The collapse of a volcanic flank can be destructive and deadly. Hydrothermal alteration is common to volcanoes worldwide and is thought to promote volcano instability by decreasing rock strength. However, some laboratory studies have shown that not all alteration reduces rock strength. Our new laboratory data for altered rhyodacites from Chaos Crags (Lassen volcanic center, California, USA) show that pore- and crack-filling mineral precipitation can reduce porosity and permeability and increase strength, Young's modulus, and cohesion. A significant reduction in permeability, by as much as four orders of magnitude, will inhibit fluid circulation and create zones of high pore fluid pressure. We explored the consequences of pore fluid pressurization on volcano stability using large-scale numerical modeling. Upscaled physical and mechanical properties for hydrothermally altered rocks were used as input parameters in our modeling. Results show that a high-pore-pressure zone within a volcano increases volcano deformation and that increasing the size of this zone increases the observed deformation. Hydrothermal alteration associated with mineral precipitation, and increases to rock strength, can therefore promote pore pressurization and volcano deformation, increasing the likelihood of volcano spreading, flank collapses, and phreatic/phreatomagmatic explosions. We conclude that porosity-decreasing alteration, explored here, and porosity-increasing alteration can both promote volcano instability and collapse, but by different mechanisms. Hydrothermal alteration should therefore be monitored at volcanoes worldwide and incorporated into hazard assessments.
A principal challenge impeding strong inference in analyses of wild populations is the lack of robust and long-term data sets. Recent advancements in analytical tools used in wildlife science may increase our ability to integrate smaller data sets and enhance the statistical power of population estimates. One such advancement, the development of spatial capture–recapture (SCR) methods, explicitly accounts for differences in spatial study designs, making it possible to equate multiple study designs in one analysis. SCR has been shown to be robust to variation in design as long as minimal sampling guidance is adhered to. However, these expectations are based on simulation and have yet to be evaluated in wild populations. Here we conduct a rigorously designed field experiment by manipulating the arrangement of artificial cover objects (ACOs) used to collect data on red-backed salamanders (Plethodon cinereus) to empirically evaluate the effects of design configuration on inference made using SCR. Our results suggest that, using SCR, estimates of space use and detectability are sensitive to study design configuration, namely the spacing and extent of the array, and that caution is warranted when assigning biological interpretation to these parameters. However, estimates of population density remain robust to design except when the configuration of detectors grossly violates existing recommendations.
Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledge is increasingly used to improve and inform biological or vulnerability assessments, especially in data-poor areas. Integrating expert knowledge on the distribution, intensity, and relative influence of human activities can guide natural resource management strategies and institutional resource allocation and prioritization. This paper introduces a dataset summarizing the expert-perceived state of inland fisheries at the basin (fishery) level. An electronic survey distributed to professional networks (June-September 2020) captured expert perceptions (n = 536) of threats, successes, and adaptive capacity to fisheries across 93 hydrological basins, 79 countries, and all major freshwater habitat types. This dataset can be used to address research questions with conservation relevance, including: demographic influences on perceptions of threat, adaptive capacities for climate change, external factors driving multi-stressor interactions, and geospatial threat assessments.
","language":"English","publisher":"Nature","doi":"10.1038/s41597-021-00949-0","usgsCitation":"Stokes, G.L., Lynch, A., Funge-Smith, S., Valbo-Jorgensen, J., Beard, Lowe, B.S., Wong, J.P., and Smidt, S.J., 2021, A global dataset of inland fisheries expert knowledge: Scientific Data, v. 8, 182, 10 p., https://doi.org/10.1038/s41597-021-00949-0.","productDescription":"182, 10 p.","ipdsId":"IP-123864","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":451498,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-021-00949-0","text":"Publisher Index Page"},{"id":388584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2021-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Stokes, Gretchen L. 0000-0003-4202-6527","orcid":"https://orcid.org/0000-0003-4202-6527","contributorId":245640,"corporation":false,"usgs":false,"family":"Stokes","given":"Gretchen","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":822094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":822095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Funge-Smith, Simon 0000-0001-9974-5333","orcid":"https://orcid.org/0000-0001-9974-5333","contributorId":245642,"corporation":false,"usgs":false,"family":"Funge-Smith","given":"Simon","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":822096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Valbo-Jorgensen, John 0000-0002-1992-5682","orcid":"https://orcid.org/0000-0002-1992-5682","contributorId":245643,"corporation":false,"usgs":false,"family":"Valbo-Jorgensen","given":"John","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":822097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":822098,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lowe, Benjamin S. 0000-0002-1879-254X","orcid":"https://orcid.org/0000-0002-1879-254X","contributorId":245641,"corporation":false,"usgs":false,"family":"Lowe","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":822099,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wong, Jesse P.","contributorId":264850,"corporation":false,"usgs":false,"family":"Wong","given":"Jesse","email":"","middleInitial":"P.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":822100,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smidt, Samuel J. 0000-0001-7728-2083","orcid":"https://orcid.org/0000-0001-7728-2083","contributorId":192816,"corporation":false,"usgs":false,"family":"Smidt","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":822101,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70222514,"text":"70222514 - 2021 - Using fission-track radiography coupled with scanning electron microscopy for efficient identification of solid-phase uranium mineralogy at a former uranium pilot mill (Grand Junction, Colorado)","interactions":[],"lastModifiedDate":"2021-08-02T13:07:59.439386","indexId":"70222514","displayToPublicDate":"2021-07-16T08:03:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Using fission-track radiography coupled with scanning electron microscopy for efficient identification of solid-phase uranium mineralogy at a former uranium pilot mill (Grand Junction, Colorado)","docAbstract":"Generally, fluvial systems are used for different objectives including energy production, water supply, recreation, and navigation. Thus, many impacts must be considered with their use. An understanding of sediment dynamics in fluvial systems is often of value for a variety of objectives, given that erosion and depositional processes can change the fluvial system morphology and can substantially alter the fluvial environment. In this sense, sediment monitoring is important because it helps to explain and quantify sediment dynamics in the environment. Hence, this study presents an innovative sediment monitoring technique: the use of the acoustic Doppler current profiler, commonly used to obtain discharge measurements, to obtain suspended sediment concentration (SSC). This paper aims to describe the application of additional corrections to the ADP-M9 signal to obtain SSC from measurement campaigns that used the ADP only for discharge measurements. The analyses were based on traditional sediment sampling methods and discharge measurements, with the ADP-M9, from 7 field campaigns at the Taquari River, a major tributary from the Alto Paraguay Basin, in the Pantanal Biome, known as the largest freshwater wetland system in the world. The correlation was assessed considering the following: (a) the equipment frequency operation mode (Smart Pulse or Fixed Frequency) and (b) by checking the influence of the sediment attenuation coefficient. Furthermore, extrapolation was conducted in filtered and unmeasured areas of the ADP to map the suspended sediment concentration over the entire cross section. Results indicate that ADP correlations can be an effective tool for estimating SSC in the Taquari River when samples cannot be collected. Correlations could be applied to past and future ADP measurements made at the location where the correlation was created, as long as similar environmental conditions are present as when the correlation was developed. The described technique can expand the amount of sediment data available at a monitoring site even with reduced traditional sampling and by leveraging instruments used for other monitoring purposes.
