Grass carp (Ctenopharyngodon idella) are non-indigenous to North America having been translocated to the United States in the 1960s as a potential non-chemical solution for nuisance aquatic vegetation. Reproductively viable grass carp now exist in many watersheds in the United States. In the Great Lakes basin, grass carp were first discovered in the 1980s with direct confirmation of successful reproduction in 2015 via collection of fertilized grass carp eggs in the Sandusky River. Early life stage monitoring also confirmed reproduction in the Maumee River in 2017. During 2018–2021, no new spawning tributaries were discovered (18 total sampling events in five Great Lakes tributaries). In 2022, fourteen eggs with characteristics similar to grass carp were identified from the Huron River which is a tributary to Lake Erie’s Central Basin. Eggs were identified to species via DNA sequencing and were determined to be grass carp eggs. The confirmation of spawning in the Huron River represents a third spawning tributary in the Lake Erie basin and expands eastward the geographic extent of known grass carp spawning locations. Presently, the ability of the Huron River to support hatching and survival of larval grass carp is unknown. Discovery of the Huron River as a grass carp spawning tributary identifies the value of continued surveillance in Great Lakes tributaries for early life stages and conducting scientific inquiries evaluating the consistency of tributary use and survival of early life stages.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102350","usgsCitation":"Hilling, C.D., Landry, A.J., Roberts, J., Thompson, N., Richter, C.A., Brown, R.E., Mayer, C.M., and Qian, S.S., 2024, First documentation of grass carp spawning in Lake Erie’s Central Basin: Journal of Great Lakes Research, v. 50, no. 3, 102350, 6 p., https://doi.org/10.1016/j.jglr.2024.102350.","productDescription":"102350, 6 p.","ipdsId":"IP-161657","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":434978,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14HMDBT","text":"USGS data release","linkHelpText":"Grass Carp (Ctenopharyngodon idella) Egg Diameter Estimates from the Huron River (Ohio), 2022"},{"id":428068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Huron River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.4658080331037,\n 41.43488681145024\n ],\n [\n -82.75342636612123,\n 41.43488681145024\n ],\n [\n -82.75342636612123,\n 41.027155764237335\n ],\n [\n -82.4658080331037,\n 41.027155764237335\n ],\n [\n -82.4658080331037,\n 41.43488681145024\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":899394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landry, Adam J.","contributorId":335743,"corporation":false,"usgs":false,"family":"Landry","given":"Adam","email":"","middleInitial":"J.","affiliations":[{"id":51831,"text":"Contractor to USGS Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":899395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes 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}],"preferred":true,"id":899396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":899397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":899398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Ryan E.","contributorId":332137,"corporation":false,"usgs":false,"family":"Brown","given":"Ryan","email":"","middleInitial":"E.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":899399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":899400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":899401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253133,"text":"tm19C1 - 2024 - West Nile virus (avian) case definition for wildlife","interactions":[{"subject":{"id":70253133,"text":"tm19C1 - 2024 - West Nile virus (avian) case definition for wildlife","indexId":"tm19C1","publicationYear":"2024","noYear":false,"displayTitle":"West Nile Virus (Avian) Case Definition for Wildlife","title":"West Nile virus (avian) case definition for wildlife"},"predicate":"IS_PART_OF","object":{"id":70251831,"text":"tm19 - 2024 - Case definitions for wildlife diseases","indexId":"tm19","publicationYear":"2024","noYear":false,"title":"Case definitions for wildlife diseases"},"id":1}],"isPartOf":{"id":70251831,"text":"tm19 - 2024 - Case definitions for wildlife diseases","indexId":"tm19","publicationYear":"2024","noYear":false,"title":"Case definitions for wildlife diseases"},"lastModifiedDate":"2024-04-22T23:53:11.155137","indexId":"tm19C1","displayToPublicDate":"2024-04-22T07:19:15","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"19-C1","displayTitle":"West Nile Virus (Avian) Case Definition for Wildlife","title":"West Nile virus (avian) case definition for wildlife","docAbstract":"Diagnostic laboratories receive carcasses and samples for diagnostic evaluation and pathogen/toxin detection. Case definitions bring clarity and consistency to the evaluation process. Their use within and between organizations allows more uniform reporting of diseases and etiologic agents. The intent of a case definition is to provide scientifically based criteria for determining (a) if an individual carcass has a specific disease and degree of confidence in that diagnosis and (b) if there is evidence of a pathogen or toxin in a carcass or sample (for example, swab, tissue sample, skin scraping, blood/serum sample, environmental sample, or other). This case definition is specific to West Nile virus and applies to all avian species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm19C1","collaboration":"Prepared in cooperation with the Canadian Wildlife Health Cooperative","usgsCitation":"Lair, S., Shearn-Bochsler, V., and Zimmer, M., 2024, West Nile virus (avian) case definition for wildlife: U.S. Geological Survey Techniques and Methods, book 19, chap. C1, 10 p., https://doi.org/10.3133/tm19C1.","productDescription":"v, 18 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140693","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":427947,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/19/c1/coverthb.jpg"},{"id":427948,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/19/c1/tm19c1.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":427949,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/19/c1/tm19c1.XML"},{"id":427950,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/19/c1/images/"},{"id":427951,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm19C1/full"}],"contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711
No abstract available.
