n ],\n [\n -84.27139914148874,\n 10.25040774483874\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Cascante, Monserrat","contributorId":354536,"corporation":false,"usgs":false,"family":"Cascante","given":"Monserrat","affiliations":[{"id":84637,"text":"University of Oregon, OVSICORI-UNA","active":true,"usgs":false}],"preferred":false,"id":935823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giachetti, Thomas 0000-0003-1360-6768","orcid":"https://orcid.org/0000-0003-1360-6768","contributorId":287591,"corporation":false,"usgs":false,"family":"Giachetti","given":"Thomas","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":935824,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Heather M. 0000-0001-9013-507X hwright@usgs.gov","orcid":"https://orcid.org/0000-0001-9013-507X","contributorId":3949,"corporation":false,"usgs":true,"family":"Wright","given":"Heather","email":"hwright@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935825,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":935826,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Avard, Geoffroy","contributorId":173679,"corporation":false,"usgs":false,"family":"Avard","given":"Geoffroy","email":"","affiliations":[{"id":27271,"text":"Observatorio Volcanológico y Sismológico de Costa Rica, Universidad Nacional, Heredia, Costa Rica","active":true,"usgs":false}],"preferred":false,"id":935827,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268481,"text":"70268481 - 2025 - A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities","interactions":[],"lastModifiedDate":"2025-06-27T15:13:12.47879","indexId":"70268481","displayToPublicDate":"2025-04-24T08:09:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities","docAbstract":"As wildfires increasingly affect water-supply watersheds, the demand for models to predict water-quality responses is increasing. This work reviews and synthesizes existing post-wildfire applications of water-quality models in the context of geographic and ecohydrological distribution, hydrologic and water-quality response process representation, model parameterization, model and input data scales, model calibration data availability, as well as calibration and performance evaluation approaches. Emphasis is placed on models that simulate water-quality output, rather than sediment and erosional response as the primary focus. Here, identified gaps and opportunities to advance the post-wildfire application of water-quality models include: 1. applying models in under-represented geographic and ecohydrologic regions, 2. simulating multiple streamflow generation mechanisms, including groundwater, with an emphasis on shifting dominant flow pathways as the landscape recovers following wildfire, 3. adding studies that include the simulation of metals, 4. incorporating more biogeochemical and in-stream processes to model applications, 5. applying finer spatial and temporal resolution of precipitation data input as well as finer spatial resolution hydrologic response units, 6. implementing fully distributed grid or element models or finer resolution response units to capture burn severity heterogeneity, 7. collecting enhanced water-quality data for model calibration and validation, 8. conducting model-intercomparison studies, and 9. developing model parameter value guidance in post-wildfire applications. These identified gaps and opportunities may assist users in deciding on key processes and approaches to consider in modeling post-wildfire water-quality conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179435","usgsCitation":"Shephard, Z.M., Partridge, T.F., Murphy, S.F., Walvoord, M.A., and Ebel, B., 2025, A review of post-wildfire adaptations of surface-water-quality models: Synthesis, gaps, and opportunities: Science of the Total Environment, v. 979, 179435, 15 p., https://doi.org/10.1016/j.scitotenv.2025.179435.","productDescription":"179435, 15 p.","ipdsId":"IP-165381","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":491530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"979","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shephard, Zachary M. 0000-0003-2994-3355","orcid":"https://orcid.org/0000-0003-2994-3355","contributorId":222581,"corporation":false,"usgs":true,"family":"Shephard","given":"Zachary","email":"","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":941495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":941499,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70266130,"text":"70266130 - 2025 - HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","interactions":[],"lastModifiedDate":"2025-04-30T14:55:18.955047","indexId":"70266130","displayToPublicDate":"2025-04-24T07:42:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12552,"text":"Scientific Data - Nature","active":true,"publicationSubtype":{"id":10}},"title":"HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa","docAbstract":"Sub-Saharan Africa (SSA) faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across SSA. Based on agricultural statistics collated by USAID’s Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 546,605 records, primarily spanning from 1980 to 2022, detailing crop production, harvested areas, and yields for 33 countries and 90 crop types, including key cereals in SSA such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it sets an important precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-025-05001-z","usgsCitation":"Lee, D., Anderson, W., Chen, X., Davenport, F., Shukla, S., Sahajpal, R., Budde, M., Rowland, J., Verdin, J., You, L., Ahouangbenon, M., Frankel