The Statewide (formerly Southern) California Earthquake Center (SCEC) conducts multidisciplinary earthquake system science research that aims to develop predictive models of earthquake processes, and to produce accurate seismic hazard information that can improve societal preparedness and resiliency to earthquake hazards. As part of this program, SCEC has developed the CyberShake platform, which calculates physics-based probabilistic seismic hazard analysis (PSHA) models for regions with high-quality seismic velocity and fault models. The CyberShake platform implements a sophisticated computational workflow that includes over 15 individual codes written by 6 developers. These codes are heterogeneous, ranging from short-running high-throughput serial CPU codes to large, long-running, parallel GPU codes. Additionally, CyberShake simulation campaigns are computationally extensive, typically producing tens of terabytes of meaningful scientific data and metadata over several months of around-the-clock execution on leadership-class supercomputers. To meet the needs of the CyberShake platform, we have developed an extreme-scale workflow stack, including the Pegasus Workflow Management System, HTCondor, Globus, and custom tools. We present this workflow software stack and identify how the CyberShake platform and supporting tools enable us to meet a variety of challenges that come with large-scale simulations, such as automated remote job submission, data management, and verification and validation. This platform enabled us to perform our most recent simulation campaign, CyberShake Study 22.12, from December 2022 to April 2023. During this time, our workflow tools executed approximately 32,000 jobs, and used up to 73% of the Summit system at Oak Ridge Leadership Computing Facility. Our workflow tools managed about 2.5 PB of total temporary and output data, and automatically staged 19 million output files totaling 74 TB back to archival storage on the University of Southern California's Center for Advanced Research Computing systems, including file-based relational data and large binary files to efficiently store millions of simulated seismograms. CyberShake extreme-scale workflows have generated simulation-based probabilistic seismic hazard models that are being used by seismological, engineering, and governmental communities.
Lotic fish species distributions are frequently predicted using remotely sensed habitat variables that characterize the adjacent landscape and serve as proxies for instream habitat. Recent advancements in statistical methodology, however, allow for leveraging fish assemblage data when predicting distributions. This is important because assemblage composition likely provides better information about instream habitat compared to landscape-derived metrics and therefore may improve predictions. To better understand the value of using multi-species fish data in species distribution modeling, we fit two conditional random fields (CRF) models to quantify the relative importance of fish assemblage co-occurrence, landscape-derived habitat variables, and interactions between these two predictor groups (i.e., effects of co-occurrence could be context-dependent) at over 1200 stream catchments in Pennsylvania, USA. We first compared predictive performance of CRF models against traditionally used single-species logistic regressions (generalized linear models; GLMs) and found that inclusion of fish assemblage data often improved predictive performance. The multi-species CRF models performed significantly better at predicting occurrence for 63% of species with an average percent increase in AUC of 25% compared to GLMs. Furthermore, the CRF identified species co-occurrences as more informative, and thus relatively more important, at predicting occurrence than the other effect types. The CRF also suggested that allowing these biotic effects to be context-dependent was important for predicting occurrence of many species. These findings illustrate the value of fish assemblage data for landscape-scale species distribution modeling and leveraging this information can improve predictions and inferences to help inform the management and conservation of freshwater fishes.
","language":"English","publisher":"Springer","doi":"10.1007/s42974-024-00183-9","usgsCitation":"Custer, C., Fischer, D., Smith, G., Henning, A., Kepler Schall, M., Shank, M.K., Wertz, T.A., and Isermann, D.A., 2024, Quantifying the relative importance of biotic and abiotic factors in landscape-based models of stream fish distributions: Community Ecology, v. 25, p. 145-196, https://doi.org/10.1007/s42974-024-00183-9.","productDescription":"52 p.","startPage":"145","endPage":"196","ipdsId":"IP-147148","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439702,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s42974-024-00183-9","text":"Publisher Index Page"},{"id":433512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Custer, Christopher A.","contributorId":341088,"corporation":false,"usgs":false,"family":"Custer","given":"Christopher A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fischer, Douglas P.","contributorId":342445,"corporation":false,"usgs":false,"family":"Fischer","given":"Douglas P.","affiliations":[{"id":36966,"text":"Pennsylvania Fish and Boat Commission","active":true,"usgs":false}],"preferred":false,"id":910109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Geoffrey","contributorId":199064,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":910110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henning, Aaron","contributorId":342450,"corporation":false,"usgs":false,"family":"Henning","given":"Aaron","email":"","affiliations":[{"id":79900,"text":"Susquehanna River Basin Commission","active":true,"usgs":false}],"preferred":false,"id":910111,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kepler Schall, Megan","contributorId":342451,"corporation":false,"usgs":false,"family":"Kepler Schall","given":"Megan","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":910112,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shank, Matthew K.","contributorId":342453,"corporation":false,"usgs":false,"family":"Shank","given":"Matthew","email":"","middleInitial":"K.","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":910113,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wertz, Timothy A.","contributorId":342455,"corporation":false,"usgs":false,"family":"Wertz","given":"Timothy","email":"","middleInitial":"A.","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":910114,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910115,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70270998,"text":"70270998 - 2024 - Thick- and thin-skinned contractional styles and the tectonic evolution of the northern Sangre de Cristo Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2025-08-27T13:49:11.414989","indexId":"70270998","displayToPublicDate":"2024-04-30T08:39:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Thick- and thin-skinned contractional styles and the tectonic evolution of the northern Sangre de Cristo Mountains, Colorado, USA","docAbstract":"The Sangre de Cristo Mountains of southern Colorado and northern New Mexico, USA, contain an unusual combination of thick- and thin-skinned contractional structures involving both basement and cover rocks in the Laramide Rocky Mountain foreland. These structures are truncated by down-faulted extensional basins to the east and west. Together with synorogenic sediments, these structures preserve a record of the rise of the Ancestral Rocky Mountains, the Laramide orogeny, and Rio Grande rifting. Laramide structures within the mountains provide clues to processes that link the three events and to necessary conditions for thin-skinned and thick-skinned contractional structures to form together in continental interiors.
To examine the full variety of structural styles, a portion of the northern Sangre de Cristo fold- and-thrust belt in Colorado was described and interpreted using geologic maps and structural cross-sections. Stratigraphic relations of the Ancestral Rocky Mountain highlands and basin fill were reconstructed from existing maps. These relations allow identification of faults inherited from the Ancestral Rocky Mountains, differentiation of thrust sheets, and in some cases, estimation of the magnitude of displacement. To examine relations between Laramide thrusts and Rio Grande rifting, kinematic data were collected from a thrust fault adjacent to rift faults.
Three thrust fault styles were recognized: thin-skinned basement, thin-skinned cover rocks, and thick-skinned basement. Thin-skinned thrusts arising from a hinterland beneath the present San Luis Valley carried sheets of Proterozoic basement rocks northeast over a Laramide foreland. These basement thrusts are interpreted to be faults of the Ancestral Rocky Mountains that reactivated during the Laramide orogeny. The Laramide foreland consists of thin-skinned thrusts and folds in sedimentary cover rocks as young as 49 Ma. Both thin-skinned thrusts in basement and cover rocks are bounded by thick-skinned basement thrusts that moved intermittently throughout the Laramide orogeny.
We infer that thin-skinned thrusts form in continental interiors where deformation is focused in weak strata of thick basin fill and in fluid-reaction weakened preexisting faults in basement rocks. Both conditions are met in the Sangre de Cristo Mountains. Basement thrusts adjacent to the San Luis Valley contain evidence of plastic contractional microstructures overprinted by extensional microstructures that may record the transition from Laramide contraction to Rio Grande extension of the crust.