","language":"English","publisher":"Biotaxa","usgsCitation":"Ray, A.M., Hubbard, J.A., Brown, O.T., Schnaubelt, E.R., Giermakowski, J., Zylstra, E.R., and Hossack, B., 2024, Albinism in American Bullfrog, Lithobates catesbeianus Shaw, 1802 tadpoles from the Gila River, New Mexico, USA: Herpetology Notes, v. 17, p. 239-242.","productDescription":"4 p.","startPage":"239","endPage":"242","ipdsId":"IP-159033","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":428523,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://d42u830l15av3.cloudfront.net/hn/article/view/84320"},{"id":428459,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.70279115291069,\n 39.2445773872339\n ],\n [\n -123.70279115291069,\n 28.62742725483139\n ],\n [\n -102.69693177791086,\n 28.62742725483139\n ],\n [\n -102.69693177791086,\n 39.2445773872339\n ],\n [\n -123.70279115291069,\n 39.2445773872339\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ray, Andrew M.","contributorId":167601,"corporation":false,"usgs":false,"family":"Ray","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":900248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hubbard, J Andy","contributorId":336535,"corporation":false,"usgs":false,"family":"Hubbard","given":"J","email":"","middleInitial":"Andy","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":900249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Owen T","contributorId":336536,"corporation":false,"usgs":false,"family":"Brown","given":"Owen","email":"","middleInitial":"T","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":900250,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schnaubelt, Elizabeth R","contributorId":336537,"corporation":false,"usgs":false,"family":"Schnaubelt","given":"Elizabeth","email":"","middleInitial":"R","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":900251,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Giermakowski, J. Tomasz","contributorId":336539,"corporation":false,"usgs":false,"family":"Giermakowski","given":"J. Tomasz","affiliations":[{"id":18859,"text":"Department of Biology and Museum of Southwestern Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":900252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zylstra, Erin R 0000-0002-2536-0403","orcid":"https://orcid.org/0000-0002-2536-0403","contributorId":218873,"corporation":false,"usgs":false,"family":"Zylstra","given":"Erin","email":"","middleInitial":"R","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":900253,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":900254,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70253177,"text":"70253177 - 2024 - Groundwater sustainability and land subsidence in California’s Central Valley","interactions":[],"lastModifiedDate":"2025-05-09T19:57:50.689646","indexId":"70253177","displayToPublicDate":"2024-04-22T06:38:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater sustainability and land subsidence in California’s Central Valley","docAbstract":"Longitudinal dispersal of migratory fish species can be interrupted by factors that fragment rivers, such as dams and reservoirs with incompatible habitats, and indirect alterations to variables, such as water temperature or turbidity. The endangered pallid sturgeon (Scaphirhynchus albus) population in the Upper Missouri River Basin in North Dakota and Montana is an example of such fragmentation and alteration due to the construction of dams. We applied a high-resolution, 2+-dimensional modelling framework composed of hydrodynamic and Lagrangian particle tracking components to simulate pallid sturgeon larval drift and dispersal along a 33-km section of the Upper Missouri River to evaluate three main issues: a comparison between multidimensional models and traditional 1-dimensional models, the sensitivity of hydrodynamics to channel morphology, and the implications of channel morphology on retention and transport-time metrics for larval fish. The results indicate that multidimensional models better represent breakthrough curves of transporting larvae compared to 1-dimensional models, especially for the long tail of slow drifters in the population. Results also indicate that channel morphology and hydraulic complexity play significant roles in larval dispersal with certain flow conditions and channel features increasing larval retention and providing potential management options to increase survival rates by adjusting flow conditions during spawning events. For example, modelling indicates increased retention times at discharges 23–38% daily flow exceedance, coincident with emergence of mid-channel sandbars. Findings additionally emphasize the need for improved understanding of biological factors that affect larval drift and dispersal.
