Occurring globally, pockmarks are seafloor depressions associated with seabed fluid escape. Pockmark ubiquity and morphologic heterogeneity result in an irregular seafloor that can be difficult to quantitatively describe. To address this challenge, we test the hypothesis that deep-learning based object detection and segmentation can be used to develop data-driven models for pockmark identification and characterization. This study describes the development, testing, and deployment of eight separate deep learning-based pockmark detection models using publicly available, gridded bathymetric data from the Belfast Bay, Maine, USA, Blue Hill Bay, Maine, USA, and Passamaquoddy Bay, New Brunswick, Canada estuarine pockmark fields. The models tested include three types of convolutional neural network architectures, as well as a generative adversarial network. We find that the data-driven models consistently resolve pockmarks from the background seafloor, allowing for quick and accurate delineation of pockmarks in a variety of seabed habitats. With these delineations we examine and compare the morphology of the muddy estuarine pockmark fields. We then compare these morphometric results to pockmark fields in two distinct settings, the sandy German Bight and the Aquitaine continental slope. We find that in all the pockmark fields a power law relationship, generally, exists between pockmark area and pockmark depth, though this relationship deteriorates with the smallest pockmarks, suggesting that there may be a minimum size needed for geomorphic stability. These results show that the training data and trained models developed here can be applied for quick detection and characterization of pockmarks where other high-resolution bathymetry is available, demonstrating the value of data-driven detection models for characterizing morphologically complex seafloors. Last, the morphologic characteristics of pockmarks identified in this study will aid future studies in relating pockmark size to environmental characteristics like seabed sediment texture and regional gradient.
The accuracy and traceability of geochronometers are of vital importance to questions asked by many Earth scientists. The widely applied 40Ar/39Ar geochronometer relies on the co-irradiation of samples with neutron fluence monitors (reference materials) of known ages; the ages and uncertainties of these monitors are critical to our ability to apply this chronometer. Previously, first principles, astronomical and optimisation calibrations have been made. The first principles method for determining the age of monitor minerals is the K-Ar method, which involves measurement of their 40K and 40Ar* abundances. The AQuA (Absolute Quantities of Argon) pipette system, which emits calibrated quantities of 40Ar* via the ideal gas law, was used to calibrate the sensitivity of the system across a range of source pressures and estimate 40Ar* abundances in neutron fluence monitors. These 40Ar abundances were combined with existing 40K abundance data for these monitors. Ages for HD-B1 and MD2 (GA1550) biotite fluence monitors were calculated and combined with intercalibration data for HD-B1 and Fish Canyon sanidine (FCs) to determine ages for FCs. Current results do not have the targeted accuracy when compared with previous calibrations; however, we show how the extensive methodology development presented here can be used towards making reliable future measurements.
","language":"English","publisher":"Wiley","doi":"10.1111/ggr.12464","usgsCitation":"Morgan, L.E., Davidheiser-Kroll, B., Kuiper, K.F., Mark, D.F., McLean, N.M., and Wijbrans, J., 2023, First principles calibration of 40Ar abundances in 40Ar/39Ar mineral neutron fluence monitors: Methodology and preliminary results: Geostandards and Geoanalytical Research, v. 47, no. 1, p. 91-104, https://doi.org/10.1111/ggr.12464.","productDescription":"14 p.","startPage":"91","endPage":"104","ipdsId":"IP-134452","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":445276,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ggr.12464","text":"Publisher Index Page"},{"id":410541,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":859090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidheiser-Kroll, Brett","contributorId":176175,"corporation":false,"usgs":false,"family":"Davidheiser-Kroll","given":"Brett","email":"","affiliations":[],"preferred":false,"id":859091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuiper, Klaudia F. 0000-0001-6345-5019","orcid":"https://orcid.org/0000-0001-6345-5019","contributorId":229575,"corporation":false,"usgs":false,"family":"Kuiper","given":"Klaudia","email":"","middleInitial":"F.","affiliations":[{"id":41675,"text":"Faculty of Earth and Life Sciences, VU University Amsterdam","active":true,"usgs":false}],"preferred":false,"id":859092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mark, Darren F. 0000-0002-0707-9773","orcid":"https://orcid.org/0000-0002-0707-9773","contributorId":229571,"corporation":false,"usgs":false,"family":"Mark","given":"Darren","email":"","middleInitial":"F.","affiliations":[{"id":41672,"text":"Isotope Geoscience Unit, Scottish Universities Environmental Research Centre (SUERC)","active":true,"usgs":false}],"preferred":false,"id":859093,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLean, Noah M. 0000-0003-0388-1862","orcid":"https://orcid.org/0000-0003-0388-1862","contributorId":299948,"corporation":false,"usgs":false,"family":"McLean","given":"Noah","email":"","middleInitial":"M.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":859094,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wijbrans, Jan 0000-0002-8091-1239","orcid":"https://orcid.org/0000-0002-8091-1239","contributorId":229585,"corporation":false,"usgs":false,"family":"Wijbrans","given":"Jan","email":"","affiliations":[{"id":41681,"text":"Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1085, 1081HV, Amsterdam, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":859095,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238075,"text":"70238075 - 2023 - A size-based stock assessment model for invasive blue catfish in a Chesapeake Bay sub-estuary during 2001–2016","interactions":[],"lastModifiedDate":"2023-01-19T16:57:34.401619","indexId":"70238075","displayToPublicDate":"2022-11-07T06:48:30","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A size-based stock assessment model for invasive blue catfish in a Chesapeake Bay sub-estuary during 2001–2016","docAbstract":"Stock assessment modeling provides a means to estimate the population dynamics of invasive fishes and may do so despite data limitations. Blue catfish (Ictalurus furcatus) were introduced to the Chesapeake Bay watershed to support recreational fisheries but also consume species of conservation need and economic importance. To assess management tradeoffs, managers need to understand the current status of the population and anticipate future population abundance and trends. A Bayesian size-based stock assessment model was used to estimate blue catfish abundance, fishing mortality, and size structure over time (2001–2016) in the tidal James River. The model estimated population size increases until around 2006, with declines in total abundance after 2011 and large blue catfish (≥80 cm total length) after 2001. These first estimates of blue catfish population dynamics in the Chesapeake Bay region provide inputs for projection models to evaluate prospective management actions and identify monitoring needs.
Mass capture of flightless geese during the summer is a common trapping technique to obtain large numbers of individuals for research and marking, but few studies have assessed the impacts of this method on the survival of after-hatch-year geese. We evaluated the effects of holding time and captured flock size on the survival of >26,000 subadult (second yr) and adult (≥third yr) greater white-fronted geese (Anser albifrons frontalis) banded in Alaska, USA, 1999–2017. We constructed models with and without capture effects to analyze our band-recovery data and used Akaike's Information Criterion to rank our model set. Models that included both capture-related variables ranked highest. Longer individual holding times negatively affected survival during the first year after banding, and effects were greatest during the earliest years of our study when holding times were generally longer and protocols to minimize negative capture effects were less refined. There was a positive relationship between survival and captured flock size. We suggest practitioners reduce holding times of geese during mass captures to the extent practicable and continually evaluate and refine their methods to minimize negative capture effects.