","language":"English","publisher":"U.S. Geological Survey","doi":"10.1130/GES02635.1","usgsCitation":"Lindsey, D.A., and Caine, J., 2024, Thick- and thin-skinned contractional styles and the tectonic evolution of the northern Sangre de Cristo Mountains, Colorado, USA: Geosphere, v. 20, no. 3, p. 678-710, https://doi.org/10.1130/GES02635.1.","productDescription":"33 p.","startPage":"678","endPage":"710","ipdsId":"IP-147576","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":495064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02635.1","text":"Publisher Index Page"},{"id":494940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"northern Sangre de Cristo Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.75,\n 38\n ],\n [\n -105.75,\n 37.5\n ],\n [\n -105.25,\n 37.5\n ],\n [\n -105.25,\n 38\n ],\n [\n -105.75,\n 38\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Lindsey, David A. 0000-0002-9466-0899 dlindsey@usgs.gov","orcid":"https://orcid.org/0000-0002-9466-0899","contributorId":773,"corporation":false,"usgs":true,"family":"Lindsey","given":"David","email":"dlindsey@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":947462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":947463,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70253227,"text":"sir20245025 - 2024 - Simulation of hydrodynamics and water temperature in a 21-mile reach of the upper Illinois River, Illinois, 2020–22","interactions":[],"lastModifiedDate":"2026-02-03T18:10:33.629761","indexId":"sir20245025","displayToPublicDate":"2024-04-30T07:15:01","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5025","displayTitle":"Simulation of Hydrodynamics and Water Temperature in a 21-Mile Reach of the Upper Illinois River, Illinois, 2020–22","title":"Simulation of hydrodynamics and water temperature in a 21-mile reach of the upper Illinois River, Illinois, 2020–22","docAbstract":"This report describes the development of a CE-QUAL-W2 river hydrodynamics and temperature model of a 21-mile reach of the Illinois River including a 3-mile reach of a major tributary, the Fox River. Model outputs consist of streamflow, water velocity, water-surface elevation, and water-temperature time series that can be used to simulate summer conditions in years with and without extensive development of harmful algal blooms (HABs). These analyses may provide a better understanding of some complex factors contributing to HAB development along the Illinois River. Such an understanding may provide more accurate HAB timing and location predictions and may help determine potential mitigating activities to prevent or limit the size and duration of HABs.
Using the observed and simulated hydrodynamic conditions in the Illinois River study reach, it was possible to compare and contrast streamflow, velocity, and temperature conditions in years with varying HAB distributions. Occurrences of extensive HABs were documented in the study reach in June 2020 and June 2021, but only a small HAB restricted to the Marseilles Lock and Dam pool occurred in the summer of 2022. The objective then was to find similarities in site conditions between 2020 and 2021 that may contrast with the conditions in 2022. Among the 3 years included in the study, the variability in simulated water temperature exceeded variability in observed streamflow and simulated velocities. The longest period of water temperatures greater than 27 degrees Celsius (°C) in the selected locations in June of the three analysis years was in the second half of June 2022, yet no study-area wide HAB was documented in 2022. Simulations indicated that after warm water temperatures were established in the reach in June 2022, a cooling period broke up the warming period. This period of cooling was greater in magnitude and duration downstream from the location of a localized HAB perhaps limiting the spread of the bloom.
Residence times differed substantially in segments representing different channel features; values ranged from 0.28 to 17.3 (days per 500 meters of channel) between the main stem and backwater areas, respectively. Variation in average June residence times was also greater among different channel features than among different years in the study period. The HABs in 2020 and 2021 at Starved Rock Dam were documented when water temperatures were about 26 °C. River backwater areas at some locations did attain these temperatures 2 to 3 days before the conditions in the main stem. Residence times in the backwater areas, however, generally exceeded 9 days, thus limiting the exchange of water carrying algal biomass into the main channel.
Hydrodynamic model calibration involved adjusting model parameters until observed and simulated daily water-surface elevations, daily streamflows, discrete velocities, and channel areas were similar. Temperature calibration was done with near-surface continuous time-series data and discrete vertical profile temperatures. Observed and simulated water temperatures generally were within 1 °C at all monitoring locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245025","usgsCitation":"Ament, M.R., and Heimann, D.C., 2024, Simulation of hydrodynamics and water temperature in a 21-mile reach of the upper Illinois River, Illinois, 2020–22 (ver. 1.1, October 2024): U.S. Geological Survey Scientific Investigations Report 2024–5025, 35 p., https://doi.org/10.3133/sir20245025.","productDescription":"Report: viii, 35 p.; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-147887","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497947,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116400.htm","linkFileType":{"id":5,"text":"html"}},{"id":462442,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5025/versionHist.txt","size":"2.7 KB","linkFileType":{"id":2,"text":"txt"}},{"id":428192,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245025/full"},{"id":428191,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":428190,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BV9EG2","text":"USGS data release","linkHelpText":"Hydrodynamic and water-temperature model of a 21-mile reach of the upper Illinois River, Illinois (ver. 1.1, October 2024)"},{"id":428189,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5025/images/"},{"id":428186,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5025/coverthb2.jpg"},{"id":428187,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5025/sir20245025.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5025"},{"id":428188,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5025/sir20245025.XML"}],"country":"United States","state":"Illinois","otherGeospatial":"Upper Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.09651850759147,\n 41.39468681338917\n ],\n [\n -89.09651850759147,\n 41.27302034876615\n ],\n [\n -88.30008450383568,\n 41.27302034876615\n ],\n [\n -88.30008450383568,\n 41.39468681338917\n ],\n [\n -89.09651850759147,\n 41.39468681338917\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: April 30, 2024; Version 1.1: October 1, 2024","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Data citation promotes accessibility and discoverability of data through measures carried out by researchers, publishers, repositories, and the scientific community. This paper examines how a data citation workflow has been implemented by the U.S. Geological Survey (USGS) by evaluating publication and data linkages. Two different methods were used to identify data citations: examining publication structural metadata and examining the full text of the publication. A growing number of USGS researchers are complying with publisher data sharing policies aimed to capture data citation information in a standardized way within associated publications. However, inconsistencies in how data citation information is documented in publications has limited the accessibility and discoverability of the data. This paper demonstrates how organizational evaluations of publication and data linkages can be used to identify obstacles in advancing data citation efforts and improve data citation workflows.