We conceptualize and test a non-intrusive barrier, comprised of an oblique bubble screen (OBS) oriented at an angle to the mean flow, to prevent the downstream dispersal of invasive carp egg surrogates. Three surrogates of different densities and diameters were tested. Secondary flows created by the OBS were tuned to redirect surrogate eggs to facilitate their capture. Surface particle image velocimetry and acoustic Doppler velocimetry were used to characterize secondary flows. We assessed the influence of airflow rate, OBS angle, mean flow velocity, and surrogate density on particle redirection. In general, redirection efficiency improves by increasing the OBS angle with respect to the cross-section. At a mean flow velocity of 0.75 metres per second (m/s), the OBS system redirected up to 60% (%) of positively buoyant particles (specific gravity SG = 0.9, and diameter d = 7.09 millimetres [mm]) and 40% of semi-buoyant particles (SG = 1.001, d = 3.1 mm). Negatively buoyant particles (SG = 1.04, and d = 5.90 mm) were redirected by the physical structure of the diffuser rather than by OBS-induced flow. The study shows that an OBS system can be used to effectively redirect carp-egg surrogates over a wide range of particle sizes and densities, allowing for selective targeting of undesired particles in streams.
Mange is a skin disease caused by mites that parasitize an animal's skin, often yielding inflamed immune responses and hair loss. At a population level, mange may reduce survival and cause population declines. Many forms of mange can be treated quite effectively when an animal is in hand; however, this is not often feasible for many free-ranging wildlife populations. Some animals, particularly territorial carnivores, will rub or roll to scent mark and transmit information about their presence to other individuals. We posited that rub stations comprised, in part, of anthelmintic medication and foreign scents that induce rubbing could be used to remotely treat mange in the wild. We deployed 39 rub stations containing lure and dye in Santa Monica Mountains National Recreation Area, Southern California, USA, October–November 2022. Carnivores rubbed or rolled at .97% of rub stations, with coyotes (Canis latrans), gray foxes (Urocyon cinereoargenteus), and bobcats (Lynx rufus) being the most abundant species. Time to first rub or roll was generally <1 wk. Several sympatric species (e.g., mule deer, Odocoileus hemionus) were detected at rub stations but did not rub. Our pilot test provides strong evidence that treating mange in wild carnivores may be possible using the remote medicinal rub stations we describe. Future efforts to add medicine to rub stations and monitor for a change in mange prevalence are a logical next step.
Sturgeons are among the most endangered fishes in the world. Identifying habitat use characteristics to inform restoration projects is crucial for recovery. However, small sample sizes, inadequate replication of studies, and limited spatial extents complicate our ability to effectively apply the findings of single studies to endangered species conservation across the larger riverscape. We synthesized information from amphidromous and anadromous sturgeons in North America to identify species-specific knowledge gaps and conduct a quantitative comparison of species–habitat relationships. We provided a qualitative summary of substrate use and synthesized estimates of depth and velocity during spawning and non-spawning activity. Generalized patterns among species were identified, such as spawning in fast water on hard substrate and then using slow water with soft substrate areas when not spawning. We noted species-specific variability during spawning that may be attributed to historical maximum length, egg characteristics, and watershed features. This study provides some of the first estimates of habitat use that can be adapted for many populations. Results can contribute to empirically grounded decision-support tools used to prioritize information needs for recovery.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0222","usgsCitation":"Gilligan-Lunda, E.K., Duarte, A., and Peterson, J., 2024, Habitat use of anadromous and amphidromous sturgeons in North America: A systematic review: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 5, p. 508-524, https://doi.org/10.1139/cjfas-2023-0222.","productDescription":"17 p.","startPage":"508","endPage":"524","ipdsId":"IP-156404","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilligan-Lunda, Erin K.","contributorId":339252,"corporation":false,"usgs":false,"family":"Gilligan-Lunda","given":"Erin","email":"","middleInitial":"K.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":339254,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":903887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903888,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257486,"text":"70257486 - 2024 - Cyclic injection leads to larger and more frequent induced earthquakes under volume-controlled conditions","interactions":[],"lastModifiedDate":"2024-08-16T15:38:11.03954","indexId":"70257486","displayToPublicDate":"2024-04-19T10:34:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Cyclic injection leads to larger and more frequent induced earthquakes under volume-controlled conditions","docAbstract":"As carbon storage technologies advance globally, methods to understand and mitigate induced earthquakes become increasingly important. Although the physical processes that relate increased subsurface pore pressure changes to induced earthquakes have long been known, reliable methods to forecast and control induced seismic sequences remain elusive. Suggested reservoir engineering scenarios for