Measuring precipitation in semi-arid landscapes is important for understanding the processes related to rainfall and run-off; however, measuring precipitation accurately can often be challenging especially within remote regions where precipitation instruments are scarce. Typically, rain-gauges are sparsely distributed and research comparing rain-gauge and RADAR precipitation estimates reveal that RADAR data are often misleading, especially for monsoon season convective storms. This study investigates an alternative way to map the spatial and temporal variation of precipitation inputs along ephemeral stream channels using Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper imagery. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG), a region covering 3,367 km2 of semiarid landscapes in southwestern Arizona, USA. The change in NDVI from a pre-to post-monsoon season image along ephemeral stream channels explained 73% of the variance in annual monsoonal precipitation totals from a nearby rain-gauge. In addition, large seasonal changes in NDVI along channels were useful in determining when and where flow events have occurred.
","language":"English","publisher":"Academic Press","doi":"10.1016/j.jaridenv.2016.04.004","usgsCitation":"Birtwhistle, A.N., Laituri, M., Bledsoe, B., and Friedman, J.M., 2016, Using NDVI to measure precipitation in semi-arid landscapes: Journal of Arid Environments, v. 131, p. 15-24, https://doi.org/10.1016/j.jaridenv.2016.04.004.","productDescription":"10 p.","startPage":"15","endPage":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070425","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471060,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2016.04.004","text":"Publisher Index Page"},{"id":438618,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZK5DSB","text":"USGS data release","linkHelpText":"Mean of the Top Ten Percent of NDVI Values in the Yuma Proving Ground during Monsoon Season, 1986-2011"},{"id":327106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":334957,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ZK5DSB","text":"Mean of the top ten percent of NDVI values in the Yuma Proving Ground during monsoon season, 1986-2011"}],"country":"United States","state":"Arizona","otherGeospatial":"Sonoran Desert, Yuma Proving Ground","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.49676513671875,\n 33.55741786324217\n ],\n [\n -114.23858642578125,\n 33.56199537293026\n ],\n [\n -114.2083740234375,\n 32.95797741405949\n ],\n [\n -113.9666748046875,\n 32.94875863715422\n ],\n [\n -113.9501953125,\n 33.0316929997831\n ],\n [\n -113.74420166015624,\n 33.04320549616557\n ],\n [\n -113.7469482421875,\n 33.33511774753217\n ],\n [\n -113.587646484375,\n 33.33741240611175\n ],\n [\n -113.56842041015625,\n 32.930318199070534\n ],\n [\n -113.90625,\n 32.76880048488168\n ],\n [\n -114.466552734375,\n 32.7295304134847\n ],\n [\n -114.466552734375,\n 32.85421076375023\n ],\n [\n -114.46105957031249,\n 32.877280526855394\n ],\n [\n -114.44183349609375,\n 32.89342578969231\n ],\n [\n -114.46380615234375,\n 32.92109653816924\n ],\n [\n -114.46380615234375,\n 32.94414888814148\n ],\n [\n -114.46105957031249,\n 32.9648908656902\n ],\n [\n -114.46929931640624,\n 32.98562797456918\n ],\n [\n -114.49127197265625,\n 32.98562797456918\n ],\n [\n -114.49676513671875,\n 33.00636021320537\n ],\n [\n -114.52972412109375,\n 33.02939031998959\n ],\n [\n -114.55169677734375,\n 33.038600678138195\n ],\n [\n -114.576416015625,\n 33.04090311724091\n ],\n [\n -114.60113525390625,\n 33.050112271849656\n ],\n [\n -114.5928955078125,\n 33.4955977448657\n ],\n [\n -114.49676513671875,\n 33.55741786324217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b82deee4b03fd6b7da3a5e","contributors":{"authors":[{"text":"Birtwhistle, Amy N.","contributorId":173888,"corporation":false,"usgs":false,"family":"Birtwhistle","given":"Amy","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laituri, Melinda","contributorId":173889,"corporation":false,"usgs":false,"family":"Laituri","given":"Melinda","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bledsoe, Brian","contributorId":173890,"corporation":false,"usgs":false,"family":"Bledsoe","given":"Brian","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":646516,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174896,"text":"70174896 - 2016 - Effect of cysteine and humic acids on bioavailability of Ag from Ag nanoparticles to a freshwater snail","interactions":[],"lastModifiedDate":"2016-07-25T11:55:44","indexId":"70174896","displayToPublicDate":"2016-04-20T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5146,"text":"NanoImpact","active":true,"publicationSubtype":{"id":10}},"title":"Effect of cysteine and humic acids on bioavailability of Ag from Ag nanoparticles to a freshwater snail","docAbstract":"Metal-based engineered nanoparticles (NPs) will undergo transformations that will affect their bioavailability, toxicity and ecological risk when released to the environment, including interactions with dissolved organic material. The purpose of this paper is to determine how interactions with two different types of organic material affect the bioavailability of silver nanoparticles (AgNPs). Silver uptake rates by the pond snail Lymnaea stagnalis were determined after exposure to 25 nmol l-1 of Ag as PVP AgNPs, PEG AgNPs or AgNO3, in the presence of either Suwannee River humic acid or cysteine, a high-affinity thiol-rich organic ligand. Total uptake rate of Ag from the two NPs was either increased or not strongly affected in the presence of 1 – 10 mg 1-1 humic acid. Humic substances contain relatively few strong ligands for Ag explaining their limited effects on Ag uptake rate. In contrast, Ag uptake rate was substantially reduced by cysteine. Three components of uptake from the AgNPs were quantified in the presence of cysteine using a biodynamic modeling approach: uptake of dissolved Ag released by the AgNPs, uptake of a polymer or large (>3kD) Ag-cysteine complex and uptake of the nanoparticle itself. Addition of 1:1 Ag:cysteine reduced concentrations of dissolved Ag, which contributed to, but did not fully explain the reductions in uptake. A bioavailable Ag-cysteine complex (> 3kD) appeared to be the dominant avenue of uptake from both PVP AgNPs and PEG AgNPs in the presence of cysteine. Quantifying the different avenues of uptake sets the stage for studies to assess toxicity unique to NPs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.impact.2016.06.006","usgsCitation":"Luoma, S.N., Stoiber, T., Croteau, M.N., Romer, I., Merrifeild, R., and Lead, J., 2016, Effect of cysteine and humic acids on bioavailability of Ag from Ag nanoparticles to a freshwater snail: NanoImpact, v. 2, p. 61-69, https://doi.org/10.1016/j.impact.2016.06.006.","productDescription":"8 p.","startPage":"61","endPage":"69","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073881","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":325469,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5790a17ee4b030378fb47425","contributors":{"authors":[{"text":"Luoma, Samuel N. 0000-0001-5443-5091 snluoma@usgs.gov","orcid":"https://orcid.org/0000-0001-5443-5091","contributorId":2287,"corporation":false,"usgs":true,"family":"Luoma","given":"Samuel","email":"snluoma@usgs.gov","middleInitial":"N.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":643033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoiber, Tasha","contributorId":173022,"corporation":false,"usgs":false,"family":"Stoiber","given":"Tasha","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":643034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":643032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romer, Isabelle","contributorId":173025,"corporation":false,"usgs":false,"family":"Romer","given":"Isabelle","email":"","affiliations":[{"id":27144,"text":"University of Birmingham, UK","active":true,"usgs":false}],"preferred":false,"id":643037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merrifeild, Ruth","contributorId":173023,"corporation":false,"usgs":false,"family":"Merrifeild","given":"Ruth","email":"","affiliations":[{"id":27143,"text":"University of South Carolina, Columbia, SC","active":true,"usgs":false}],"preferred":false,"id":643035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lead, Jamie","contributorId":173024,"corporation":false,"usgs":false,"family":"Lead","given":"Jamie","affiliations":[{"id":27143,"text":"University of South Carolina, Columbia, SC","active":true,"usgs":false}],"preferred":false,"id":643036,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169300,"text":"fs20163017 - 2016 - Coal-tar-based pavement sealcoat—Potential concerns for human health and aquatic life","interactions":[],"lastModifiedDate":"2017-06-30T10:18:22","indexId":"fs20163017","displayToPublicDate":"2016-04-20T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3017","title":"Coal-tar-based pavement sealcoat—Potential concerns for human health and aquatic life","docAbstract":"Sealcoat is the black, viscous liquid sprayed or painted on many asphalt parking lots, driveways, and playgrounds to protect and enhance the appearance of the underlying asphalt. Studies by the U.S. Geological Survey (USGS), academic institutions, and State and local agencies have identified coal-tar-based pavement sealcoat as a major source of polycyclic aromatic hydrocarbon (PAH) contamination in urban and suburban areas and a potential concern for human health and aquatic life.