","language":"English","publisher":"The Committee on Data of the International Science Council (CODATA)","doi":"10.5334/dsj-2024-024","usgsCitation":"Donovan, G.C., and Langseth, M., 2024, Are researchers citing their data? A case study from the U.S. Geological Survey: Data Science Journal, v. 23, 24 p., https://doi.org/10.5334/dsj-2024-024.","productDescription":"24 p.","ipdsId":"IP-156543","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":439712,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5334/dsj-2024-024","text":"Publisher Index Page"},{"id":434969,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPC9M2","text":"USGS data release","linkHelpText":"U.S. Geological Survey Data Citation Analysis, 2016-2022"},{"id":428265,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Donovan, Grace C. 0000-0002-6632-4564","orcid":"https://orcid.org/0000-0002-6632-4564","contributorId":219931,"corporation":false,"usgs":true,"family":"Donovan","given":"Grace","email":"","middleInitial":"C.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":899849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Langseth, Madison 0000-0002-4472-9106 mlangseth@usgs.gov","orcid":"https://orcid.org/0000-0002-4472-9106","contributorId":191744,"corporation":false,"usgs":true,"family":"Langseth","given":"Madison","email":"mlangseth@usgs.gov","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":899850,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70254552,"text":"70254552 - 2024 - Role of edaphic, hydrologic, and land cover variables in determining dissolved organic carbon in Missouri (USA) reservoirs and streams","interactions":[],"lastModifiedDate":"2024-06-03T11:35:41.932352","indexId":"70254552","displayToPublicDate":"2024-04-30T06:28:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2592,"text":"Lake and Reservoir Management","active":true,"publicationSubtype":{"id":10}},"title":"Role of edaphic, hydrologic, and land cover variables in determining dissolved organic carbon in Missouri (USA) reservoirs and streams","docAbstract":"In Missouri, distinct geophysical gradients influence statewide patterns in water quality. Here, we quantify the spatiotemporal variability of dissolved organic carbon (DOC) in reservoirs and streams and the edaphic, hydrologic, and land cover variables that account for cross-system variation. Datasets included statewide inventories collected over decades and studies with greater temporal resolution (n = >6350 DOC measurements). Among reservoirs, the smallest DOC concentration was measured in a spring-fed system within a forested watershed, and the largest was where agricultural biosolids were applied to the land (range 1.0–15.9 mg/L, overall mean 5.8 mg/L). Reservoir values increased from the southern forested Highlands (mean 4.7 mg/L) to the northern agricultural Plains (mean 7.0 mg/L). Stream DOC was similar to reservoir values (overall mean in streams 6.3 mg/L; Highlands mean 4.0 mg/L; Plains mean 6.6 mg/L), despite differences in study design and collection period. Reservoir DOC increased in spring, indicative of allochthonous loading, with small autochthonous additions during a broad summer peak. Temporal variability in DOC was low relative to macronutrients and chlorophyll in both reservoirs and streams, indicating DOC may be a sensitive and readily detected indicator of temporal change in these systems. In regression analyses, watershed features accounted for more than 60% of overall cross-system variability in DOC in both reservoirs and streams. Driver-response relations, however, differed between regions. This analysis extends our understanding of environmental influences on surface water chemistry in Missouri and indicates DOC is nearly as predictable as macronutrients using landscape-level features.
An important provision of the Minamata Convention on Mercury is to monitor and evaluate the effectiveness of the adopted measures and its implementation. Here, we describe for the first time currently available biotic mercury (Hg) data on a global scale to improve the understanding of global efforts to reduce the impact of Hg pollution on people and the environment. Data from the peer-reviewed literature were compiled in the Global Biotic Mercury Synthesis (GBMS) database (>550,000 data points). These data provide a foundation for establishing a biomonitoring framework needed to track Hg concentrations in biota globally. We describe Hg exposure in the taxa identified by the Minamata Convention: fish, sea turtles, birds, and marine mammals. Based on the GBMS database, Hg concentrations are presented at relevant geographic scales for continents and oceanic basins. We identify some effective regional templates for monitoring methylmercury (MeHg) availability in the environment, but overall illustrate that there is a general lack of regional biomonitoring initiatives around the world, especially in Africa, Australia, Indo-Pacific, Middle East, and South Atlantic and Pacific Oceans. Temporal trend data for Hg in biota are generally limited. Ecologically sensitive sites (where biota have above average MeHg tissue concentrations) have been identified throughout the world. Efforts to model and quantify ecosystem sensitivity locally, regionally, and globally could help establish effective and efficient biomonitoring programs. We present a framework for a global Hg biomonitoring network that includes a three-step continental and oceanic approach to integrate existing biomonitoring efforts and prioritize filling regional data gaps linked with key Hg sources. We describe a standardized approach that builds on an evidence-based evaluation to assess the Minamata Convention’s progress to reduce the impact of global Hg pollution on people and the environment.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-024-02747-x","usgsCitation":"Evers, D.C., Ackerman, J.T., Akerblom, S., Bally, D., Basu, N., Bishop, K., Bodin, N., Veitberg Braaten, H.F., Burton, M., Bustamante, P., Chen, C.Y., Chetelat, J., Christian, L., Dietz, R., Drevnick, P., Eagles-Smith, C., Fernandez, L., Hammerschlag, N., Harmelin-Vivien, M., Harte, A., Kruemmel, E., Lailson-Brito, J., Medina, G., Rodriguez, C., Stenhouse, I., Sunderland, E.M., Takeuchi, A., Tear, T., Vega, C., Wilson, S., and Wu, P., 2024, Global mercury concentrations in biota: Their use as a basis for a global biomonitoring framework: Ecotoxicology, v. 33, p. 325-396, https://doi.org/10.1007/s10646-024-02747-x.","productDescription":"72 p.","startPage":"325","endPage":"396","ipdsId":"IP-159055","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":439720,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10646-024-02747-x","text":"Publisher Index Page"},{"id":428320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","noUsgsAuthors":false,"publicationDate":"2024-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Evers, David C.","contributorId":96160,"corporation":false,"usgs":false,"family":"Evers","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":899935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":899936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Akerblom, Staffan 0000-0001-7607-9518","orcid":"https://orcid.org/0000-0001-7607-9518","contributorId":335950,"corporation":false,"usgs":false,"family":"Akerblom","given":"Staffan","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":899937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bally, Dominique","contributorId":335951,"corporation":false,"usgs":false,"family":"Bally","given":"Dominique","email":"","affiliations":[{"id":80590,"text":"African Center for Environmental Health","active":true,"usgs":false}],"preferred":false,"id":899938,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Basu, Niladri","contributorId":60085,"corporation":false,"usgs":false,"family":"Basu","given":"Niladri","email":"","affiliations":[],"preferred":false,"id":899939,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bishop, Kevin","contributorId":147940,"corporation":false,"usgs":false,"family":"Bishop","given":"Kevin","affiliations":[],"preferred":false,"id":899940,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bodin, Nathalie 0000-0001-8464-0213","orcid":"https://orcid.org/0000-0001-8464-0213","contributorId":335953,"corporation":false,"usgs":false,"family":"Bodin","given":"Nathalie","email":"","affiliations":[{"id":80591,"text":"Research Institute for Sustainable Development Seychelles Fishing 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Panamá","active":true,"usgs":false}],"preferred":false,"id":899959,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Stenhouse, Iain","contributorId":194567,"corporation":false,"usgs":false,"family":"Stenhouse","given":"Iain","affiliations":[],"preferred":false,"id":899960,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sunderland, Elsie M.","contributorId":151016,"corporation":false,"usgs":false,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[{"id":18166,"text":"Harvard University, Cambridge, M","active":true,"usgs":false}],"preferred":false,"id":899961,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Takeuchi, Akinori","contributorId":335965,"corporation":false,"usgs":false,"family":"Takeuchi","given":"Akinori","email":"","affiliations":[{"id":80601,"text":"Japanese National Institute for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":899962,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Tear, Timothy","contributorId":139999,"corporation":false,"usgs":false,"family":"Tear","given":"Timothy","email":"","affiliations":[{"id":13347,"text":"Grumeti Fund","active":true,"usgs":false}],"preferred":false,"id":899963,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Vega, Claudia","contributorId":335966,"corporation":false,"usgs":false,"family":"Vega","given":"Claudia","email":"","affiliations":[{"id":80602,"text":"Centro de Innovaccion Cientifica Amazonica","active":true,"usgs":false}],"preferred":false,"id":899964,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Wilson, Simon","contributorId":218345,"corporation":false,"usgs":false,"family":"Wilson","given":"Simon","email":"","affiliations":[{"id":39809,"text":"Arctic Monitoring and Assessment Programme (AMAP) Secretariat","active":true,"usgs":false}],"preferred":false,"id":899965,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Wu, Pianpian 0000-0002-2037-9164","orcid":"https://orcid.org/0000-0002-2037-9164","contributorId":335963,"corporation":false,"usgs":false,"family":"Wu","given":"Pianpian","email":"","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":899958,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70254152,"text":"70254152 - 2024 - Snow avalanches are a primary climate-linked driver of mountain ungulate populations","interactions":[],"lastModifiedDate":"2024-05-10T12:04:31.268887","indexId":"70254152","displayToPublicDate":"2024-04-29T07:01:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17775,"text":"Nature Communications Biology","active":true,"publicationSubtype":{"id":10}},"title":"Snow avalanches are a primary climate-linked driver of mountain ungulate populations","docAbstract":"Snow is a major, climate-sensitive feature of the Earth’s surface and catalyst of fundamentally important ecosystem processes. Understanding how snow influences sentinel species in rapidly changing mountain ecosystems is particularly critical. Whereas effects of snow on food availability, energy expenditure, and predation are well documented, we report how avalanches exert major impacts on an ecologically significant mountain ungulate - the coastal Alaskan mountain goat (Oreamnos americanus). Using long-term GPS data and field observations across four populations (421 individuals over 17 years), we show that avalanches caused 23−65% of all mortality, depending on area. Deaths varied seasonally and were directly linked to spatial movement patterns and avalanche terrain use. Population-level avalanche mortality, 61% of which comprised reproductively important prime-aged individuals, averaged 8% annually and exceeded 22% when avalanche conditions were severe. Our findings reveal a widespread but previously undescribed pathway by which snow can elicit major population-level impacts and shape demographic characteristics of slow-growing populations of mountain-adapted animals.