Habitat suitability is a consequence of interacting environmental factors. In riparian ecosystems, suitable plant habitat is influenced by interactions between stream hydrology and climate, hereafter referred to as “hydroclimate”. We tested the hypothesis that hydroclimate variables would improve the fit of ecological niche models for a suite of riparian species using occurrence data from the western United States. We focus on the climate conditions (temperature, precipitation and vapor pressure deficit) during the months of lowest and highest streamflow as integrative hydroclimate metrics of resource and stress levels. We found that the inclusion of hydroclimate variables improved model fit for all species in the western USA dataset. We then tested the utility of the improved habitat suitability models by projecting them onto a regulated segment of the Colorado River to assess potential impacts of streamflow seasonality on vegetation metrics of management concern. Species frequency derived from independent survey data in the Colorado River segment was significantly higher for species with predicted suitable habitat than for species without predicted suitable habitat. Under different simulated hydrographs for the Colorado River, overall species richness was predicted to be greatest with peak streamflows during summer, and native-to-non-native species ratios were predicted to be greatest with lowest streamflows in winter. Summer high flows were particularly associated with higher predicted habitat suitability for species that have increased in cover over recent decades (e.g., Pluchea sericea, Baccharis species). We conclude that hydroclimate covariates can be useful tools for predicting how riparian vegetation communities respond to changes in the seasonal timing of low and high streamflows.
Two radioactive elements, uranium (U) and radon (Rn), which are of potential concern in New Hampshire (NH) groundwater, are investigated. Exceedance probability maps are tools to highlight locations where the concentrations of undesirable substances in the groundwater may be elevated. Two forms of statistical analysis are used to create exceedance probability maps for U and Rn in NH groundwater. The first, Boosted Regression Tree (BRT), was selected for estimating U exceedance values. It computes exceedance values directly using the Bernoulli distribution function. The second method of statistical analysis used for Rn to determine exceedance probabilities is ordinary least squares (OLS) regression. In the process of determining exceedance probabilities for U and Rn, the utility of a new dataset is investigated. That new predictor dataset is the Multi-Order Hydrologic Position (MOHP) dataset. MOHP raster datasets have been produced nationally for the conterminous United States at a 30-m resolution. The concept behind MOHP is that, for any given point on the earth's surface, there is the potential for a longer groundwater flow path as one goes deeper beneath the land surface. MOHP predictors were tested in both models. Three MOHP predictors were found useful in the BRT model and two in the OLS model. MOHP data were found useful as predictors along with other site characteristics in predicting U and Rn exceedance probabilities in New Hampshire groundwater.
Plant element stoichiometry and stoichiometric flexibility strongly regulate ecosystem responses to global change. Here, we tested three potential mechanistic drivers (climate, soil nutrients, and plant taxonomy) of both using paired foliar and soil nutrient data from terrestrial forested National Ecological Observatory Network sites across the USA. We found that broad patterns of foliar nitrogen (N) and foliar phosphorus (P) are explained by different mechanisms. Plant taxonomy was an important control over all foliar nutrient stoichiometries and concentrations, especially foliar N, which was dominantly related to taxonomy and did not vary across climate or soil gradients. Despite a lack of site-level correlations between N and environment variables, foliar N exhibited intraspecific flexibility, with numerous species-specific correlations between foliar N and various environmental factors, demonstrating the variable spatial and temporal scales on which foliar chemistry and stoichiometric flexibility can manifest. In addition to plant taxonomy, foliar P and N:P ratios were also linked to soil nutrient status (extractable P) and climate, especially actual evapotranspiration rates. Our findings highlight the myriad factors that influence foliar chemistry and show that broad patterns cannot be explained by a single consistent mechanism. Furthermore, differing controls over foliar N versus P suggests that each may be sensitive to global change drivers on distinct spatial and temporal scales, potentially resulting in altered ecosystem N:P ratios that have implications for processes ranging from productivity to carbon sequestration.
We produced a globally distributed catalog of earthquakes and nuclear explosions with calibrated hypocenters, referred to as the Global Catalog of Calibrated Earthquake Locations (GCCEL). This dataset currently contains 18,782 events in 289 clusters with >3.2 million arrival times observed at 19,258 stations. The term “calibrated” refers to the property that the hypocenters are minimally biased by unknown Earth structure. In addition, we calculate uncertainties using empirically determined variability of the arrival‐time data itself, specific to each calibrated cluster of hypocenters. Outliers in the arrival‐time dataset are removed based on measured variability of the data. In each cluster, we estimate the empirically determined uncertainty for each set of station‐phase arrival times. We use a version of the hypocentroidal decomposition multiple event relocation algorithm specifically adapted for calibrated relocations of clusters of seismic events. Most clusters are calibrated by fitting the subset of direct crustal first arrivals (Pg and Sg) with a locally appropriate travel‐time model to estimate the cluster hypocentroid. A few clusters are calibrated by aligning the pattern of relative locations in space and time with one or more events for which a ground‐truth hypocenter is available from an independent source with known uncertainty, such as a nuclear explosion. Epicentral uncertainties in GCCEL typically range from 1 to 5 km with a 90% confidence interval. Most events have depth constraint from one or more sources, usually with an uncertainty of ≤5 km. GCCEL is a significant resource for research at local, regional, and global scales because it provides minimally biased absolute hypocenters, meaningful associated error estimates, and curated arrival times as a reference dataset that can be used as prior constraints in the development of new regional, national, and global earthquake catalogs; validation of new location techniques; and the generation of advanced Earth models.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220217","usgsCitation":"Bergman, E.A., Benz, H.M., Yeck, W.L., Karasözen, E., Engdahl, E., Ghods, A., Hayes, G., and Earle, P.S., 2023, A global catalog of calibrated earthquake locations: Seismological Research Letters, v. 94, no. 1, p. 485-495, https://doi.org/10.1785/0220220217.","productDescription":"11 p.","startPage":"485","endPage":"495","ipdsId":"IP-134306","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":435566,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95R8K8G","text":"USGS data release","linkHelpText":"Global Catalog of Calibrated Earthquake Locations"},{"id":408855,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"94","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Bergman, Eric A. 0000-0002-7069-8286","orcid":"https://orcid.org/0000-0002-7069-8286","contributorId":84513,"corporation":false,"usgs":false,"family":"Bergman","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":856034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":856035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":856036,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karasözen, Ezgi","contributorId":298619,"corporation":false,"usgs":false,"family":"Karasözen","given":"Ezgi","affiliations":[{"id":64627,"text":"Alaska Earthquake Center, University of Alaska-Fairbanks","active":true,"usgs":false}],"preferred":false,"id":856037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Engdahl, E. Robert","contributorId":298620,"corporation":false,"usgs":false,"family":"Engdahl","given":"E. Robert","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":856038,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ghods, Abdolreza","contributorId":244222,"corporation":false,"usgs":false,"family":"Ghods","given":"Abdolreza","email":"","affiliations":[{"id":48866,"text":"Institute for Advanced Studies in Basic Sciences","active":true,"usgs":false}],"preferred":false,"id":856039,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hayes, Gavin P. 0000-0003-3323-0112","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":6157,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":856040,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":856041,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247690,"text":"70247690 - 2023 - Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning","interactions":[],"lastModifiedDate":"2023-08-11T14:31:34.353066","indexId":"70247690","displayToPublicDate":"2022-10-18T09:27:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning","docAbstract":"Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low-altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost-prohibitive. Therefore, automated methods for counting are critical to cost-efficient application of remote sensing for wildlife surveys covering large areas. We conducted nocturnal surveys of sandhill cranes (Antigone canadensis) during spring migration in the Central Platte River Valley of Nebraska, USA, using midwave thermal infrared sensors. We developed a framework for automated counting of sandhill cranes from thermal imagery using deep learning, assessed and compared the performance of two automated counting models, and quantified the effect of spatial resolution on counting accuracy. Aerial thermal imagery data were collected in March 2018 and 2021; 40 images were analyzed. We applied two deep learning models: an object detection approach, Faster R-CNN and a recently developed pixel-density estimation approach, ASPDNet. Model performance was determined using data independent of the training imagery. The effect of spatial resolution was quantified with a beta regression on relative error. Our results showed model accuracy of 9% mean percent error for ASPDNet and 18% for Faster R-CNN. Most error was related to the undercounting of sandhill cranes. ASPDNet had <50% of the error of Faster R-CNN as measured by mean percent error, root-mean-squared error and mean absolute error. Spatial resolution affected accuracy of both models, with error rate increasing with coarser resolution, particularly with Faster R-CNN. Deep learning models, particularly pixel-density estimators, can accurately automate counting of migratory birds in a dense, aggregate setting such as nocturnal roosting sites.
