Volcanic ashfall forecasts are highly dependent on eruption source parameters (ESPs) and synoptic weather conditions at the time and location of the eruption. In New Zealand, MetService and GNS Science have been jointly developing an ashfall forecast system that incorporates four-dimensional high-resolution numerical weather prediction (NWP) and ESPs into the HYSPLIT model, a state-of-the art hybrid Eulerian and Lagrangian dispersion model widely used for volcanic ash. However, these forecasts are based on discrete ESPs combined with a deterministic weather forecast and thus provide no information on output uncertainty. This shortcoming hinders stakeholder decision making, particularly near the geographical margin of forecasted ashfall and in areas with large gradients in forecasted ash deposition. Our study presents a new approach that incorporates uncertainty from both eruptive and meteorological inputs to deliver uncertainty in the model output. To this end, we developed probability density functions (PDFs) for three key ESPs (plume height, mass eruption rate, eruption duration) tailored to New Zealand’s volcanoes and combine them with NWP ensemble datasets to generate probabilistic ashfall forecasts using the HYSPLIT model. We show that the Latin Hypercube Sampling (LHS) technique can be used to representatively span this four-dimensional parameter space and allow us to add uncertainty quantification to rapid response forecast systems. For a case study of a hypothetical eruption at Tongariro, New Zealand we suggest that large parts of New Zealand’s North Island would not receive adequate warning for potential ashfall if uncertainties were not included in the forecasts. We also propose new probabilistic summary products to support public information and emergency responders decision making.
Understanding patterns of genetic structure and adaptive variation in natural populations is crucial for informing conservation and management. Past genetic research using 11 microsatellite loci identified six genetic stocks of lake whitefish (Coregonus clupeaformis) within Lake Michigan, USA. However, ambiguity in genetic stock assignments suggested those neutral microsatellite markers did not provide adequate power for delineating lake whitefish stocks in this system, prompting calls for a genomics approach to investigate stock structure. Here, we generated a dense genomic dataset to characterize population structure and investigate patterns of neutral and adaptive genetic diversity among lake whitefish populations in Lake Michigan. Using Rapture sequencing, we genotyped 829 individuals collected from 17 baseline populations at 197,588 SNP markers after quality filtering. Although the overall pattern of genetic structure was similar to the previous microsatellite study, our genomic data provided several novel insights. Our results indicated a large genetic break between the northwestern and eastern sides of Lake Michigan, and we found a much greater level of population structure on the eastern side compared to the northwestern side. Collectively, we observed five genomic islands of adaptive divergence on five different chromosomes. Each island displayed a different pattern of population structure, suggesting that combinations of genotypes at these adaptive regions are facilitating local adaptation to spatially heterogenous selection pressures. Additionally, we identified a large linkage disequilibrium block of ~8.5 Mb on chromosome 20 that is suggestive of a putative inversion but with a low frequency of the minor haplotype. Our study provides a comprehensive assessment of population structure and adaptive variation that can help inform the management of Lake Michigan's lake whitefish fishery and highlights the utility of incorporating adaptive loci into fisheries management.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.13475","usgsCitation":"Shi, Y., Homola, J.J., Euclide, P., Isermann, D.A., Caroffino, D., and McPhee, M., 2022, High-density genomic data reveal fine-scale population structure and pronounced islands of adaptive divergence in lake whitefish (Coregonus clupeaformis) from Lake Michigan: Evolutionary Applications, v. 15, no. 11, p. 1776-1791, https://doi.org/10.1111/eva.13475.","productDescription":"16 p.","startPage":"1776","endPage":"1791","ipdsId":"IP-137991","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481072,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.13475","text":"Publisher Index Page"},{"id":480916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.55725950240699,\n 45.37753522596927\n ],\n [\n -87.39148683074085,\n 44.817230157751304\n ],\n [\n -88.03397794894306,\n 42.98217615918885\n ],\n [\n -87.66163831918826,\n 42.48489493230767\n ],\n [\n -86.3437295978575,\n 42.617675976389194\n ],\n [\n -86.40170815429047,\n 43.587414879850954\n ],\n [\n -84.89518851103124,\n 46.04290477047883\n ],\n [\n -86.20220438971735,\n 46.00276060146369\n ],\n [\n -87.55725950240699,\n 45.37753522596927\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Yue","contributorId":349037,"corporation":false,"usgs":false,"family":"Shi","given":"Yue","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":923948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homola, Jared Joseph 0000-0003-3821-7224","orcid":"https://orcid.org/0000-0003-3821-7224","contributorId":303741,"corporation":false,"usgs":true,"family":"Homola","given":"Jared","email":"","middleInitial":"Joseph","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923949,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Euclide, Peter T.","contributorId":349039,"corporation":false,"usgs":false,"family":"Euclide","given":"Peter T.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923950,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923951,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caroffino, David C.","contributorId":349042,"corporation":false,"usgs":false,"family":"Caroffino","given":"David C.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923952,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McPhee, Megan V.","contributorId":349044,"corporation":false,"usgs":false,"family":"McPhee","given":"Megan V.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":923953,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256603,"text":"70256603 - 2022 - Microhabitat use of larval fish in a South Carolina Piedmont stream","interactions":[],"lastModifiedDate":"2024-08-23T16:53:58.777118","indexId":"70256603","displayToPublicDate":"2022-11-13T11:41:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2299,"text":"Journal of Freshwater Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Microhabitat use of larval fish in a South Carolina Piedmont stream","docAbstract":"Understanding habitat use and nursery areas of larval fish is a key component to managing and conserving riverine fishes. Yet, freshwater researchers often focus only on adult fishes, resulting in a limited understanding of the habitat requirements for the early life stages of freshwater fishes. The goal of this study was to quantify the larval fish microhabitat use of three fish families in Twelvemile Creek, a fifth-order tributary of Lake Hartwell (Savannah River basin) in the Piedmont ecoregion of South Carolina, USA. We used handheld dipnets to sample larval fishes along 20 equidistant transects spaced 10 m apart weekly from May to July 2021 along a 200 m stream reach. We also collected microhabitat data at each larval fish capture location. Most captured individuals were in the metalarval stage and were identified to the family level. A partial distance-based redundancy analysis indicated that water velocity contributed to changes in larval fish assemblage structure. Larval fishes occupied a subset of the available habitat that was characterized by low water velocity, non-Podostemum substrate, and shallow habitats close to the shore or bed rock structure. We also detected temporal patterns in larval fish counts, with peak Percidae and Leuciscidae counts in late July and the highest Catostomidae counts in late May–early June. Our results suggest that larval fishes select habitats with low water velocity and shallow habitats close to shore microhabitat characteristics, and that riffle-pool sequences may serve as a nursery habitat for Percidae, Catostomidae and Leuciscidae metalarvae.