Birds are used as bioindicators of environmental mercury (Hg) contamination, and toxicity reference values are needed for injury assessments. We conducted a comprehensive review, summarized data from 168 studies, performed a series of Bayesian hierarchical meta-analyses, and developed new toxicity reference values for the effects of methylmercury (MeHg) on birds using a benchmark dose analysis framework. Lethal and sublethal effects of MeHg on birds were categorized into nine biologically relevant endpoint categories and three age classes. Effective Hg concentrations where there was a 10% reduction (EC10) in the production of juvenile offspring (0.55 µg/g wet wt adult blood-equivalent Hg concentrations, 80% credible interval: [0.33, 0.85]), histology endpoints (0.49 [0.15, 0.96] and 0.61 [0.09, 2.48]), and biochemical markers (0.77 [<0.25, 2.12] and 0.57 [0.35, 0.92]) were substantially lower than those for survival (2.97 [2.10, 4.73] and 5.24 [3.30, 9.55]) and behavior (6.23 [1.84, >13.42] and 3.11 [2.10, 4.64]) of juveniles and adults, respectively. Within the egg age class, survival was the most sensitive endpoint (EC10 = 2.02 µg/g wet wt adult blood-equivalent Hg concentrations [1.39, 2.94] or 1.17 µg/g fresh wet wt egg-equivalent Hg concentrations [0.80, 1.70]). Body morphology was not particularly sensitive to Hg. We developed toxicity reference values using a combined survival and reproduction endpoints category for juveniles, because juveniles were more sensitive to Hg toxicity than eggs or adults. Adult blood-equivalent Hg concentrations (µg/g wet wt) and egg-equivalent Hg concentrations (µg/g fresh wet wt) caused low injury to birds (EC1) at 0.09 [0.04, 0.17] and 0.04 [0.01, 0.08], moderate injury (EC5) at 0.6 [0.37, 0.84] and 0.3 [0.17, 0.44], high injury (EC10) at 1.3 [0.94, 1.89] and 0.7 [0.49, 1.02], and severe injury (EC20) at 3.2 [2.24, 4.78] and 1.8 [1.28, 2.79], respectively. Maternal dietary Hg (µg/g dry wt) caused low injury to juveniles at 0.16 [0.05, 0.38], moderate injury at 0.6 [0.29, 1.03], high injury at 1.1 [0.63, 1.87], and severe injury at 2.4 [1.42, 4.13]. We found few substantial differences in Hg toxicity among avian taxonomic orders, including for controlled laboratory studies that injected Hg into eggs. Our results can be used to quantify injury to birds caused by Hg pollution.
This study focuses on understanding the association of rare earth elements (REE; lanthanides + yttrium + scandium) with organic matter from the Middle Devonian black shales of the Appalachian Basin. Developing a better understanding of the role of organic matter (OM) and thermal maturity in REE partitioning may help improve current geochemical models of REE enrichment in a wide range of black shales. We studied relationships between whole rock REE content and total organic carbon (TOC) and compared the correlations with a suite of global oil shales that contain TOC as high as 60 wt.%. The sequential leaching of the Appalachian shale samples was conducted to evaluate the REE content associated with carbonates, Fe–Mn oxyhydroxides, sulfides, and organics. Finally, the residue from the leaching experiment was analyzed to assess the mineralogical changes and REE extraction efficiency. Our results show that heavier REE (HREE) have a positive correlation with TOC in our Appalachian core samples. However, data from the global oil shales display an opposite trend. We propose that although TOC controls REE enrichment, thermal maturation likely plays a critical role in HREE partitioning into refractory organic phases, such as pyrobitumen. The REE inventory from a core in the Appalachian Basin shows that (1) the total REE ranges between 180 and 270 ppm and the OM-rich samples tend to contain more REE than the calcareous shales; (2) there is a relatively higher abundance of middle REE (MREE) to HREE than lighter REE (LREE); (3) there is a disproportionate increase in Y and Tb with TOC likely due to the rocks being over-mature; and (4) the REE extraction demonstrates that although the OM has higher HREE concentration, the organic leachates contain more LREE, suggesting it is more challenging to extract HREE from OM than using traditional leaching techniques.
","language":"English","publisher":"MDPI","doi":"10.3390/en17092107","usgsCitation":"Bhattacharya, S., Sharma, S., Agrawal, V., Dix, M.C., Zanoni, G., Birdwell, J.E., Wylie, A.S., and Wagner, T., 2024, Influence of organic matter thermal maturity on rare earth element distribution: A study of Middle Devonian black shales from the Appalachian Basin, USA: Energies, v. 17, no. 9, 2107, 23 p., https://doi.org/10.3390/en17092107.","productDescription":"2107, 23 p.","ipdsId":"IP-160281","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":439736,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/en17092107","text":"Publisher Index Page"},{"id":428357,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Middle Devonian Appalachian Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.019722212101,\n 35.09354117626262\n ],\n [\n -80.0893868493873,\n 35.83616426236574\n ],\n [\n -75.48876301910367,\n 41.23650512855389\n ],\n [\n -74.6944038739024,\n 43.48785346597265\n ],\n [\n -79.3043542917768,\n 42.997887934674736\n ],\n [\n -83.72355980871455,\n 37.83971543304291\n ],\n [\n -85.019722212101,\n 35.09354117626262\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"9","noUsgsAuthors":false,"publicationDate":"2024-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bhattacharya, Shailee","contributorId":336153,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Shailee","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":900057,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sharma, Shikha","contributorId":336154,"corporation":false,"usgs":false,"family":"Sharma","given":"Shikha","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":900058,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Agrawal, Vikas","contributorId":336156,"corporation":false,"usgs":false,"family":"Agrawal","given":"Vikas","email":"","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":900059,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dix, Michael C.","contributorId":336159,"corporation":false,"usgs":false,"family":"Dix","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":80761,"text":"Consultant (formerly with PremierCorex)","active":true,"usgs":false}],"preferred":false,"id":900060,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zanoni, Giovanni","contributorId":336160,"corporation":false,"usgs":false,"family":"Zanoni","given":"Giovanni","email":"","affiliations":[{"id":80763,"text":"RohmTek, Houston, TX","active":true,"usgs":false}],"preferred":false,"id":900061,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":900062,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wylie, Albert S. Jr.","contributorId":336282,"corporation":false,"usgs":false,"family":"Wylie","given":"Albert","suffix":"Jr.","email":"","middleInitial":"S.","affiliations":[{"id":80764,"text":"Independent researcher, Mohawk, MI","active":true,"usgs":false}],"preferred":false,"id":900063,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wagner, Tom","contributorId":336283,"corporation":false,"usgs":false,"family":"Wagner","given":"Tom","email":"","affiliations":[],"preferred":false,"id":900064,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70254148,"text":"70254148 - 2024 - Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: The hotspot learning and identification network","interactions":[],"lastModifiedDate":"2024-05-09T11:59:38.152536","indexId":"70254148","displayToPublicDate":"2024-04-26T06:55:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Automatic identification and quantification of volcanic hotspots in Alaska using HotLINK: The hotspot learning and identification network","docAbstract":"An increase in volcanic thermal emissions can indicate subsurface and surface processes that precede, or coincide with, volcanic eruptions. Space-borne infrared sensors can detect hotspots—defined here as localized volcanic thermal emissions—in near-real-time. However, automatic hotspot detection systems are needed to efficiently analyze the large quantities of data produced. While hotspots have been automatically detected for over 20 years with simple thresholding algorithms, new computer vision technologies, such as convolutional neural networks (CNNs), can enable improved detection