","language":"English","publisher":"Zoological Society of London","doi":"10.1002/rse2.301","usgsCitation":"Luz-Ricca, E., Landolt, K.L., Pickens, B.A., and Koneff, M.D., 2023, Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning: Remote Sensing in Ecology and Conservation, v. 9, no. 2, p. 182-194, https://doi.org/10.1002/rse2.301.","productDescription":"13 p.","startPage":"182","endPage":"194","ipdsId":"IP-137740","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":445346,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.301","text":"Publisher Index Page"},{"id":435568,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DZKFQ3","text":"USGS data release","linkHelpText":"Aerial thermal imagery of the Central Platte River Valley and bounding box annotations of sandhill cranes"},{"id":419747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.4602621702348,\n 40.829394227542565\n ],\n [\n -99.14401866960671,\n 40.829394227542565\n ],\n [\n -99.14401866960671,\n 40.57838905213882\n ],\n [\n -98.4602621702348,\n 40.57838905213882\n ],\n [\n -98.4602621702348,\n 40.829394227542565\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Luz-Ricca, Emilio","contributorId":298780,"corporation":false,"usgs":false,"family":"Luz-Ricca","given":"Emilio","email":"","affiliations":[{"id":6686,"text":"College of William and Mary","active":true,"usgs":false}],"preferred":false,"id":880036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landolt, Kyle Lawrence 0000-0002-6738-8586","orcid":"https://orcid.org/0000-0002-6738-8586","contributorId":298782,"corporation":false,"usgs":true,"family":"Landolt","given":"Kyle","email":"","middleInitial":"Lawrence","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":880037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pickens, Bradley A.","contributorId":140926,"corporation":false,"usgs":false,"family":"Pickens","given":"Bradley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":880038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koneff, Mark D.","contributorId":191128,"corporation":false,"usgs":false,"family":"Koneff","given":"Mark","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":880039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237623,"text":"70237623 - 2023 - Streamlined approach for assessing embedded consumption of lithium and cobalt in the United States","interactions":[],"lastModifiedDate":"2023-03-01T16:55:41.096762","indexId":"70237623","displayToPublicDate":"2022-10-17T08:34:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2351,"text":"Journal of Industrial Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Streamlined approach for assessing embedded consumption of lithium and cobalt in the United States","docAbstract":"In today's complex global supply chains, time and data intensive analyses are required to understand global flows of mineral commodities from mine to consumer, particularly for mineral commodities in products (electronics, automobiles, etc.) that contain multiple parts with many mineral commodities. National and regional analyses require additional time and data to incorporate international trade flows. However, data limitations and time constraints often prohibit global and national material flow analyses for minor metals. Here we present a methodological approach to circumvent these constraints by utilizing readily available industry-level global data from the United Nations Statistics Division and national industrial data to estimate total requirements for a mineral commodity. We apply this approach to lithium and cobalt use in the United States for the year 2018 and distinguish between apparent raw material consumption versus inferred embedded consumption of lithium and cobalt materials in all forms. The results show that more than half of the United States’ total requirements for both lithium and cobalt is in parts and products that were manufactured outside the United States. In large part, this is due to limited US manufacturing capability for lithium-ion battery materials and cells and the United States’ high import reliance for electronics that use those batteries.
","language":"English","publisher":"Wiley","doi":"10.1111/jiec.13337","usgsCitation":"Alonso, E., Pineault, D., and Nassar, N.T., 2023, Streamlined approach for assessing embedded consumption of lithium and cobalt in the United States: Journal of Industrial Ecology, v. 27, no. 1, p. 33-42, https://doi.org/10.1111/jiec.13337.","productDescription":"10 p.","startPage":"33","endPage":"42","ipdsId":"IP-130705","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":445353,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jiec.13337","text":"Publisher Index Page"},{"id":408379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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through arctic and subarctic zones","docAbstract":"Influenza A viruses (IAV) circulate endemically among many wild aquatic bird populations that seasonally migrate between wintering grounds in southern latitudes to breeding ranges along the perimeter of the circumpolar arctic. Arctic and subarctic zones are hypothesized to serve as ecologic drivers of the intercontinental movement and reassortment of IAVs due to high densities of disparate populations of long distance migratory and native bird species present during breeding seasons. Iceland is a staging ground that connects the East Atlantic and North Atlantic American flyways, providing a unique study system for characterizing viral flow between eastern and western hemispheres. Using Bayesian phylodynamic analyses, we sought to evaluate the viral connectivity of Iceland to proximal regions and how inter-species transmission and reassortment dynamics in this region influence the geographic spread of low and highly pathogenic IAVs. Findings demonstrate that IAV movement in the arctic and subarctic reflects wild bird migration around the perimeter of the circumpolar north, favouring short-distance flights between proximal regions rather than long distance flights over the polar interior. Iceland connects virus movement between mainland Europe and North America, consistent with the westward migration of wild birds from mainland Europe to Northeastern Canada and Greenland. Though virus diffusion rates were similar among avian taxonomic groups in Iceland, gulls play an outsized role as sinks of IAVs from other avian hosts prior to onward migration. These data identify patterns of virus movement in northern latitudes and inform future surveillance strategies related to seasonal and emergent IAVs with potential public health concern.