","language":"English","publisher":"Springer","doi":"10.1007/s10661-021-09266-w","usgsCitation":"Wosiacki, L.F., Koji Suekame, H., Wood, M.S., Verissimo Goncalves, F., and Bleninger, T., 2021, Mapping of suspended sediment transport using acoustic methods in a Pantanal tributary: Environmental Monitoring and Assessment, v. 193, 493, 19 p., https://doi.org/10.1007/s10661-021-09266-w.","productDescription":"493, 19 p.","ipdsId":"IP-120116","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":387258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Taquari River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -57.041015625,\n -22.105998799750566\n ],\n [\n -54.31640625,\n -19.84939395842278\n ],\n [\n -52.9541015625,\n -18.437924653474393\n ],\n [\n -53.78906249999999,\n -17.853290114098\n ],\n [\n -53.7451171875,\n -17.26672782352052\n ],\n [\n -54.4921875,\n -17.434510551522894\n ],\n [\n -56.82128906249999,\n -16.63619187839765\n ],\n [\n -57.041015625,\n -14.944784875088372\n ],\n [\n -59.501953125,\n -14.349547837185362\n ],\n [\n -60.16113281250001,\n -14.902321826141796\n ],\n [\n -60.1171875,\n -16.1724728083975\n ],\n [\n -58.447265625,\n -16.13026201203474\n ],\n [\n -58.18359375,\n -16.762467717941593\n ],\n [\n -57.78808593749999,\n -17.560246503294888\n ],\n [\n -57.7001953125,\n -18.729501999072138\n ],\n [\n -58.3154296875,\n -20.055931265194438\n ],\n [\n -57.919921875,\n -21.943045533438166\n ],\n [\n -57.041015625,\n -22.105998799750566\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"193","noUsgsAuthors":false,"publicationDate":"2021-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Wosiacki, Liege F.K.","contributorId":261197,"corporation":false,"usgs":false,"family":"Wosiacki","given":"Liege","email":"","middleInitial":"F.K.","affiliations":[{"id":52772,"text":"Federal University of Parana, Curitiba, Brazil","active":true,"usgs":false}],"preferred":false,"id":819460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Koji Suekame, Hugo","contributorId":261198,"corporation":false,"usgs":false,"family":"Koji Suekame","given":"Hugo","email":"","affiliations":[{"id":52773,"text":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil","active":true,"usgs":false}],"preferred":false,"id":819461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":819462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verissimo Goncalves, Fabio","contributorId":261199,"corporation":false,"usgs":false,"family":"Verissimo Goncalves","given":"Fabio","email":"","affiliations":[{"id":52773,"text":"Federal University of Mato Grosso do Sul, Campo Grande, Brazil","active":true,"usgs":false}],"preferred":false,"id":819463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bleninger, Tobias","contributorId":261200,"corporation":false,"usgs":false,"family":"Bleninger","given":"Tobias","email":"","affiliations":[{"id":52772,"text":"Federal University of Parana, Curitiba, Brazil","active":true,"usgs":false}],"preferred":false,"id":819464,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222091,"text":"70222091 - 2021 - Influence of filter pore size on composition and relative abundance of bacterial communities and select host-specific MST markers in coastal waters of southern Lake Michigan","interactions":[],"lastModifiedDate":"2021-07-19T23:11:51.462326","indexId":"70222091","displayToPublicDate":"2021-07-15T18:02:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"title":"Influence of filter pore size on composition and relative abundance of bacterial communities and select host-specific MST markers in coastal waters of southern Lake Michigan","docAbstract":"Water clarity is often the primary guiding factor in determining whether a prefiltration step is needed to increase volumes processed for a range of microbial endpoints. In this study, we evaluate the effect of filter pore size on the bacterial communities detected by 16S rRNA gene sequencing and incidence of two host-specific microbial source tracking (MST) markers in a range of coastal waters from southern Lake Michigan, using two independent data sets collected in 2015 (bacterial communities) and 2016–2017 (MST markers). Water samples were collected from river, shoreline, and offshore areas. For bacterial communities, each sample was filtered through a 5.0-μm filter, followed by filtration through a 0.22-μm filter, resulting in 70 and 143 filter pairs for bacterial communities and MST markers, respectively. Following DNA extraction, the bacterial communities were compared using 16S rRNA gene amplicons of the V3–V4 region sequenced on a MiSeq Illumina platform. Presence of human (Bacteroides HF183) and gull (Gull2, Catellicoccus marimammalium) host-specific MST markers were detected by qPCR. Actinobacteriota, Bacteroidota, and Proteobacteria, collectively represented 96.9% and 93.9% of the relative proportion of all phyla in the 0.22- and 5.0-μm pore size filters, respectively. There were more families detected in the 5.0-μm pore size filter (368) than the 0.22-μm (228). There were significant differences in the number of taxa between the two filter sizes at all levels of taxonomic classification according to linear discriminant analysis (LDA) effect size (LEfSe) with as many as 986 taxa from both filter sizes at LDA effect sizes greater than 2.0. Overall, the Gull2 marker was found in higher abundance on the 5.0-μm filter than 0.22 μm with the reverse pattern for the HF183 marker. This discrepancy could lead to problems with identifying microbial sources of contamination. Collectively, these results highlight the importance of analyzing pre- and final filters for a wide range of microbial endpoints, including host-specific MST markers routinely used in water quality monitoring programs. Analysis of both filters may increase costs but provides more complete genomic data via increased sample volume for characterizing microbial communities in coastal waters.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2021.665664","usgsCitation":"Byappanahalli, M., Nevers, M., Shively, D., Nakatsu, C.H., Kinzelman, J.L., and Phanikumar, M.S., 2021, Influence of filter pore size on composition and relative abundance of bacterial communities and select host-specific MST markers in coastal waters of southern Lake Michigan: Frontiers in Microbiology, v. 12, 665664, 11 p., https://doi.org/10.3389/fmicb.2021.665664.","productDescription":"665664, 11 p.","ipdsId":"IP-126915","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":451506,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2021.665664","text":"Publisher Index Page"},{"id":387255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Wisconsin","city":"Chicago, East Chicago, Racine, Whiting","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.82745361328125,\n 42.67132949822802\n ],\n [\n -87.76290893554688,\n 42.67132949822802\n ],\n [\n -87.76290893554688,\n 42.76012111926778\n ],\n [\n -87.82745361328125,\n 42.76012111926778\n ],\n [\n -87.82745361328125,\n 42.67132949822802\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.57442474365234,\n 41.77643254375403\n ],\n [\n -87.56412506103514,\n 41.77643254375403\n ],\n [\n -87.56412506103514,\n 41.78609706039752\n ],\n [\n -87.57442474365234,\n 41.78609706039752\n ],\n [\n -87.57442474365234,\n 41.77643254375403\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": 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0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":819477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":819478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shively, Dawn 0000-0002-6119-924X dshively@usgs.gov","orcid":"https://orcid.org/0000-0002-6119-924X","contributorId":201533,"corporation":false,"usgs":true,"family":"Shively","given":"Dawn","email":"dshively@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":819479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nakatsu, Cindy H 0000-0003-0663-180X","orcid":"https://orcid.org/0000-0003-0663-180X","contributorId":215593,"corporation":false,"usgs":false,"family":"Nakatsu","given":"Cindy","email":"","middleInitial":"H","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":819480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kinzelman, Julie L.","contributorId":236944,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","email":"","middleInitial":"L.","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":819481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":819482,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221835,"text":"sir20215031 - 2021 - Optimization of the Idaho National Laboratory water-quality aquifer monitoring network, southeastern Idaho","interactions":[],"lastModifiedDate":"2021-07-16T12:31:02.274219","indexId":"sir20215031","displayToPublicDate":"2021-07-15T07:17:18","publicationYear":"2021","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":"2021-5031","displayTitle":"Optimization of the Idaho National Laboratory Water-Quality Aquifer Monitoring Network, Southeastern Idaho","title":"Optimization of the Idaho National Laboratory water-quality aquifer monitoring network, southeastern Idaho","docAbstract":"Long-term monitoring of water-quality data collected from wells at the Idaho National Laboratory (INL) has provided essential information for delineating the movement of radiochemical and chemical wastes in the eastern Snake River Plain aquifer, southeastern Idaho. Since 1949, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, has maintained as many as 200 wells in the INL water-quality monitoring network. A network design tool, distributed as an R package, was developed to evaluate and optimize groundwater monitoring in the existing network based on water-quality data collected at 153 sampling sites since January 1, 1989. The objective of the optimization design tool is to reduce well monitoring redundancy while retaining sufficient data to reliably characterize water-quality conditions in the aquifer. A spatial optimization was used to identify a set of wells whose removal leads to the smallest increase in the deviation between interpolated concentration maps using the existing and reduced monitoring networks while preserving significant long-term trends and seasonal components in the data. Additionally, a temporal optimization was used to identify reductions in sampling frequencies by minimizing the redundancy in sampling events.