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02705060.2022.2144957","usgsCitation":"Bower, L.M., and Peoples, B., 2022, Microhabitat use of larval fish in a South Carolina Piedmont stream: Journal of Freshwater Ecology, v. 37, no. 1, p. 583-596, https://doi.org/10.1080/02705060.2022.2144957.","productDescription":"14 p.","startPage":"583","endPage":"596","ipdsId":"IP-144322","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445889,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2022.2144957","text":"Publisher Index Page"},{"id":433114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Twelvemile Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.03663027265466,\n 34.94019201717681\n ],\n [\n -83.03663027265466,\n 34.54806571836822\n ],\n [\n -82.48826562971826,\n 34.54806571836822\n ],\n [\n -82.48826562971826,\n 34.94019201717681\n ],\n [\n -83.03663027265466,\n 34.94019201717681\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Bower, Luke Max 0000-0002-0739-858X","orcid":"https://orcid.org/0000-0002-0739-858X","contributorId":341034,"corporation":false,"usgs":true,"family":"Bower","given":"Luke","email":"","middleInitial":"Max","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908253,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peoples, B.K.","contributorId":341333,"corporation":false,"usgs":false,"family":"Peoples","given":"B.K.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908254,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238160,"text":"70238160 - 2022 - GCPs free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-Mounted GNSS RTK","interactions":[],"lastModifiedDate":"2022-11-15T12:55:04.571611","indexId":"70238160","displayToPublicDate":"2022-11-12T06:53:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"GCPs free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-Mounted GNSS RTK","docAbstract":"In most birds, parental incubation of eggs is necessary for embryo development and survival. Using a combination of weekly nest visits, temperature dataloggers, infrared video cameras, and GPS tracking of hens, we documented several instances of duck eggs hatching after being abandoned by the incubating female. Of 2826 Mallard (Anas platyrhynchos) and Gadwall (Mareca strepera) nests monitored 2015–2019 in Suisun Marsh, California, 48 (1.7%) were abandoned during late incubation (≥ 20 days). Of these, we identified six (12.5%) where at least one egg hatched 2–9 days after abandonment. In all six cases, eggshell membranes were found in the nest (indicating hatch), and ducklings were observed at three nests. Abandoned nests were unattended for an average of 5.9 days before eggs hatched; during this time, mean nest temperatures (23.6°C–29.0°C) were substantially lower than before nest abandonment (31.7°C–36.4°C). We estimated that abandonment resulted in a 9% longer time period between clutch completion and hatch (0–4 days longer) and a lower rate of egg hatching success (36%). Our results provide evidence that some older embryos (≥ 20 days) in mild climates can survive without parental incubation for several days and continue to develop (at a reduced rate) to the point of successfully hatching.
Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and uncertainties of the input data and of the estimated model parameters, which, although optimized for the given calibration data, remain uncertain. The input data errors are caused by uncertainties in the forcing data, such as precipitation and other climatological data, and in the published discharges used for calibration. In the numerical algorithms used for calibration, the published discharges are often assumed to be without error, but they are themselves uncertain, typically having been computed using ratings, which are models fitted to uncertain discharge measurements.
In this study, uncertainty of published daily discharge data and how the discharge uncertainty is transmitted to the parameter values of the Hydrological Simulation Program–FORTRAN (HSPF) rainfall-runoff model and to the simulated discharge at both calibration and prediction locations were investigated for the Lake Michigan diversion in northeastern Illinois and northwestern Indiana. The HSPF model used in this study is used by the U.S. Army Corps of Engineers as part of quantifying the diversion of water from Lake Michigan by the State of Illinois. In this study, the model is calibrated jointly at two watersheds in the study area; the resulting model is considered the base model in this study. Seven other gaged watersheds in the study area are used for testing predictive simulations. A Bayesian rating curve estimation (BaRatin) approach, the BaRatin stage-period-discharge (SPD) method, was used to estimate the uncertainty of the published discharge from the calibration watersheds. To characterize the effect of the discharge uncertainty on parameter values, the HSPF model parameters were recalibrated to 17 nonrandomly selected pairs of discharge series from the BaRatin SPD analysis. To provide an indicator of the effect of parameter uncertainty to compare to the effect of discharge uncertainty, 1,000 parameter sets also were randomly generated from the estimated parameter covariance matrix of the base model. The recalibrated and random parameter sets were then used in HSPF simulations of discharge at the two calibration watersheds and at the seven prediction watersheds. Selected discharge summary statistics—the period-of-study (POS, water years 1997 to 2015) mean discharge, selected flow-duration curve (FDC) quantiles, and water year mean discharges—are used to characterize the variability between simulated and published discharge.
A normalized variability index (VN) is used as a measure of the uncertainty of flow statistics arising from the uncertainty of the sources considered in this study. When this index is at least 1, the variability of the simulations is large enough to explain the median error between simulated and published values, although offsetting errors from other sources are also likely. When the index is appreciably less than 1, the variability of the simulations is clearly insufficient to explain the median error between simulated and published values. At the two calibration watersheds and for results of the two simulation sets considered together, the VN values ranged from 0.2 to 0.8 for POS mean discharge, from 0.3 to 0.6 in the median for a set of FDC quantiles, and from 0.1 to 0.2 in the median for water year mean discharges. These values indicate that substantial uncertainty remains unexplained. Even though two watersheds were used in calibration, that calibration was highly constrained because it was applied to the watersheds simultaneously and was subject to parameter regularization that constrained the adjustment of the parameters from their initial values. These constraints were applied to avoid overfitting to the calibration watersheds and thus to increase the likelihood that the resulting parameters would give accurate results at watersheds not used in the calibration, but they created a parameter transfer error in the calibration watershed results shown by the balancing of errors between the two watersheds. Additional remaining error sources include model structural error and meteorological forcing error to the degree that the calibration was unable to adjust the parameters to account for these errors. At the prediction watersheds, the corresponding VN values were almost always substantially lower than those values at the calibration watersheds. This result is expected because the prediction watersheds have additional uncertainty, including parameter transfer error.