Davis, K., Kebede, E., Ehrmann, S., Justice, C., and Meyer, C., 2025, HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa: Scientific Data - Nature, v. 12, 690, 13 p., https://doi.org/10.1038/s41597-025-05001-z.","productDescription":"690, 13 p.","ipdsId":"IP-171185","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":487839,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-025-05001-z","text":"Publisher Index Page"},{"id":485134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"sub-Saharan Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -17.424723015261094,\n 17.16083963368456\n ],\n [\n -14.217821051176188,\n 5.539623170864749\n ],\n [\n 4.256539808755463,\n -0.30131645411445973\n ],\n [\n 12.866067481920282,\n -36.38147254759492\n ],\n [\n 38.836875517610935,\n -36.542196807270734\n ],\n [\n 51.14863109990904,\n 4.494257003339012\n ],\n [\n 38.836875517610935,\n 17.16083963368456\n ],\n [\n -17.424723015261094,\n 17.16083963368456\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Donghoon 0000-0001-5438-903X","orcid":"https://orcid.org/0000-0001-5438-903X","contributorId":292417,"corporation":false,"usgs":false,"family":"Lee","given":"Donghoon","email":"","affiliations":[{"id":62899,"text":"Climate Hazards Center, University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":934708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Weston","contributorId":353902,"corporation":false,"usgs":false,"family":"Anderson","given":"Weston","affiliations":[{"id":84523,"text":"NASA Goddard Space Flight Center, Greenbelt, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":934709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Xuan","contributorId":204821,"corporation":false,"usgs":false,"family":"Chen","given":"Xuan","email":"","affiliations":[{"id":36987,"text":"Louisiana State University, College of Coast and 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Matthieu","contributorId":353905,"corporation":false,"usgs":false,"family":"Ahouangbenon","given":"Matthieu","affiliations":[{"id":84528,"text":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716 USA","active":true,"usgs":false}],"preferred":false,"id":934718,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Frankel Davis, Kyle","contributorId":209958,"corporation":false,"usgs":false,"family":"Frankel Davis","given":"Kyle","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":934719,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kebede, Endalkachew","contributorId":353906,"corporation":false,"usgs":false,"family":"Kebede","given":"Endalkachew","affiliations":[{"id":84528,"text":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716 USA","active":true,"usgs":false}],"preferred":false,"id":934720,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ehrmann, Steffen","contributorId":353907,"corporation":false,"usgs":false,"family":"Ehrmann","given":"Steffen","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":934721,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Justice, Christina","contributorId":347086,"corporation":false,"usgs":false,"family":"Justice","given":"Christina","email":"","affiliations":[{"id":37106,"text":"Cherokee Nation","active":true,"usgs":false}],"preferred":false,"id":934722,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Meyer, Carsten","contributorId":193124,"corporation":false,"usgs":false,"family":"Meyer","given":"Carsten","email":"","affiliations":[],"preferred":false,"id":934723,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","interactions":[{"subject":{"id":70266022,"text":"ofr20211030T - 2025 - System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","indexId":"ofr20211030T","publicationYear":"2025","noYear":false,"chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor"},"predicate":"IS_PART_OF","object":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"id":1}],"isPartOf":{"id":70221266,"text":"ofr20211030 - 2021 - System characterization of Earth observation sensors","indexId":"ofr20211030","publicationYear":"2021","noYear":false,"title":"System characterization of Earth observation sensors"},"lastModifiedDate":"2025-04-24T14:11:03.291909","indexId":"ofr20211030T","displayToPublicDate":"2025-04-23T12:23:04","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":"2021-1030","chapter":"T","displayTitle":"System Characterization Report on Resourcesat-2A Linear Imaging Self Scanning-3 Sensor","title":"System characterization report on Resourcesat-2A Linear Imaging Self Scanning-3 sensor","docAbstract":"This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-3 sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence since 2021. These reports present and detail the methodology and procedures for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 for continuity of data and improved temporal resolution. The Resourcesat-2 platform (which includes Resourcesat-2A) is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Linear Imaging Self Scanning-3 and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2022 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances.
To summarize the results, we have determined that this sensor provides an interior geometric performance with mean offsets in the range of 1.75 meters (m; 0.06 pixel) to 6.83 m (0.23 pixel) in easting and −1.83 m (−0.06 pixel) to 1.81 m (0.06 pixel) in northing in band-to-band registration and a root mean square error in the range of 3.81 m (0.13 pixel) to 8.19 m (0.27 pixel) in easting and 2.21 m (0.09 pixel) to 4.72 m (0.16 pixel) in northing.