capabilities. Here we introduce HotLINK: the Hotspot Learning and Identification Network, a CNN trained to detect hotspots with a dataset of −3,800 satellite-based, Visible Infrared Imaging Radiometer Suite (VIIRS) images from Mount Veniaminof and Mount Cleveland volcanoes, Alaska. We find that our model achieves an accuracy of 96% (F1-score 0.92) when evaluated on −1,700 unseen images from the same volcanoes, and 95% (F1-score 0.67) when evaluated on −3,000 images from six additional Alaska volcanoes (Augustine Volcano, Bogoslof Island, Okmok Caldera, Pavlof Volcano, Redoubt Volcano, Shishaldin Volcano). In comparison with an existing threshold-based hotspot detection algorithm, MIROVA (Coppola et al., Geological Society, London, Special Publications, 2016, 426, 181–205), our model detects 22% more hotspots and produces 12% fewer false positives. Additional testing on −700 labeled Moderate Resolution Imaging Spectroradiometer (MODIS) images from Mount Veniaminof demonstrates that our model is applicable to this sensor’s data as well, achieving an accuracy of 98% (F1-score 0.95). We apply HotLINK to 10 years of VIIRS data and 22 years of MODIS data for the eight aforementioned Alaska volcanoes and calculate the radiative power of detected hotspots. From these time series we find that HotLINK accurately characterizes background and eruptive periods, similar to MIROVA, but also detects more subtle warming signals, potentially related to volcanic unrest. We identify three advantages to our model over its predecessors: 1) the ability to detect more subtle volcanic hotspots and produce fewer false positives, especially in daytime images; 2) probabilistic predictions provide a measure of detection confidence; and 3) its transferability, i.e., the successful application to multiple sensors and multiple volcanoes without the need for threshold tuning, suggesting the potential for global application.
Many forests globally are experiencing increases in large, high-severity wildfires, often with increasingly inadequate post-fire tree regeneration. To identify areas that might need post-fire planting, forest managers have a growing need for seedling reference densities – the natural seedling densities expected to be adequate to regenerate a forest – to compare with observed post-fire seedling densities. The most useful reference densities will meet five criteria: they will (1) be specific to natural post-fire reproduction rather than planted seedlings (because planted seedlings can have substantially greater survival than natural seedlings, thus underestimating adequate natural reproduction), (2) apply to the first few years following fire (when management decisions and actions are most likely), (3) be specific to each of those post-fire years (because post-fire seedling densities can change rapidly with time since fire), (4) be associated with estimates of uncertainty, and (5) include consideration of novel environmental conditions during management applications (because most reference densities will be based on data collected under more environmentally benign conditions). The world’s most massive tree species, the giant sequoia (Sequoiadendron giganteum) of California’s Sierra Nevada, recently experienced historically unprecedented wildfires that killed an estimated 13–19% of mature sequoias across their native range. Seedlings germinating after these fires then experienced exceptional summer heat and the two most severe summer droughts of the 121-year historical record. To help inform management responses to these events, we used seedling censuses from past fires (mostly prescribed fires) to calculate sequoia seedling reference densities meeting the five criteria. The reference densities had three striking features, which are partly attributable to giant sequoia’s status as a pioneer species. First, despite being inherently conservative, the reference densities were quite high. For example, mean first-year reference density was 172,599 seedlings ha−1. Second, reference densities declined precipitously with time since fire: the mean fifth-year reference density was only 5% of the mean first-year density. Third, the reference densities were associated with relatively substantial uncertainty, a consequence of density variations among seedling plots; for example, the 95% credible interval for first-year reference density was 64,377 to 313,438 seedlings ha−1. Despite this uncertainty, a case-study sequoia grove that recently burned in a high-severity wildfire had second-year post-fire seedling densities that were significantly (and dramatically) lower than the corresponding second-year reference density, suggesting inadequate post-fire reproduction. Our results highlight the value of the five criteria for reference densities – criteria that, in current practice, are rarely all met.
The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set of metrics to better document HAB occurrences and forecast future events. Using data from seven sites in the Illinois River Basin, we computed metrics focused on HAB conditions related to excess algal growth and hypoxia. Daily mean chlorophyll and dissolved oxygen (DO) concentrations, gross primary productivity (GPP), and net ecosystem productivity (NEP) rates, focused on water quality status, identifying the timing of the transition from a clear-water to an algal dominated state. Early warning indicators (EWIs), the first-order autoregressive process (Ar1) and standard deviation (SD) of chlorophyll concentrations, focused on future events, forecasting blooms. Metrics were compared to either literature-derived or statistical-based thresholds and were normalized by total number of daily samples for an exceedance rate. Exceedances of a daily mean chlorophyll concentration averaged 50 % across all sites using a 10 µg L−1 threshold but increasing the threshold to 50 μg L−1 reduced the average exceedance rate to 5 %. The average exceedance rate for GPP (∼8 g O2 m2d−1 threshold) was 15 %, similar to the daily amplitude DO concentration (∼3 mg L−1 threshold), but the average for NEP (0 g O2 m2 d−1 threshold) was higher, at 28 %. The number of days with at least 1 continuous DO concentration below the threshold of 5, 3, or 2 mg L−1, had basin wide exceedance rates of 9 %, 3 %, and 2 %, respectively. Thresholds for EWIs, Ar1 and SD, were exceeded at 5 of the 7 sites with high chlorophyll concentrations and GPP rates. The correlation between proxies for algal biomass (chlorophyll concentration) and productivity (GPP) was strongest for sites in the middle region of the basin, with R2 values between 0.54 and 0.74. Although, cyanotoxin concentrations are the most commonly used metrics by states to define an inland water HAB, there is a paucity of publicly available data. The wider availability of chlorophyll and oxygen data combined with the results from this study suggest that biomass and productivity state and event-based metrics may be a promising way to assess and predict the vulnerability of rivers to some of the deleterious effects of HABs at broad spatial scales.
In response to recent shifts towards open science that emphasize transparency, reproducibility, and access to research data, the US Geological Survey (USGS) conducted a study to assess the degree to which USGS data assets meet the FAIR data principles (Findable, Accessible, Interoperable, and Reusable). The USGS designed and applied a methodology for quantitative analysis of FAIR characteristics. A new rubric was derived from a crosswalk of existing FAIR evaluation frameworks and customized for the USGS. The rubric, consisting of 62 yes/no questions, was applied to 392 metadata records of USGS data products published between 1987 and 2022. Results were analyzed to show which FAIR characteristics were most and least present in the metadata and how these scores changed after the implementation of data policy requirements in 2016. Aggregated scores showed specific areas of strength and needed improvements. The greatest increases in FAIR scores over time were for elements that were required by new data policies, especially in the ‘Findable’ category. Based on the results, this paper presents strategies to further improve USGS alignment with FAIR. The suggested strategies are organized in four key areas: USGS data repository characteristics, training and communities of practice, data management policy considerations, and metadata standards, tools, and best practices.