The work described in this report provides preliminary estimates of a limited range of sources of error in predicted discharge uncertainty. Future work would be beneficial to obtain a better statistical characterization of the effect of the uncertainty of calibration discharge series and to address additional sources of uncertainty, such as from precipitation input data used in calibration and prediction and from structural (model) errors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225102","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Soong, D.T., and Over, T.M., 2022, Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2022–5102, 54 p., https://doi.org/10.3133/sir20225102.","productDescription":"Report: ix, 54 p.; 2 Data releases; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-120412","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":409202,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S2IID","text":"USGS data release","linkHelpText":"National Land Cover Database (NLCD) 2011 Land Cover Conterminous United States"},{"id":409201,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UC21B0","text":"USGS data release","linkHelpText":"Models, inputs, and outputs for estimating the uncertainty of discharge simulations for the Lake Michigan Diversion using the Hydrological Simulation Program – FORTRAN model"},{"id":409196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5102/coverthb.jpg"},{"id":409197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.pdf","text":"Report","size":"8.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5102"},{"id":409198,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.XML"},{"id":409199,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5102/images"},{"id":409200,"rank":5,"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"}],"country":"United States","state":"Illinois, Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.34881814497747,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 42.492126793048925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Swarms are bursts of earthquakes without an obvious mainshock. Some have been observed to be associated with transient aseismic fault slip, while others are thought to be related to fluids. However, the association is rarely quantitative due to insufficient data quality. We use high-quality GPS/GNSS, InSAR, and relocated seismicity to study a swarm of >2,000 earthquakes which occurred between 30 September and 6 October 2020, near Westmorland, California. Using 5 min sampled Global Positioning System (GPS) supplemented with InSAR, we document a spontaneous shallow Mw 5.2 slow slip event that preceded the swarm by 2–15 hr. The earthquakes in the early phase were predominantly non-interacting and driven primarily by the slow slip event resulting in a nonlinear expansion. A stress-driven model based on the rate-and-state friction successfully explains the overall spatial and temporal evolution of earthquakes, including the time lag between the onset of the slow slip event and the swarm. Later, a distinct back front and a square root of time expansion of clustered seismicity on en-echelon fault structures suggest that fluids helped sustain the swarm. Static stress triggering analysis using Coulomb stress and statistics of interevent times suggest that 45%–65% of seismicity was driven by the slow slip event, 10%–35% by inter-earthquake interactions, and 10%–30% by fluids. Our model also provides constraints on the friction parameter and the pore pressure and suggests that this swarm behaved like an aftershock sequence but with the mainshock replaced by the slow slip event.
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only because of adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into known adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth, (2) better understand when juvenile sucker mortality is greatest, and (3) help identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey (USGS) monitoring program, begun in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker apparent production, juvenile sucker apparent survival, apparent growth, species composition, and health.
Upper Klamath Lake indices of year-class strength suggest that the 2020 age-0 cohort is one of the lowest since standardized monitoring began. Despite apparently low over-winter survival, the relatively large 2019 cohort persisted in our 2020 samples and continues to contribute to the populations. Although the 2019 cohort age-0 suckers were composed mainly of Lost River suckers, the age-1 suckers from the 2019 cohort were mainly shortnose suckers. Lost River suckers comprised the largest proportion of the 2020 year-class and were only captured in July and August. Shortnose suckers were mainly captured in August and September and comprised a smaller proportion of the 2020 year-class.
Age distribution of suckers captured in Clear Lake indicates greater juvenile survival than in Upper Klamath Lake. Most juvenile suckers captured were age-3 and age-4 suckers classified as the combination of Klamath largescale suckers (Catostomus snyderi) and shortnose suckers from the Lost River Basin, from the 2016 and 2017 cohorts. A lack of age-0 suckers captured in Clear Lake during years with the low inflow or lake levels initially lead us to believe that low water prevented spawning and year class formation. However, recent data indicate that some cohorts that were not captured as age-0 suckers were detected in later years at age-1 or age-2. This finding indicates that juvenile suckers in Clear Lake may spend one or more years in the tributaries or that sampling efficacy for age-0 suckers varies among years because of water depth.
The first 5 years of this monitoring program indicated different patterns in recruitment and survival of juvenile suckers between Upper Klamath and Clear Lakes. Since the monitoring program began in 2015, age-0 sucker catch rates, interpreted as indices of year-class strength, were greatest in Upper Klamath Lake in 2016 and 2019. In those years Lost River suckers made up the majority of age-0 sucker catches; however, in 2017 and 2020 the age-1 sucker catches from these cohorts were mainly composed of shortnose suckers or suckers with genetic markers of both Klamath largescale and shortnose suckers, indicating a low overwinter survival for Lost River suckers even when the age-0 catches were high. Age-0 suckers do not fully recruit to our sampling gear in Upper Klamath Lake until August, experience high mortality by September, and are almost undetectable by the following July or August in most years. In Clear Lake, suckers frequently are not captured until age-1 or age-2 and annual survival appears much greater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221099","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Martin, B.A., Kelsey, C.M., Burdick, S.M., and Bart, R.J., 2022, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2020 monitoring report: U.S. Geological Survey Open-File Report 2022–1099, 27 p., https://doi.org/10.3133/ofr20221099.","productDescription":"vi, 27 p.","onlineOnly":"Y","ipdsId":"IP-141866","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":409276,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221099/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1099"},{"id":409274,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1099/coverthb.jpg"},{"id":409278,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1099/ofr20221099.XML"},{"id":409277,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1099/images"},{"id":409275,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1099/ofr20221099.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1099"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Upper Klamath Lake, Clear Lake Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.23841270893135,\n 42.66770378348696\n ],\n [\n -122.23841270893135,\n 41.77275507129002\n ],\n [\n -121.00794395893129,\n 41.77275507129002\n ],\n [\n -121.00794395893129,\n 42.66770378348696\n ],\n [\n -122.23841270893135,\n 42.66770378348696\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Director, North Central Climate Adaptation Science Center
U.S. Geological Survey
University of Colorado - Boulder
Sustainability, Energy and Environment Community
4001 Discovery Dr., Suite 348
Boulder, CO 80303
The Powder River Basin (PRB) is a world-class oil province, in large part thanks to contributions from premier source rocks, Cretaceous Mowry and Niobrara shales. Both formations are also unconventional reservoirs. A critical aspect of evaluating production potential and finding sweet spots is the nature of the pore systems in these fine-grained source-rock reservoirs. Variation by stratigraphic interval is important for selecting optimum target zones for horizontal wells. Understanding variation in pore type, size, and connectivity and relationships with mineralogy and fabric help in determining prospectivity in different parts of the basin. Deciphering controls on pore-system development helps predict intervals and locations of optimum reservoir quality.
Imaging of Niobrara and Mowry samples from a range of thermal maturities provided observations and data on pore systems, organic matter (OM) types and associations with mineralogy and fabric, wettability, and microporosity associated with both diagenetic and detrital clays. Imaging techniques included scanning electron microscopy, organic petrography and correlative scanning electron microscopy, and mapping of mineralogy through energy dispersive spectroscopy.
Mean solid bitumen (BRo) and vitrinite reflectance (VRo) values indicate all samples are in the oil window with values ranging from 0.52 to 1.15%. Organic fluorescence is prominent in amorphous OM, solid bitumen and some vitrinite in the early oil window. The fluorescence is extinguished at higher thermal maturity. Carbonate pellets (in Niobrara) mainly contain migrated solid bitumen and residual live oil and little or no terrigenous OM (vitrinite and inertinite). However, terrigenous OM is common in siliceous/argillaceous laminae in both formations, where it occurs with amorphous OM, some of which has converted in situ to a solid bitumen petroleum residue.