Spatial optimization uses an islands genetic algorithm to identify near-optimal network designs removing 10, 20, 30, 40, and 50 wells from the existing monitoring network. With this method, choosing a greater number of wells to remove results in greater cost savings and decreased accuracy of the average relative difference between interpolated maps of the reduced-dataset and the full-dataset. The genetic search algorithm identified reduced networks that best capture the spatial patterns of the average concentration plume while preserving long-term temporal trends at individual wells. Concentration data for 10 analyte types are integrated in a single optimization so that all datasets may be evaluated simultaneously. A constituent was selected for inclusion in the spatial optimization problem when the observations were sufficient to (1) establish a two-range variability model, (2) classify at least one concentration time series as a continuous record block, and (3) make a prediction using the quantile-kriging interpolation method. The selected constituents include sodium, chloride, sulfate, nitrate, carbon tetrachloride, 1,1-dichloroethylene, 1,1,1-trichloroethane, trichloroethylene, tritium, strontium-90, and plutonium-238.
In temporal optimization, an iterative-thinning method was used to find an optimal sampling frequency for each analyte-well pair. Optimal frequencies indicate that for many of the wells, samples may be collected less frequently and still be able to characterize the concentration over time. The optimization results indicated that the sample-collection interval may be increased by an of average of 273 days owing to temporal redundancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215031","collaboration":"DOE/ID-22252Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Coconino aquifer (C aquifer) is a regionally extensive multiple-aquifer system supplying water for municipal, agricultural, and industrial use in northeastern Arizona, northwestern New Mexico, and southeastern Utah. This report focuses on the C aquifer in the arid to semi-arid area between St. Johns, Ariz., and Flagstaff, Ariz., along the Interstate-40 corridor where an increase in groundwater withdrawals coupled with ongoing drought conditions increase the potential for substantial water-level decline within the aquifer.
The U.S. Geological Survey (USGS) C-aquifer Monitoring Program began in 2005 to establish baseline groundwater and surface-water conditions and to quantify physical and water-chemistry responses to pumping stresses and climate. This report presents data previously reported in Brown and Macy (2012) that extend back as far as the 1950s, along with new data collected from the USGS C-aquifer Monitoring Program since that publication, from water years 2012 to 2019.
Water levels in 17 wells are measured quarterly as part of the C-aquifer Monitoring Program, and five of those are continuously monitored at 15-minute intervals. Water levels in an additional 18 wells in the study area are measured periodically by the USGS or other agencies. The largest historical change in water level in the study area was a decrease of 81.20 feet in Lake Mary 1 Well near Flagstaff between 1962 and 2018. Changes in water levels were greatest around major pumping centers and in the eastern extent of the study area.
Surface-water water-quality parameters (pH, water temperature, specific conductance, and dissolved oxygen) and streamflow discharge measurements were collected and analyzed along perennial, groundwater-fed reaches of Clear Creek, Chevelon Creek, and the Little Colorado River during nine baseflow investigations of varying extent between 2005 and 2019. Both Clear Creek and Chevelon Creek gain in flow from the beginning of their perennial reaches to their outflow into the Little Colorado River. The Little Colorado River has relatively steady streamflow in the reach between where the two tributaries enter the river. Chevelon Creek showed an increase in median specific conductance during all baseflow investigations of nearly 4,000 microsiemens per centimeter (μS/cm) from near the headwaters to the confluence with the Little Colorado River; Clear Creek also showed an increase in median specific conductance of almost 5,000 μS/cm from headwaters to confluence. Water temperature, dissolved oxygen, and pH do not show substantial trends along the reaches of Clear Creek, Chevelon Creek, or the Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211051","collaboration":"Prepared in cooperation with the Navajo Nation and the City of Flagstaff","usgsCitation":"Jones, C.J.R., and Robinson, M.J., 2021, Groundwater and surface-water data from the C-aquifer monitoring program, Northeastern Arizona, 2012–2019: U.S. Geological Survey Open-File Report 2021–1051, 34 p., https://doi.org/10.3133/ofr20211051.","productDescription":"vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-115787","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":387185,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20121196","text":"Open-File Report 2012-1196","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data from C-aquifer Monitoring Program, Northeastern Arizona, 2005-11"},{"id":387177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1051/covrthb.jpg"},{"id":387178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1051/ofr20211051.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.829833984375,\n 34.27083595165\n ],\n [\n -109.149169921875,\n 34.27083595165\n ],\n [\n -109.149169921875,\n 36.146746777814364\n ],\n [\n -111.829833984375,\n 36.146746777814364\n ],\n [\n -111.829833984375,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The Arctic is undergoing rapid and accelerating change in response to global warming, altering biodiversity patterns, and ecosystem function across the region. For Arctic endemic species, our understanding of the consequences of such change remains limited. Spectacled eiders (Somateria fischeri), a large Arctic sea duck, use remote regions in the Bering Sea, Arctic Russia, and Alaska throughout the annual cycle making it difficult to conduct comprehensive surveys or demographic studies. Listed as Threatened under the U.S. Endangered Species Act, understanding the species response to climate change is critical for effective conservation policy and planning. Here, we developed an integrated population model to describe spectacled eider population dynamics using capture–mark–recapture, breeding population survey, nest survey, and environmental data collected between 1992 and 2014. Our intent was to estimate abundance, population growth, and demographic rates, and quantify how changes in the environment influenced population dynamics. Abundance of spectacled eiders breeding in western Alaska has increased since listing in 1993 and responded more strongly to annual variation in first-year survival than adult survival or productivity. We found both adult survival and nest success were highest in years following intermediate sea ice conditions during the wintering period, and both demographic rates declined when sea ice conditions were above or below average. In recent years, sea ice extent has reached new record lows and has remained below average throughout the winter for multiple years in a row. Sea ice persistence is expected to further decline in the Bering Sea. Our results indicate spectacled eiders may be vulnerable to climate change and the increasingly variable sea ice conditions throughout their wintering range with potentially deleterious effects on population dynamics. Importantly, we identified that different demographic rates responded similarly to changes in sea ice conditions, emphasizing the need for integrated analyses to understand population dynamics.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.7873","usgsCitation":"Dunham, K., Tucker, A., Koons, D., Abebe, A., Dobson, F., and Grand, J.B., 2021, Demographic responses to climate change in a threatened Arctic species: Ecology and Evolution, v. 11, no. 15, p. 10627-10643, https://doi.org/10.1002/ece3.7873.","productDescription":"17 p.","startPage":"10627","endPage":"10643","ipdsId":"IP-123223","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451515,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.7873","text":"Publisher Index Page"},{"id":396660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Coastal Plain, Arctic Russia, Yukon-Kuskokwim Delta","volume":"11","issue":"15","noUsgsAuthors":false,"publicationDate":"2021-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunham, K.D.","contributorId":287550,"corporation":false,"usgs":false,"family":"Dunham","given":"K.D.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":836868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, A.M.","contributorId":287552,"corporation":false,"usgs":false,"family":"Tucker","given":"A.M.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":836869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koons, D.N.","contributorId":287553,"corporation":false,"usgs":false,"family":"Koons","given":"D.N.","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":836870,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Abebe, A.","contributorId":287556,"corporation":false,"usgs":false,"family":"Abebe","given":"A.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":836871,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobson, F.S.","contributorId":287558,"corporation":false,"usgs":false,"family":"Dobson","given":"F.S.