\nKey Findings:
\nHuman Health Concerns—As coal-tar-based sealcoat ages, it wears into small particles with high levels of PAHs that can be tracked into homes and incorporated into house dust. For people who live adjacent to coal-tar-sealcoated pavement, ingestion of PAH-contaminated house dust and soil results in an elevated potential cancer risk, particularly for young children. Exposure to PAHs, especially early in childhood, has been linked by health professionals to an increased risk of lung, skin, bladder, and respiratory cancers.
\nAquatic Life Concerns—Runoff from coal-tar-sealcoated pavement, even runoff collected more than 3 months after sealcoat application, is acutely toxic to fathead minnows and water fleas, two species commonly used to assess toxicity to aquatic life. Exposure to even highly diluted runoff from coal-tar-sealcoated pavement can cause DNA damage and impair DNA repair. These findings demonstrate that coal-tar-sealcoat runoff can remain a risk to aquatic life for months after application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163017","usgsCitation":"Mahler, B.J., Woodside, M.D., and VanMetre, P.C., 2016, Coal-tar-based pavement sealcoat—Potential concerns for human health and aquatic life: U.S. Geological Survey Fact Sheet 2016–3017, 6 p., https://dx.doi.org/10.3133/fs20163017.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067036","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":320231,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3017/coverthb.jpg"},{"id":320232,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3017/fs20163017.pdf","text":"Report","size":"6.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3017"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754
http://tx.usgs.gov/sealcoat.html
Methylmercury contamination of the environment is an important issue globally, and birds are useful bioindicators for mercury monitoring programs. The available data on mercury contamination of birds in western North America were synthesized. Original data from multiple databases were obtained and a literature review was conducted to obtain additional mercury concentrations. In total, 29219 original bird mercury concentrations from 225 species were compiled, and an additional 1712 mean mercury concentrations, representing 19998 individuals and 176 species, from 200 publications were obtained. To make mercury data comparable across bird tissues, published equations of tissue mercury correlations were used to convert all mercury concentrations into blood-equivalent mercury concentrations. Blood-equivalent mercury concentrations differed among species, foraging guilds, habitat types, locations, and ecoregions. Piscivores and carnivores exhibited the greatest mercury concentrations, whereas herbivores and granivores exhibited the lowest mercury concentrations. Bird mercury concentrations were greatest in ocean and salt marsh habitats and lowest in terrestrial habitats. Bird mercury concentrations were above toxicity benchmarks in many areas throughout western North America, and multiple hotspots were identified. Additionally, published toxicity benchmarks established in multiple tissues were summarized and translated into a common blood-equivalent mercury concentration. Overall, 66% of birds sampled in western North American exceeded a blood-equivalent mercury concentration of 0.2 μg/g wet weight (ww; above background levels), which is the lowest-observed effect level, 28% exceeded 1.0 μg/g ww (moderate risk), 8% exceeded 3.0 μg/g ww (high risk), and 4% exceeded 4.0 μg/g ww (severe risk). Mercury monitoring programs should sample bird tissues, such as adult blood and eggs, that are most-easily translated into tissues with well-developed toxicity benchmarks and that are directly relevant to bird reproduction. Results indicate that mercury contamination of birds is prevalent in many areas throughout western North America, and large-scale ecological attributes are important factors influencing bird mercury concentrations.
\nUnstable hybrid swarms that arise following the introduction of non-native species can overwhelm native congeners, yet the stability of invasive hybrid swarms has not been well documented over time. Here we examine genetic variation and clinal stability across a recently formed hybrid swarm involving native blacktail shiner (Cyprinella venusta) and non-native red shiner (C. lutrensis) in the Upper Coosa River basin, which is widely considered to be a global hotspot of aquatic biodiversity. Examination of phenotypic, multilocus genotypic, and mitochondrial haplotype variability between 2005 and 2011 revealed that the proportion of hybrids has increased over time, with more than a third of all sampled individuals exhibiting admixture in the final year of sampling. Comparisons of clines over time indicated that the hybrid swarm has been rapidly progressing upstream, but at a declining and slower pace than rates estimated from historical collection records. Clinal comparisons also showed that the hybrid swarm has been expanding and contracting over time. Additionally, we documented the presence of red shiner and hybrids farther downstream than prior studies have detected, which suggests that congeners in the Coosa River basin, including all remaining populations of the threatened blue shiner (Cyprinella caerulea), are at greater risk than previously thought.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.12371","usgsCitation":"Glotzbecker, G.J., Walters, D., and Blum, M.J., 2016, Rapid movement and instability of an invasive hybrid swarm: Evolutionary Applications, v. 9, no. 6, p. 741-755, https://doi.org/10.1111/eva.12371.","productDescription":"15 p.","startPage":"741","endPage":"755","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068483","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.12371","text":"Publisher Index Page"},{"id":320335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Coosa River basin, Weiss Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.5340576171875,\n 34.268566186749894\n ],\n [\n -85.50521850585938,\n 34.250405862125\n ],\n [\n -85.4791259765625,\n 34.229970811273084\n ],\n [\n -85.46127319335938,\n 34.20953080048952\n ],\n [\n -85.45578002929688,\n 34.16977214177208\n ],\n [\n -85.54229736328125,\n 34.12203701907784\n ],\n [\n -85.61370849609375,\n 34.09474769880029\n ],\n [\n -85.6439208984375,\n 34.127721186043985\n ],\n [\n -85.69335937499999,\n 34.14136162745489\n ],\n [\n -85.72494506835936,\n 34.116352469972746\n ],\n [\n -85.73181152343749,\n 34.076549928891744\n ],\n [\n -85.78674316406249,\n 34.03900467904445\n ],\n [\n -85.8087158203125,\n 34.0219331594475\n ],\n [\n -85.88012695312499,\n 33.996888718429396\n ],\n [\n -85.91995239257812,\n 34.00144280255186\n ],\n [\n -85.92819213867188,\n 34.05152161016494\n ],\n [\n -85.91995239257812,\n 34.102707993174874\n ],\n [\n -85.91445922851562,\n 34.16068181711789\n ],\n [\n -85.84579467773438,\n 34.20612364990816\n ],\n [\n -85.77850341796875,\n 34.26062152714047\n ],\n [\n -85.70846557617186,\n 34.29239566218292\n ],\n [\n -85.64804077148438,\n 34.30827822549175\n ],\n [\n -85.61920166015625,\n 34.30600947175969\n ],\n [\n -85.58761596679688,\n 34.301471780404476\n ],\n [\n -85.5560302734375,\n 34.28558793000448\n ],\n [\n -85.5340576171875,\n 34.268566186749894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-27","publicationStatus":"PW","scienceBaseUri":"57189a1be4b0ef3b7caaf797","contributors":{"authors":[{"text":"Glotzbecker, Gregory J.","contributorId":168704,"corporation":false,"usgs":false,"family":"Glotzbecker","given":"Gregory","email":"","middleInitial":"J.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":627046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blum, Michael J.","contributorId":168705,"corporation":false,"usgs":false,"family":"Blum","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":627047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169137,"text":"sir20165032 - 2016 - Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013","interactions":[],"lastModifiedDate":"2016-04-20T12:57:06","indexId":"sir20165032","displayToPublicDate":"2016-04-20T09:30:00","publicationYear":"2016","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":"2016-5032","title":"Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013","docAbstract":"The U.S. Geological Survey, in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board, developed and calibrated a Soil and Water Assessment Tool watershed model of the Double Mountain Fork Brazos River watershed in western Texas to simulate monthly mean streamflow and to evaluate the effects of brush management on water yields in the watershed, particularly to Lake Alan Henry, for calendar years 1994–2013. Model simulations were done to quantify the possible change in water yield of individual subbasins in the Double Mountain Fork Brazos River watershed as a result of the replacement of shrubland (brush) with grassland. The simulation results will serve as a tool for resource managers to guide brush-management efforts.
\nThe model was calibrated from 1994 through 2008 and validated from 2009 through 2013 with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08079600 Double Mountain Fork Brazos River at Justiceburg, Texas (hereinafter referred to as the “Justiceburg gage”). Simulated monthly mean streamflow showed agreement with measured monthly mean streamflow for the 1994–2013 study period: the percentage bias was +6, the coefficient of determination was 0.73, and the Nash–Sutcliffe coefficient of model efficiency was 0.71.