We have measured an exterior geometric error offset in the range of −21.29 to 6.88 m in easting and −7.35 to −2.63 m in northing, and the root mean square error is in the range of 7.19 to 21.43 m in easting and 3.64 to 8.19 m in northing in comparison to the Landsat 8 Operational Land Imager.
The measured radiometric performance was in the range of −0.002 to 0.031 in offset and 0.701 to 0.940 in slope, and the spatial performance was in the range of 1.204 to 1.265 pixels for full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.251 to 0.277.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many coastal urban areas are prone to extreme pluvial flooding due to limitations in stormwater system capacity, with the additional potential for flooding compounded by storm surge, tides, and waves. Understanding and simulating these processes can improve prediction and flood risk management. Here, we adapt the Coupled Ocean–Atmosphere–Wave–Sediment Transport modeling framework (COAWST) to simulate pluvial flooding from post-tropical Cyclone Ida (2021) in the Jamaica Bay watershed of New York City (NYC). We modify the model to capture the volumetric effects of rainfall and parameterize soil infiltration and a stormwater conveyance system as the drainage rate. We generate a spatially continuous flood map of Ida with a root-mean-square error (RMSE) of 20 cm when compared to high-water marks, useful for understanding Ida's impacts and subsequent mitigation planning. Results show that over 23 km2 and 4621 buildings were flooded deeper than 0.3 m during Ida. Sensitivity analyses are used to study the broader risk from events like Ida (pluvial flooding) as well as potential compound (pluvial–coastal) flooding. Spatial shifting of the storm track within a typical 12 h forecast uncertainty reveals a worst-case scenario that increases this flooded area to 62 km2 (5907 buildings). Shifting Ida's rainfall to coincide with high tide increases this flooded area by 1 km2, a relatively small change due to the lack of significant storm surge. The application of COAWST to this storm event addresses a broader goal of developing the capability to model compound pluvial–coastal flooding by simultaneously representing coastal storm processes such as rain, tide, waves, erosion, and atmosphere–wave–ocean interactions. The sensitivity analysis results underscore the need for detailed flood risk assessments, showing that Ida, already NYC's worst rain event, could have been even more devastating with slight shifts in the storm track.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-29-2043-2025","usgsCitation":"Kasaei, S., Orton, P.M., Ralston, D.K., and Warner, J., 2025, Pluvial and potential compound flooding in a coupled coastal modeling framework: New York City during post-tropical Cyclone Ida (2021): Hydrology and Earth System Sciences, v. 29, no. 8, p. 2043-2058, https://doi.org/10.5194/hess-29-2043-2025.","productDescription":"16 p.","startPage":"2043","endPage":"2058","ipdsId":"IP-168323","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":502086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-29-2043-2025","text":"Publisher Index Page"},{"id":502009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","city":"New York City","otherGeospatial":"Jamaica Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.01902277171598,\n 40.81541595710692\n ],\n [\n -74.01902277171598,\n 40.5619948325492\n ],\n [\n -73.61280152477734,\n 40.5619948325492\n ],\n [\n -73.61280152477734,\n 40.81541595710692\n ],\n [\n -74.01902277171598,\n 40.81541595710692\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"29","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasaei, Shima","contributorId":369142,"corporation":false,"usgs":false,"family":"Kasaei","given":"Shima","affiliations":[{"id":28243,"text":"Stevens Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":958528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Orton, Phillip M.","contributorId":369143,"corporation":false,"usgs":false,"family":"Orton","given":"Phillip","middleInitial":"M.","affiliations":[{"id":28243,"text":"Stevens Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":958529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ralston, David K.","contributorId":369144,"corporation":false,"usgs":false,"family":"Ralston","given":"David","middleInitial":"K.","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":958530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":958531,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266105,"text":"70266105 - 2025 - The relationship between body condition, body composition, and growth in amphibians","interactions":[],"lastModifiedDate":"2025-04-25T15:39:07.679497","indexId":"70266105","displayToPublicDate":"2025-04-23T10:36:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The relationship between body condition, body composition, and growth in amphibians","docAbstract":"Body condition of animals is often assumed to reflect advantages in survival or reproduction, but body condition indices may not reflect body composition, or condition may be unrelated to fitness-associated traits. The relationship between body condition indices and composition has rarely been quantified in amphibians, and body condition has not previously been related to growth in adult amphibians. We used laboratory (quantitative magnetic resonance) and field methods to