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":836872,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836873,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221762,"text":"70221762 - 2021 - Comparison of preservation and extraction methods on five taxonomically disparate coral microbiomes","interactions":[],"lastModifiedDate":"2021-09-15T13:44:45.088065","indexId":"70221762","displayToPublicDate":"2021-07-14T08:42:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of preservation and extraction methods on five taxonomically disparate coral microbiomes","docAbstract":"All animals are host to a multitude of microorganisms that are essential to the animal’s health. Host-associated microbes have been shown to defend against potential pathogens, provide essential nutrients, interact with the host’s immune system, and even regulate mood. However, it can be difficult to preserve and obtain nucleic acids from some host-associated microbiomes, making studying their microbial communities challenging. Corals are an example of this, in part due to their potentially remote, underwater locations, their thick surface mucopolysaccharide layer, and various inherent molecular inhibitors. This study examined three different preservatives (RNAlater, DNA/RNA Shield, and liquid nitrogen) and two extraction methods (the Qiagen PowerBiofilm kit and the Promega Maxwell RBC kit with modifications) to determine if there was an optimum combination for examining the coral microbiome. These methods were employed across taxonomically diverse coral species, including deep-sea/shallow, stony/soft, and zooxanthellate/azooxanthellate: Lophelia pertusa, Paragorgia johnsoni, Montastraea cavernosa, Porites astreoides, and Stephanocoenia intersepta. Although significant differences were found between preservative types and extraction methods, these differences were subtle, and varied in nature from coral species to coral species. Significant differences between coral species were far more profound than those detected between preservative or extraction method. We suggest that the preservative types presented here and extraction methods using a bead-beating step provide enough consistency to compare coral microbiomes across various studies, as long as subtle differences in microbial communities are attributed to dissimilar methodologies. Additionally, the inclusion of internal controls such as a mock community and extraction blanks can help provide context regarding data quality, improving downstream analyses.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.684161","usgsCitation":"Pratte, Z.A., and Kellogg, C.A., 2021, Comparison of preservation and extraction methods on five taxonomically disparate coral microbiomes: Frontiers in Marine Science, v. 8, 684161, 13 p., https://doi.org/10.3389/fmars.2021.684161.","productDescription":"684161, 13 p.","ipdsId":"IP-127754","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":451519,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2021.684161","text":"Publisher Index Page"},{"id":436274,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96GBWDM","text":"USGS data release","linkHelpText":"Coral Microbiome Preservation and Extraction Method Comparison-Raw Data"},{"id":389261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2021-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":818655,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":818656,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222564,"text":"70222564 - 2021 - A reactive transport approach to modeling cave seepage water chemistry I: Carbon isotope transformations","interactions":[],"lastModifiedDate":"2021-09-14T16:45:51.996528","indexId":"70222564","displayToPublicDate":"2021-07-14T07:58:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"A reactive transport approach to modeling cave seepage water chemistry I: Carbon isotope transformations","docAbstract":"The majority of Critical Zone research has emphasized silicate lithologies, which are typified by relatively slow rates of reactivity and incongruent weathering. However, the relatively simpler weathering of carbonate-dominated lithology can result in secondary mineral deposits, such as speleothems, which provide a long-term archive for Critical Zone processes. In particular, carbon isotopic variability in speleothems has the potential to provide records of changes in vegetation, soil respiration, carbon stabilization in deep soils, and/or chemical weathering in the host rock. Despite this opportunity to reconstruct many Critical Zone processes, multiple influences can also make interpretion of these speleothem carbon isotope records challenging. The integration of observational data and simulations specific to karst systems offers an interpretive framework for these unique time-averaged records accumulated through the evolution of carbonate landscapes. Here, we present a forward and process-based reactive transport simulation based on a multi-year monitoring study of Blue Spring Cave in central Tennessee, USA. The simulations describe the fluid-driven weathering of limestone including explicit tracking of dissolved calcium, stable carbon, and radiocarbon isotope ratios based on reaction rates calibrated through laboratory batch reaction data. We find that calcium concentrations and radiocarbon isotope ratios are strongly influenced by the combination of fluid flow rate and soil CO2 content, and require rapid gas phase communication between the overlying soil boundary condition and interior karst to sustain both elevated limestone weathering rates and relatively modern radiocarbon signatures. Stable carbon isotopes are largely dictated by temperature-dependent equilibrium fractionation among contemporaneous species. These simulations are extended to a wide range of parameter space to demonstrate the environmental factors that these isotope proxies record.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2021.06.041","usgsCitation":"Druhan, J., Lawrence, C., Covey, A., Giannetta, M., and Oster, J., 2021, A reactive transport approach to modeling cave seepage water chemistry I: Carbon isotope transformations: Geochimica et Cosmochimica Acta, v. 311, p. 374-400, https://doi.org/10.1016/j.gca.2021.06.041.","productDescription":"27 p.","startPage":"374","endPage":"400","ipdsId":"IP-125015","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":451520,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2021.06.041","text":"Publisher Index Page"},{"id":436277,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90OTSDY","text":"USGS data release","linkHelpText":"Data from a reactive transport modeling study of cave seepage water chemistry"},{"id":387713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"311","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Druhan, Jennifer","contributorId":245460,"corporation":false,"usgs":false,"family":"Druhan","given":"Jennifer","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":820565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Corey 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":202373,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":820566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Covey, Aaron","contributorId":261749,"corporation":false,"usgs":false,"family":"Covey","given":"Aaron","email":"","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":820567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Giannetta, Max","contributorId":261750,"corporation":false,"usgs":false,"family":"Giannetta","given":"Max","email":"","affiliations":[{"id":35161,"text":"University of Illinois, Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":820568,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oster, Jessica","contributorId":223020,"corporation":false,"usgs":false,"family":"Oster","given":"Jessica","email":"","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":820569,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221897,"text":"ofr20211061 - 2021 - Spatial and temporal distribution of radio-tagged Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers in Clear Lake Reservoir and associated spawning tributaries, Northern California, 2015–17","interactions":[],"lastModifiedDate":"2021-07-14T18:43:40.52114","indexId":"ofr20211061","displayToPublicDate":"2021-07-13T13:15:14","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1061","displayTitle":"Spatial and Temporal Distribution of Radio-Tagged Lost River (Deltistes luxatus) and Shortnose (Chasmistes brevirostris) Suckers in Clear Lake Reservoir and Associated Spawning Tributaries, Northern California, 2015–17","title":"Spatial and temporal distribution of radio-tagged Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers in Clear Lake Reservoir and associated spawning tributaries, Northern California, 2015–17","docAbstract":"Data from a multi-year radio telemetry study were used to assess seasonal distribution patterns for two long-lived, federally endangered catostomids across substantially different water conditions in Clear Lake Reservoir, northern California. Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers, two species endemic to the Klamath Basin, were implanted with radio transmitters in each of 3 years in an effort to expand our understanding of seasonal sucker movements within the reservoir and their migrations in spawning tributaries. Clear Lake Reservoir and its tributaries are part of a critical management unit within the Lost River Basin Recovery Unit for populations of Lost River and shortnose suckers. We documented residency and migratory behaviors and how behaviors were affected by lake surface elevations and water management practices.