\nThe calibrated watershed model was used to perform brush-management simulations. The National Land Cover Database 2006, which was the land-cover data used to develop the watershed model, was modified to simulate shrubland replacement with grassland in each of the 35 model subbasins. After replacement of shrubland with grassland in areas with land slope less than 20 percent and excluding riparian areas, the modeled 20-year (1994 through 2013) water yields to Lake Alan Henry increased by 114,000 acre-feet or about 5,700 acre-feet per year. In terms of the increase in water yield per acre of shrubland replaced with grassland, the average annual increase in water yield was 17,300 gallons per acre. Within the modeled subbasins, the increase in average annual water yield ranged from 5,850 to 34,400 gallons per acre of shrubland replaced with grassland. Subbasins downstream from the Justiceburg gage had a higher average annual increase in water yield (21,700 gallons per acre) than subbasins upstream from the streamflow-gaging station (16,800 gallons per acre).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165032","collaboration":"Prepared in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board","usgsCitation":"Harwell, G.R., Stengel, V.G., and Bumgarner, J.R., 2016, Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013: U.S. Geological Survey Scientific Investigations Report 2016–5032, 39 p., https://dx.doi.org/10.3133/sir20165032.","productDescription":"Report: viii, 39 p.; Precipitation and Temperature Data","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072426","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":320033,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5032/sir20165032_data.zip","text":"Precipitation and Temperature Data","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016–5032 Precipitation and Temperature Data"},{"id":319869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5032/coverthb.jpg"},{"id":319870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5032/sir20165032.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5032"}],"country":"United States","state":"Texas","otherGeospatial":"Double Mountain Fork Brazos River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.7,\n 33.165\n ],\n [\n -101.7,\n 32.835\n ],\n [\n -100.9,\n 32.835\n ],\n [\n -100.9,\n 33.165\n ],\n [\n -101.7,\n 33.165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4733
This report describes a method for estimating ecologically relevant low-flow metrics that quantify late‑season streamflow regime for ungaged sites in the upper Grande Ronde River Basin, Oregon. The analysis presented here focuses on sites sampled by the Columbia River Inter‑Tribal Fish Commission as part of their efforts to monitor habitat restoration to benefit spring Chinook salmon recovery in the basin. Streamflow data were provided by the U.S. Geological Survey and the Oregon Water Resources Department. Specific guidance was provided for selection of streamgages, development of probabilistic frequency distributions for annual 7-day low-flow events, and regionalization of the frequency curves based on multivariate analysis of watershed characteristics. Evaluation of the uncertainty associated with the various components of this protocol indicates that the results are reliable for the intended purpose of hydrologic classification to support ecological analysis of factors contributing to juvenile salmon success. They should not be considered suitable for more standard water-resource evaluations that require greater precision, especially those focused on management and forecasting of extreme low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20165041","collaboration":"Prepared in cooperation with Columbia River Inter-Tribal Fish Commission","usgsCitation":"Kelly, V.J., and White, Seth, 2016, A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5041, 41 p.,\nhttps://dx.doi.org/10.3133/sir20165041.","productDescription":"Report: vi, 41 p.; Appendixes A-F","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059110","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":320198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5041 Report PDF"},{"id":320199,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041_appendixes.xlsx","text":"Appendixes A-F","size":"75 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5041 Appendixes"},{"id":320197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5041/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Grande Ronde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 44\n ],\n [\n -115,\n 47\n ],\n [\n -119,\n 47\n ],\n [\n -119,\n 44\n ],\n [\n -115,\n 44\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
Many barrier islands in the United States are eroding and losing elevation substantively because of storm surge, waves, and sea-level changes. This is particularly true for the deltaic barrier system in Louisiana. Breton Island is near the mouth of the Mississippi River at the southern end of the Chandeleur barrier island chain in southeast Louisiana. This report expands on previous geomorphic studies of Breton Island by incorporating additional historic and recent datasets. Multiple analyses focus on longand short-term shoreline change, as well as episodic events and anthropogenic modification. Analyses periods include long term (1869–2014), long-term historic (1869–1950), post-Mississippi River-Gulf Outlet (1950–2014), pre/post-Hurricane Katrina (2004–5), and recent (2005–14). In addition to shoreline change, barrier island geomorphology is evaluated using island area, elevation, and sediment volume change. In the long term (1869–2014), Breton Island was affected by landward transgression, island narrowing, and elevation loss. Major storm events exacerbated the long-term trends. In the recent period (2005–14), Breton Island eroded at a slower rate than in the long-term and gained area and total sediment volume. The recent accretion is likely because of the lack of major storms since Hurricane Katrina in 2005.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161039","usgsCitation":"Terrano, J.F., Flocks, J.G., and Smith, K.E.L., 2016, Analysis of shoreline and geomorphic change for Breton Island, Louisiana, from 1869 to 2014: U.S. Geological Survey Open-File Report 2016–1039, 34 p.,\nhttps://dx.doi.org/10.3133/ofr20161039.","productDescription":"Report: viii, 34 p.; Data Releases","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070444","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320056,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1039/coverthb.jpg"},{"id":320057,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1039/ofr20161039.pdf","text":"Report","size":"3.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1039"},{"id":324797,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70G3H6G","text":"USGS data release - Topobathymetric Lidar Survey of Breton and Gosier Islands, Louisiana, January 16 and 18, 2014"},{"id":324798,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7XS5SGM","text":"USGS data release - A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for Breton Island, Louisiana: 1869–2014"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island, Breton National Wildlife Refuge, Chandeleur barrier island chain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.17087554931639,\n 29.50729642400116\n ],\n [\n -89.17757034301758,\n 29.506698839472033\n ],\n [\n -89.18306350708008,\n 29.503262659979747\n ],\n [\n -89.18684005737305,\n 29.49579230227246\n ],\n [\n -89.18478012084961,\n 29.493849919009545\n ],\n [\n -89.1840934753418,\n 29.492206334848714\n ],\n [\n -89.17928695678711,\n 29.492804004901785\n ],\n [\n -89.17671203613281,\n 29.49235575269256\n ],\n [\n -89.17722702026367,\n 29.48862024048175\n ],\n [\n -89.18169021606444,\n 29.483390291999466\n ],\n [\n -89.18254852294922,\n 29.477861195816843\n ],\n [\n -89.1789436340332,\n 29.473676814427723\n ],\n [\n -89.1734504699707,\n 29.473527369040777\n ],\n [\n -89.16847229003906,\n 29.478907264175373\n ],\n [\n -89.1650390625,\n 29.48742484748479\n ],\n [\n -89.16435241699219,\n 29.497585238377603\n ],\n [\n -89.16521072387695,\n 29.501918036260868\n ],\n [\n -89.1653823852539,\n 29.506101251415647\n ],\n [\n -89.16778564453125,\n 29.508043399702284\n ],\n [\n -89.17087554931639,\n 29.50729642400116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The Working Group on Utah Earthquake Probabilities has assessed the probability of large earthquakes in the Wasatch Front region. There is a 43 percent probability of one or more magnitude 6.75 or greater earthquakes and a 57 percent probability of one or more magnitude 6.0 or greater earthquakes in the region in the next 50 years. These results highlight the threat of large earthquakes in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163019","collaboration":"Prepared in cooperation with the Utah Geological Survey, URS Corporation, University of Utah Seismograph Stations, and University of Utah.","usgsCitation":"DuRoss, C.B., 2016, Earthquake forecast for the Wasatch Front region of the Intermountain West: U.S. Geological Survey Fact Sheet 2016–3019, 2 p., https://doi.dx.org/10.3133/fs20163019. \n","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073668","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":320043,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3019/fs20163019.pdf","text":"Report","size":"6.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3019"},{"id":320042,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3019/coverthb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Wasatch Front Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.26904296874999,\n 39.21948715423953\n ],\n [\n -113.26904296874999,\n 42.187829010590825\n ],\n [\n -110.8465576171875,\n 42.187829010590825\n ],\n [\n -110.8465576171875,\n 39.21948715423953\n ],\n [\n -113.26904296874999,\n 39.21948715423953\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geologic Hazards Science Center
Box 25046, Mail Stop 966
Denver, CO 80225
http://geohazards.cr.usgs.gov/
","tableOfContents":"In 2013, Landsat 8 began adding high quality, global, moderate-resolution imagery to the more than 40-year archive of Landsat imagery. To assess the potential effects of the availability of Landsat 8 imagery on users and their work, the U.S. Geological Survey (USGS) Land Remote Sensing Program (LRS) initiated a survey of Landsat users. The objectives of the survey were to
\n1. Characterize various Landsat user groups, such as United States (U.S.) and international users and Landsat 8 and non-Landsat 8 users;
2. Identify any differences among user groups in uses and preferences;
3. Measure the importance of and satisfaction with Landsat 8 attributes;
4. Assess the importance to users of the frequency of usable imagery; and
5. Determine any challenges in using Landsat 8.
The online survey was sent to 51,617 Landsat users registered with USGS in May 2014. Almost 13,000 people responded to the survey for a response rate of 25 percent (n = 12,966). Current Landsat users (users who had used Landsat in their work in the year prior to the survey) composed 89 percent of the sample (n = 11,549) and past Landsat users composed 11 percent (n = 1,417). The results reported here apply to current Landsat users registered with the USGS Earth Resources Observation and Science (EROS) Center.