One key finding is the widespread presence of migrated OM at very early oil window maturity. Distribution of such OM and associated wettability alteration is fabric-controlled, at all levels of thermal maturity studied. Clay morphology and abundance and supporting rigid mineral grain framework strongly influence pore development, preservation, and connectivity in both formations. Carbonate content is a good proxy for reservoir quality in Niobrara intervals due to association of porous solid bitumen with calcareous fecal pellets. High recrystallized microquartz content is associated with the best reservoir intervals in the Mowry.
","language":"English","publisher":"Elsesvier","doi":"10.1016/j.coal.2022.104134","usgsCitation":"Olson, T., Michalchuk, B., Hackley, P.C., Valentine, B.J., Parker, J., and San Martin, R., 2022, Pore systems and organic petrology of cretaceous Mowry and Niobrara source-rock reservoirs, Powder River Basin, Wyoming, USA: International Journal of Coal Geology, v. 264, 104134, 13 p., https://doi.org/10.1016/j.coal.2022.104134.","productDescription":"104134, 13 p.","ipdsId":"IP-142426","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":418454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Powder River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.22939078183438,\n 45.01127679602621\n ],\n [\n -106.22939078183438,\n 42.73172365239171\n ],\n [\n -104.01110426262045,\n 42.73172365239171\n ],\n [\n -104.01110426262045,\n 45.01127679602621\n ],\n [\n -106.22939078183438,\n 45.01127679602621\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"264","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Olson, Terri","contributorId":312451,"corporation":false,"usgs":false,"family":"Olson","given":"Terri","email":"","affiliations":[{"id":67672,"text":"Digital Rock Petrophysics LLC","active":true,"usgs":false}],"preferred":false,"id":876154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michalchuk, Brad","contributorId":312452,"corporation":false,"usgs":false,"family":"Michalchuk","given":"Brad","email":"","affiliations":[{"id":67673,"text":"Anschutz Exploration and Production","active":true,"usgs":false}],"preferred":false,"id":876155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":876156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":876157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parker, Jason","contributorId":312453,"corporation":false,"usgs":false,"family":"Parker","given":"Jason","email":"","affiliations":[{"id":67675,"text":"FIB-X","active":true,"usgs":false}],"preferred":false,"id":876158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"San Martin, Ricardo","contributorId":312454,"corporation":false,"usgs":false,"family":"San Martin","given":"Ricardo","email":"","affiliations":[{"id":67675,"text":"FIB-X","active":true,"usgs":false}],"preferred":false,"id":876159,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237910,"text":"sir20225081 - 2022 - Suspended-sediment transport and water management, Jemez Canyon Dam, New Mexico, 1948–2018","interactions":[],"lastModifiedDate":"2022-11-08T18:02:54.491628","indexId":"sir20225081","displayToPublicDate":"2022-11-08T11:41:15","publicationYear":"2022","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":"2022-5081","displayTitle":"Suspended-Sediment Transport and Water Management, Jemez Canyon Dam, New Mexico, 1948–2018","title":"Suspended-sediment transport and water management, Jemez Canyon Dam, New Mexico, 1948–2018","docAbstract":"Construction and operation of dams provide sources of clean drinking water, support large-scale irrigation, generate hydroelectricity, control floods, and improve river navigation. Yet these benefits are not without cost. Dams affect the natural flow regime, downstream sediment fluxes, and riverine and riparian ecosystems. The Jemez Canyon Dam in New Mexico was constructed in 1953 by the U.S. Army Corps of Engineers with authorizations for flood control and sediment retention. Water managers of the dam use various operational techniques to restore peak streamflow, improve sediment management, and restore altered ecosystem processes, while maintaining the authorized purposes of the dam. This study focuses on four distinct reservoir management operation periods implemented at the Jemez Canyon Dam: (1) predam (pre-1953), (2) a seasonal 24-hour hold pool (1953–79), (3) a permanent pool (1979–2001), and (4) dry reservoir (2001–18).
Results of this study indicate successful flood control and reduction in peak instantaneous streamflow events following construction of the dam, specifically documented in 1958 and 2013. During the second water management operation period, moderate sediment retention (also defined as trap efficiency, which is the percentage of incoming sediments trapped within a reservoir during a given time) occurred (between 41.0 and 67.0 percent of sediments were retained). During the third period (1979–2001), between 61.2 and 99.8 percent of sediments were retained. During the fourth period (2001–18), at least 1,909 acre-feet of accumulated sediment were remobilized. The estimated dam trap efficiency during the fourth water management operation period was −37.2 percent, indicating that more sediments were being removed from the Jemez Canyon Reservoir than were being deposited. These remobilized sediments supplemented the natural sediment delivery in the Jemez River to the middle Rio Grande. The current (2022) dry reservoir operation allows sediment delivery during periods when flooding is not a concern while still providing flood control when needed.
Suspended-sediment particle size data indicate potential coarsening of suspended sediments during the fourth water management operation period, likely resulting from erosion of coarse bed sediments deposited in the reservoir. Suspended-sediment particle size data during the first and fourth water management operation periods indicate that finer sediment mobilized during monsoon season than during snowmelt. Also, suspended-sediment concentrations during the predam and post-hold pool periods indicate concentrations were higher during monsoon season than during snowmelt. Seasonal variations in suspended-sediment concentration and particle size may help dam managers make operational decisions by increasing the understanding of particle size, concentration, and variation of suspended sediment during a given year. The seasonality of suspended-sediment transport can also vary, depending not only on concentration and particle size, but on precipitation. The maximum annual suspended-sediment loads occurred during all three seasonal categories analyzed in this study: snowmelt, monsoon, and the remainder of the year. This indicates that, in addition to sediment particle size and concentration, understanding the variability of transport mechanisms of suspended-sediment load can also guide optimal water management operations at a dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225081","isbn":"978-1-4113-4481-5","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Brown, J.E., Matherne, A.M., Reale, J.K., and Miltenberger, K.E., 2022, Suspended-sediment transport and water management, Jemez Canyon Dam, New Mexico, 1948–2018: U.S. Geological Survey Scientific Investigations Report 2022–5081, 30 p., https://doi.org/10.3133/sir20225081.","productDescription":"Report: vii, 30 p.; 2 Datasets","numberOfPages":"42","onlineOnly":"N","ipdsId":"IP-107586","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":408900,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5081/coverthb.jpg"},{"id":408901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5081/sir20225081.pdf","text":"Report","size":"2.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5081"},{"id":408902,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5081/sir20225081.XML"},{"id":409231,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225081/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408905,"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":408904,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS Earth Resources Observation and Science Center database","linkHelpText":"—EarthExplorer"},{"id":408903,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5081/images"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.61655311950582,\n 35.438783063650746\n ],\n [\n -106.61655311950582,\n 35.32720598341298\n ],\n [\n -106.46641220000733,\n 35.32720598341298\n ],\n [\n -106.46641220000733,\n 35.438783063650746\n ],\n [\n -106.61655311950582,\n 35.438783063650746\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
This paper introduces a method for determining spatially-distributed, 2-D bedload rates using repeat, high-resolution surveys of the bed topography. As opposed to existing methods, bedform parameters and bedload rates are computed from bed elevation profiles interpolated along the local bedform velocities. The bedform velocity fields are computed applying Large-Scale Particle Image Velocimetry, initially developed for surface velocity measurements, to pairs of successive Digital Elevation Models (DEMs). The bathymetry data are interpolated along the direction of each bedform velocity and the mean height of the closest bedform is computed. The dune shape factor is also evaluated along each bedform direction of travel. The local bedload fluxes can be computed by multiplying the bedform velocity by its mean height averaged over the successive two DEMs, and they can be time-averaged over a series of DEM pairs. This method is applied to a high-resolution acoustical survey of an approximately 300 m long by 40 m wide reach of the Colorado River in Grand Canyon upstream from Diamond Creek, USA. The repeat period was about 6–10 min and bed elevation was interpolated every 0.25 m. The obtained results provide insight to the spatial and temporal variability of bedload rates, bedform parameters and bedload fluxes through cross-sections. The method can be applied to other repeated acoustical surveys of river reaches provided that the space and time resolutions are high enough to capture the local movement of bedforms.
Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horseshoe, on estimating interspecific interactions with VAR and VARSS models. We first perform a large-scale simulation study, examining the performance of alternative priors across various levels of observation error. Results from these simulations show that for sparse matrices, the regularized horseshoe prior minimizes the bias and variance across all inter-specific interactions. We then apply the Bayesian VAR model with regularized priors to a output from a large marine food web model (37 species) from the west coast of the USA. Results from this analysis indicate that regularization improves predictive performance of the VAR model, while still identifying important inter-specific interactions.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.14332","usgsCitation":"Ward, E.J., Marshall, K.N., Scheuerell, M.D., 2022, Regularizing priors for Bayesian VAR applications to large ecological datasets: PeerJ, v. 10, e14332, 18 p., https://doi.org/10.7717/peerj.14332.","productDescription":"e14332, 18 p.","ipdsId":"IP-144838","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500605,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.14332","text":"Publisher Index Page"},{"id":500429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, Eric J.","contributorId":366786,"corporation":false,"usgs":false,"family":"Ward","given":"Eric","middleInitial":"J.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":956224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Kristin N.","contributorId":366787,"corporation":false,"usgs":false,"family":"Marshall","given":"Kristin","middleInitial":"N.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":956225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956226,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237990,"text":"tm17A1 - 2022 - Rapidly assessing social characteristics of drought preparedness and decision making: A guide for practitioners","interactions":[],"lastModifiedDate":"2022-11-07T17:14:33.667614","indexId":"tm17A1","displayToPublicDate":"2022-11-07T11:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"17-A1","displayTitle":"Rapidly Assessing Social Characteristics of Drought Preparedness and Decision Making: A Guide for Practitioners","title":"Rapidly assessing social characteristics of drought preparedness and decision making: A guide for practitioners","docAbstract":"This guide is intended to provide managers, decision makers, and other practitioners with advice on conducting a rapid assessment of the social dimensions of drought. Findings from a rapid assessment can provide key social context that may aid in decision making, such as when preparing a drought plan, allocating local drought resilience funding, or gathering the support of local agencies and organizations for collective action related to drought mitigation.
Part I—In the introduction to Part I, we describe the unique problems associated with drought—particularly its slow onset and long duration, which make it difficult to define drought—and highlight five major types of drought (see Box 1). We introduce a few social dimensions of drought (such as economic and institutional perspectives), demonstrate how these dimensions can be interrelated, and describe a few of the modern challenges (such as transformational change and cascading risks) that practitioners face.
We also provide background on the rapid assessment method, first describing it as a “snapshot” of the social landscape, then providing some key advantages of the method (it can be quicker and cheaper than more in-depth methods), and lastly describing how secondary data and other methods can help overcome some of the disadvantages of rapid assessments.
Then, after summarizing the process of developing this guide, we outline the process of using the guide. Importantly, we compare the guide to a travel guide, which provides many different types of information and is best approached with specific interests in mind. Ultimately, we hope for this guide to be malleable enough that it can be helpful to researchers and practitioners in many different contexts, using many different research methods. Related to how to use the guide, we characterize the type of person who might be motivated to use this guide. We also specify key qualifications for a researcher conducting a rapid assessment, drawing particular attention to training on ethical considerations.
We sketch out key considerations when choosing social dimensions of drought to focus on, and the type of data used for analysis. First, it is important to note that in this guide we provide nine important social dimensions of drought, but this is by no means a comprehensive list, and a researcher may find that other dimensions better fit their local context. Second, we provide some pros and cons to a narrow (focusing on just a few dimensions or at a smaller scale) versus broad research focus. Lastly, we describe the pros and cons of using primary versus secondary data (one strategy is to use both, sequentially) and qualitative versus quantitative data.
Ultimately, Part I of this guide functions as an exploration of the various decisions a researcher will make when designing a rapid assessment. These decisions will inform the type of findings and other outcomes that result from the rapid assessment.
Part II—Part II of this guide introduces nine key social dimensions of drought: defining the problem of drought, individual perceptions, social relationships, technology, economics and livelihoods, water governance, decision making, information, and social vulnerability. Each section provides background and key considerations related to a particular dimension, as well as ideas for how to explore the dimension via a rapid assessment.