evaluate the relationship between body composition and the four common body condition indices for wildlife studies (body mass index, Fulton’s index, scaled mass index, and residual index) in two frog and one salamander species in Montana, USA. We then assessed the relationship between body condition and summertime somatic growth during a 3-yr mark-recapture study of one of our study species (Columbia spotted frogs, Rana luteiventris). Correlation of body condition indices with fat and lean mass differed across species, sexes, and whether components were represented as percentages or were scaled based on size. Scaled mass index, residual index, and Fulton’s index were most often well correlated (r > 0.6) with scaled body components, but Fulton’s index was strongly correlated with body length. Scaled mass and residual indices predicted scaled fat relatively well and were uncorrelated with body length. Heavier condition predicted higher growth rates of Columbia spotted frogs, regardless of the index used. Frogs of heavy body condition (90th percentile residual index) grew 0.04 and 0.05 mm/day greater than frogs of light condition (10th percentile) for average length males and females, respectively. Frogs of short body length (10th percentile) grew 0.11 and 0.19 mm/day more than long (90th percentile) males and females, respectively. By examining the relationship between body condition indices and body composition and revealing a link between condition and future growth, our results provide an empirical basis for choosing the most appropriate condition index, as well as a potential link to fitness-related traits.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0320954","usgsCitation":"Hinderer, R., Hossack, B.R., and Eby, L., 2025, The relationship between body condition, body composition, and growth in amphibians: PLoS ONE, v. 20, no. 4, e0320954, 15 p., https://doi.org/10.1371/journal.pone.0320954.","productDescription":"e0320954, 15 p.","ipdsId":"IP-170065","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":487777,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0320954","text":"Publisher Index Page"},{"id":485063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hinderer, Ross K.","contributorId":353872,"corporation":false,"usgs":false,"family":"Hinderer","given":"Ross K.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":934603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":1177,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":934604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eby, Lisa A.","contributorId":353873,"corporation":false,"usgs":false,"family":"Eby","given":"Lisa A.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":934605,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266082,"text":"70266082 - 2025 - Additional common milkweed would help Canada meet its share of the trinational eastern migratory monarch butterfly recovery target","interactions":[],"lastModifiedDate":"2025-04-24T14:58:37.413649","indexId":"70266082","displayToPublicDate":"2025-04-23T09:52:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16690,"text":"Facets","active":true,"publicationSubtype":{"id":10}},"title":"Additional common milkweed would help Canada meet its share of the trinational eastern migratory monarch butterfly recovery target","docAbstract":"The eastern migratory monarch butterfly (Danaus plexippus) population has declined by ∼84% between 1993 and 2024. Population recovery in the Midwestern United States is limited by the availability of the monarch's main host plant for egg laying—common milkweed (Asclepias syriaca). The extent to which common milkweed availability is limiting in other breeding regions is unknown. Our objective was to determine whether Canada has enough common milkweed to support its share of the trinational eastern migratory monarch population recovery target, given ∼29 stems of common milkweed are needed to contribute one adult monarch into the fall migratory population. To meet this objective, we estimated the number of common milkweed stems in Canada using published common milkweed availability estimates by land cover type. We also estimated the size of the Canadian monarch population if the recovery target was achieved using published estimates of wintering monarch density in Mexico, fall migration survival rates, and the relative proportion of monarchs entering fall migration from Canada. We estimate that Canada currently has 484 million common milkweed stems (range: 111 million–1 billion stems) and increasing this amount by 1.61 times (i.e., by ∼295 million stems), or equivalently, by 61%, would support the recovery target.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/facets-2024-0063","usgsCitation":"Mitchell, G.W., Kirby, P., Duffe, J., Fahrig, L., Girard, J., Johnston, M., Larrivee, M., Martin, A., Momeni-Dehaghi, I., Pasher, J., Rezek, E., Shapiro, E., Thogmartin, W.E., and Pouliot, D., 2025, Additional common milkweed would help Canada meet its share of the trinational eastern migratory monarch butterfly recovery target: Facets, v. 10, 14 p., https://doi.org/10.1139/facets-2024-0063.","productDescription":"14 p.","ipdsId":"IP-163947","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487900,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/facets-2024-0063","text":"Publisher Index Page"},{"id":484979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Greg W.","contributorId":317902,"corporation":false,"usgs":false,"family":"Mitchell","given":"Greg","email":"","middleInitial":"W.