\nUsers from 161 countries responded to the survey. Of those, 19 percent were citizens or permanent residents of the United States and 81 percent resided in other countries. More than 70 percent of current users had used Landsat 8 in the year prior to the survey. The majority of Landsat 8 users (65 percent) were established users who used Landsat imagery regularly both before and after Landsat 8 imagery became available. The average current Landsat user was male, 36 years old, and highly educated, with 9 years of experience using satellite imagery or geographic information system (GIS) software. Landsat 8 users had, on average, two more years of experience than non-Landsat 8 users. Users were employed predominantly by academic institutions (65 percent), followed by private businesses (13 percent), Federal governments (10 percent), State and local governments (6 percent), and nonprofit organizations (6 percent).
\nOf the Landsat imagery obtained in the past year by current users, on average 31 percent came from a Landsat 8 sensor. An equivalent amount came from the Landsat 7 ETM+ sensor (33 percent); slightly less came from Landsats 4 and 5 TM sensors (27 percent). Much less came from Landsats 1 through 5 MSS sensors (5 percent). Overall, more than a third of users’ work used Landsat imagery (38 percent). Of this work, on average, 37 percent of the work was operational. Landsat 8 users considered a greater proportion of their work operational than non-Landsat 8 users (39 percent compared with 29 percent). Environmental sciences and management were the most commonly selected primary applications (selected by 42 percent of users). Land use/land cover (23 percent) was the second most commonly selected primary application, followed by education (12 percent), agriculture (9 percent), and planning and development (6 percent).
\nLandsat 8 users were asked to rank the importance of certain attributes in determining whether to use Landsat 8 imagery in their work. The archive was ranked most important, followed by cost, spatial resolution, extent of coverage, data quality, and frequency of revisit. Users were asked how satisfied they were with these same attributes as they currently apply to Landsat 8 imagery. On average, users were most satisfied with lack of cost, extent of coverage, data quality, and the archive, but they were satisfied with all attributes.
\nUsers were asked how often they needed Landsat imagery to meet various requirements for their primary application. The survey question specifically asked how often users needed usable imagery, which differs from how often they would like the Landsat satellites to acquire an image. Users were asked to identify their needed frequency of usable imagery for the following levels:
\n1. Threshold level—the minimum frequency of usable imagery needed to be of any value to their primary application.
2. Breakthrough level—the frequency of usable imagery that would result in a significant improvement for their primary application of the imagery.
3. Target level—the frequency of usable imagery that would only provide a limited additional increase in the expected performance for their primary application.
To meet the threshold level, three-quarters of users needed usable imagery every 17 days or less frequently. At the breakthrough level, two-thirds of users (64 percent) needed a usable image every 5–16 days. The current constellation of two satellites (Landsat 7 and 8) is capable of meeting the threshold and breakthrough needs of most users at least some of the time, but a single satellite would be highly unlikely to do so. Two-fifths of users (40 percent) felt that usable imagery provided every 4 days or more frequently would meet their target level which the current Landsat constellation cannot provide. Landsat 8 users were significantly more likely than non-Landsat 8 users to need usable imagery more frequently to meet their target levels. Additionally, U.S. Landsat 8 users were significantly more likely than other Landsat users to need usable imagery more frequently in order meet both their breakthrough and target levels.
\nTo explore the effect of the availability of Landsat 8 imagery on Landsat imagery use in general, established users (those who had consistently used Landsat imagery both before and after Landsat 8 imagery became available) using Landsat 8 imagery were asked about changes in the amount of Landsat imagery they used. The majority of established users using Landsat 8 imagery (60 percent) reported an average increase of 51 percent in the number of scenes obtained after Landsat 8 imagery became available. Landsat 8 users were asked if they had encountered challenges in using Landsat 8 whereas non-Landsat 8 users were asked if such challenges had played a role in why they were not using Landsat 8 imagery. Although many users did not encounter challenges when using or trying to use Landsat 8 data, slightly less than 30 percent did encounter issues with processing the data to a usable point. The most common issue reported was not being able to create or have access to a surface reflectance corrected product. Other challenges were related to the file sizes of images being too large to download, store, or analyze. There were no statistically significant differences between Landsat 8 and non-Landsat 8 users in terms of challenges encountered when using or trying to use the imagery, which indicates that users were not unduly discouraged by the challenges they may have encountered. When asked about potential consequences of not using Landsat 8, more than half of the non-Landsat 8 users did not report detrimental effects on their work from not using the imagery. Of those who did report detrimental effects, decreased quality of work, decreased scope of work, and increased time spent on work were the most common.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161032","usgsCitation":"Miller, H.M., 2016, Users and uses of Landsat 8 satellite imagery—2014 survey results: U.S. Geological Survey Open-File Report 2016–1032, 27 p., https://dx.doi/org/10.3133/ofr20161032.","productDescription":"vi, 27 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069774","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1032/ofr20161032.pdf","text":"Report","size":"2.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1032"},{"id":320058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1032/coverthb.jpg"}],"contact":"Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Box 25046, MS-939
Fort Collins, CO 80526-8118
Concentrations of H2O and CO2 in olivine-hosted melt inclusions can be used to estimate crystallization depths for the olivine host. However, the original dissolved CO2concentration of melt inclusions at the time of trapping can be difficult to measure directly because in many cases substantial CO2 is transferred to shrinkage bubbles that form during post-entrapment cooling and crystallization. To investigate this problem, we heated olivine from the 1959 Kīlauea Iki and 1960 Kapoho (Hawai‘i) eruptions in a 1-atm furnace to temperatures above the melt inclusion trapping temperature to redissolve the CO2 in shrinkage bubbles. The measured CO2 concentrations of the experimentally rehomogenized inclusions (⩽590 ppm for Kīlauea Iki [n=10]; ⩽880 ppm for Kapoho, with one inclusion at 1863 ppm [n=38]) overlap with values for naturally quenched inclusions from the same samples, but experimentally rehomogenized inclusions have higher within-sample median CO2 values than naturally quenched inclusions, indicating at least partial dissolution of CO2 from the vapor bubble during heating. Comparison of our data with predictions from modeling of vapor bubble formation and published Raman data on the density of CO2 in the vapor bubbles suggests that 55-85% of the dissolved CO2 in the melt inclusions at the time of trapping was lost to post-entrapment shrinkage bubbles. Our results combined with the Raman data demonstrate that olivine from the early part of the Kīlauea Iki eruption crystallized at <6 km depth, with the majority of olivine in the 1-3 km depth range. These depths are consistent with the interpretation that the Kīlauea Iki magma was supplied from Kīlauea’s summit magma reservoir (∼2-5 km depth). In contrast, olivine from Kapoho, which was the rift zone extension of the Kīlauea Iki eruption, crystallized over a much wider range of depths (∼1-16 km). The wider depth range requires magma transport during the Kapoho eruption from deep beneath the summit region and/or from deep beneath Kīlauea’s east rift zone. The deeply derived olivine crystals and their host magma mixed with stored, more evolved magma in the rift zone, and the mixture was later erupted at Kapoho.