Part III—Part III of this guide provides two hypothetical examples of how one might use this guide to aid the practitioner in implementing the lessons learned here. In the first example, a watershed group uses two dimensions, defining the problem of drought and social relationships, to inform a community meeting about protecting fisheries from drought. In the second example, a resource manager uses the economics and livelihoods and social vulnerability dimensions to inform the development of a livestock grazing drought management plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm17A1","usgsCitation":"Clifford, K.R., Goolsby, J.B., Cravens, A.E., and Cooper, A.E., 2022, Rapidly assessing social characteristics of drought preparedness and decision making: A guide for practitioners: U.S. Geological Survey Techniques and Methods 17-A1, 41 p., https://doi.org/10.3133/tm17A1.","productDescription":"vii, 41 p.","onlineOnly":"Y","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":409066,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/17/a1/tm17a1.xml"},{"id":409065,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/17/a1/images"},{"id":409061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/17/a1/coverthb.jpg"},{"id":409062,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/17/a1/tm17a1.pdf","text":"Report","size":"1.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 17-A1"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
We describe and interpret deposits associated with the final Ubehebe Crater-forming, phreatomagmatic explosive phase of the multivent, monogenetic Ubehebe volcanic center. Ubehebe volcano is located in Death Valley, California, USA. Pyroclastic deposits occur in four main facies: (1) lapilli- and blockdominated beds, (2) thinly bedded lapilli tuff, (3) laminated and cross-laminated ash, and (4) massive lapilli ash/tuff. Lapilli- and block-dominated beds are found mostly within several hundred meters of the crater and transition outward into discontinuous lenses of lapilli and blocks; they are interpreted to have been deposited by ballistic processes associated with crater-forming explosions. Thinly bedded lapilli tuff is found mainly within several hundred meters, and laminated and cross-laminated ash extends at least 9 km from the crater center. Dune forms are common within ~2 km of the crater center, while finer-grained, distal deposits tend to exhibit planar lamination. These two facies (thinly bedded lapilli tuff and laminated and cross-laminated ash) are interpreted to record multiple pyroclastic surges (dilute pyroclastic currents). Repeated couplets of coarse layers overlain by finer-grained, laminated horizons suggest that many or most of the surges were transient, likely recording individual explosions, and they traveled over complex topography in some areas. These two factors complicate the application of classical sediment-transport theory to quantify surge properties. However, dune-form data provide possible constraints on the relationships between suspended load sedimentation and bed-load transport that are consistent using two independent approaches. Massive lapilli ash/tuff beds occur in drainages below steep slopes and can extend up to ~1 km onto adjacent valley floors beneath large catchments. Although they are massive in texture, their grain-size characteristics are shared with laminated and cross-laminated ash facies, with which they are locally interbedded. These are interpreted to record concentrated granular flows sourced by remobilized pyroclastic surge deposits, either during surge transport or shortly after, while the surge deposits retained their elevated initial pore-gas pressures. Although similar surge-derived concentrated flows have been described elsewhere (e.g., Mount St. Helens, Washington, USA, and Soufriére Hills, Montserrat, West Indies), to our knowledge Ubehebe is the first case where such processes have been identified at a maar volcano. These concentrated flows followed paths that were independent of the pyroclastic surges and represent a potential hazard at similar maar volcanoes in areas with complex terrain.
Diagnostic absorption features in hyperspectral data can be used to identify a specific mineral or mineral associations. However, it is unknown how accurate hyperspectral mapping can be for identifying alteration mineral compositions at the resolution required to describe structures such as fossil intrusions, or whether it can accurately quantify the alteration present. This study compared petrographic observation with visible, near-infrared (VNIR), and shortwave infrared (SWIR) hyperspectral remote sensing at laboratory- (centimetre-scale) and aerial- (metre-scale) scales to characterise the abundance of surface hydrothermal rock alteration in and around a shallow fossil intrusion on Pinnacle Ridge, Mt. Ruapehu, New Zealand. We classified a high-resolution aerial hyperspectral image to develop a new surface alteration map using Spectral Angle Mapper (SAM) algorithm. The petrographic thin-section and the laboratory and aerial hyperspectral imaging revealed a spectrum of hydrous alteration phases as indicated by the presence of an absorption feature at 2207 nm. Moderate correlation exists between the depth of the absorption feature at 2207 nm and the point counting-derived alteration percent values, indicating reliability of laboratory-based hyperspectral analytical methods. In contrast, aerial hyperspectral data failed to provide any clear correlations to field-mapped alteration using a band-depth approach, and we interpret this due to ‘oversampling’ of surface (supergene) alteration, spectral mixing, and sensor limitations (e.g., bandwidth, signal-to-noise ratio). The hyperspectral image-derived alteration map, created using supervised image classification, can loosely be translated to a geotechnical map where porosity and permeability play a major role in localizing hydrothermal fluid flow and the formation of alteration mineral associations.
Evaluation of sea-level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully-linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario-based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022EA002286","usgsCitation":"Gutierrez, B.T., Zeigler, S., Lentz, E.E., Sturdivant, E., and Plant, N., 2022, Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability: Earth and Space Science, v. 9, no. 11, e2022EA002286, 24 p., https://doi.org/10.1029/2022EA002286.","productDescription":"e2022EA002286, 24 p.","ipdsId":"IP-133519","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":445946,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022ea002286","text":"Publisher Index Page"},{"id":435628,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R63EMY","text":"USGS data release","linkHelpText":"LinkedBNs_4Habitat - 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","language":"English","publisher":"Elsevier","doi":"10.1016/j.softx.2022.101215","usgsCitation":"McCann, M., Anderson, D.L., Sherwood, C.R., Bruder, B., Bak, A.S., and Brodie, K., 2022, CoastalImageLib: An open-source Python package for creating common coastal image products: SoftwareX, v. 20, 101215, 7 p., https://doi.org/10.1016/j.softx.2022.101215.","productDescription":"101215, 7 p.","ipdsId":"IP-132131","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":489196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.softx.2022.101215","text":"Publisher Index Page"},{"id":409114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCann, Maile","contributorId":298807,"corporation":false,"usgs":false,"family":"McCann","given":"Maile","email":"","affiliations":[{"id":64688,"text":"Sonny Astani Department of Civil & Environmental Engineering University of Southern California","active":true,"usgs":false}],"preferred":false,"id":856486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Dylan L.","contributorId":187533,"corporation":false,"usgs":false,"family":"Anderson","given":"Dylan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":856487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":856488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bruder, Brittany","contributorId":298808,"corporation":false,"usgs":false,"family":"Bruder","given":"Brittany","email":"","affiliations":[{"id":64689,"text":"Coastal and Hydraulics Laboratory, US Army Engineer Research and Development Center, Kitty Hawk, NC","active":true,"usgs":false}],"preferred":false,"id":856489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bak, A. Spicer","contributorId":298809,"corporation":false,"usgs":false,"family":"Bak","given":"A.","email":"","middleInitial":"Spicer","affiliations":[{"id":64689,"text":"Coastal and Hydraulics Laboratory, US Army Engineer Research and Development Center, Kitty