mitigating induced earthquakes typically involve modulation of the injection rate. Some operators have implemented periodic shutdowns (i.e., effective cycling of injection rates) to allow reservoir pressures to equilibrate (e.g., Paradox Valley) or shut‐in wells after the occurrence of an event of concern (e.g., Basel, Switzerland). Other proposed scenarios include altering injection rates, actively managing pressures through coproduction of fluids, and preinjection brine extraction. In this work, we use 3D physics‐based earthquake simulations to understand the effects of different injection scenarios on induced earthquake rates, maximum event magnitudes, and postinjection seismicity. For comparability, the modeled injection considers the same cumulative volume over the project’s operational life but varies the schedule and rates of fluid injected. Simulation results show that cyclic injection leads to more frequent and larger events than constant injection. Furthermore, with intermittent injection scenario, a significant number of events are shown to occur during pauses in injection, and the seismicity rate remains elevated for longer into the postinjection phase compared to the constant injection scenario.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220230330","usgsCitation":"Kroll, K.A., and Cochran, E.S., 2024, Cyclic injection leads to larger and more frequent induced earthquakes under volume-controlled conditions: Seismological Research Letters, v. 95, p. 2105-2117, https://doi.org/10.1785/0220230330.","productDescription":"13 p.","startPage":"2105","endPage":"2117","ipdsId":"IP-157897","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489102,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/2377896","text":"External Repository"},{"id":432863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"95","noUsgsAuthors":false,"publicationDate":"2024-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kroll, Kayla A.","contributorId":146335,"corporation":false,"usgs":false,"family":"Kroll","given":"Kayla","email":"","middleInitial":"A.","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":910519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910520,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70253174,"text":"70253174 - 2024 - Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States","interactions":[],"lastModifiedDate":"2024-04-23T11:58:30.217736","indexId":"70253174","displayToPublicDate":"2024-04-19T06:55:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17467,"text":"Machine Learning with Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States","docAbstract":"The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
To better understand the potential for amplified ground shaking at sites that house critical infrastructure, the U.S. Geological Survey (USGS) evaluated shear-wave velocities (VS) at six strong-motion recording stations in Southern California Edison facilities in southern California. We calculated VS30 (time-averaged shear-wave velocity in the upper 30 meters [m]), which is a parameter used in ground-motion prediction equations (GMPEs) to account for site amplification (Building Safety Seismic Council, 2003; Holtzer and others, 2005; Baltay and Boatwright, 2015). Previous site-characterization studies using multiple methods in Alameda, Napa, and Sonoma Counties, Calif., and in British Columbia (Catchings and others, 2017, 2019; Chan and others, 2018a, 2018b) show that some sites have significant lateral variability; thus, a single measurement of VS30 nearest to the strong-motion recording station may not accurately account for the actual subsurface velocity variations. In the summer of 2017, we recorded body and surface waves along linear profiles (118–174 m long) using active-source seismic methods (226-kilogram [kg] accelerated weight-drop and 3.5-kg sledgehammer impacts) near strong-motion recording stations. We used S-wave refraction tomography and a multichannel analysis of surface waves (MASW) method (using common midpoint cross-correlation; CMPCC) to evaluate two-dimensional (2-D) VS from body and surface waves, respectively. We evaluated VS from both Rayleigh- and Love-waves.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241016","usgsCitation":"Chan, J.H., Catchings, R.D., Goldman, M.R., Criley, C.J., and Sickler, R.R., 2024, Evaluation of 2-D shear-wave velocity models and VS30 at six strong-motion recording stations in southern California using multichannel analysis of surface waves and refraction tomography: U.S. Geological Survey Open-File Report 2024–1016, 58 p., https://doi.org/10.3133/ofr20241016.","productDescription":"Report: vii, 58 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-132062","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":499241,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116364.htm","linkFileType":{"id":5,"text":"html"}},{"id":427913,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990O55F","text":"USGS Data Release","description":"Chan, J.H., Catchings, R.D., Goldman, M.R, Criley, C.J., and Sickler, R.R., 2021, High-resolution seismic data acquired at six Southern California seismic network (SCSN) recording stations in 2017: U.S. Geological Survey data release, https://doi.org/10.5066/P990O55F.","linkHelpText":"High-resolution seismic data acquired at six Southern California seismic network (SCSN) recording stations in 2017"},{"id":427918,"rank":6,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1016/coverthb.jpg"},{"id":427915,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1016/images"},{"id":427914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1016/ofr20241016.pdf","text":"Report","size":"20 