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Health","active":true,"usgs":false}],"preferred":false,"id":855487,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Nutter, Felicia B.","contributorId":8070,"corporation":false,"usgs":false,"family":"Nutter","given":"Felicia","email":"","middleInitial":"B.","affiliations":[{"id":6936,"text":"Tufts University","active":true,"usgs":false}],"preferred":false,"id":855488,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Runstadler, Jonathan A.","contributorId":24706,"corporation":false,"usgs":false,"family":"Runstadler","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":855489,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Hill, Nichola J.","contributorId":189563,"corporation":false,"usgs":false,"family":"Hill","given":"Nichola","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":855490,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70239010,"text":"70239010 - 2023 - Phylogenetic risk assessment is robust for forecasting the impact of European insects on North American conifers","interactions":[],"lastModifiedDate":"2023-03-15T14:33:00.796771","indexId":"70239010","displayToPublicDate":"2022-10-11T08:05:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Phylogenetic risk assessment is robust for forecasting the impact of European insects on North American conifers","docAbstract":"Some introduced species cause severe damage, although the majority have little impact. Robust predictions of which species are most likely to cause substantial impacts could focus efforts to mitigate those impacts or prevent certain invasions entirely. Introduced herbivorous insects can reduce crop yield, fundamentally alter natural and managed forest ecosystems, and are unique among invasive species in that they require certain host plants to succeed. Recent studies have demonstrated that understanding the evolutionary history of introduced herbivores and their host plants can provide robust predictions of impact. Specifically, divergence times between hosts in the native and introduced ranges of a nonnative insect can be used to predict the potential impact of the insect should it establish in a novel ecosystem. However, divergence time estimates vary among published phylogenetic datasets, making it crucial to understand if and how the choice of phylogeny affects prediction of impact. Here, we tested the robustness of impact prediction to variation in host phylogeny by using insects that feed on conifers and predicting the likelihood of high impact using four different published phylogenies. Our analyses ranked 62 insects that are not established in North America and 47 North American conifer species according to overall risk and vulnerability, respectively. We found that results were robust to the choice of phylogeny. Although published vascular plant phylogenies continue to be refined, our analysis indicates that those differences are not substantial enough to alter the predictions of invader impact. Our results can assist in focusing biosecurity programs for conifer pests and can be more generally applied to nonnative insects and their potential hosts by prioritizing surveillance for those insects most likely to be damaging invaders.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2761","usgsCitation":"Uden, D.R., Mech, A.M., Havill, N.P., Schulz, A.N., Ayers, M.P., Herms, D.A., Hoover, A.M., Gandhi, K.J., Hufbauer, R.A., Liebhold, A.M., Marisco, T.D., Raffa, K.F., Thomas, K.A., Tobin, P.C., and Allen, C., 2023, Phylogenetic risk assessment is robust for forecasting the impact of European insects on North American conifers: Ecological Applications, v. 33, no. 2, e2761, 16 p., https://doi.org/10.1002/eap.2761.","productDescription":"e2761, 16 p.","ipdsId":"IP-137618","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445369,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2761","text":"Publisher Index Page"},{"id":410794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Uden, Daniel R.","contributorId":219904,"corporation":false,"usgs":false,"family":"Uden","given":"Daniel","email":"","middleInitial":"R.","affiliations":[{"id":40095,"text":"Nebraska Cooperative Fish and Wildlife Unit, School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE","active":true,"usgs":false}],"preferred":false,"id":859669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mech, Angela M.","contributorId":219892,"corporation":false,"usgs":false,"family":"Mech","given":"Angela","email":"","middleInitial":"M.","affiliations":[{"id":40087,"text":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA. Corresponding email: ammech@wcu.edu. Present address: Department of Geosciences and Natural Resources, Western Carolina University, Cullowhee, NC","active":true,"usgs":false}],"preferred":false,"id":859670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Havill, Nathan P.","contributorId":219900,"corporation":false,"usgs":false,"family":"Havill","given":"Nathan","email":"","middleInitial":"P.","affiliations":[{"id":40091,"text":"Northern Research Station, USDA Forest Service, Hamden, CT","active":true,"usgs":false}],"preferred":false,"id":859671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulz, Ashley N.","contributorId":219894,"corporation":false,"usgs":false,"family":"Schulz","given":"Ashley","email":"","middleInitial":"N.","affiliations":[{"id":40088,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR","active":true,"usgs":false}],"preferred":false,"id":859672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayers, Matthew P","contributorId":219486,"corporation":false,"usgs":false,"family":"Ayers","given":"Matthew","email":"","middleInitial":"P","affiliations":[{"id":40011,"text":"Department of Biological Sciences, Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":859673,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herms, Daniel A.","contributorId":219895,"corporation":false,"usgs":false,"family":"Herms","given":"Daniel","email":"","middleInitial":"A.","affiliations":[{"id":40089,"text":"The Davey Tree Expert Company, Kent, OH","active":true,"usgs":false}],"preferred":false,"id":859674,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hoover, Angela Marie 0000-0003-0401-5587","orcid":"https://orcid.org/0000-0003-0401-5587","contributorId":265174,"corporation":false,"usgs":true,"family":"Hoover","given":"Angela","email":"","middleInitial":"Marie","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859675,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gandhi, Kamal JK","contributorId":300212,"corporation":false,"usgs":false,"family":"Gandhi","given":"Kamal","email":"","middleInitial":"JK","affiliations":[{"id":65048,"text":"D.B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA","active":true,"usgs":false}],"preferred":false,"id":859676,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hufbauer, Ruth A.","contributorId":219901,"corporation":false,"usgs":false,"family":"Hufbauer","given":"Ruth","email":"","middleInitial":"A.","affiliations":[{"id":40092,"text":"Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":859677,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Liebhold, Andrew M.","contributorId":219902,"corporation":false,"usgs":false,"family":"Liebhold","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":40093,"text":"USDA Forest Service Northern Research Station, Morgantown, WV","active":true,"usgs":false}],"preferred":false,"id":859678,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Marisco, Travis D","contributorId":300213,"corporation":false,"usgs":false,"family":"Marisco","given":"Travis","email":"","middleInitial":"D","affiliations":[{"id":65050,"text":"Department of Biological Sciences, Arkansas State University, Jonesboro, AR, USA","active":true,"usgs":false}],"preferred":false,"id":859679,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Raffa, Kenneth F.","contributorId":219903,"corporation":false,"usgs":false,"family":"Raffa","given":"Kenneth","email":"","middleInitial":"F.","affiliations":[{"id":40094,"text":"Department of Entomology, University of Wisconsin, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":859680,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thomas, Kathryn A. 0000-0002-7131-8564 kathryn_a_thomas@usgs.gov","orcid":"https://orcid.org/0000-0002-7131-8564","contributorId":167,"corporation":false,"usgs":true,"family":"Thomas","given":"Kathryn","email":"kathryn_a_thomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859681,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Tobin, Patrick C.","contributorId":200172,"corporation":false,"usgs":false,"family":"Tobin","given":"Patrick","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":859682,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Allen, Craig R.","contributorId":246029,"corporation":false,"usgs":false,"family":"Allen","given":"Craig R.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":859683,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70241456,"text":"70241456 - 2023 - Lake Superior Kiyi reproductive biology","interactions":[],"lastModifiedDate":"2023-03-21T11:58:09.344621","indexId":"70241456","displayToPublicDate":"2022-10-07T06:55:44","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Lake Superior Kiyi reproductive biology","docAbstract":"The Lake Superior Kiyi Coregonus kiyi is an understudied species being considered for reintroduction into Laurentian Great Lakes where it no longer occurs. Herein, we provide descriptions of Kiyi reproductive biology with the intention of guiding potential gamete collections for propagation.