Hawk, NC","active":true,"usgs":false}],"preferred":false,"id":856490,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brodie, Katherine","contributorId":266146,"corporation":false,"usgs":false,"family":"Brodie","given":"Katherine","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":856491,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237852,"text":"ofr20221085 - 2022 - Systematic mapping of the ocean-continent transform plate boundary of the Queen Charlotte fault system, southeastern Alaska and western British Columbia—A preliminary bathymetric terrain model","interactions":[],"lastModifiedDate":"2026-03-30T20:38:03.192379","indexId":"ofr20221085","displayToPublicDate":"2022-11-02T08:15:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1085","displayTitle":"Systematic Mapping of the Ocean-Continent Transform Plate Boundary of the Queen Charlotte Fault System, Southeastern Alaska and Western British Columbia—A Preliminary Bathymetric Terrain Model","title":"Systematic mapping of the ocean-continent transform plate boundary of the Queen Charlotte fault system, southeastern Alaska and western British Columbia—A preliminary bathymetric terrain model","docAbstract":"In 2015, U.S. Geological Survey scientists in collaboration with scientists from other institutions began a study of the Queen Charlotte fault—the first systematic study of the fault in more than three decades. The primary goal of the study was to gain a better understanding of the earthquake, tsunami, and underwater-landslide hazards throughout southeastern Alaska, as well as gather data to develop geologic models that can be applied to similar plate boundaries around the globe, such as the San Andreas fault system in southern California, the Alpine fault in New Zealand, and the North Anatolian fault in Turkey. A bathymetric terrain model was compiled from six different multibeam surveys of the previously unmapped Queen Charlotte fault offshore of southeastern Alaska and Haida Gwaii archipelago.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221085","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration","usgsCitation":"Andrews, B.D., Brothers, D.S., Dartnell, P., Barrie, J.V., Haeussler, P.J., Green, K.M., Greene, H.G., Miller, N.C., Kluesner, J.W., and ten Brink, U.S., 2022, Systematic mapping of the ocean-continent transform plate boundary of the Queen Charlotte fault system, southeastern Alaska and western British Columbia—A preliminary bathymetric terrain model: U.S. Geological Survey Open-File Report 2022–1085, 2 sheets, 7-p. pamphlet, https://doi.org/10.3133/ofr20221085.","productDescription":"Pamphlet: iii, 7 p.; 2 Sheets: 60.50 × 42.50 inches and 60.00 × 42.00 inches; Data Release","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-128196","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501832,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113792.htm","linkFileType":{"id":5,"text":"html"}},{"id":408793,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2022/1085/ofr20221085_sheet2.pdf","text":"Sheet 2","size":"101 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Contents of sheet replicated in the HTML version of the report linked to above"},{"id":408792,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2022/1085/ofr20221085_sheet1.pdf","text":"Sheet 1","size":"72.3 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Contents of sheet replicated in the HTML version of the report linked to above"},{"id":408791,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1085/images/"},{"id":408787,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1085/coverthb.jpg"},{"id":408788,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1085/ofr20221085_pamphlet.pdf","text":"Pamphlet","size":"5.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1085"},{"id":408790,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1085/ofr20221085.XML"},{"id":408789,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221085/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1085"},{"id":408794,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YGDHIQ","text":"USGS data release","linkHelpText":"A bathymetric terrain model of multibeam sonar data collected between 2005 and 2018 along the Queen Charlotte fault system in the eastern Gulf of Alaska from Cross Sound, Alaska, to Queen Charlotte Sound, Canada"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Queen Charlotte Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -140.92029001527425,\n 58\n ],\n [\n -140.92029001527425,\n 46.46240819189495\n ],\n [\n -124.69923324285543,\n 46.46240819189495\n ],\n [\n -124.69923324285543,\n 58\n ],\n [\n -140.92029001527425,\n 58\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
Background: Remotely sensed burned area products are critical to support fire modelling, policy, and management but often require further processing before use.
Aim: We calculated fire history metrics from the Landsat Burned Area Product (1984–2020) across the conterminous U.S. (CONUS) including (1) fire frequency, (2) time since last burn (TSLB), (3) year of last burn, (4) longest fire-free interval, (5) average fire interval length, and (6) contemporary fire return interval (cFRI).
Methods: Metrics were summarised by ecoregion and land ownership, and related to historical and cheatgrass datasets to demonstrate further applications of the products.
Key results: The proportion burned ranged from 0.7% in the Northeast Mixed Woods to 74.1% in the Kansas Flint Hills. The Flint Hills and Temperate Prairies showed the highest burn frequency, while the Flint Hills and the Sierra Nevada and Klamath Mountains showed the shortest TSLB. Compared to private, public land had greater burned area (19 of 31 ecoregions) and shorter cFRI (25 of 31 ecoregions).
Conclusions: Contemporary fire history metrics can help characterise recent fire regimes across CONUS.
Implications: In regions with frequent fire, comparison of contemporary with target fire regimes or invasive species datasets enables the efficient incorporation of burned area data into decision-making.
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Center","active":true,"usgs":true}],"preferred":true,"id":857022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teske, Casey","contributorId":224732,"corporation":false,"usgs":false,"family":"Teske","given":"Casey","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":857023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noble, Joe","contributorId":257938,"corporation":false,"usgs":false,"family":"Noble","given":"Joe","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":857024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Jim","contributorId":191054,"corporation":false,"usgs":false,"family":"Smith","given":"Jim","email":"","affiliations":[],"preferred":false,"id":857025,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262187,"text":"70262187 - 2022 - Evaluation of host fishes for the Brook Floater (Alasmidonta varicosa) from populations in Massachusetts and Maine, USA","interactions":[],"lastModifiedDate":"2025-01-15T17:26:32.534473","indexId":"70262187","displayToPublicDate":"2022-11-01T11:26:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"displayTitle":"Evaluation of host fishes for the Brook Floater (Alasmidonta varicosa) from populations in Massachusetts and Maine, USA","title":"Evaluation of host fishes for the Brook Floater (Alasmidonta varicosa) from populations in Massachusetts and Maine, USA","docAbstract":"The Brook Floater (Alasmidonta varicosa) mussel is globally vulnerable and has disappeared from much of its historical range. Information on Brook Floater host fish use is needed for ecological and conservation purposes, but previous laboratory studies provide conflicting results. We evaluated host fish use by Brook Floater from populations in Massachusetts and Maine, USA. We conducted three experiments using a total of 10 fish species from six families, and we estimated glochidial attachment rate and juvenile metamorphosis rate. Across fish species, attachment ranged from 51.0% to 84.6% and metamorphosis ranged from 4.9% to 80.9%. Fish species and inoculation density (viable glochidia/mL) only weakly predicted attachment, and the number of glochidia that attached to fish did not affect metamorphosis rate. Juvenile metamorphosis was successful on all fish species tested, supporting evidence that Brook Floater is a host generalist. Fish species was an important factor in predicting metamorphosis rates in all experiments. The highest metamorphosis was on Slimy Sculpin (Cottus cognatus) (80.9% ± 2.6 SD) and Brook Trout (Salvelinus fontinalis) (71.6%), but metamorphosis on Brook Trout varied according to source and was lowest on hatchery-raised fish (12.8% ± 0.3 SD). These data contribute to our understanding of the life history of Brook Floater by identifying potential host fishes, and our results can inform propagation efforts for this species in the northeastern USA.