Adult suckers were captured during autumn trammel net sampling in the west lobe of the reservoir and implanted with internal radio transmitters. A total of 163 suckers were radio-tagged (75 in 2014, 64 in 2015, and 24 in 2016); 27 more shortnose suckers were tagged than Lost River suckers to reflect the larger population of shortnose suckers in the reservoir. Sex ratios were approximately equal for each species. Aerial telemetry surveys were used to monitor radio-tagged fish from January 20 to December 2 each year and to document the upstream extent of spawning migrations in the tributaries. Surveys were scheduled more frequently during the spawning season (February–June) when suckers are known to move out of the reservoir and into spawning tributaries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211061","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Banet, N.V., Hewitt, D.A., Dolan-Caret, A., and Harris, A.C., 2021, Spatial and temporal distribution of radio-tagged Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers in Clear Lake Reservoir and associated spawning tributaries, Northern California, 2015–17: U.S. Geological Survey Open-File Report 2021–1061, 37 p., https://doi.org/10.3133/ofr20211061.","productDescription":"vi, 37 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120279","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":387167,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1061/ofr20211061_landing.html","text":"Animated movements and migrations","description":"OFR 2021-1061 Animated movements and migrations."},{"id":387166,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1061/ofr20211061.pdf","text":"Report","size":"12.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1061"},{"id":387165,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1061/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25610351562499,\n 41.78616105896385\n ],\n [\n -121.03637695312499,\n 41.78616105896385\n ],\n [\n -121.03637695312499,\n 41.93548729665268\n ],\n [\n -121.25610351562499,\n 41.93548729665268\n ],\n [\n -121.25610351562499,\n 41.78616105896385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Estimates of species abundance are critical to understand population processes and to assess and select management actions. However, capturing and marking individuals for abundance estimation, while providing robust information, can be economically and logistically prohibitive, particularly for species with cryptic behavior. Camera traps can be used to collect data at temporal and spatial scales necessary for estimating abundance, but the use of camera traps comes with limitations when target species are not uniquely identifiable (i.e., “unmarked”). Abundance estimation is particularly useful in the management of invasive species, with herpetofauna being recognized as some of the most pervasive and detrimental invasive vertebrate species. However, the use of camera traps for these taxa presents additional challenges with relevancy across multiple taxa. It is often necessary to use lures to attract animals in order to obtain sufficient observations, yet lure attraction can influence species’ landscape use and potentially induce bias in abundance estimators. We investigated these challenges and assessed the feasibility of obtaining reliable abundance estimates using camera-trapping data on a population of invasive brown treesnakes (Boiga irregularis) in Guam. Data were collected using camera traps in an enclosed area where snakes were subject to high-intensity capture–recapture effort, resulting in presumed abundance of 116 snakes (density = 23/ha). We then applied spatial count, random encounter and staying time, space to event, and instantaneous sampling estimators to photo-capture data to estimate abundance and compared estimates to our presumed abundance. We found that all estimators for unmarked populations performed poorly, with inaccurate or imprecise abundance estimates that limit their usefulness for management in this system. We further investigated the sensitivity of these estimators to the use of lures (i.e., violating the assumption that animal behavior is unchanged by sampling) and camera density in a simulation study. Increasing the effective distances of a lure (i.e., lure attraction) and camera density both resulted in biased abundance estimates. Each estimator rarely recovered truth or suffered from convergence issues. Our results indicate that, when limited to unmarked estimators and the use of lures, camera traps alone are unlikely to produce abundance estimates with utility for brown treesnake management.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2410","usgsCitation":"Amburgey, S.M., Yackel Adams, A.A., Gardner, B., Hostetter, N., Siers, S., McClintock, B., and Converse, S.J., 2021, Evaluation of camera trap-based abundance estimators for unmarked populations: Ecological Applications, v. 31, no. 7, e02410, 19 p.; Data Release, https://doi.org/10.1002/eap.2410.","productDescription":"e02410, 19 p.; Data Release","ipdsId":"IP-126135","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451529,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2410","text":"External Repository"},{"id":436279,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JV1QU5","text":"USGS data release","linkHelpText":"Camera trap data of Brown Treesnakes at mouse-lure 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B.T.","contributorId":29108,"corporation":false,"usgs":true,"family":"McClintock","given":"B.T.","email":"","affiliations":[],"preferred":false,"id":902205,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902200,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70222607,"text":"70222607 - 2021 - NGA-East Ground-Motion Characterization model part I: Summary of products and model development","interactions":[],"lastModifiedDate":"2021-08-09T12:55:45.893875","indexId":"70222607","displayToPublicDate":"2021-07-13T07:53:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"NGA-East Ground-Motion Characterization model part I: Summary of products and model development","docAbstract":"In this article, we present an overview of the research project NGA-East, Next Generation Attenuation for Central and Eastern North America (CENA), and summarize the key methodology and products. The project was tasked with developing a new ground motion characterization (GMC) model for CENA. The final NGA-East GMC model includes a set of 17 median ground motion models (GMMs) for peak ground acceleration and velocity (PGA, PGV) and response spectral ordinates for periods ranging from 0.01 to 10 s. The NGA-East GMMs are applicable to horizontal components of ground motions on very hard rock, for the moment magnitude range of 4.0–8.2, and distances of up to 1500 km. The aleatory standard deviations of GMMs are also provided for site-specific analysis (single-station standard deviation) and for general probabilistic seismic hazard analyses (PSHA) applications (ergodic standard deviation). In addition, adjustment factors are provided for source depth and hanging-wall effects, as well as for hazard computations at sites in the Gulf Coast Region. During the course of the project, several innovative technologies were developed and implemented to increase the transparency and repeatability of the GMC building process. This involved expanding on a set of candidate median GMMs to define and capture an appropriate range of epistemic uncertainty in ground motions. We also developed a new approach for modeling the aleatory variability that was completely independent of the median GMMs. The development made extensive use of the CENA database but also borrowed data from other parts of the world when relevant and led to an integrated suite of models. Through this repeatable process, epistemic uncertainty could be quantified more objectively than before, relying less on expert opinion. The NGA-East project went through a comprehensive Seismic Senior Hazard Analysis Committee (SSHAC) Level 3 peer review process before its release.