MB"},{"id":427917,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241016/full"},{"id":427916,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1016/ofr20241016.xml"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.02522723790126,\n 35.189340133329225\n ],\n [\n -121.02522723790126,\n 33.05031372839122\n ],\n [\n -115.5979811441514,\n 33.05031372839122\n ],\n [\n -115.5979811441514,\n 35.189340133329225\n ],\n [\n -121.02522723790126,\n 35.189340133329225\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The Ocean Exploration Trust 2023 field season","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","doi":"10.62878/vud148","usgsCitation":"Mueller, M., Morrison, C., Sowers, D., and Romanek, C., 2024, Collaborative mapping and characterization offshore of the Hawaiian Islands, 3 p., https://doi.org/10.62878/vud148.","productDescription":"3 p.","startPage":"60","endPage":"62","ipdsId":"IP-162348","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":483872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.79226809491234,\n 18.85892200077457\n ],\n [\n -156.72514139674018,\n 20.25295786943228\n ],\n [\n -157.5120022734238,\n 21.09913250792914\n ],\n [\n -158.17224185960652,\n 18.014414343272335\n ],\n [\n -158.02753181331997,\n 17.10038293098839\n ],\n [\n -156.03776867687873,\n 17.04850850228614\n ],\n [\n -155.79226809491234,\n 18.85892200077457\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Mueller, Mark","contributorId":331034,"corporation":false,"usgs":false,"family":"Mueller","given":"Mark","email":"","affiliations":[{"id":79094,"text":"Bureau of Ocean Energy Management, Office of Environmental Programs","active":true,"usgs":false}],"preferred":false,"id":932043,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrison, Cheryl L. 0000-0001-9425-691X","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":239844,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":932044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sowers, Derek","contributorId":214036,"corporation":false,"usgs":false,"family":"Sowers","given":"Derek","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932045,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanek, Christopher","contributorId":207026,"corporation":false,"usgs":false,"family":"Romanek","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":932046,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265886,"text":"70265886 - 2024 - Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California","interactions":[],"lastModifiedDate":"2025-04-18T14:40:37.364459","indexId":"70265886","displayToPublicDate":"2024-04-18T09:33:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California","docAbstract":"Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131226","usgsCitation":"Earll, M.M., Henson, W.R., Lockwood, B., and Boyce, S.E., 2024, Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California: Journal of Hydrology, v. 636, 131226, 17 p., https://doi.org/10.1016/j.jhydrol.2024.131226.","productDescription":"131226, 17 p.","ipdsId":"IP-118873","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":484763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.92596725165258,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 37.07637156314877\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"636","noUsgsAuthors":false,"publicationDate":"2024-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Earll, Marisa M. 0000-0002-4367-2013 mearll@usgs.gov","orcid":"https://orcid.org/0000-0002-4367-2013","contributorId":223723,"corporation":false,"usgs":true,"family":"Earll","given":"Marisa","email":"mearll@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockwood, Brian","contributorId":80202,"corporation":false,"usgs":true,"family":"Lockwood","given":"Brian","email":"","affiliations":[],"preferred":false,"id":933960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261736,"text":"70261736 - 2024 - Mechanisms, detections, and impacts of species redistributions under climate change","interactions":[],"lastModifiedDate":"2024-12-20T15:48:11.066677","indexId":"70261736","displayToPublicDate":"2024-04-18T09:23:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7460,"text":"Nature Reviews Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms, detections, and impacts of species redistributions under climate change","docAbstract":"Shifts in species distributions are a common ecological response to climate change, and global temperature rise is often hypothesized as the primary driver. However, the directions and rates of distribution shifts are highly variable across species, systems, and studies, complicating efforts to manage and anticipate biodiversity responses to anthropogenic change. In this Review, we summarize approaches to documenting species range shifts, discuss why observed range shifts often do not match our expectations, and explore the impacts of species range shifts on nature and society. The majority (59%) of documented range shifts are directionally consistent with climate change, based on the BioShifts database of range shift observations. However, many observed species have not shifted or have shifted in directions opposite to temperature-based expectations. These lagging or expectation-contrary shifts might be explained by additional biotic or abiotic factors driving range shifts, including additional non-temperature climatic drivers, habitat characteristics, and species interactions, which are not normally considered in range shift documentations. Understanding and managing range shifts will require increasing and connecting observational biological data, generalizing range shift patterns across systems, and predicting shifts at management-relevant timescales.