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\"}}]}","volume":"25","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":348389,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":923429,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923430,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelton, Peter D.","contributorId":348390,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":923431,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, David","contributorId":348391,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923432,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Warren, Timothy","contributorId":348393,"corporation":false,"usgs":false,"family":"Warren","given":"Timothy","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923433,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237964,"text":"ofr20221096 - 2022 - Assessing the efficacy of using a parentage-based tagging survival model to evaluate two sources of mortality for juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon","interactions":[],"lastModifiedDate":"2023-09-18T20:04:08.872815","indexId":"ofr20221096","displayToPublicDate":"2022-11-01T08:50:41","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1096","displayTitle":"Assessing the Efficacy of Using a Parentage-Based Tagging Survival Model to Evaluate Two Sources of Mortality for Juvenile Chinook Salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon","title":"Assessing the efficacy of using a parentage-based tagging survival model to evaluate two sources of mortality for juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon","docAbstract":"We conducted a study to assess the efficacy of using a parentage-based tagging survival model (PBT N-mixture model) to evaluate two sources of mortality for juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon. The model was originally developed to evaluate reservoir mortality because of predation from piscivorous fish. However, recent studies have also found that juvenile Chinook salmon experience high infection rates from parasitic copepods (Salmincola californiensis), which are known to negatively affect performance and survival. Our study was conducted to determine if the PBT N-mixture model could separately estimate mortality because of predation from non-native fish and mortality resulting from copepod infection. This assessment was conducted in two parts: (1) data collected in Lookout Point Reservoir during 2018 were re-analyzed; and (2) a simulation was conducted to evaluate a multi-year study that included inter-annual variation in copepod infection rate and two subsampling strategies (10 fish per month, 30 fish per month) to characterize monthly copepod infection rate. Results from each of these efforts suggest that the survival model is unlikely to provide reliable survival estimates for the two mortality sources that we evaluated. The re-analysis of 2018 data showed that “predation only” and “copepod only” models estimated a negative coefficient for the respective covariate, but the model that included both covariates provided coefficient estimates that differed from the other models and were highly uncertain. Similarly, the simulation results showed that most models failed to correctly estimate the magnitude and direction of mortality due to predation and copepods. These results suggest that additional data will be required if a model is desired that can separately estimate mortality effects due to both predation and copepods in the future. The existing data are limited by factors including low detection probabilities from previous field studies, existing uncertainties about copepod effects on mortality in a natural setting and expected limitations in the number of years that a field study could realistically be expected to receive funding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221096","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hance, D.J., Kock, T.J., Perry, R.W., and Pope, A.C., 2022, Assessing the efficacy of using a parentage-based tagging survival model to evaluate two sources of mortality for juvenile Chinook salmon (Oncorhynchus tshawytscha) in Lookout Point Reservoir, Oregon: U.S. Geological Survey Open-File Report 2022–1096, 14 p., https://doi.org/10.3133/ofr20221096.","productDescription":"v, 14 p.","onlineOnly":"Y","ipdsId":"IP-141621","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408997,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1096/ofr20221096.XML"},{"id":408996,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1096/images"},{"id":408995,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221096/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1096"},{"id":408994,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1096/ofr20221096.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1096"},{"id":408993,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1096/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Lookout Point Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.84577497588536,\n 43.952222617898286\n ],\n [\n -122.84577497588536,\n 43.78093867902544\n ],\n [\n -122.51343855010415,\n 43.78093867902544\n ],\n [\n -122.51343855010415,\n 43.952222617898286\n ],\n [\n -122.84577497588536,\n 43.952222617898286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the Earth. Therefore, our overarching goal in this study was to use these data to explore advances that can be made in agricultural research. We selected PRISMA and DESIS images during the 2020 growing season in California's Central Valley to study seven major crops. PRISMA and DESIS images were highly correlated (R 2of 0.9–0.95). Out of the 235 DESIS bands (400–1000 nm) and 238 PRISMA bands (400–2500 nm), 26 (11%) and 45 (19%) bands, respectively, were optimal to study agricultural crops. These optimal bands provided crop type classification accuracies of 83–90%. Hyperspectral vegetation indices to estimate plant pigment content, stress, biomass, moisture, and cellulose/lignin content were also identified.
","language":"English","publisher":"American Society for Photogrammetry and Remote Sensing","doi":"10.14358/PERS.22-00039R2","usgsCitation":"Aneece, I.P., and Thenkabail, P., 2022, New generation hyperspectral sensors DESIS and PRISMA provide improved agricultural crop classifications: Photogrammetric Engineering & Remote Sensing, v. 88, no. 11, p. 715-729, https://doi.org/10.14358/PERS.22-00039R2.","productDescription":"15 p.","startPage":"715","endPage":"729","ipdsId":"IP-137552","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":445966,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.22-00039r2","text":"Publisher Index Page"},{"id":435634,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98LO5D4","text":"USGS data release","linkHelpText":"DESIS and PRISMA spectral library of agricultural crops in California's Central Valley in the 2020 Growing Season"},{"id":410361,"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 -121,\n 37.15\n ],\n [\n -121,\n 36.75\n ],\n [\n -120.333,\n 36.75\n ],\n [\n -120.333,\n 37.15\n ],\n [\n -121,\n 37.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":858748,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":858749,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238321,"text":"70238321 - 2022 - Gaining decision-maker confidence through community consensus: Developing environmental DNA standards for data display on the USGS Nonindigenous Aquatic Species database","interactions":[],"lastModifiedDate":"2022-11-16T12:50:55.92911","indexId":"70238321","displayToPublicDate":"2022-11-01T06:49:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Gaining decision-maker confidence through community consensus: Developing environmental DNA standards for data display on the USGS Nonindigenous Aquatic Species database","docAbstract":"To advance national efforts for the detection and biosurveillance of aquatic invasive species (AIS), we employed a community consensus process to enable the incorporation of environmental DNA (eDNA) detection data into the U.S. Geological Survey’s (USGS) Nonindigenous Aquatic Species (NAS) database (https://nas.er.usgs.gov/eDNA/). Our goal was to identify minimum standards and best practices for the verification of eDNA data by working closely with AIS eDNA community practitioners and natural resource managers across government, private and academic sectors. To better inform management decisions, verified AIS eDNA data will be displayed on a separate mapping layer alongside visual sighting data with the inclusion of additional information on the eDNA methods employed to collect and produce the data. To allow for eDNA data display, we produced consensus derived online documents including a submission application and data submission template and are developing a guidance document for detailing the eDNA data submission process. We also developed a communication plan including a mechanism for reporting detections to appropriate managers for consideration prior to display. The products of these efforts are an application and data submission process that will be used in the new environmental DNA data layer on the Nonindigenous Aquatic Species (NAS) database. Herein, we detail how we engaged the eDNA community for consensus of our standards, share lessons learned from the process, and describe the benefits of such an approach at instilling confidence among the research and decision-maker community. |