Climate change uncertainty poses serious challenges to conservation efforts. One emerging conservation strategy is to identify and conserve climate change refugia: areas relatively buffered from contemporary climate change that enable persistence of valued resources. This management paradigm may be pursued at broad scales by leveraging existing resources and placing them into a tangible framework to stimulate further collaboration that fosters management decision-making. Here, we describe a framework for moving toward operationalizing climate change refugia conservation at an ecoregion scale with an analysis for the Sierra Nevada ecoregion (CA, USA). Structured within the Climate Change Refugia Conservation Cycle, we identify a preliminary suite of conservation priorities for the ecoregion, and demonstrate how existing mapping, data, and applications could be used for identifying, prioritizing, managing, and monitoring refugia. We focus on six stakeholder-identified conservation priorities, including two process-based refugial priorities (snow and fire), and four ecosystem-based refugial priorities (meadows, giant sequoia, old growth forests, and alpine communities). This pilot overview of concepts and resources provides a foundation for both near-term implementation and further discussion in moving from science to conservation practice. Such an approach may provide new practical insights for ecosystem management at ecoregion scales in the face of climate change.
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Survey","interactions":[],"lastModifiedDate":"2021-07-13T18:50:07.976","indexId":"70221882","displayToPublicDate":"2021-07-11T13:48:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8959,"text":"International Journal of Digital Curation","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging existing technology: Developing a trusted digital repository for the U.S. Geological Survey","docAbstract":"As Federal Government agencies in the United States pivot to increase access to scientific data (Sheehan, 2016), the U.S. Geological Survey (USGS) has made substantial progress (Kriesberg et al., 2017). USGS authors are required to make federally funded data publicly available in an approved data repository (USGS, 2016b). This type of public data product, known as a USGS data release, serves as a method for publishing reviewed and approved data. In this paper, we present major milestones in the approach the USGS took to transition an existing technology platform to a Trusted Digital Repository. We describe both the technical and the non-technical actions that contributed to a successful outcome.We highlight how initial workflows revealed patterns that were later automated, and the ways in which assessments and user feedback influenced design and implementation. The paper concludes with lessons learned, such as the importance of a community of practice, application programming interface (API)-driven technologies, iterative development, and user-centered design. This paper is intended to offer a potential roadmap for organizations pursuing similar goals.
","language":"English","publisher":"International Journal of Digital Curation","doi":"10.2218/ijdc.v16i1.741","usgsCitation":"Hutchison, V.B., Norkin, T., Langseth, M., Ignizio, D., Zolly, L., McClees-Funinan, R., and Liford, A.N., 2021, Leveraging existing technology: Developing a trusted digital repository for the U.S. Geological Survey: International Journal of Digital Curation, v. 16, no. 1, 23 p., https://doi.org/10.2218/ijdc.v16i1.741.","productDescription":"23 p.","ipdsId":"IP-119128","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":451552,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2218/ijdc.v16i1.741","text":"Publisher Index Page"},{"id":387162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchison, Vivian B. 0000-0001-5301-3698 vhutchison@usgs.gov","orcid":"https://orcid.org/0000-0001-5301-3698","contributorId":173674,"corporation":false,"usgs":true,"family":"Hutchison","given":"Vivian","email":"vhutchison@usgs.gov","middleInitial":"B.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norkin, Tamar 0000-0003-0797-3940 tnorkin@usgs.gov","orcid":"https://orcid.org/0000-0003-0797-3940","contributorId":5882,"corporation":false,"usgs":true,"family":"Norkin","given":"Tamar","email":"tnorkin@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":false,"id":819207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langseth, Madison 0000-0002-4472-9106 mlangseth@usgs.gov","orcid":"https://orcid.org/0000-0002-4472-9106","contributorId":191744,"corporation":false,"usgs":true,"family":"Langseth","given":"Madison","email":"mlangseth@usgs.gov","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ignizio, Drew 0000-0001-8054-5139 dignizio@usgs.gov","orcid":"https://orcid.org/0000-0001-8054-5139","contributorId":172696,"corporation":false,"usgs":true,"family":"Ignizio","given":"Drew","email":"dignizio@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":819209,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zolly, Lisa 0000-0003-3595-7809 lisa_zolly@usgs.gov","orcid":"https://orcid.org/0000-0003-3595-7809","contributorId":484,"corporation":false,"usgs":true,"family":"Zolly","given":"Lisa","email":"lisa_zolly@usgs.gov","affiliations":[],"preferred":true,"id":819210,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McClees-Funinan, Ricardo 0000-0002-3254-1843 rmcclees-funinan@usgs.gov","orcid":"https://orcid.org/0000-0002-3254-1843","contributorId":5988,"corporation":false,"usgs":true,"family":"McClees-Funinan","given":"Ricardo","email":"rmcclees-funinan@usgs.gov","affiliations":[{"id":37226,"text":"Core Science Analytics, Synthesis, and Libraries","active":true,"usgs":true}],"preferred":true,"id":819211,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liford, Amanda N. 0000-0002-6992-2543","orcid":"https://orcid.org/0000-0002-6992-2543","contributorId":257671,"corporation":false,"usgs":true,"family":"Liford","given":"Amanda","email":"","middleInitial":"N.","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819212,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238782,"text":"70238782 - 2021 - Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands","interactions":[],"lastModifiedDate":"2022-12-12T14:20:29.236213","indexId":"70238782","displayToPublicDate":"2021-07-10T08:06:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands","docAbstract":"Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. 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Benjamin","contributorId":298276,"corporation":false,"usgs":false,"family":"Poulter","given":"Benjamin","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":858693,"contributorType":{"id":1,"text":"Authors"},"rank":92},{"text":"Jackson, Robert B. 0000-0001-8846-7147","orcid":"https://orcid.org/0000-0001-8846-7147","contributorId":34252,"corporation":false,"usgs":false,"family":"Jackson","given":"Robert","email":"","middleInitial":"B.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":858694,"contributorType":{"id":1,"text":"Authors"},"rank":93}]}} ,{"id":70221786,"text":"ofr20211064 - 2021 - Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers","interactions":[],"lastModifiedDate":"2021-07-09T18:52:23.587817","indexId":"ofr20211064","displayToPublicDate":"2021-07-09T11:55:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1064","displayTitle":"Instruments, Methods, Rationale, and Derived Data Used to Quantify and Compare the Trapping Efficiencies of Four Types of Pressure-Difference Bedload Samplers","title":"Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers","docAbstract":"Bedload and ancillary data were collected to calculate and compare the bedload trapping efficiencies of four types of pressure-difference bedload samplers as part of episodic, sediment-recirculating flume experiments at the St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, in January–March 2006. The bedload-sampler experiments, which were conceived, organized, and led by the U.S. Geological Survey’s Office of Surface Water, were part of a broader suite of experiments performed in the rectangular, concrete-lined, sediment-recirculating Main Channel Facility (“main channel flume”). Collectively referred to as “StreamLab06,” the experiments were conducted under the auspices of the National Center for Earth-Surface Dynamics, University of Minnesota.
Four pressure-difference-type bedload samplers—a standard Helley-Smith, US BLH-84, Elwha, and Toutle River-2—were deployed by using hand-held rods in the main flume in a series of trials during steady flows as part of the first two of seven phases of the StreamLab06 experiments. The Phase I flows were released over a sand bed. Gravel composed the bed during the Phase II flows. Bedload samples were collected during flows ranging from 2.0 cubic meters per second (near the incipient motion of bed material) to 5.5 cubic meters per second. A total of 2,030 bedload samples were collected—1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.