","language":"English","publisher":"CODATA","doi":"10.5334/dsj-2024-022","usgsCitation":"Hutchison, V.B., Norkin, T., Zolly, L., and Hsu, L., 2024, State of the data: Assessing the FAIRness of USGS data: Data Science Journal, v. 23, 20 p., https://doi.org/10.5334/dsj-2024-022.","productDescription":"20 p.","ipdsId":"IP-155592","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":439743,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5334/dsj-2024-022","text":"Publisher Index Page"},{"id":428533,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2024-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchison, Vivian B. 0000-0001-5301-3698 vhutchison@usgs.gov","orcid":"https://orcid.org/0000-0001-5301-3698","contributorId":173674,"corporation":false,"usgs":true,"family":"Hutchison","given":"Vivian","email":"vhutchison@usgs.gov","middleInitial":"B.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":900358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norkin, Tamar 0000-0003-0797-3940 tnorkin@usgs.gov","orcid":"https://orcid.org/0000-0003-0797-3940","contributorId":5882,"corporation":false,"usgs":true,"family":"Norkin","given":"Tamar","email":"tnorkin@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":false,"id":900359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zolly, Lisa 0000-0003-3595-7809 lisa_zolly@usgs.gov","orcid":"https://orcid.org/0000-0003-3595-7809","contributorId":484,"corporation":false,"usgs":true,"family":"Zolly","given":"Lisa","email":"lisa_zolly@usgs.gov","affiliations":[],"preferred":true,"id":900360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":900361,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253191,"text":"sir20245009 - 2024 - Status of water quality in groundwater resources used for drinking-water supply in the southeastern San Joaquin Valley, 2013–15—California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2025-08-07T20:31:29.798566","indexId":"sir20245009","displayToPublicDate":"2024-04-25T13:17:53","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5009","displayTitle":"Status of Water Quality in Groundwater Resources Used for Drinking-Water Supply in the Southeastern San Joaquin Valley, 2013–15: California GAMA Priority Basin Project","title":"Status of water quality in groundwater resources used for drinking-water supply in the southeastern San Joaquin Valley, 2013–15—California GAMA Priority Basin Project","docAbstract":"The California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) investigated water quality of groundwater resources used for drinking-water supplies in the Madera-Chowchilla, Kings, Kaweah, Tule, and Tulare Lake groundwater subbasins of the southeastern San Joaquin Valley during 2013–15. The study focused primarily on groundwater resources used for domestic-supply wells in the southeastern San Joaquin Valley (SESJV-D), which correspond mostly to shallower parts of aquifer systems, compared to the groundwater resources used for public-supply wells in the southeastern San Joaquin Valley (SESJV-P). The investigation had three components: (1) characterization of the status of water quality in the SESJV-D, (2) comparison between water quality in the SESJV-D and SESJV-P, and (3) identification of natural and anthropogenic factors that potentially could affect water quality in these resources.
The characterization of water quality in the SESJV-D was based on data collected from 198 domestic wells sampled during 2013–15 by the U.S. Geological Survey (USGS); characterization of water quality in the SESJV-P was based on data collected from 124 wells sampled by the USGS during 2005–18 and an additional 1,577 wells with publicly available data reported to the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW). Measured concentrations were compared to regulatory and non-regulatory drinking-water quality benchmarks. A grid-based method was used to estimate the areal proportions of each study area and the whole southeastern San Joaquin Valley with high (greater than benchmark concentration), moderate (greater than half of the benchmark for inorganic and one-tenth of the benchmark for organic), and low concentrations relative to those benchmarks.
Natural and anthropogenic factors that could affect groundwater quality for the SESJV-D were identified in the context of the hydrogeologic setting of the southeastern San Joaquin Valley. The considered factors represented hydrologic conditions and position in the groundwater flow system (well depth, lateral position, presence of hydric soils, percentage of coarse-grained sediment, and aridity index), land-use characteristics (percentages of agricultural, urban, and natural land use, percentage of orchard or vineyard land use, and densities of septic tanks and underground storage tanks near the wells), and geochemical conditions (groundwater age class, oxidation-reduction class, pH, and dissolved oxygen and bicarbonate concentrations). Factors are compared between SESJV-D and SESJV-P at the scale of the five study areas.
One or more inorganic constituents with U.S. Environmental Protection Agency (EPA) or California maximum contaminant levels (MCLs) were detected at high concentrations in 47 percent of the SESJV-D and in 32 percent of the SESJV-P. The inorganic constituents most commonly present at high concentrations in the SESJV-D were nitrate, uranium, and arsenic. Within the SESJV-D, the proportion of the study area with high concentrations of inorganic constituents ranged from 19 percent in Madera-Chowchilla to 60 percent in Kings and Tulare Lake. One or more inorganic constituents with California State Water Resources Control Board Division of Drinking Water secondary maximum contaminant levels (SMCL-CAs) were detected at high concentrations in 14 percent of the SESJV-D and in 19 percent of the SESJV-P. The constituents most commonly present at high concentrations were manganese, iron, and total dissolved solids (TDS). Although the proportion of SESJV-D and SESJV-P with high concentrations of TDS greater than the upper SMCL were similar at 4 percent, the proportion of the SESJV-D with moderate concentrations (between the recommended and upper SMCL-CA), 30 percent, was greater than the proportion of the SESJV-P with moderate concentrations, 12 percent.
One or more organic constituents with MCLs were present at high concentrations in 19 percent of the SESJV-D and in 12 percent of the SESJV-P. All the constituents detected at high concentrations in the SESJV-D were fumigants, primarily 1,2,3-trichloropropane (1,2,3-TCP) and 1,2-dibromo-3-chloropropane (DBCP). Fumigants also were the constituents most commonly detected at high concentrations in the SESJV-P, although high concentrations of solvents also were detected. The SESJV-D dataset included analysis of many organic constituents without MCL benchmarks and with detection levels far below drinking water benchmark concentrations; detections at these low concentrations can be used as tracers of anthropogenic influence on groundwater. Pesticides and degradates of pesticides were detected in 60 percent of the SESJV-D; the most frequently detected pesticides were the herbicides simazine, didealkylatrazine (CAAT, a degradate of simazine and atrazine), diuron, and bromacil.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245009","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Burow, K.R., Shelton, J.L., and Fram, M.S., 2024, Status of water quality in groundwater resources used for drinking-water supply in the southeastern San Joaquin Valley, 2013–15—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2024–5009, 135 p., https://doi.org/10.3133/sir20245009.","productDescription":"Report: xiii, 135 p.; Data Release","numberOfPages":"136","onlineOnly":"Y","ipdsId":"IP-094434","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":428122,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245009/full"},{"id":493742,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116370.htm","linkFileType":{"id":5,"text":"html"}},{"id":428123,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DCTLXV","text":"USGS Data Release","description":"Balkan, M., Burow, K.R., and Shelton, J.L., and Fram, M.S., 2024, Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project: U.S. Geological Survey data release, accessed January, 22, 2024, at https://doi.org/10.5066/P9DCTLXV","linkHelpText":"Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project"},{"id":428120,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.xml"},{"id":428118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5009/covrthb.jpg"},{"id":428121,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5009/images"},{"id":428119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.36728753741212,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 37.719936264455484\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Atmospheric rivers are associated with some of the largest flood-producing precipitation events in western North America, particularly California. Insight into past extreme precipitation can be reconstructed from sedimentary archives on millennial timescales. Here we document atmospheric river activity near Leonard Lake, California, over 3,200 years, using a key metric of atmospheric river intensity, that is silicon/aluminum enriched layers that are highly correlated with modern records of integrated vapor transport. The late twentieth century had the highest median integrated vapor transport since the onset of the Medieval Climate Anomaly, with integrated vapor transport increasing during the Little Ice Age. The reconstruction suggests California has experienced pluvial episodes that exceeded any in the meteorologic instrumental era, with the largest episodes occurring two and three millennia ago. These results provide critical data to help avoid underestimation of potential risks and aid future planning scenarios.