","language":"English","publisher":"Nature","doi":"10.1038/s43017-024-00527-z","usgsCitation":"Lawlor, J.A., Comte, L., Grenouillet, G., Baecher, J.A., Bandara, R., Bertrand, R., Chen, I., Diamond, S.E., Lancaster, L.T., Moore, N., Murienne, J., Oliveira, B.F., Pecl, G.T., Pinsky, M., Rolland, J., Rubenstein, M.A., Scheffers, B.R., Thompson, L., van Amerom, B., Villalobos, F., Weiskopf, S.R., and Sunday, J., 2024, Mechanisms, detections, and impacts of species redistributions under climate change: Nature Reviews Earth & Environment, v. 5, p. 351-368, https://doi.org/10.1038/s43017-024-00527-z.","productDescription":"18 p.","startPage":"351","endPage":"368","ipdsId":"IP-154985","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":467014,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://u-picardie.hal.science/hal-04554209","text":"External Repository"},{"id":465400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2024-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Lawlor, Jake A.","contributorId":347427,"corporation":false,"usgs":false,"family":"Lawlor","given":"Jake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":921691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Comte, Lise","contributorId":304119,"corporation":false,"usgs":false,"family":"Comte","given":"Lise","email":"","affiliations":[{"id":65974,"text":"College of Arts and Sciences, Illinois State University","active":true,"usgs":false}],"preferred":false,"id":921692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grenouillet, Gael","contributorId":347428,"corporation":false,"usgs":false,"family":"Grenouillet","given":"Gael","email":"","affiliations":[],"preferred":false,"id":921693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baecher, J. 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Tasmania","active":true,"usgs":false}],"preferred":false,"id":921704,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pinsky, Malin","contributorId":191589,"corporation":false,"usgs":false,"family":"Pinsky","given":"Malin","email":"","affiliations":[],"preferred":false,"id":921705,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rolland, Jonathan","contributorId":347437,"corporation":false,"usgs":false,"family":"Rolland","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":921706,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rubenstein, Madeleine A. 0000-0001-8569-781X mrubenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-8569-781X","contributorId":203206,"corporation":false,"usgs":true,"family":"Rubenstein","given":"Madeleine","email":"mrubenstein@usgs.gov","middleInitial":"A.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science 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","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Fair, J.H., Letcher, B., and Goodling, P.J., 2024, Updates to the Flow Photo Explorer tool: Flow Photo Explorer, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-164874","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":433247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":433246,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://content.govdelivery.com/accounts/USDOIGS/bulletins/397081f","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":909819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909820,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70253895,"text":"70253895 - 2024 - Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","interactions":[],"lastModifiedDate":"2024-05-20T15:42:40.394048","indexId":"70253895","displayToPublicDate":"2024-04-18T09:07:55","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","title":"Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","docAbstract":"Control of invasive grass carp (Ctenopharyngodon idella) populations in the Western Lake Erie Basin merits adaptive management guided by the best available science. Presently (2024), capture of mature grass carp in rivers during spawning season is most efficient, so knowing when and where grass carp are spawning is essential information for natural resource agencies. Using bidirectional drift modeling and grass carp ichthyoplankton samples captured in the Maumee River during the 2017–2019 spawning seasons, this study identified 12 probable grass carp spawning areas in the lower 96.5-kilometers of the Maumee River. These spawning areas were located both above and below the Grand Rapids/Providence low-head dams. Three areas showed evidence of multiyear use, while nine had multi-event use. Spawning activity had no definitive diel variation and occurred at an average photoperiod of 15.15 