Bedload was captured in five contiguous weigh drums inside a slot spanning the full width of the main flume channel 8.5 meters downstream from the cross-section in which the bedload samplers were deployed. The contents of each drum were automatically weighed and recorded as a time series about every 1.1 seconds. Each drum automatically, independently, and episodically dumped its contents into the bottom of the slot upon the accumulation of a pre-determined mass of entrapped sediment, after which the drum continued to capture and weigh bedload. An auger at the bottom of the slot evacuated the accumulating sediment to a side-channel pump that piped the captured sediments upstream and discharged them back to the flume.
Bedload-transport rates were calculated from measurements of the masses of material trapped by the bedload samplers and from the data produced by the automated bedload capture-and-weigh system of the main channel flume. These data were used to compute at-a-point and mean bedload-transport rates for subsequent use in developing bedload-trapping efficiency (calibration) coefficients for each bedload sampler and for comparing the relative trapping efficiencies of the manually deployed bedload samplers. The data were collected to enable the use of several computational methods for deriving bedload-trapping coefficients.
Continuous ancillary data including stage, water discharge, and water temperature were automatically collected and stored. Flow depths were manually measured and recorded concurrent with each at-a-point bedload-sampler deployment. Other information obtained during parts of the experiments included longitudinal water-surface slope, bedload particle-size distributions, and suspended-sediment concentrations and percent sand analyzed from samples collected by depth integration with a US DH-48 isokinetic suspended-sediment sampler.
This report describes the types and availability of the bedload and ancillary data derived through the StreamLab06 experiments. The data are available from the St. Anthony Falls Laboratory and the U.S. Geological Survey through a data release. Also included are selected descriptive and historical information as well as the background, experimental design, experimental caveats, and other factors relevant to the production of the bedload-transport and ancillary data produced through Phases I and II of the StreamLab06 experiments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211064","usgsCitation":"Gray, J.R., Schwarz, G.E., Dean, D.J., Czuba, J.A., and Groten, J.T., 2021, Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers: U.S. Geological Survey Open-File Report 2021–1064, 61 p., https://doi.org/10.3133/ofr20211064.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098017","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386969,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1064/ofr20211064.pdf","text":"Report","size":"70.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1064"},{"id":386970,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1064/coverthb.jpg"},{"id":386971,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBB2YF","text":"USGS data release","linkHelpText":"Data describing the trapping efficiency of four types of pressure-difference bedload samplers, St. Anthony Falls Laboratory, Minneapolis, Minnesota, 2006"}],"contact":"Chief, Analysis and Prediction Branch
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 415
Reston, VA 20192
During the time period 2001–2006, the U.S. Geological Survey reported mercury-concentration measurements that exceeded the North Carolina water-quality criterion (NCWQC) of 0.012 microgram per liter for total recoverable mercury in streams and reservoirs across the Triangle Area of North Carolina. Mercury data were sparse, however, generally consisting of only one or two water samples per year. Additional monitoring and data analysis were needed to better determine the occurrence and distribution of mercury in the Triangle Area for all seasons and waterbody types as well as associations between mercury concentrations and water-quality and land-use parameters. Water at fifteen reservoir and 14 stream sites across the Triangle Area was sampled at various times between August 2007 and June 2009, with water samples collected from both the surfaces and bottoms of the water columns in reservoirs and from the surfaces of streams. A bed sediment sample was also collected at all reservoir sites and at all but one stream site. A total of 301 water samples was collected at reservoir sites. Filtered and total recoverable mercury were detected in at least one water sample collected from each reservoir site. A total of 77 water samples was collected from stream sites with filtered mercury detected in samples from one-half of these sites, and total recoverable mercury detected in at least one water sample from all but two sites. Total recoverable and filtered mercury concentrations exceeded the NCWQC for mercury more frequently in reservoir than in stream samples. Differences in sampling frequencies among seasons and between streams and reservoirs, however, may have negatively biased overall estimates of mercury concentrations in streams relative to reservoirs. Filtered mercury concentrations in surface-water samples from reservoirs and total recoverable mercury concentrations in bottom samples from reservoirs were highest in the fall, whereas no seasonal trends in filtered or total recoverable mercury were detected from stream samples. Total mercury concentrations were calculated for the bulk sample on the basis of the percentage of the grains in the bulk sample whose diameters that were smaller than 0.0625 millimeters. Total mercury concentrations in bed sediment were generally higher for samples from reservoir sites compared to streams sites, although the highest total mercury concentration in bed sediment was from a stream site. Concentrations of total recoverable mercury in water samples from stream sites all fell within the general range for streams and lakes without on-site significant anthropogenic sources (for example, mercury mines or industrial pollution), whereas samples collected from eight reservoir sites had total mercury concentrations in a range characteristic of sites affected by mercury mines or industrial pollution. Results suggested that litterfall may be a source of mercury in streams, whereas atmospheric deposition is likely a dominant source for reservoirs; however, high concentrations of filtered and total recoverable mercury concentrations in the fall season in some reservoir-water samples may warrant further analysis of potential hydrologic factors. Mercury concentrations in all water and bed sediment samples were below levels expected to cause adverse effects to humans and aquatic biota, indicating that mercury levels at the study sites in the Triangle Area were unlikely to cause an immediate health risk to humans or aquatic organisms. The high variability among several sample replicates for total recoverable mercury, however, indicated that inferences from total recoverable mercury concentrations can be tenuous.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215027","collaboration":"Prepared in cooperation with the Triangle Area Water Supply Monitoring Project Steering Committee","usgsCitation":"McKee, A.M., Fitzgerald, S., and Giorgino, M., 2021, Occurrence and distribution of mercury in streams and reservoirs in the Triangle Area of North Carolina, July 2007–June 2009: U.S. Geological Survey Scientific Investigations Report 2021–5027, 42 p., https://doi.org/10.3133/sir20215027.","productDescription":"Report: x, 42 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-114002","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":387025,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4EMC7","text":"USGS Data Release","linkHelpText":"Water and bed sediment data associated with the occurrence and distribution of mercury in streams and reservoirs in the Triangle Area of North Carolina, July 2007 -June 2009"},{"id":387023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5027/coverthb.jpg"},{"id":387024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5027/sir20215027.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5027"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.64263916015625,\n 35.31512519050729\n ],\n [\n -78.12103271484375,\n 35.31512519050729\n ],\n [\n -78.12103271484375,\n 36.49859745028132\n ],\n [\n -79.64263916015625,\n 36.49859745028132\n ],\n [\n -79.64263916015625,\n 35.31512519050729\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Coral disease is a growing problem for coral reefs globally and diseases have been linked to thermal stress, excess nutrients, overfishing and other human impacts. The Red Sea is a unique environment for corals with a strong environmental gradient characterized by temperature extremes and high salinities, but minimal terrestrial runoff or riverine input and their associated pollution. Yet, relatively little is known about coral diseases in this region. Disease surveys were conducted at 22 reefs within three regions (Yanbu, Thuwal, Al Lith) in the central Red Sea along the Saudi Arabian coast. Surveys occurred in October 2015, which coincided with a hyperthermal-induced bleaching event. Our objectives were to 1) document types, prevalence, and distribution of coral diseases in a region with minimal terrestrial input, 2) compare regional differences in diseases and bleaching along a latitudinal gradient of environmental conditions, and 3) use histopathology to characterize disease lesions at the cellular level. Coral reefs of the central Red Sea had a widespread but a surprisingly low prevalence of disease (<0.5%), based on the examination of >75,750 colonies. Twenty diseases were recorded affecting 16 coral taxa and included black band disease, white syndromes, endolithic hypermycosis, skeletal eroding band, growth anomalies and focal bleached patches. The three most common diseases were Acropora white syndrome (59.1% of the survey sites), Porites growth anomalies (40.9%), and Porites white syndrome (31.8%). Sixteen out of 30 coral genera within transects had lesions and Acropora, Millepora and Lobophyllia were the most commonly affected. Cell-associated microbial aggregates were found in four coral genera including a first report in Stylophora. Differences in disease prevalence, coral cover, amount of heat stress as measured by degree heating weeks (DHW) and extent of bleaching was evident among sites. Disease prevalence was not explained by coral cover or DHW, and a negative relationship between coral bleaching and disease prevalence was found. The northern-most sites off the coast of Yanbu had the highest average disease prevalence and highest average DHW values but no bleaching. Our study provides a foundation and baseline data for coral disease prevalence in the central Red Sea, which is projected to increase as a consequence of increased frequency and severity of ocean warming.