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near seven bridges at six highway crossings of the Missouri and Mississippi Rivers on the periphery of Missouri from June 13–22, 2022. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 feet longitudinally and generally extending laterally across the active channel from bank to bank during minor flood-flow conditions. These surveys provided channel geometry and hydraulic conditions at the time of the surveys and provided characteristics of scour holes that may be useful in developing or verifying predictive guidelines or equations for computing potential scour depth. These data also may be useful to the Missouri Department of Transportation as a minor flood-flow assessment of the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every in-channel pier. Scour holes were present at most piers for which bathymetry could be obtained, except those on banks or surrounded by riprap. Occasionally, scour holes were minor and difficult to discern from nearby dunes and ripples. All bridge sites in this study were surveyed and documented in previous studies. Although partial exposure of substructural support elements was observed at several piers, at most sites the exposure most likely is minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 12 and 13 at structure L0135 on State Highway 51 at Chester, Illinois, where the bedrock material was fully exposed around the piers.
The average difference between the bathymetric surfaces between 2022 and 2018 varied from 0.41 foot higher to 1.86 feet lower. Between 2022 and 2014, the average difference between the bathymetric surfaces varied from 1.02 feet higher to 4.69 feet lower. Only the two sites on the Missouri River and the Caruthersville site were surveyed in 2011; for those sites, the average difference between the bathymetric surfaces varied from 5.83 feet higher to 1.34 feet lower. The most substantial overall net gain of sediment in a reach was between 2011 and 2022 at structure A1700 near Caruthersville, Mo. (site 38). This result was expected because structure A1700 is downstream from the confluences of the Missouri and Ohio Rivers, and therefore subject to the largest streamflows, the largest streamflow fluctuations, and the most substantial sediment flux, as has historically been observed at this site.
The presence of riprap blankets, pier size and nose shape, and alignment to flow had a substantial effect on the size of the scour hole observed for a given pier. Piers that were surrounded by riprap blankets had scour holes that were substantially smaller (to nonexistent) compared to piers at which no rock or riprap were present. New riprap blankets were surveyed at pier 3 of structure L0098 at Brownville, Nebraska, and at piers 15–18 of structure A1700 near Caruthersville, Mo., that effectively mitigated the scour holes historically observed at these piers. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several of the structures had piers that were skewed to primary approach flow. Scour holes near these piers consistently displayed greater depth on the side of the pier with impinging flow and deposition on the leeward side of the pier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245032","collaboration":"Prepared in cooperation with Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2024, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, June 13–22, 2022: U.S. Geological Survey Scientific Investigations Report 2024–5032, 82 p., https://doi.org/10.3133/sir20245032.","productDescription":"Report: x, 82 p.; Data Release; Dataset","numberOfPages":"96","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-151591","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":428058,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245032/full"},{"id":428056,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5032/sir20245032.XML"},{"id":428054,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5032/coverthb.jpg"},{"id":428055,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5032/sir20245032.pdf","text":"Report","size":"36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5032"},{"id":499459,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116369.htm","linkFileType":{"id":5,"text":"html"}},{"id":428060,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":428059,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K66GYC","text":"USGS data release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, June 13–22, 2022"},{"id":428057,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5032/images/"}],"country":"United States","state":"Missouri","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.91926710848416,\n 39.14873660952682\n ],\n [\n -90.91926710848416,\n 38.25709077908323\n ],\n [\n -89.90852492098409,\n 38.25709077908323\n ],\n [\n -89.90852492098409,\n 39.14873660952682\n ],\n [\n -90.91926710848416,\n 39.14873660952682\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.85237257723388,\n 39.36990790433225\n ],\n [\n -94.85237257723388,\n 38.70430347732409\n ],\n [\n -94.14924757723371,\n 38.70430347732409\n ],\n [\n -94.14924757723371,\n 39.36990790433225\n ],\n [\n -94.85237257723388,\n 39.36990790433225\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Wildlife reintroduction is an important conservation tool for threatened species, yet identifying appropriate source populations poses a challenge. In particular, the possibility of outbreeding depression is cited as a constraint limiting the range of candidate source populations for translocation. When multiple source lineages are mixed during reintroduction, genetic monitoring is necessary to evaluate whether sources contribute equally to subsequent generations and whether they are interbreeding as expected. Moreover, statistical analysis of genetic data should account for complex life histories that might affect the timescale of admixture and genetic drift. Here, we use samples collected over a 23-year period and a stochastic age-structured model to analyze the genetic mixing process in reintroduced Brook Trout (Salvelinus fontinalis) populations in the Southern Appalachians. Each restored population was seeded with two to three source populations. Previous research inferred reproductive isolation between source populations leading to a proposal of splitting the species into multiple taxa. In contrast, we found patterns of ancestry that were consistent with random mating and no advantage for one source lineage over any other. Brook Trout from different source streams are mixing as expected in the restoration sites. This result does not support the hypothesis that Brook Trout in the Southern Appalachian Mountains includes several distinct species. Mixing different sources from the same watershed seems to be an effective way to increase genetic diversity of reintroduced populations while minimizing risk to source populations.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01620-y","usgsCitation":"Smith, R.J., Kazyak, D.C., Kulp, M.A., Lubinski, B.A., and Fitzpatrick, B.M., 2024, Genetic structure of restored Brook Trout populations in the Southern Appalachian Mountains indicates successful reintroductions: Conservation Genetics, v. 25, p. 1007-1020, https://doi.org/10.1007/s10592-024-01620-y.","productDescription":"14 p.","startPage":"1007","endPage":"1020","ipdsId":"IP-158952","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":428350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.90629478474587,\n 35.85116809750147\n ],\n [\n -84.21639200734252,\n 35.85116809750147\n ],\n [\n -84.21639200734252,\n 35.2665417042201\n ],\n [\n -82.90629478474587,\n 35.2665417042201\n ],\n [\n -82.90629478474587,\n 35.85116809750147\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Rebecca J.","contributorId":229064,"corporation":false,"usgs":false,"family":"Smith","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":41574,"text":"National Park Service, Yellowstone National Park, PO Box 168, 22 Stable Street, Yellowstone National Park, WY, 82190, USA","active":true,"usgs":false}],"preferred":false,"id":900031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":900032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":900033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":900034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzpatrick, Benjamin M.","contributorId":336140,"corporation":false,"usgs":false,"family":"Fitzpatrick","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[{"id":80760,"text":"1. Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee","active":true,"usgs":false}],"preferred":false,"id":900035,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70253193,"text":"70253193 - 2024 - Spatiotemporal patterns in habitat use of natal and non-natal adult Atlantic sturgeon in two spawning rivers","interactions":[],"lastModifiedDate":"2024-04-26T12:11:30.607528","indexId":"70253193","displayToPublicDate":"2024-04-24T07:07:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal patterns in habitat use of natal and non-natal adult Atlantic sturgeon in two spawning rivers","docAbstract":"Monitoring movement across an organism’s ontogeny is often challenging, particularly for long-lived or wide-ranging species. When empirical data are unavailable, general knowledge about species’ ecology may be used to make assumptions about habitat use across space or time. However, inferences about habitat use based on population-level ecology may overlook important eco-evolutionary contributions from individuals with heterogenous ethologies and could diminish the efficacy of conservation and management.