h. The maturation metric ADD15, or annual degree days above 15 degrees Celsius, generally exceeded the 655 threshold for spawning; however, some spawning occurred when ADD15 ≤235, indicating spawners likely matured in a warmwater discharge. The probable spawning areas were generally characterized by mean velocities between 0.4 and 2.1 m per second (with locally higher velocities possible), areas of high turbulence produced by dam spillways or bedrock outcroppings, channel constrictions, confluences, islands, and bridges with piers in the water. Spawning suitability indices (SSI), based on velocity, varied considerably between spawning areas and SSI models. These results could be used to inform control efforts and predict potential grass carp spawning locations in other rivers under threat of invasion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102347","usgsCitation":"Jackson, P.R., Cigrand, C.V., Kocovsky, P.M., King, N.R., Kasprak, A., Lindroth, E., Doyle, H.F., Qian, S.S., and Mayer, C.M., 2024, Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling: Journal of Great Lakes Research, v. 50, 102347, 17 p., https://doi.org/10.1016/j.jglr.2024.102347.","productDescription":"102347, 17 p.","ipdsId":"IP-142521","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439799,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102347","text":"Publisher Index Page"},{"id":434983,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CQ6FYX","text":"USGS data release","linkHelpText":"Hydraulic Model Archive and Fluvial Egg Drift Simulator (FluEgg) Results for Simulations of Invasive Carp Egg and Larval Drift in the Maumee River, Ohio (ver. 1.1, July 2023)"},{"id":428353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.38518414361491,\n 41.68593497191691\n ],\n [\n -83.45979759899954,\n 41.74305978003929\n ],\n [\n -83.66454217332985,\n 41.60155527334871\n ],\n [\n -83.8481872826162,\n 41.471249401310274\n ],\n [\n -84.12554495228478,\n 41.444009750259625\n ],\n [\n -84.20397403397705,\n 41.37227829616401\n ],\n [\n -84.41243215532246,\n 41.2918512649257\n ],\n [\n -84.35316138453301,\n 41.241526198376704\n ],\n [\n -84.09303055462573,\n 41.30190322738565\n ],\n [\n -84.060513011197,\n 41.379455129599705\n ],\n [\n -83.7812377285593,\n 41.39524150503729\n ],\n [\n -83.38518414361491,\n 41.68593497191691\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":900020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Nicole R.","contributorId":239495,"corporation":false,"usgs":false,"family":"King","given":"Nicole","email":"","middleInitial":"R.","affiliations":[{"id":47892,"text":"University of Toledo Lake Erie Center, 6200 Bay Shore Road, Oregon, OH","active":true,"usgs":false}],"preferred":false,"id":900021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":900022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindroth, Evan M. 0000-0002-9746-4359","orcid":"https://orcid.org/0000-0002-9746-4359","contributorId":336138,"corporation":false,"usgs":false,"family":"Lindroth","given":"Evan M.","affiliations":[{"id":80757,"text":"Maricopa County Flood Control District","active":true,"usgs":false}],"preferred":false,"id":900023,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doyle, Henry F. 0000-0001-9942-8602 hfdoyle@usgs.gov","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":243432,"corporation":false,"usgs":true,"family":"Doyle","given":"Henry","email":"hfdoyle@usgs.gov","middleInitial":"F.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900024,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":900025,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":900026,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70253125,"text":"70253125 - 2024 - Network connectivity contributes to native small-bodied fish assemblages in the upper Mississippi River system","interactions":[],"lastModifiedDate":"2024-06-03T15:03:49.975123","indexId":"70253125","displayToPublicDate":"2024-04-18T07:15:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17465,"text":"Journal of Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Network connectivity contributes to native small-bodied fish assemblages in the upper Mississippi River system","docAbstract":"In a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions.
The objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles (Aquila chrysaetos). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection).
Using tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection.
Eagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles.
Here we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.