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kirby, Patrick","contributorId":353821,"corporation":false,"usgs":false,"family":"Kirby","given":"Patrick","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duffe, Jason","contributorId":353824,"corporation":false,"usgs":false,"family":"Duffe","given":"Jason","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fahrig, Lenore","contributorId":340627,"corporation":false,"usgs":false,"family":"Fahrig","given":"Lenore","email":"","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":934535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Girard, Judith","contributorId":353827,"corporation":false,"usgs":false,"family":"Girard","given":"Judith","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnston, Mark K.","contributorId":353830,"corporation":false,"usgs":false,"family":"Johnston","given":"Mark K.","affiliations":[{"id":84513,"text":"Field Museum of Natural History, Chicago","active":true,"usgs":false}],"preferred":false,"id":934537,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larrivee, Maxim","contributorId":353831,"corporation":false,"usgs":false,"family":"Larrivee","given":"Maxim","affiliations":[{"id":84516,"text":"Insectarium de Montréal","active":true,"usgs":false}],"preferred":false,"id":934538,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Amanda E.","contributorId":353832,"corporation":false,"usgs":false,"family":"Martin","given":"Amanda E.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934539,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Momeni-Dehaghi, Iman","contributorId":353833,"corporation":false,"usgs":false,"family":"Momeni-Dehaghi","given":"Iman","affiliations":[{"id":84517,"text":"Carlton University","active":true,"usgs":false}],"preferred":false,"id":934540,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pasher, Jon","contributorId":353834,"corporation":false,"usgs":false,"family":"Pasher","given":"Jon","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934541,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rezek, Elizabeth","contributorId":353835,"corporation":false,"usgs":false,"family":"Rezek","given":"Elizabeth","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934542,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shapiro, Elisabeth","contributorId":353836,"corporation":false,"usgs":false,"family":"Shapiro","given":"Elisabeth","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":934543,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":934544,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pouliot, Darren","contributorId":330656,"corporation":false,"usgs":false,"family":"Pouliot","given":"Darren","email":"","affiliations":[{"id":78952,"text":"CCRS, Canada","active":true,"usgs":false}],"preferred":false,"id":934545,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70266396,"text":"70266396 - 2025 - Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus","interactions":[],"lastModifiedDate":"2025-05-06T14:11:54.318665","indexId":"70266396","displayToPublicDate":"2025-04-23T09:02:42","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus","title":"Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus","docAbstract":"Black Carp Mylopharyngodon piceus were imported into the United States in the 1970s and 1980s for use in aquaculture; escape occurred and reported wild captures increased. Lacking species-specific capture methods, we assessed fisheries dependent incidental Black Carp catches for a common method, hoop nets, by kernel density analysis to identify an area of increased reporting and compare frequency of reports for water temperature, river stage, and capture date to identify seasonality. We then used fisheries independent effort to identify co-occurrence of species via non-metric multi-dimensional scaling and fit Black Carp catch and environmental covariates by generalized linear models to assess site-specific environmental covariates facilitating capture. The best approximating distribution was refitted for predictions and inference. The greatest density of fisheries dependent hoop net captures (39 %) was near the confluence of the Missouri and Mississippi rivers, primarily from July-September. Captures were characterized by median water temperature 26.7°C, river stage 5.02 m, and 223 day-of-year (DOY; mid-August). Ordination of fisheries independent catch identified similarity in environmental covariates of Smallmouth Buffalo Ictiobus bubalus and Black Carp. The probability of capturing ≥ 1 Black Carp increased with DOY, decreased with increasing current velocity, and increased with depth. Most captures occurred in outside bends (87 %) or side channels (12 %). Probability of Black Carp capture was low but increased in summer and early fall when stage is lower, facilitating reduced current velocity and access to deeper areas. Results may be validated beyond this river segment to test if site-specific hydrology or habitat characteristics facilitated increased commercial and biologist capture and for replication.