We analyzed over a decade of acoustic telemetry data to understand individual differences in habitat use of federally endangered adult Atlantic sturgeon (Acipenser o. oxyrinchus) in the Delaware and Hudson rivers during spawning season. In particular, we sought to understand whether sex or natal origin could predict patterns in habitat use, as there is a long-held assumption that adult Atlantic sturgeon seldom stray into non-natal rivers.
In both rivers, migration timing, spawning habitat occupancy, and maximum upstream migration distance were similar between natal and non-natal individuals. While non-natal individuals represented only 13% of fish detected in the Hudson River, nearly half of all tagged fish detected in the Delaware River were non-natal and generally occupied freshwater habitats longer than natal individuals. In both systems males had more heterogenous patterns of habitat use and longer duration of occupancy than did females.
This study demonstrates the importance of non-natal rivers for fulfilling ontogenetic habitat requirements in Atlantic sturgeon. Our results may also highlight an opportunity to improve conservation and management by extending habitat designations to account for more heterogenous patterns in individual habitat use in non-natal freshwater environments.
Moderate- or high-severity fires promote increases in runoff and erosion, leading to a greater likelihood of extreme geomorphic responses, including debris flows. In the first several years following fire, the majority of debris flows initiate when runoff rapidly entrains sediment on steep slopes. From a hazard perspective, it is important to be able to anticipate when and where watershed responses will be dominated by debris flows rather than flood flows. Rainfall intensity averaged over a 15 min duration, I15, in particular, has been identified as a key predictor of debris flow likelihood. Developing effective warning systems and predictive models for post-fire debris flow hazards therefore relies on high-temporal resolution rainfall data at the time debris flows initiate. In this study, we documented the geomorphic response of a series of watersheds following a wildfire in western New Mexico, USA, with an emphasis on constraining debris flow timing within rainstorms to better characterize debris-flow-triggering rainfall intensities. We estimated temporal changes in soil hydraulic properties and ground cover in areas burned at different severities over >2 years to offer explanations for observed differences in spatial and temporal patterns in debris flow activity. We observed 16 debris flows, all of which initiated during the first several months following the fire. The average recurrence interval of the debris-flow-triggering I15 is 1.3 years, which highlights the susceptibility of recently burned watersheds to runoff-generated debris flows in this region. All but one of the debris flows initiated in watersheds burned primarily at moderate or high soil burn severity. Since soil hydraulic properties appeared to be relatively resilient to burning, we attribute reduced debris flow activity at later times to decreases in the fraction of bare ground. Results provide additional constraints on the rainfall characteristics that promote post-fire debris flow initiation in a region where fire size and severity have been increasing.
Carotenoid-dependent ornaments can reflect animals’ diet and foraging behaviors. However, this association should be spatially flexible and variable among populations to account for geographic variation in optimal foraging behaviors. We tested this hypothesis using populations of a marine predator (the brown booby, Sula leucogaster) that forage across a gradient in ocean depth in and near the Gulf of California. Specifically, we quantified green chroma for two skin traits (foot and gular color) and their relationship to foraging location and diet of males, as measured via global positioning system tracking and stable carbon isotope analysis of blood plasma. Our three focal colonies varied in which foraging attributes were linked to carotenoid-rich ornaments. For gular skin, our data showed a shift from a benthic prey-green skin association in the shallow waters in the north to a pelagic prey-green skin association in the deepest waters to the south. Mean foraging trip duration and distance of foraging site from coast also predicted skin coloration in some colonies. Finally, brown booby colonies varied in which trait (foot versus gular skin color) was associated with foraging metrics. Overall, our results indicate that male ornaments reflect quality of diet and foraging–information that may help females select mates who are adapted to local foraging conditions and therefore, are likely to provide better parental care. More broadly, our results stress that diet-dependent ornaments are closely linked to animals’ environments and that we cannot assume ornaments or ornament signal content are ubiquitous within species, even when ornaments appear similar among populations.
The U.S. Geological Survey collected water velocity and water quality data from salt marshes in Great Channel, southwest of Stone Harbor, New Jersey, and near Thompsons Beach, New Jersey, to evaluate restoration effectiveness after Hurricane Sandy and monitor postrestoration marsh health. Time series data of turbidity and water velocity were collected from 2018 to 2019 and 2022 to 2023 at both sites. Water samples were collected and analyzed for suspended-sediment concentration (SSC), which was used to derive a regression model to estimate a time series of SSC data from turbidity data. The SSC time series data were then combined with the water velocity data to calculate the flood-ebb SSC differential. This report presents the data collection methods, the repeated median regression model used to estimate SSC from turbidity, and the flood-ebb SSC differential calculations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1193","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"De Meo, O.A., Bales, R.D., Ganju, N.K., Marsjanik, E.D., and Suttles, S.E., 2024, Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23: U.S. Geological Survey Data Report 1193, 12 p., https://doi.org/10.3133/dr1193.","productDescription":"Report: iv, 12 p.; 2 Data Releases","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-162873","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499107,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116368.htm","linkFileType":{"id":5,"text":"html"}},{"id":427955,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1193/dr1193.XML"},{"id":427958,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CS5U6N","text":"USGS data release","linkHelpText":"Supplementary data in support of oceanographic and water quality times-series measurements made at Thompsons Beach and Stone Harbor, NJ from September 2018 to February 2023"},{"id":427957,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z0Z8DM","text":"USGS data release","linkHelpText":"Time-series measurements of oceanographic and water quality data collected at Thompsons Beach and Stone Harbor, New Jersey, USA, September 2018 to September 2019 and March 2022 to May 2023"},{"id":427956,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1193/images/"},{"id":427954,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1193/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1193"},{"id":427953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1193/dr1193.pdf","text":"Report","size":"3.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1193"},{"id":427952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1193/coverthb.jpg"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.65514977013649,\n 39.123992018486376\n ],\n [\n -74.84847491860745,\n 39.123992018486376\n ],\n [\n -74.84847491860745,\n 39.01856524124548\n ],\n [\n -74.65514977013649,\n 39.01856524124548\n ],\n [\n -74.65514977013649,\n 39.123992018486376\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7, 8, and 9 for quarter 4 (October–December) of 2023. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
This is the second quarterly report to include analysis results for Landsat 9, which was launched in September 2021. The inclusion of Landsat 9 analysis results was dependent on two factors: a complete reprocessing of the Landsat 9 data archive and enough time elapsing to begin formulating lifetime trends. In April 2023, all Landsat 9 image data acquired since the satellite’s launch were reprocessed to take advantage of calibration updates identified by the ECCOE Landsat Cal/Val Team. Additional information about the Landsat 9 reprocessing effort is available at https://www.usgs.gov/landsat-missions/news/upcoming-reprocessing-all-landsat-9-data. Additional information about Landsat 9 prelaunch, commissioning, and early on-orbit imaging performance is available at https://www.mdpi.com/journal/remotesensing/special_issues/15B4V2K92K.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241026","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Shaw, J.L., Denevan, A., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Barsi, J., Kaita, E., Levy, R., Miller, J., and Ding, L., 2024, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2023 (ver. 1.1, December 2024): U.S. Geological Survey Open-File Report 2024–1026, 62 p., https://doi.org/10.3133/ofr20241026.","productDescription":"Report: ix, 62 p.; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-162286","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":427978,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1026/coverthb2.jpg"},{"id":427981,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1026/images/"},{"id":427979,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1026/ofr20241026.pdf","text":"Report","size":"5.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1026"},{"id":427980,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1026/ofr20241026.XML"},{"id":427982,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":427983,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241026/full"},{"id":464893,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1026/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: April 22, 2024; Version 1.1: December 11, 2024","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198