The location of estuarine organisms varies based on geophysical cycles and environmental conditions, which can strongly bias understanding of organism abundance and distribution. In the San Francisco Estuary, California, extensive monitoring surveys have provided insight into the life history and ecology of certain commercially important or legislatively protected fish species. However, there remains substantial uncertainty in factors influencing the vertical and lateral distributions of many other nekton species in the San Francisco Estuary, including longfin smelt Spirinchus thaleichthys, for whom such distributional information may highly influence interpretation of existing data. We carried out paired sampling using surface and demersal gears to address three questions: (1) Does diel phase influence the vertical position of nekton (e.g., surface versus demersal)? (2) Do environmental conditions, specifically turbidity, influence the vertical and lateral positions of nekton (e.g., center channel versus peripheral shoal)? (3) Does tidal variability influence vertical and lateral distributions of nekton? We documented variability in sampled nekton densities across diel phase (day/night), vertical position (surface/bottom), and lateral position (channel/shoal). Tidal phase and turbidity concentration influenced vertical and lateral distributions for some species at certain locations. Although infrequently encountered, we documented associations of longfin smelt with the lower water column and shoal habitats, with some evidence for upward vertical shifts in low light conditions brought about by nightfall or elevated turbidity. Observed habitat associations provide insight into how interacting geophysical and environmental factors may influence the distribution of nekton and thus the vulnerability of individual species to detection by sampling gears.
The risk of lampricide applications (such as 4-nitro-3-[trifluoromethyl]phenol [TFM]) to nontarget fauna continues to be a concern within the Great Lakes Fishery Commission Sea Lamprey Control Program, especially among imperiled aquatic species—such as native freshwater mussels. The Grand River (Ohio, USA) is routinely treated for larval sea lampreys (Petromyzon marinus), and this river contains populations of the federally threatened mussel Obovaria subrotunda. Given this spatial overlap, information on the sensitivity of O. subrotunda to TFM is needed. Our objectives were to assess the toxicity of TFM to (1) adult Obovaria olivaria (a surrogate for O. subrotunda), (2) glochidial larvae of O. olivaria and O. subrotunda, (3) juveniles of O. olivaria and O. subrotunda, and (4) adult Percina maculata (host for O. subrotunda glochidia). In acute toxicity tests, TFM was not toxic to glochidia and adult mussels at exposure concentrations that exceed typical treatment rates. Although significant dose–response relationships were observed in hosts and juveniles, survival was ≥95% (Percina maculata), ≥93% (O. olivaria), and ≥74% (O. subrotunda) at typical treatment rates. However, the steep slope of these dose–response relationships indicates that an approximately 20% difference in the treatment level can result in nearly an order of magnitude difference in survival. Collectively, these data indicate that routine sea lamprey control operations are unlikely to acutely affect these species or their host. However, given that many mussel species are long-lived (30–100 years), the risks posed by lampricide treatments in the Great Lakes would be further informed by research on the potential long-term effects of lampricides on imperiled species. Environ Toxicol Chem 2024;00:1–8. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.
Adaptive capacity (AC), defined as the ability of a species to cope with or adjust to climate change, is a critical determinant of species vulnerability and has been widely applied in wildlife contexts (IPCC 2014, Thurman et al. 2020). This process can be further defined with respect to intrinsic capacities versus extrinsic constraints on AC (Beever et al. 2016, Thurman et al. 2020). Previous applications of AC to biological systems have largely occurred through climate change vulnerability assessments (CCVAs; Thurman et al. 2020), and select examples exist in wildlife applications (Beever et al. 2023, Thurman et al. 2022). However, there is little understanding and application of adaptive capacity to inland fishes given previous emphases on trait-based approaches and population-level management. Therefore, there are substantial opportunities to improve our understanding of AC for inland fishes to explore climate impacts and the mechanisms by which interventions affect AC and resulting climate vulnerability.
","language":"English","publisher":"Midwest Climate Adaptation Science Center","usgsCitation":"Embke, H.S., Bunnell, D.B., LeDee, O.E., and Suski, C., 2024, Adaptive capacity of Inland Fishes Workshop Summary, 7 p.","productDescription":"7 p.","ipdsId":"IP-162579","costCenters":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":501539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427805,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://mwcasc.umn.edu/node/1041"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":898876,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David B. 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":195888,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","email":"dbunnell@usgs.gov","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":898877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeDee, Olivia E. 0000-0002-7791-5829 oledee@usgs.gov","orcid":"https://orcid.org/0000-0002-7791-5829","contributorId":242820,"corporation":false,"usgs":true,"family":"LeDee","given":"Olivia","email":"oledee@usgs.gov","middleInitial":"E.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":898878,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suski, Cory","contributorId":367838,"corporation":false,"usgs":false,"family":"Suski","given":"Cory","affiliations":[],"preferred":false,"id":898879,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}