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2025.107368","usgsCitation":"Kroboth, P., Colvin, M.E., and Broaddus, C., 2025, Fisheries dependent and independent data inform a capture technique for an emerging invasive fish species in the mainstem Mississippi River; Black Carp Mylopharyngodon piceus: Fisheries Research, v. 285, 107368, 12 p., https://doi.org/10.1016/j.fishres.2025.107368.","productDescription":"107368, 12 p.","ipdsId":"IP-167531","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":487576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2025.107368","text":"Publisher Index Page"},{"id":485444,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Missouri","otherGeospatial":"MIssissippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.22145042868262,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.666188258783194\n ],\n [\n -90.1030809646113,\n 38.90746978465282\n ],\n [\n -90.22145042868262,\n 38.90746978465282\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"285","noUsgsAuthors":false,"publicationDate":"2025-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroboth, Patrick 0000-0002-9447-4818","orcid":"https://orcid.org/0000-0002-9447-4818","contributorId":216578,"corporation":false,"usgs":true,"family":"Kroboth","given":"Patrick","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colvin, Michael E. 0000-0002-6581-4764","orcid":"https://orcid.org/0000-0002-6581-4764","contributorId":331490,"corporation":false,"usgs":true,"family":"Colvin","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Broaddus, Courtney 0000-0003-3851-3584","orcid":"https://orcid.org/0000-0003-3851-3584","contributorId":354595,"corporation":false,"usgs":true,"family":"Broaddus","given":"Courtney","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":935822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266309,"text":"70266309 - 2025 - Comparison of lapilli otoliths and pectoral fin rays for estimating age of Northern Pikeminnows","interactions":[],"lastModifiedDate":"2025-05-05T15:32:14.722693","indexId":"70266309","displayToPublicDate":"2025-04-23T08:25:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2900,"text":"Northwest Science","onlineIssn":"2161-9859","printIssn":"0029-344X","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of lapilli otoliths and pectoral fin rays for estimating age of Northern Pikeminnows","docAbstract":"The Northern Pikeminnow Ptychocheilus oregonensis is a piscivorous cyprinid native to western North America. Information on the best structure for estimating the age of Northern Pikeminnows is a key knowledge gap that may limit inquiries on management efforts. Thus, the objective of this study was to evaluate between-reader precision and concordance between age estimates for lapilli otoliths and pectoral fin rays from Northern Pikeminnows. Age estimates from lapilli otoliths were compared to those from pectoral fin rays of 150 Northern Pikeminnows captured from Lake Cascade, Idaho, in April–May 2022. Exact percent agreement of estimated ages between the readers was higher for fin rays (75.3%) than otoliths (50.0%), with a mean coefficient of variation of 3.5 and 8.7, respectively. Readers also assigned a confidence rating (0–3; higher value reflects higher confidence in age estimate) to each structure. Confidence ratings were higher for fin ray age estimates (mean ± SD; 1.6 ± 0.6) than otolith estimates (1.1 ± 0.7) between readers. A consensus age was estimated for each structure and fish. Agreement between consensus age estimates for otoliths and fin rays was 26.7% with a coefficient of variation of 14.0. Our findings suggest that fin rays were easier to collect, process, and read than otoliths, and resulted in more precise age estimates than otoliths. Results from our study provide guidance on the best structures for estimating the age of Northern Pikeminnows that can be used to inform management efforts.
","language":"English","publisher":"BioOne","doi":"10.3955/046.098.0106","usgsCitation":"Wilson, C., Marciniak, B., Thomas, M., Messner, J., Corsi, M., and Quist, M.C., 2025, Comparison of lapilli otoliths and pectoral fin rays for estimating age of Northern Pikeminnows: Northwest Science, v. 98, no. 1, p. 79-85, https://doi.org/10.3955/046.098.0106.","productDescription":"7 p.","startPage":"79","endPage":"85","ipdsId":"IP-166941","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Lake Cascade","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.15719052395744,\n 44.765077659324646\n ],\n [\n -116.15719052395744,\n 44.472061689535565\n ],\n [\n -116.02527503288141,\n 44.472061689535565\n ],\n [\n -116.02527503288141,\n 44.765077659324646\n ],\n [\n -116.15719052395744,\n 44.765077659324646\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"98","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Caleb M.","contributorId":354458,"corporation":false,"usgs":false,"family":"Wilson","given":"Caleb M.","affiliations":[],"preferred":false,"id":935724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marciniak, Bryce","contributorId":354376,"corporation":false,"usgs":false,"family":"Marciniak","given":"Bryce","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":935524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Mike","contributorId":354377,"corporation":false,"usgs":false,"family":"Thomas","given":"Mike","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":935525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Messner, Jordan","contributorId":354378,"corporation":false,"usgs":false,"family":"Messner","given":"Jordan","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":935526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corsi, Matthew P.","contributorId":171811,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew P.","affiliations":[],"preferred":false,"id":935527,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935528,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}