Data were collected on Kiyi spawning timing, spawning locations, spawning season catch rates, length at sexual maturity, sex ratios, fecundity, egg size, and larval occurrences in Lake Superior from 1996–2021. These data were compared to observations made a century prior in Lakes Michigan, Ontario, and Superior.
Contemporary Kiyi spawning occurred between late December and late January when surface water temperatures cooled to <4°C. Spawning Kiyi were caught almost exclusively in 38.1-mm stretch mesh, as compared to larger meshes (50.8–76.2 mm). Capture depths for developing, ripe, running, and spent female Kiyi were similar and ranged from 82 to 221 m. Fifty percent of female and male Kiyi were classified as sexually mature at ~150 mm total length. Fecundity estimates ranged from 1,578 to 6,720 eggs/female. Mean diameter of unfertilized eggs was 1.7 mm. Recently hatched larval Kiyi were collected at the surface during May–July at 62 of the 113 locations sampled throughout the lake in 2019.
Our work suggests that Kiyi gamete collection efforts from mid-December through January using 38.1-mm gill-net panels set at bathymetric depths of at least 100 m would maximize the collection of spawning Kiyi and reduce the bycatch of other Coregonus species. Future research questions include the following: (1) “Do Kiyi form spawning aggregations at specific spawning areas, or do they spawn indiscriminately across the lake?”; (2) “Do Kiyi spawn near the bottom or up in the water column?”; (3) “What is the relationship between fall lake overturn and Kiyi spawn timing?”; and (4) “Could summer larval and age-0 Kiyi collections provide an opportunity for establishing a captive broodstock?”
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10389","usgsCitation":"Vinson, M., Herbert, M.E., Ackiss, A.S., Dobosenski, J.A., Evrard, L.M., Gorman, O., Lyons, J.F., Phillips, S.B., and Yule, D.L., 2023, Lake Superior Kiyi reproductive biology: Transactions of the American Fisheries Society, v. 152, no. 1, p. 75-93, https://doi.org/10.1002/tafs.10389.","productDescription":"19 p.","startPage":"75","endPage":"93","ipdsId":"IP-138990","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":445372,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10389","text":"Publisher Index Page"},{"id":414424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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\"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":866884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herbert, Matthew E.","contributorId":189192,"corporation":false,"usgs":false,"family":"Herbert","given":"Matthew","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":866885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ackiss, Amanda Susanne 0000-0002-8726-7423","orcid":"https://orcid.org/0000-0002-8726-7423","contributorId":272165,"corporation":false,"usgs":true,"family":"Ackiss","given":"Amanda","email":"","middleInitial":"Susanne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":866886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobosenski, Jamie A.","contributorId":239602,"corporation":false,"usgs":false,"family":"Dobosenski","given":"Jamie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":866887,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Evrard, Lori M. 0000-0001-8582-5818 levrard@usgs.gov","orcid":"https://orcid.org/0000-0001-8582-5818","contributorId":2720,"corporation":false,"usgs":true,"family":"Evrard","given":"Lori","email":"levrard@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science 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0000-0003-0179-6533","orcid":"https://orcid.org/0000-0003-0179-6533","contributorId":302071,"corporation":false,"usgs":true,"family":"Phillips","given":"Sydney","email":"","middleInitial":"B","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":866891,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":866892,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238965,"text":"70238965 - 2023 - Using continuous surveys to evaluate precision and bias of inferences from design-based reach-scale sampling of stream habitat","interactions":[],"lastModifiedDate":"2023-02-02T17:51:33.580257","indexId":"70238965","displayToPublicDate":"2022-10-04T06:34:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Using continuous surveys to evaluate precision and bias of inferences from design-based reach-scale sampling of stream habitat","docAbstract":"Understanding the spatial and temporal habitat use of a population is a necessary step for recovery planning. For Chinook salmon (Oncorhynchus tshawytscha), variation in their migration and habitat use complicate predicting how restoring habitats could impact total recruitment. To evaluate how juvenile life history variation affects a population’s response to potential restoration, we developed a stage-structured model for a Chinook salmon population in a northern California river with a seasonally closed estuary. We modeled the timing of juvenile migration and estuarine use as a function of freshwater conditions and fish abundance. We used the model to evaluate the sensitivity of the population to different estuary and freshwater restoration scenarios that could affect population parameters at different life stages. The population’s run size increased most in response to freshwater restoration that enhanced spawning productivity (egg and fry survival), followed by spawner capacity. In contrast, estuary restoration scenarios affected only a subset of Chinook salmon (average 15%), and as a result, did not have a large impact on the total recruitment of a cohort. Under current condition, estuary rearing fish were over six times less likely to survive than fish that migrate to the ocean in the spring or early summer before estuary closure. Because estuary residents experienced low survival in the estuary and in the ocean, improvements to both estuary survival and growth would be needed to increase their total survival. When life cycle monitoring data is available, life cycle models such as ours generate predictions at scales relevant to conservation and are an advantageous approach to managing and conserving anadromous salmon that use multiple habitats throughout their life cycle.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2022.106511","usgsCitation":"Chen, E.K., Som, N.A., Deibner-Hanson, J., Anderson, D.G., and Henderson, M., 2023, A life cycle model for evaluating estuary residency and restoration potential in Chinook salmon: Fisheries Research, v. 257, 106511, 12 p., https://doi.org/10.1016/j.fishres.2022.106511.","productDescription":"106511, 12 p.","ipdsId":"IP-135740","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":445393,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2022.106511","text":"Publisher Index Page"},{"id":429651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Humboldt County","otherGeospatial":"Redwood Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.10880012967473,\n 41.33028967561785\n ],\n [\n -124.10880012967473,\n 41.09072792742461\n ],\n [\n -123.87469855224558,\n 41.09072792742461\n ],\n [\n -123.87469855224558,\n 41.33028967561785\n ],\n [\n -124.10880012967473,\n 41.33028967561785\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"257","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Emily K.","contributorId":337295,"corporation":false,"usgs":false,"family":"Chen","given":"Emily","email":"","middleInitial":"K.","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":902335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Som, Nicholas A.","contributorId":36039,"corporation":false,"usgs":true,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":902336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deibner-Hanson, John","contributorId":337299,"corporation":false,"usgs":false,"family":"Deibner-Hanson","given":"John","email":"","affiliations":[{"id":7067,"text":"Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":902337,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, David G.","contributorId":337301,"corporation":false,"usgs":false,"family":"Anderson","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":81007,"text":"Redwood National and State Parks","active":true,"usgs":false}],"preferred":false,"id":902338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":902339,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240136,"text":"70240136 - 2023 - Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers","interactions":[],"lastModifiedDate":"2023-01-30T12:46:08.681028","indexId":"70240136","displayToPublicDate":"2022-09-27T06:42:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing pesticides of potential concern and identifying potential mixture effects in Great Lakes tributaries using passive samplers","docAbstract":"To help meet the objectives of the Great Lakes Restoration Initiative with regard to increasing knowledge about toxic substances, 223 pesticides and pesticide transformation products were monitored in 15 Great Lakes tributaries using polar organic chemical integrative samplers. A screening-level assessment of their potential for biological effects was conducted by computing toxicity quotients (TQs) for chemicals with available US Environmental Protection Agency (USEPA) Aquatic Life Benchmark values. In addition, exposure activity ratios (EAR) were calculated using information from the USEPA ToxCast database. Between 16 and 81 chemicals were detected per site, with 97 unique compounds detected overall, for which 64 could be assessed using TQs or EARs. Ten chemicals exceeded TQ or EAR levels of concern at two or more sites. Chemicals exceeding thresholds included seven herbicides (2,4-dichlorophenoxyacetic acid, diuron, metolachlor, acetochlor, atrazine, simazine, and sulfentrazone), a transformation product (deisopropylatrazine), and two insecticides (fipronil and imidacloprid). Watersheds draining agricultural and urban areas had more detections and higher concentrations of pesticides compared with other land uses. Chemical mixtures analysis for ToxCast assays associated with common modes of action defined by gene targets and adverse outcome pathways (AOP) indicated potential activity on biological pathways related to a range of cellular processes, including xenobiotic metabolism, extracellular signaling, endocrine function, and protection against oxidative stress. Use of gene ontology databases and the AOP knowledgebase within the R-package ToxMixtures highlighted the utility of ToxCast data for identifying and evaluating potential biological effects and adverse outcomes of chemicals and mixtures. Results have provided a list of high-priority chemicals for future monitoring and potential biological effects warranting further evaluation in laboratory and field environments. Environ Toxicol Chem 2023;42:340–366. Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Sea ice loss is fundamentally altering the Arctic marine environment. Yet there is a paucity of data on the adaptability of food webs to ecosystem change, including predator-prey interactions. Polar bears (Ursus maritimus) are an important subsistence resource for Indigenous people and an apex predator that relies entirely on the under-ice food web to meet their energy needs. Here, we assessed whether polar bears maintained dietary energy density by prey switching in response to spatial-temporal variation in prey availability. We compared the macronutrient composition of diets inferred from stable carbon and nitrogen isotopes in polar bear guard hair (primarily representing summer/fall diet) during periods when bears had low and high survival 2004-2016, between bears that summered on land versus pack ice, and between bears occupying different regions of the Alaskan and Canadian Beaufort Sea. Polar bears consumed diets with lower energy density during periods of low survival suggesting that concurrent increased dietary proportions of beluga whales (Delphinapterus leucas) did not offset reduced proportions of ringed seals (Pusa hispida). Diets with the lowest energy density and proportions from ringed seal blubber were consumed by bears in the western Beaufort Sea (Alaska) during a period when polar bear abundance declined. Intake required to meet energy requirements of an average free-ranging adult female polar bear was 2.1 kg/day on diets consumed during years with high survival but rose to 3.0 kg/day when survival was low. Although bears that summered onshore in the Alaskan Beaufort Sea had higher fat diets than bears that summered on the pack ice, access to the remains of subsistence-harvested bowhead whales (Balaena mysticetus) contributed little to improving diet energy density. Because most bears in this region remain with the sea ice year-round, prey-switching and consumption of whale carcasses onshore appear insufficient to augment diets when availability of their primary prey, ringed seals, is reduced. Our results show that a strong predator-prey relationship between polar bears and ringed seals continues in the Beaufort Sea. The method of estimating dietary blubber using predator hair, demonstrated here, provides a new metric to monitor predator-prey relationships that affect individual health and population demographics.
Current understanding of global late Quaternary fire history is largely drawn from sedimentary charcoal data. Since publication, CharAnalysis increasingly has been relied upon as a robust method for analyzing these data. However, several underlying assumptions of the algorithm have not been tested. This study uses replicated charcoal count data to examine the assumption of Poisson distribution and reproducibility of peak detection. Results show <10% of the replicate counts are Poisson distributed, a maximum peak replication rate of 60%, and, for >90% of the data, intra-level count differences were larger than the threshold used to identify significance in inter-level differences. A pronounced “edge effect” was observed at the beginning and end of the records, cautioning against validation of results based on sections corresponding to the historical period. The proximal cause for low reproducibility is likely a lack of spatial randomness of charcoal particles at the scale of a core diameter. Until and unless decomposition methods can be developed that accommodate the observed limitations inherent in particle count data, best practices for interpreting charcoal records may be to rely on qualitative interpretations based on smoothed influx values and minimum particle count values in the hundreds.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2022.43","usgsCitation":"Anderson, L., Presnetsova, L.S., Wahl, D., Phelps, G., and Gous, A., 2023, Assessing reproducibility in sedimentary macroscopic charcoal count data: Quaternary Research, v. 111, p. 177-196, https://doi.org/10.1017/qua.2022.43.","productDescription":"20 p.","startPage":"177","endPage":"196","ipdsId":"IP-123535","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":435576,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D27CPD","text":"USGS data release","linkHelpText":"ReplicateChar package"},{"id":407617,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Lysanna 0000-0001-5650-9744 landerson@usgs.gov","orcid":"https://orcid.org/0000-0001-5650-9744","contributorId":5339,"corporation":false,"usgs":true,"family":"Anderson","given":"Lysanna","email":"landerson@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850450,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Presnetsova, Liubov S. 0000-0002-1351-8541 lpresnetsova@usgs.gov","orcid":"https://orcid.org/0000-0002-1351-8541","contributorId":296053,"corporation":false,"usgs":true,"family":"Presnetsova","given":"Liubov","email":"lpresnetsova@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850451,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wahl, David 0000-0002-0451-3554","orcid":"https://orcid.org/0000-0002-0451-3554","contributorId":206113,"corporation":false,"usgs":true,"family":"Wahl","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850452,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phelps, Geoffrey 0000-0003-1958-2736 gphelps@usgs.gov","orcid":"https://orcid.org/0000-0003-1958-2736","contributorId":127489,"corporation":false,"usgs":true,"family":"Phelps","given":"Geoffrey","email":"gphelps@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850453,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gous, Alan","contributorId":296054,"corporation":false,"usgs":false,"family":"Gous","given":"Alan","email":"","affiliations":[{"id":63976,"text":"ICME, Stanford University","active":true,"usgs":false}],"preferred":false,"id":850454,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70242723,"text":"70242723 - 2023 - High-resolution 3D forest structure explains ecomorphological trait variation in assemblages of saproxylic beetles","interactions":[],"lastModifiedDate":"2023-05-10T19:15:20.332938","indexId":"70242723","displayToPublicDate":"2022-09-23T06:57:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution 3D forest structure explains ecomorphological trait variation in assemblages of saproxylic beetles","docAbstract":"We test the behavior of the United States (US) West Coast ShakeAlert earthquake early warning (EEW) system during temporally close earthquake pairs to understand current performance and limitations. We consider performance metrics based on source parameter and ground‐motion forecast accuracy, as well as on alerting timeliness. We generate ground‐motion times series for synthesized earthquake sequences from real data by combining the signals from pairs of well‐recorded earthquakes (4.4≤M≤7.1) using time shifts ranging from −60 to +180 s. We examine fore‐ and aftershock sequences, near‐simultaneous events in different source regions, and simulated out‐of‐network and offshore earthquakes. We find that the operational ShakeAlert algorithms Earthquake Point‐source Integrated Code (EPIC) and Finite‐Fault Rupture Detector (FinDer) and the Propagation of Local Undamped Motion (PLUM) method perform largely as expected: EPIC provides the best source location estimates and is often fastest but can underestimate magnitudes or, in extreme cases, miss large earthquakes; FinDer provides real‐time line‐source models and unsaturated magnitude estimates for large earthquakes but currently cannot process concurrent events and may mislocate offshore earthquakes; PLUM identifies pockets of strong ground motion, but can overestimate alert areas. Implications for system performance are: (1) spatially and temporally close events are difficult to identify separately; (2) challenging scenarios with foreshocks that are close in space and time can lead to missed alerts for large earthquakes; and (3) in these situations the algorithms can often estimate ground motion better than source parameters. To improve EEW, our work suggests revisiting the current algorithm weighting in ShakeAlert, to continue developments that focus on using ground‐motion data to aggregate alerts from multiple algorithms, and to investigate methods to optimally leverage algorithm ground‐motion estimates. For testing and certification of EEW performance in ShakeAlert and other EEW systems where applicable, we also suggest that 25 of our 73 scenarios become part of the baseline data set.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220088","usgsCitation":"Bose, M., Andrews, J., O’Rourke, C.T., Kilb, D.L., Lux, A., Bunn, J., and McGuire, J., 2023, Testing the ShakeAlert earthquake early warning system using synthesized earthquake sequences: Seismological Research Letters, v. 94, no. 1, p. 243-259, https://doi.org/10.1785/0220220088.","productDescription":"17 p.","startPage":"243","endPage":"259","ipdsId":"IP-141699","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.12875665417036,\n 32.529141687918056\n ],\n [\n -114.63698449380014,\n 32.72614838079981\n ],\n [\n -114.48442701459375,\n 32.93979339517037\n ],\n [\n -114.66749598964131,\n 33.12737512996701\n ],\n [\n -114.66749598964131,\n 33.365536989744484\n ],\n [\n -114.50476801182128,\n 33.79765966999783\n ],\n [\n -114.37255152984241,\n 34.135061265713915\n ],\n [\n -114.16914155756731,\n 34.269646597615036\n ],\n [\n -114.62681399518621,\n 35.006011225518876\n ],\n [\n -116.50835623873114,\n 36.49168810234471\n ],\n [\n -121.76650402204325,\n 36.26240794936248\n ],\n [\n -120.71894266482627,\n 35.105916993794835\n ],\n [\n -120.92235263710137,\n 34.546539072317\n ],\n [\n -120.92235263710137,\n 34.303292988567804\n ],\n [\n -120.8816706426466,\n 33.79765966999783\n ],\n [\n -119.4171188422655,\n 32.477677373072126\n ],\n [\n -117.12875665417036,\n 32.529141687918056\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"94","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Bose, Maren","contributorId":222639,"corporation":false,"usgs":false,"family":"Bose","given":"Maren","email":"","affiliations":[{"id":40575,"text":"Swiss Seismological Service, Swiss Federal Institute of Technology Zürich (ETH Zürich), Zürich, Switzerland","active":true,"usgs":false}],"preferred":false,"id":853549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andrews, Jennifer","contributorId":187764,"corporation":false,"usgs":false,"family":"Andrews","given":"Jennifer","affiliations":[],"preferred":false,"id":853550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Rourke, Colin T 0000-0001-5403-4685","orcid":"https://orcid.org/0000-0001-5403-4685","contributorId":290635,"corporation":false,"usgs":true,"family":"O’Rourke","given":"Colin","email":"","middleInitial":"T","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kilb, Deborah L.","contributorId":216380,"corporation":false,"usgs":false,"family":"Kilb","given":"Deborah","email":"","middleInitial":"L.","affiliations":[{"id":37799,"text":"SCRIPPS","active":true,"usgs":false}],"preferred":false,"id":853552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lux, Angela","contributorId":297155,"corporation":false,"usgs":false,"family":"Lux","given":"Angela","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":853553,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bunn, Julian","contributorId":216379,"corporation":false,"usgs":false,"family":"Bunn","given":"Julian","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":853554,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853555,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256632,"text":"70256632 - 2023 - Abundance-occupancy patterns of black bass in an impounded river","interactions":[],"lastModifiedDate":"2024-08-27T16:19:28.491506","indexId":"70256632","displayToPublicDate":"2022-09-20T11:11:36","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Abundance-occupancy patterns of black bass in an impounded river","docAbstract":"A positive relationship has been documented for a wide diversity of taxa between the percentage of transects sampled in which a species is recorded (i.e., occupancy) and the average abundance of the species at transects where recorded. This positive relationship implies that abundance increases faster than occupancy, so populations that occupy more sites also tend to occupy them at higher abundances. Plainly, there is a limit to the sites available for a species to occupy, so as the population expands numerically, abundance at a site must also increase. The pattern may differ across species and geography depending on aspects such as species vital rates, resource use, and resource availability. I investigated abundance–occupancy patterns of three black basses Micropterus spp. in reservoirs of the mainstem Tennessee River, USA. The data set included relative abundance estimates made at 7,237 sites in nine reservoirs sampled during 1997–2018, for 43,243 black bass, including 67% Largemouth Bass Micropterus salmoides, 14% Smallmouth Bass M. dolomieu, and 19% Spotted Bass M. punctulatus. As relative abundance increased due to natural annual population fluctuations, occupancy also increased, but faster for Largemouth Bass and more slowly for Smallmouth Bass and Spotted Bass. Largemouth Bass spread abundance more thinly over many sites, and Smallmouth Bass and Spotted Bass spread abundance more thickly over fewer sites. The recognition that black bass populations that decline in occupancy face the additional burden of disproportionally larger decreases in abundance per site, or that black bass that decline in abundance per site face decreases in occupancy, has various conservation and habitat management implications.