Aquatic connectivity projects, such as removing dams and modifying culverts, have substantial benefits. The restoration of natural flow conditions improves water quality, sediment transport, aquatic and riparian habitat, and fish passage. These projects can also decrease hazards faced by communities by lowering water-surface elevations of flood waters and by removing the risk of dam breaches associated with aging or inadequate infrastructure.
This report documents and provides results of one- and two-dimensional hydraulic models developed for selected rivers and streams in the northeastern United States where a dam was removed or a culvert was retrofitted. The models were developed for conditions before and after the dam removal or culvert modification. The discharges applied in the models included monthly discharges and flood discharges for the annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent.
This study, by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, demonstrates the benefits resulting from dam removal and retrofitting undersized culverts in terms of decreased water-surface elevations during flooding and improved fish passage. The U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System was used to model the sites in one- and two-dimensional hydraulics, and decreases in the 1-percent annual exceedance probability discharge water-surface elevation were found at all sites studied. The decreases in water-surface elevation at sites in which the impoundment was removed ranged from 1.3 to 10.4 feet. One site, Bradford Dam in Westerly, Rhode Island, had only a 0.2-foot decrease, but at that site the dam was replaced by a series of weirs to retain the upstream impoundment and allow fish passage.
Minimal differences were found between the water-surface elevations computed by the one- and two-dimensional models. The two-dimensional models, however, provide the additional benefit of detailed velocity and depth data throughout the channel at a resolution not possible with a one-dimensional model. These velocity and depth data allowed for assessment of the suitability for fish passage at the sites. Fish passage was improved at all the sites by removing the dams and retrofitting the culvert. Prolonged swim velocity criteria for selected fish species were maintained throughout three of the nine study sites, and burst swim velocity criteria were met at all study sites.
Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
We simulated the concurrent rapid motion of landslides on an unstable slope at Barry Arm, Alaska. Movement of landslides into the adjacent fjord displaced fjord water and generated a tsunami, which propagated out of Barry Arm. Rather than assuming an initial sea surface height, velocity, and location for the tsunami, we generated the tsunami directly using a model capable of simulating the dynamics of both water and landslide material. The fjord below most of the landslide source area was occupied by the Barry Glacier until about 2012; therefore, our direct simulation of tsunami generation by landslide motion required new topographic and bathymetric data, which was collected in 2020. The topographic data also constrained landslide geometries and volumes. We considered four scenarios based on two landslide volumes and two landslide mobilities—a more mobile, contractive landslide and a less mobile, noncontractive landslide. The larger of the two volumes is 689 × 106 cubic meters (m3)—larger than the volume estimate in a previous study—and reflects the largest plausible volume given current observational data. The considered scenario that generated the largest wave heights resulted in forecast wave heights of over 200 meters (m) in the northern part of Barry Arm, adjacent to the landslide source area and runup on the opposite fjord wall in excess of 500 m. Simulated wave heights in excess of 5 m in southern Barry Arm and in Harriman Fjord occurred within 10–15 minutes (min) of landslide motion. The simulated tsunami reached Whittier, Alaska, approximately 20 min after initial rapid landslide motion, with peak heights of just over 2 m in Passage Fjord, 500 m offshore Whittier, occurring 26 min after initial rapid motion. Time of peak wave heights was consistent with previous modeling. Although results are preliminary and can be refined with additional observations and analyses, they provide a refined assessment of the upper bound of the hazard presented by the Barry Arm landslides. The results herein support the National Oceanic and Atmospheric Administration’s National Tsunami Warning Center mission to detect, forecast, and warn for tsunamis in Alaska.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211071","usgsCitation":"Barnhart, K.R., Jones, R.P., George, D.L., Coe, J.A., and Staley, D.M., 2021, Preliminary assessment of the wave generating potential from landslides at Barry Arm, Prince William Sound, Alaska: U.S. Geological Survey Open-File Report 2021–1071, 28 p., https://doi.org/10.3133/ofr20211071.","productDescription":"Report: v, 28 p.; Data Release","ipdsId":"IP-130004","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":386958,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XVJDNP","text":"USGS data release","linkHelpText":"Select model results from simulations of hypothetical rapid failures of landslides into Barry Arm Fjord, Prince William Sound, Alaska"},{"id":386957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1071/ofr20211071.pdf","text":"Report","size":"13.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1071"},{"id":386956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1071/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm, Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.90869140625,\n 60.77659627851085\n ],\n [\n -147.95562744140625,\n 60.77659627851085\n ],\n [\n -147.95562744140625,\n 61.18165309177166\n ],\n [\n -148.90869140625,\n 61.18165309177166\n ],\n [\n -148.90869140625,\n 60.77659627851085\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
International agreements such as the Convention on Biological Diversity (CBD) have committed to conserve, and sustainably and equitably use, biodiversity. The CBD is a vital instrument for global conservation because it guides 195 countries and the European Union in setting priorities and allocating resources, and requires regular reporting on progress. However, the CBD and similar policy agreements have often neglected genetic diversity. This is a critical gap because genetic diversity underlies adaptation to environmental change and ecosystem resilience. Here we aim to inform future policy, monitoring, and reporting efforts focused on limiting biodiversity loss by conducting the largest yet evaluation of how Parties to the CBD report on genetic diversity. A large, globally representative sample of 114 CBD National Reports was examined to assess reported actions, progress, values and indicators related to genetic diversity. Although the importance of genetic diversity is recognized by most Parties to the CBD, genetic diversity targets mainly addressed variation within crops and livestock (a small fraction of all species). Reported actions to conserve genetic diversity primarily concerned ex situ facilities and legislation, rather than monitoring and in situ intervention. The most commonly reported status indicators are not well correlated to maintaining genetic diversity. Lastly, few reports mentioned genetic monitoring using DNA data, indigenous use and knowledge of genetic diversity, or development of strategies to conserve genetic diversity. We make several recommendations for the post-2020 CBD Biodiversity Framework, and similar efforts such as IPBES, to improve awareness, assessment, and monitoring of genetic diversity, and facilitate consistent and complete reporting in the future.