\nThe Florida Everglades freshwater landscape exhibits a distribution of islands covered by woody vegetation and bordered by marshes and wet prairies. Known as “tree islands”, these ecogeomorphic features can be found in few other low gradient, nutrient limited freshwater wetlands. In the last few decades, however, a large percentage of tree islands have either shrank or disappeared in apparent response to altered water depths and other stressors associated with human impacts on the Everglades. Because the processes determining the formation and spatial organization of tree islands remain poorly understood, it is still unclear what controls the sensitivity of these landscapes to altered conditions. We hypothesize that positive feedbacks between woody plants and soil accretion are crucial to emergence and decline of tree islands. Likewise, positive feedbacks between phosphorus (P) accumulation and trees explain the P enrichment commonly observed in tree island soils. Here, we develop a spatially-explicit model of tree island formation and evolution, which accounts for these positive feedbacks (facilitation) as well as for long range competition and fire dynamics. It is found that tree island patterns form within a range of parameter values consistent with field data. Simulated impacts of reduced water levels, increased intensity of drought, and increased frequency of dry season/soil consuming fires on these feedback mechanisms result in the decline and disappearance of tree islands on the landscape.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.ecocom.2016.03.007","usgsCitation":"Carr, J., D’Odorico, P., Engel, V.C., and Redwine, J., 2016, Tree island pattern formation in the Florida Everglades: Ecological Complexity, v. 26, p. 37-44, https://doi.org/10.1016/j.ecocom.2016.03.007.","productDescription":"8 p.","startPage":"37","endPage":"44","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064593","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471064,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/7vh3x450","text":"External Repository"},{"id":320122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":626849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D’Odorico, P.","contributorId":56528,"corporation":false,"usgs":true,"family":"D’Odorico","given":"P.","email":"","affiliations":[],"preferred":false,"id":626850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engel, Victor C. 0000-0002-3858-7308 vengel@usgs.gov","orcid":"https://orcid.org/0000-0002-3858-7308","contributorId":2329,"corporation":false,"usgs":true,"family":"Engel","given":"Victor","email":"vengel@usgs.gov","middleInitial":"C.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":626851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Redwine, 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extensively in size. Because individuals in certain sizes of groups often have higher apparent fitness than those in other groups, why wide group size variation persists in most populations remains unexplained. We used a 30-y mark–recapture study of colonially breeding cliff swallows (Petrochelidon pyrrhonota) to show that the survival advantages of different colony sizes fluctuated among years. Colony size was under both stabilizing and directional selection in different years, and reversals in the sign of directional selection regularly occurred. Directional selection was predicted in part by drought conditions: birds in larger colonies tended to be favored in cooler and wetter years, and birds in smaller colonies in hotter and drier years. Oscillating selection on colony size likely reflected annual differences in food availability and the consequent importance of information transfer, and/or the level of ectoparasitism, with the net benefit of sociality varying under these different conditions. Averaged across years, there was no net directional change in selection on colony size. The wide range in cliff swallow group size is probably maintained by fluctuating survival selection and represents the first case, to our knowledge, in which fitness advantages of different group sizes regularly oscillate over time in a natural vertebrate population.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1600218113","usgsCitation":"Brown, C.B., Brown, M.B., Roche, E.A., O'brien, V., and Page, C.E., 2016, Fluctuating survival selection explains variation in avian group size: Proceedings of the National Academy of Sciences of the United States of America, v. 113, no. 18, p. 5113-518, https://doi.org/10.1073/pnas.1600218113.","productDescription":"6 p.","startPage":"5113","endPage":"518","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071213","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1600218113","text":"Publisher Index Page"},{"id":320512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"18","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-18","publicationStatus":"PW","scienceBaseUri":"571f3fb8e4b071321fe56a26","contributors":{"authors":[{"text":"Brown, Charles B.","contributorId":168888,"corporation":false,"usgs":false,"family":"Brown","given":"Charles","email":"","middleInitial":"B.","affiliations":[{"id":25379,"text":"Dept of Biol Sc, Univ of Tulsa, Tulsa OK","active":true,"usgs":false}],"preferred":false,"id":627592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Mary Bomberger","contributorId":150841,"corporation":false,"usgs":false,"family":"Brown","given":"Mary","email":"","middleInitial":"Bomberger","affiliations":[{"id":18117,"text":"School of Natl Res, Univ of NE, Lincoln","active":true,"usgs":false}],"preferred":false,"id":627593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roche, Erin A. eroche@usgs.gov","contributorId":5558,"corporation":false,"usgs":true,"family":"Roche","given":"Erin","email":"eroche@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":627591,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O'brien, Valerie A","contributorId":168889,"corporation":false,"usgs":false,"family":"O'brien","given":"Valerie A","affiliations":[{"id":25380,"text":"Div of Sc & Math, Tulsa Community College-Metro Campus,Tulsa OK","active":true,"usgs":false}],"preferred":false,"id":627594,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Page, Catherine E.","contributorId":168890,"corporation":false,"usgs":false,"family":"Page","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":25379,"text":"Dept of Biol Sc, Univ of Tulsa, Tulsa OK","active":true,"usgs":false}],"preferred":false,"id":627595,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70184231,"text":"70184231 - 2016 - Postseismic gravity change after the 2006–2007 great earthquake doublet and constraints on the asthenosphere structure in the central Kuril Islands","interactions":[],"lastModifiedDate":"2017-03-06T10:56:04","indexId":"70184231","displayToPublicDate":"2016-04-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Postseismic gravity change after the 2006–2007 great earthquake doublet and constraints on the asthenosphere structure in the central Kuril Islands","docAbstract":"Large earthquakes often trigger viscoelastic adjustment for years to decades depending on the rheological properties and the nature and spatial extent of coseismic stress. The 2006 Mw8.3 thrust and 2007 Mw8.1 normal fault earthquakes of the central Kuril Islands resulted in significant postseismic gravity change in Gravity Recovery and Climate Experiment (GRACE) but without a discernible coseismic gravity change. The gravity increase of ~4 μGal, observed consistently from various GRACE solutions around the epicentral area during 2007–2015, is interpreted as resulting from gradual seafloor uplift by ~6 cm produced by postseismic relaxation. The GRACE data are best fit with a model of 25–35 km for the elastic thickness and ~1018 Pa s for the Maxwell viscosity of the asthenosphere. The large measurable postseismic gravity change (greater than coseismic change) emphasizes the importance of viscoelastic relaxation in understanding tectonic deformation and fault-locking scenarios in the Kuril subduction zone.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL068167","usgsCitation":"Han, S., Sauber, J., and Pollitz, F., 2016, Postseismic gravity change after the 2006–2007 great earthquake doublet and constraints on the asthenosphere structure in the central Kuril Islands: Geophysical Research Letters, v. 43, no. 7, p. 3169-3177, https://doi.org/10.1002/2016GL068167.","productDescription":"9 p.","startPage":"3169","endPage":"3177","ipdsId":"IP-074384","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":471066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068167","text":"Publisher Index Page"},{"id":336856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Kuril Islands","volume":"43","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-05","publicationStatus":"PW","scienceBaseUri":"58be8339e4b014cc3a3a99e3","contributors":{"authors":[{"text":"Han, Shin-Chan","contributorId":187537,"corporation":false,"usgs":false,"family":"Han","given":"Shin-Chan","affiliations":[],"preferred":false,"id":680768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sauber, Jeanne","contributorId":71734,"corporation":false,"usgs":true,"family":"Sauber","given":"Jeanne","email":"","affiliations":[],"preferred":false,"id":680769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":680668,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70182739,"text":"70182739 - 2016 - Volcanic lightning and plume behavior reveal evolving hazards during the April 2015 eruption of Calbuco volcano, Chile","interactions":[],"lastModifiedDate":"2017-02-28T11:33:23","indexId":"70182739","displayToPublicDate":"2016-04-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Volcanic lightning and plume behavior reveal evolving hazards during the April 2015 eruption of Calbuco volcano, Chile","docAbstract":"Soon after the onset of an eruption, model forecasts of ash dispersal are used to mitigate the hazards to aircraft, infrastructure and communities downwind. However, it is a significant challenge to constrain the model inputs during an evolving eruption. Here we demonstrate that volcanic lightning may be used in tandem with satellite detection to recognize and quantify changes in eruption style and intensity. Using the eruption of Calbuco volcano in southern Chile on 22-23 April 2015, we investigate rates of umbrella cloud expansion from satellite observations, occurrence of lightning, and mapped characteristics of the fall deposits. Our remote-sensing analysis gives a total erupted volume that is within uncertainty of the mapped volume (0.56 ±0.28 km3 bulk). Observations and volcanic plume modeling further suggest that electrical activity was enhanced both by ice formation in the ash clouds >10 km asl and development of a low-level charge layer from ground-hugging currents.