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10839","usgsCitation":"Miranda, L.E., 2023, Abundance-occupancy patterns of black bass in an impounded river: Fisheries Magazine, v. 48, no. 1, p. 29-37, https://doi.org/10.1002/fsh.10839.","productDescription":"9 p.","startPage":"29","endPage":"37","ipdsId":"IP-137964","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445426,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fsh.10839","text":"Publisher Index Page"},{"id":433205,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee, Virginia","otherGeospatial":"Tennessee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.51831141093221,\n 35.17213463864647\n ],\n [\n -80.29834780281698,\n 37.3753674850671\n ],\n [\n -82.08385156173836,\n 36.851533341166785\n ],\n [\n -84.1055592223345,\n 36.473202359426324\n ],\n [\n -84.79636517937904,\n 36.154121202168554\n ],\n [\n -85.30188507516615,\n 35.601194925841924\n ],\n [\n -85.95061532416173,\n 35.402289193777634\n ],\n [\n -87.42526534692949,\n 35.40224034005327\n ],\n [\n -87.77087113180701,\n 36.242548221501394\n ],\n [\n -87.72037388310109,\n 36.95958455464218\n ],\n [\n -88.40292522706517,\n 37.168037652329204\n ],\n [\n -88.72311956270738,\n 37.006711794546405\n ],\n [\n -88.6388154242051,\n 35.34035627790165\n ],\n [\n -88.28490090405815,\n 34.7193901919844\n ],\n [\n -86.49011524065774,\n 34.12174705237837\n ],\n [\n -85.48751726389709,\n 34.61564094795365\n ],\n [\n -84.05191776424546,\n 35.049424366433044\n ],\n [\n -82.51702712914107,\n 35.26411210649212\n ],\n [\n -80.51831141093221,\n 35.17213463864647\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-10-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908403,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250894,"text":"70250894 - 2023 - Structural properties of the Southern San Andreas fault zone in northern Coachella Valley from magnetotelluric imaging","interactions":[],"lastModifiedDate":"2024-01-11T13:56:43.841598","indexId":"70250894","displayToPublicDate":"2022-09-08T07:53:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Structural properties of the Southern San Andreas fault zone in northern Coachella Valley from magnetotelluric imaging","docAbstract":"The Southern San Andreas fault (SSAF) poses one of the largest seismic risks in California. Yet, there is much ambiguity regarding its deeper structural properties around Coachella Valley, in large part due to the relative paucity of everyday seismicity. Here, we image a multistranded section of the SSAF using a non-seismic method, namely magnetotelluric (MT) soundings, to help inform depth-dependent fault zone geometry, fluid content and porosity. The acquired MT data and resultant inversion models highlight a conductive column encompassing the SSAF zone that includes a 2–3 km wide vertical to steeply northeast dipping conductor down to ∼4 km depth (maximum of ∼1 Ω·m at 2 km depth) and another prominent conductor in the ductile crust (∼1 Ω·m at 12 km depth and slightly southwest of the surface SSAF). We estimate porosities of 18–44 per cent for the conductive uppermost 500 m, a 10–15 per cent porosity at 2 km depth and that small amounts (0.1–3 per cent) of interconnected hypersaline fluids produce the deeper conductor. Located northeast of this conductive region is mostly resistive crust indicating dry crystalline rock that extends down to ∼20 km in places. Most of the local seismicity is associated with this resistive region. Located farther northeast still is a conductive region at >13 km depth and separate from the one to the southwest. The imaged anomalies permit two interpretations. The SSAF zone is vertical to steeply northeast dipping in the upper crust and (1) is near vertical at greater depth creating mostly an impermeable barrier for northeast fluid migration or (2) continues to dip northeast but is relatively dry and resistive up to ∼13 km depth where it manifests as a secondary deep ductile crustal conductor. Taken together with existing knowledge, the first interpretation is more likely but more MT investigations are required.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac356","usgsCitation":"Share-MacParland, P., Peacock, J., Constable, S.C., Vernon, F.L., and Wang, S., 2023, Structural properties of the Southern San Andreas fault zone in northern Coachella Valley from magnetotelluric imaging: Geophysical Journal International, v. 232, no. 1, p. 694-704, https://doi.org/10.1093/gji/ggac356.","productDescription":"11 p.","startPage":"694","endPage":"704","ipdsId":"IP-124157","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":445442,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/11250/3051610","text":"External Repository"},{"id":424322,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"northern Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.0,\n 34.25\n ],\n [\n -117.0,\n 33.5\n ],\n [\n -115.75,\n 33.5\n ],\n [\n -115.75,\n 34.25\n ],\n [\n -117.0,\n 34.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"232","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-09-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Share-MacParland, Pieter-Ewald 0000-0001-6674-1491","orcid":"https://orcid.org/0000-0001-6674-1491","contributorId":299108,"corporation":false,"usgs":false,"family":"Share-MacParland","given":"Pieter-Ewald","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":891962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":891963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Constable, Steve C. 0000-0001-6324-3470","orcid":"https://orcid.org/0000-0001-6324-3470","contributorId":333113,"corporation":false,"usgs":false,"family":"Constable","given":"Steve","email":"","middleInitial":"C.","affiliations":[{"id":79733,"text":"Institute of Geophysics and Planetary Physics, University of California at San Diego","active":true,"usgs":false}],"preferred":false,"id":891964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vernon, Frank L. 0000-0002-9379-4000","orcid":"https://orcid.org/0000-0002-9379-4000","contributorId":333114,"corporation":false,"usgs":false,"family":"Vernon","given":"Frank","email":"","middleInitial":"L.","affiliations":[{"id":79734,"text":"Institute of Geophysics and Planetary Science, University of California at San Diego","active":true,"usgs":false}],"preferred":false,"id":891965,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Shunguo","contributorId":333115,"corporation":false,"usgs":false,"family":"Wang","given":"Shunguo","email":"","affiliations":[{"id":79736,"text":"Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway","active":true,"usgs":false}],"preferred":false,"id":891966,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239910,"text":"70239910 - 2023 - Using machine learning techniques with incomplete polarity datasets to improve earthquake focal mechanism determination","interactions":[],"lastModifiedDate":"2023-01-25T12:41:17.885197","indexId":"70239910","displayToPublicDate":"2022-09-07T06:40:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Using machine learning techniques with incomplete polarity datasets to improve earthquake focal mechanism determination","docAbstract":"Earthquake focal mechanisms are traditionally produced using P‐wave first‐motion polarities and commonly require well‐recorded seismicity. A recent approach that is less dependent on high signal‐to‐noise exploits similar waveforms to produce relative polarity measurements between earthquake pairs. Utilizing these relative polarity measurements, it is possible to produce composite focal mechanisms for clusters within microseismic sequences using regional networks. However, missing or low‐confidence polarity measurements still limit our ability to calculate high‐quality composite focal mechanisms. Here, we replaced unreliable polarity measurements with estimates using iterative random forests, an unsupervised ensemble machine learning method. Using the imputed (“replaced”) polarity data, we then categorically clustered the events into families. As a case study, we applied this modified composite mechanism workflow to a multistation template matched catalog of an earthquake swarm that occurred during 2020 near the Maacama fault in northern California. We found that our modified methodology produced higher‐quality earthquake families and improved composite focal mechanisms, with fault‐plane uncertainties <35° for 94% of the families compared with 34% of families using the previous methodology.
The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI models. While a number of datasets have been published, very few have centered on the natural terrain and its landforms. To bridge this gulf, this paper introduces a first-of-its-kind benchmark dataset, GeoImageNet, which supports natural feature detection in a supervised machine-learning paradigm. A distinctive feature of this dataset is the fusion of multi-source data, including both remote sensing imagery and DEM in depicting spatial objects of interest. This multi-source dataset allows a GeoAI model to extract rich spatio-contextual information to gain stronger confidence in high-precision object detection and recognition. The image dataset is tested with a multi-source GeoAI extension against two well-known object detection models, Faster-RCNN and RetinaNet. The results demonstrate the robustness of the dataset in aiding GeoAI models to achieve convergence and the superiority of multi-source data in yielding much higher prediction accuracy than the commonly used single data source.