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL068076","usgsCitation":"Van Eaton, A.R., Amigo, A., Bertin, D., Mastin, L.G., Giacosa, R.E., Gonzalez, J., Valderrama, O., Fontijn, K., and Behnke, S., 2016, Volcanic lightning and plume behavior reveal evolving hazards during the April 2015 eruption of Calbuco volcano, Chile: Geophysical Research Letters, v. 43, no. 7, p. 3563-3571, https://doi.org/10.1002/2016GL068076.","productDescription":"9 p. 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The surface accumulation rate in the current climate can be constrained by the crater retention age. We scale NPLD crater diameters to account for icy target strength and compare surface age using a new production function for recent small impacts on Mars to the previously used model of Hartmann (2005). Our results indicate that ice is accumulating in these craters several times faster than previously thought, with a 100 m diameter crater being completely infilled within centuries. Craters appear to have a diameter-dependent lifetime, but the data also permit a complete resurfacing of the NPLD at ~1.5 ka.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2016GL068434","usgsCitation":"Landis, M., Byrne, S., Daubar, I., Herkenhoff, K.E., and Dundas, C.M., 2016, A revised surface age for the North Polar Layered Deposits of Mars : Geophysical Research Letters, v. 43, no. 7, p. 3060-3068, https://doi.org/10.1002/2016GL068434.","productDescription":"9 p.","startPage":"3060","endPage":"3068","numberOfPages":"9","ipdsId":"IP-070849","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":471067,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl068434","text":"Publisher Index Page"},{"id":330832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"43","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-05","publicationStatus":"PW","scienceBaseUri":"5821a0dde4b02f1a881de974","contributors":{"authors":[{"text":"Landis, Margaret E.","contributorId":176713,"corporation":false,"usgs":false,"family":"Landis","given":"Margaret E.","affiliations":[{"id":25655,"text":"Lunar and Planetary Laboratory, 1629 E. University Blvd., The University of Arizona, Tucson, AZ 85721, United States","active":true,"usgs":false}],"preferred":false,"id":653227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrne, Shane","contributorId":53513,"corporation":false,"usgs":false,"family":"Byrne","given":"Shane","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":653228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daubar, Ingrid J.","contributorId":34431,"corporation":false,"usgs":true,"family":"Daubar","given":"Ingrid J.","affiliations":[],"preferred":false,"id":653229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herkenhoff, Kenneth E. 0000-0002-3153-6663 kherkenhoff@usgs.gov","orcid":"https://orcid.org/0000-0002-3153-6663","contributorId":2275,"corporation":false,"usgs":true,"family":"Herkenhoff","given":"Kenneth","email":"kherkenhoff@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653165,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176538,"text":"70176538 - 2016 - Estimates of lake trout (Salvelinus namaycush) diet in Lake Ontario using two and three isotope mixing models","interactions":[],"lastModifiedDate":"2016-09-21T12:44:11","indexId":"70176538","displayToPublicDate":"2016-04-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimates of lake trout (Salvelinus namaycush) diet in Lake Ontario using two and three isotope mixing models","docAbstract":"Recent development of multi-dimensional stable isotope models for estimating both foraging patterns and niches have presented the analytical tools to further assess the food webs of freshwater populations. One approach to refine predictions from these analyses is to include a third isotope to the more common two-isotope carbon and nitrogen mixing models to increase the power to resolve different prey sources. We compared predictions made with two-isotope carbon and nitrogen mixing models and three-isotope models that also included sulphur (δ34S) for the diets of Lake Ontario lake trout (Salvelinus namaycush). We determined the isotopic compositions of lake trout and potential prey fishes sampled from Lake Ontario and then used quantitative estimates of resource use generated by two- and three-isotope Bayesian mixing models (SIAR) to infer feeding patterns of lake trout. Both two- and three-isotope models indicated that alewife (Alosa pseudoharengus) and round goby (Neogobius melanostomus) were the primary prey items, but the three-isotope models were more consistent with recent measures of prey fish abundances and lake trout diets. The lake trout sampled directly from the hatcheries had isotopic compositions derived from the hatchery food which were distinctively different from those derived from the natural prey sources. Those hatchery signals were retained for months after release, raising the possibility to distinguish hatchery-reared yearlings and similarly sized naturally reproduced lake trout based on isotopic compositions. Addition of a third-isotope resulted in mixing model results that confirmed round goby have become an important component of lake trout diet and may be overtaking alewife as a prey resource.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2016.03.010","usgsCitation":"Colborne, S.F., Rush, S.A., Paterson, G., Johnson, T.B., Lantry, B.F., and Fisk, A.T., 2016, Estimates of lake trout (Salvelinus namaycush) diet in Lake Ontario using two and three isotope mixing models: Journal of Great Lakes Research, v. 42, no. 3, p. 695-702, https://doi.org/10.1016/j.jglr.2016.03.010.","productDescription":"8 p.","startPage":"695","endPage":"702","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066386","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":328808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"New York, Ontario","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.3714599609375,\n 43.59630591596548\n ],\n [\n -76.3055419921875,\n 43.51668853502906\n ],\n [\n -76.4208984375,\n 43.393073720674415\n ],\n [\n -76.5582275390625,\n 43.337164854911094\n ],\n [\n -76.8548583984375,\n 43.197167282501276\n ],\n [\n -77.2064208984375,\n 43.177141346631714\n ],\n [\n -77.5634765625,\n 43.104993581605505\n ],\n [\n -77.92053222656249,\n 43.24520272203356\n ],\n [\n -78.2940673828125,\n 43.24520272203356\n ],\n [\n -78.6566162109375,\n 43.249203966977845\n ],\n [\n -79.013671875,\n 43.193162620926074\n ],\n [\n -79.3212890625,\n 43.100982876188546\n ],\n [\n -79.617919921875,\n 43.137069765760344\n ],\n [\n -79.716796875,\n 43.197167282501276\n ],\n [\n -79.6728515625,\n 43.28520334369384\n ],\n [\n -79.5684814453125,\n 43.32118142926661\n ],\n [\n -79.365234375,\n 43.27320591705845\n ],\n [\n -79.22241210937499,\n 43.305193797650546\n ],\n [\n -78.9862060546875,\n 43.37710501700073\n ],\n [\n -78.651123046875,\n 43.40903821777055\n ],\n [\n -78.44238281249999,\n 43.440954591707445\n ],\n [\n -78.16223144531249,\n 43.45291889355465\n ],\n [\n -77.92053222656249,\n 43.44494295526125\n ],\n [\n -77.4700927734375,\n 43.35314407444698\n ],\n [\n -77.2613525390625,\n 43.35713822211053\n ],\n [\n -77.025146484375,\n 43.38508989465156\n ],\n [\n -76.75048828125,\n 43.4249985081581\n ],\n [\n -76.5802001953125,\n 43.51668853502906\n ],\n [\n -76.4813232421875,\n 43.560491112629286\n ],\n [\n -76.3714599609375,\n 43.59630591596548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57f7c6bce4b0bc0bec09cb12","contributors":{"authors":[{"text":"Colborne, Scott F.","contributorId":174737,"corporation":false,"usgs":false,"family":"Colborne","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":649186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rush, Scott A.","contributorId":127332,"corporation":false,"usgs":false,"family":"Rush","given":"Scott","email":"","middleInitial":"A.","affiliations":[{"id":6778,"text":"University of Windsor, Windsor, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":649187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paterson, Gordon","contributorId":12755,"corporation":false,"usgs":true,"family":"Paterson","given":"Gordon","email":"","affiliations":[],"preferred":false,"id":649188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Timothy B.","contributorId":49753,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":649189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lantry, Brian F. 0000-0001-8797-3910 bflantry@usgs.gov","orcid":"https://orcid.org/0000-0001-8797-3910","contributorId":3435,"corporation":false,"usgs":true,"family":"Lantry","given":"Brian","email":"bflantry@usgs.gov","middleInitial":"F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":649190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fisk, Aaron T.","contributorId":127340,"corporation":false,"usgs":false,"family":"Fisk","given":"Aaron","email":"","middleInitial":"T.","affiliations":[{"id":6778,"text":"University of Windsor, Windsor, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":649191,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170268,"text":"ofr20151212 - 2016 - Total dissolved gas and water temperature in the lower Columbia River, Oregon and Washington, water year 2015","interactions":[],"lastModifiedDate":"2016-04-18T08:33:49","indexId":"ofr20151212","displayToPublicDate":"2016-04-15T19:00:00","publicationYear":"2016","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":"2015-1212","title":"Total dissolved gas and water temperature in the lower Columbia River, Oregon and Washington, water year 2015","docAbstract":"An analysis of total-dissolved-gas (TDG) and water-temperature data collected at eight fixed monitoring stations on the lower Columbia River in Oregon and Washington in water year 2015 indicated the following:
\nDirector, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
The Rio Rico and Nogales (Arizona) 1:24,000-scale quadrangles are located in the Basin and Range Province of southern Arizona, and the southern edge of the map is the international border with Sonora, Mexico. The major urban area is Nogales, a bi-national city known as “the gateway to Mexico.” Rocks exposed in the map area range in age from Jurassic through Quaternary. Major physiographic, geologic, and hydrologic features in the map area include the southern San Cayetano Mountains, Grosvenor Hills, and Sonoita Creek in the northern part, and Mount Benedict and the Mount Benedict horst block in the southcentral part. The horst block is bounded by the Santa Cruz River on the east and Nogales Wash on the west.
\nThe objectives of our mapping were to define the geologic framework for the Nogales area and the upper Santa Cruz basin to support ongoing multidisciplinary projects. This new work will improve understanding of the Nogales Formation to more fully assess its groundwater resource potential. We significantly revised the Miocene Nogales Formation based on geologic mapping combined with new geochronologic, geophysical, and petrographic studies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3354","usgsCitation":"Page, W.R., Menges, C.M., Gray, Floyd, Berry, M.E., Bultman, M.W., Cosca, M.A., and VanSistine, D.P., 2016, Geologic map of the Rio Rico and Nogales 7.5’ quadrangles, Santa Cruz County, Arizona: U.S. Geological Survey Scientific Investigations Map 3354, 32 p. pamphlet, 2 sheets, scale 1:24,000, https://dx.doi.org/10.3133/sim3354.","productDescription":"Pamphlet: v, 32 p.; 3 Sheets: 37.93 x 40.26 inches or smaller; Appendix; Metadata: text, xml; Read Me; Spatial Data: Base maps, Geodatabase, Shapefiles.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059210","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":320004,"rank":8,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_shapefiles.zip","text":"Shapefiles","size":"1.94 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3354 Shapefiles"},{"id":320000,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_sheet2.pdf","text":"Cross sections","size":"184 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Cross sections"},{"id":320006,"rank":9,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3354/sim3354.met","text":"Metadata text","size":"24.0 kB","description":"SIM 3354 Metadata Text"},{"id":319994,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_sheet1.pdf","text":"Geologic map","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Geologic Map"},{"id":320007,"rank":10,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_basemaps.zip","text":"Base maps","size":"18.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3354 Base maps"},{"id":319992,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_pamphlet.pdf","text":"Pamphlet","size":"21.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Pamphlet"},{"id":319993,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3354 ReadMe"},{"id":320009,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_appendix.xlsx","text":"Combined 40Ar/39Ar data","size":"68.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIM 3354 Combined 40Ar/39Ar data"},{"id":320002,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_gdb.zip","text":"Geodatabase","size":"39.5 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3354 Geodatabase"},{"id":320052,"rank":12,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_metadata.xml","text":"Metadata xml","size":"20.0 kB","description":"SIM 3354 Metadata xml"},{"id":319996,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3354/sim3354_sheet1_geo.pdf","text":"Georeferenced geologic map","size":"146.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3354 Georeferenced geologic map"},{"id":319989,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3354/coverthb.jpg"}],"country":"United States","state":"Arizona","county":"Santa Cruz County","otherGeospatial":"Rio Rico and Nogales 7.5' Quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 31.5\n ],\n [\n -111,\n 31.3325\n ],\n [\n -110.875,\n 31.3329\n ],\n [\n -110.875,\n 31.5\n ],\n [\n -111,\n 31.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
This paper represents the first attempt to show, by means other than just petrographic ones, that one type of macrinite, herein designated copromacrinite, may result from macrofauna feces. For that purpose a combination of coal petrography, X-ray photoelectron spectroscopy, and elemental-analysis continuous-flow isotope ratio mass spectrometry methods were used to determine nitrogen functionalities and δ13C andδ15N compositions in 1) vitrinite-rich, 2) fusinite + semifusinite-rich, and 3) macrinite-rich (with a possible coprolitic origin) samples of the high volatile A bituminous Peach Orchard coal (Bolsovian; Middle Pennsylvanian) from Magoffin County, Kentucky. There were no significant differences between pyridinic-N and quaternary-N abundance in the three samples, however, pyrrolic-N was higher (~ 54%) in the macrinite-rich sample than in the other two samples (~ 38%). The data suggest that pyridinic-N and quaternary-N are independent of maceral group composition and that pyrrolic-N is dependent on maceral composition (fusinite + semifusinite versus macrinite). δ13C values obtained for bulk and demineralized coal of the vitrinite- and fusinite + semifusinite-rich samples are similar with δ13C values of − 24.80 ± 0.01‰ VPDB and − 24.61 ± 0.09‰ VPDB for bulk samples and − 24.81 ± 0.07‰ VPDB and − 24.52 ± 0.04‰ VPDB for demineralized samples. These values are within the expected range for vitrinite-rich samples and the slightly higher δ13C value of the fusinite + semifusinite-rich sample is expected as δ13C values for inertinite are higher than for vitrinite. However, there was a significant shift to a lower δ13C value (− 26.80 ± 0.01‰ VPDB for the bulk sample value) for the macrinite-rich sample. Because the samples are basically isorank, and δ13C (and δ15N) shifts do not occur during maturation until anthracite rank, the difference may be related to the presence or composition of the macrinite within the sample which lacks heat-effect indicators, such as devolatilization vacuoles and distorted pores. δ15N values are also similar for bulk and demineralized coal of the vitrinite- and fusinite + semifusinite-rich samples, and the bulk values were heavier in this samples (3.07 ± 0.03‰ Air and 2.92 ± 0.10‰ Air, respectively), and much lighter (− 2.83 ± 0.09‰ Air) for the macrinite-rich sample.
\nThe study of Peach Orchard coal samples using reflected-light microscopy, isotopic composition, and nitrogen-forms analyses revealed that the macrinite-rich sample contains macrinite with coprolitic features (e.g. oxidation rind, mix of undigested palynomorphs, frequent and randomly located funginite, agglutination pulp of semifusinite reflectance, internal lack of bedding fabric, and suggestion of structures resulting from intestines and stomach walls), more pyrrolic-N (~ 16%), and lower δ13C (~ 2‰ VPDB) and δ15N (~ 4‰ Air) values than the vitrinite and semifusinite + fusinite rich samples. These findings suggest that the maceral macrinite has multiple origins based on petrography and measurable chemical differences between the macrinite, vitrinite, and semifusinite + fusinite fractions within the coal. Assuming that copromacrinite observed is an excretion then the anomalies observed may result from the symbiotic relations between the macrofauna (e.g. cockroaches) and microbiota during the digestive processes, and the nitrogen balance mechanisms inside macrofauna body.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2016.05.004","usgsCitation":"Valentim, B., Algarra, M., Guedes, A., Ruppert, L.F., and Hower, J., 2016, Notes on the origin of copromacrinite based on nitrogen functionalities and δ13C and δ15N determined on samples from the Peach Orchard coal bed, southern Magoffin County, Kentucky: International Journal of Coal Geology, v. 160-161, p. 63-72, https://doi.org/10.1016/j.coal.2016.05.004.","productDescription":"10 p.","startPage":"63","endPage":"72","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062704","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":324653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","county":"Magoffin 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Portugal.","active":true,"usgs":false}],"preferred":false,"id":564117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guedes, Alexandra","contributorId":145467,"corporation":false,"usgs":false,"family":"Guedes","given":"Alexandra","email":"","affiliations":[{"id":16122,"text":"Centro de Geologia da Universidade do Porto and Departamento de Geociências, Ambiente e Ordenamento do Território, Faculdade de Ciências da Universidade do Porto, Rua Campo Alegre, 687, 4169-007 Porto, Portugal.","active":true,"usgs":false}],"preferred":false,"id":564118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruppert, Leslie F. 0000-0002-7453-1061 lruppert@usgs.gov","orcid":"https://orcid.org/0000-0002-7453-1061","contributorId":660,"corporation":false,"usgs":true,"family":"Ruppert","given":"Leslie","email":"lruppert@usgs.gov","middleInitial":"F.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":564115,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hower, James C. 0000-0003-4694-2776","orcid":"https://orcid.org/0000-0003-4694-2776","contributorId":34561,"corporation":false,"usgs":false,"family":"Hower","given":"James C.","affiliations":[{"id":16123,"text":"University of Kentucky, Center for Applied Energy Research, 2540 Research Park Drive, Lexington, KY 40511, United States.","active":true,"usgs":false}],"preferred":false,"id":564119,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70177884,"text":"70177884 - 2016 - A new organic reference material, L-glutamic acid, USGS41a, for δ13C and δ15N measurements − a replacement for USGS41","interactions":[],"lastModifiedDate":"2016-10-25T15:41:02","indexId":"70177884","displayToPublicDate":"2016-04-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3233,"text":"Rapid Communications in Mass Spectrometry","active":true,"publicationSubtype":{"id":10}},"title":"A new organic reference material, L-glutamic acid, USGS41a, for δ13C and δ15N measurements − a replacement for USGS41","docAbstract":"The widely used l-glutamic acid isotopic reference material USGS41, enriched in both 13C and 15N, is nearly exhausted. A new material, USGS41a, has been prepared as a replacement for USGS41.
USGS41a was prepared by dissolving analytical grade l-glutamic acid enriched in 13C and 15N together with l-glutamic acid of normal isotopic composition. The δ13C and δ15N values of USGS41a were directly or indirectly normalized with the international reference materials NBS 19 calcium carbonate (δ13CVPDB = +1.95 mUr, where milliurey = 0.001 = 1 ‰), LSVEC lithium carbonate (δ13CVPDB = −46.6 mUr), and IAEA-N-1 ammonium sulfate (δ15NAir = +0.43 mUr) and USGS32 potassium nitrate (δ15N = +180 mUr exactly) by on-line combustion, continuous-flow isotope-ratio mass spectrometry, and off-line dual-inlet isotope-ratio mass spectrometry.
USGS41a is isotopically homogeneous; the reproducibility of δ13C and δ15N is better than 0.07 mUr and 0.09 mUr, respectively, in 200-μg amounts. It has a δ13C value of +36.55 mUr relative to VPDB and a δ15N value of +47.55 mUr relative to N2 in air. USGS41 was found to be hydroscopic, probably due to the presence of pyroglutamic acid. Experimental results indicate that the chemical purity of USGS41a is substantially better than that of USGS41.
The new isotopic reference material USGS41a can be used with USGS40 (having a δ13CVPDB value of −26.39 mUr and a δ15NAir value of −4.52 mUr) for (i) analyzing local laboratory isotopic reference materials, and (ii) quantifying drift with time, mass-dependent isotopic fractionation, and isotope-ratio-scale contraction for isotopic analysis of biological and organic materials. Published in 2016. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/rcm.7510","usgsCitation":"Qi, H., Coplen, T.B., Mroczkowski, S.J., Brand, W.A., Brandes, L., Geilmann, H., and Schimmelmann, A., 2016, A new organic reference material, L-glutamic acid, USGS41a, for δ13C and δ15N measurements − a replacement for USGS41: Rapid Communications in Mass Spectrometry, v. 30, no. 7, p. 859-866, https://doi.org/10.1002/rcm.7510.","productDescription":"8 p.","startPage":"859","endPage":"866","ipdsId":"IP-071905","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":330380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58106f98e4b0f497e7961117","contributors":{"authors":[{"text":"Qi, Haiping 0000-0002-8339-744X haipingq@usgs.gov","orcid":"https://orcid.org/0000-0002-8339-744X","contributorId":507,"corporation":false,"usgs":true,"family":"Qi","given":"Haiping","email":"haipingq@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":652015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":652016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mroczkowski, Stanley J. 0000-0001-8026-6025 smroczko@usgs.gov","orcid":"https://orcid.org/0000-0001-8026-6025","contributorId":2628,"corporation":false,"usgs":true,"family":"Mroczkowski","given":"Stanley","email":"smroczko@usgs.gov","middleInitial":"J.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":652017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brand, Willi A.","contributorId":33091,"corporation":false,"usgs":false,"family":"Brand","given":"Willi","email":"","middleInitial":"A.","affiliations":[{"id":13365,"text":"Max-Planck Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":652018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brandes, Lauren lbrandes@usgs.gov","contributorId":176264,"corporation":false,"usgs":true,"family":"Brandes","given":"Lauren","email":"lbrandes@usgs.gov","affiliations":[],"preferred":true,"id":652019,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Geilmann, Heike","contributorId":41303,"corporation":false,"usgs":false,"family":"Geilmann","given":"Heike","email":"","affiliations":[{"id":13365,"text":"Max-Planck Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":652020,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schimmelmann, Arndt","contributorId":140051,"corporation":false,"usgs":false,"family":"Schimmelmann","given":"Arndt","affiliations":[{"id":13366,"text":"Indiana University, Bloomington, Indiana, USA","active":true,"usgs":false}],"preferred":false,"id":652021,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70171351,"text":"70171351 - 2016 - Fish community response to dam removal in a Maine coastal river tributary","interactions":[],"lastModifiedDate":"2016-05-27T13:17:27","indexId":"70171351","displayToPublicDate":"2016-04-14T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Fish community response to dam removal in a Maine coastal river tributary","docAbstract":"Sedgeunkedunk Stream, a third-order tributary to the Penobscot River in Maine, historically has supported several anadromous fishes including Atlantic Salmon Salmo salar, Alewife Alosa pseudoharengus, and Sea Lamprey Petromyzon marinus. Two small dams constructed in the 1800s reduced or eliminated spawning runs entirely. In 2009, efforts to restore marine–freshwater connectivity in the system culminated in removal of the lowermost dam (Mill Dam) providing access to 4.7 km of lotic habitat and unimpeded passage into the lentic habitat of Fields Pond. In anticipation of these barrier removals, we initiated a modified before-after-control-impact study, and monitored stream fish assemblages in fixed treatment and reference sites. Electrofishing surveys were conducted twice yearly since 2007. Results indicated that density, biomass, and diversity of the fish assemblage increased at all treatment sites upstream of the 2009 dam removal. No distinct changes in these metrics occurred at reference sites. We documented recolonization and successful reproduction of Atlantic Salmon, Alewife, and Sea Lamprey in previously inaccessible upstream reaches. These results clearly demonstrate that dam removal has enhanced the fish assemblage by providing an undisrupted stream gradient linking a small headwater lake and tributary with a large coastal river, its estuary, and the Atlantic Ocean.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/00028487.2015.1007164","usgsCitation":"Zydlewski, J.D., Hogg, R.S., Coghlan, S.M., and Gardner, C., 2016, Fish community response to dam removal in a Maine coastal river tributary: Transactions of the American Fisheries Society, v. 144, no. 3, p. 445-455, https://doi.org/10.1080/00028487.2015.1007164.","productDescription":"10 p.","startPage":"445","endPage":"455","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044287","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":321829,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","county":"Penobscot","city":"Bangor","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.378662109375,\n 44.398467142258504\n ],\n [\n -69.378662109375,\n 45.092913646051144\n ],\n [\n -68.2086181640625,\n 45.092913646051144\n ],\n [\n -68.2086181640625,\n 44.398467142258504\n ],\n [\n -69.378662109375,\n 44.398467142258504\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"144","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-04-14","publicationStatus":"PW","scienceBaseUri":"57496fafe4b07e28b665cc64","contributors":{"authors":[{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":630722,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogg, Robert S.","contributorId":169677,"corporation":false,"usgs":false,"family":"Hogg","given":"Robert","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":630723,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coghlan, Stephen M. Jr.","contributorId":169678,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen","suffix":"Jr.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":630724,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gardner, Cory","contributorId":169679,"corporation":false,"usgs":false,"family":"Gardner","given":"Cory","email":"","affiliations":[],"preferred":false,"id":630725,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170267,"text":"70170267 - 2016 - Dairy-impacted wastewater is a source of iodinated disinfection byproducts in the environment","interactions":[],"lastModifiedDate":"2018-08-07T12:47:28","indexId":"70170267","displayToPublicDate":"2016-04-14T10:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Dairy-impacted wastewater is a source of iodinated disinfection byproducts in the environment","docAbstract":"Iodinated disinfection byproducts (DBPs) are among the most toxic DBPs, but they are not typically measured in treated water. Iodinated DBPs can be toxic to humans, and they also have the potential to affect aquatic communities. Because of the specific use of iodine and iodine-containing compounds in dairies, such livestock operations can be a potential source of iodinated DBPs in corresponding receiving water bodies. DBPs [trihalomethanes (THMs), including iodinated THMs] were measured within dairy processing facilities (milking and cheese manufacturing) and surface waters that receive dairy-impacted effluents [either directly from the dairy or through wastewater treatment plants (WWTPs)] in three areas of the United States (California, New York, and Wisconsin). Iodo-THMs comprised 15−29% of the total THMs in surface water near WWTP effluents that were impacted by dairy waste and 0−100% of the total THMs in samples from dairy processing facilities.
","language":"English","publisher":"American Chemical Society","publisherLocation":"Washington, DC","doi":"10.1021/acs.estlett.6b00109","usgsCitation":"Hladik, M., Hubbard, L.E., Kolpin, D.W., and Focazio, M.J., 2016, Dairy-impacted wastewater is a source of iodinated disinfection byproducts in the environment: Environmental Science & Technology, v. 3, no. 5, p. 190-193, https://doi.org/10.1021/acs.estlett.6b00109.","productDescription":"4 p.","startPage":"190","endPage":"193","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073754","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":320062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, New York, 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In particular, we test several hypotheses explaining broad-scale species richness patterns on several species richness data sets: (i) total turtles, (ii) freshwater turtles only, (iii) aquatic turtles, (iv) terrestrial turtles only, (v) Emydidae, and (vi) Kinosternidae. In addition to spatial data, we used a combination of 25 abiotic variables in spatial regression models to predict species richness patterns. Our results provide support for multiple hypotheses related to broad-scale patterns of species richness, and in particular, hypotheses related to climate, productivity, water availability, topography, and latitude. In general, species richness patterns were positively associated with temperature, precipitation, diversity of streams, coefficient of variation of elevation, and net primary productivity. 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