Recreational hunting in the United States has traditional and cultural importance, and generates substantial economic benefits to individual hunters themselves. This paper conducts a meta-analysis of existing nonmarket valuation estimates for hunting in the United States to explore sources and implications of variation and uncertainty in these estimates. A multi-level meta-regression model is estimated to forecast point estimates for different hunting contexts, as well as to construct bounds of uncertainty around these estimates. The results and discussion provide insight to practitioners who need to conduct or understand benefit transfer, as well as those particularly interested in the value of hunting in the U.S.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2018.07.001","usgsCitation":"Huber, C., Meldrum, J., and Richardson, L., 2018, Improving confidence by embracing uncertainty: A meta-analysis of U.S. hunting values for benefit transfer: Ecosystem Services, v. 33, no. B, p. 225-236, https://doi.org/10.1016/j.ecoser.2018.07.001.","productDescription":"12 p.","startPage":"225","endPage":"236","ipdsId":"IP-091230","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":358189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"B","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f78e4b0fc368eb53843","contributors":{"authors":[{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":747463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meldrum, James R. 0000-0001-5250-3759 jmeldrum@usgs.gov","orcid":"https://orcid.org/0000-0001-5250-3759","contributorId":195484,"corporation":false,"usgs":true,"family":"Meldrum","given":"James","email":"jmeldrum@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":747464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Leslie","contributorId":197525,"corporation":false,"usgs":false,"family":"Richardson","given":"Leslie","affiliations":[],"preferred":false,"id":747465,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199950,"text":"70199950 - 2018 - Machine learning for ecosystem services","interactions":[],"lastModifiedDate":"2018-10-05T14:40:59","indexId":"70199950","displayToPublicDate":"2018-10-05T14:40:55","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1477,"text":"Ecosystem Services","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning for ecosystem services","docAbstract":"Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behaviour of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available ‘big data’ and assist applying ecosystem service models across scales, analysing and predicting the flows of these services to disaggregated beneficiaries. We use the Weka and ARIES software to produce two examples of DDM: firewood use in South Africa and biodiversity value in Sicily, respectively. Our South African example demonstrates that DDM (64–91% accuracy) can identify the areas where firewood use is within the top quartile with comparable accuracy as conventional modelling techniques (54–77% accuracy). The Sicilian example highlights how DDM can be made more accessible to decision makers, who show both capacity and willingness to engage with uncertainty information. Uncertainty estimates, produced as part of the DDM process, allow decision makers to determine what level of uncertainty is acceptable to them and to use their own expertise for potentially contentious decisions. We conclude that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoser.2018.04.004","usgsCitation":"Willcock, S., Martinez-Lopez, J., Hooftman, D.A., Bagstad, K.J., Balbi, S., Marzo, A., Prato, C., Sciandrello, S., Signorello, G., Voigt, B., Villa, F., Bullock, J.M., and Athanasiadis, I., 2018, Machine learning for ecosystem services: Ecosystem Services, v. 33, no. Part B, p. 165-174, https://doi.org/10.1016/j.ecoser.2018.04.004.","productDescription":"10 p.","startPage":"165","endPage":"174","ipdsId":"IP-091205","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":468333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecoser.2018.04.004","text":"Publisher Index Page"},{"id":358188,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"Part B","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f79e4b0fc368eb53845","contributors":{"authors":[{"text":"Willcock, Simon 0000-0001-9534-9114","orcid":"https://orcid.org/0000-0001-9534-9114","contributorId":201576,"corporation":false,"usgs":false,"family":"Willcock","given":"Simon","email":"","affiliations":[{"id":36207,"text":"Bangor University","active":true,"usgs":false}],"preferred":false,"id":747437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinez-Lopez, Javier 0000-0003-4857-3396","orcid":"https://orcid.org/0000-0003-4857-3396","contributorId":208480,"corporation":false,"usgs":false,"family":"Martinez-Lopez","given":"Javier","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":747438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hooftman, Danny A.P.","contributorId":208490,"corporation":false,"usgs":false,"family":"Hooftman","given":"Danny","email":"","middleInitial":"A.P.","affiliations":[{"id":37805,"text":"NERC Centre for Ecology and Hydrology","active":true,"usgs":false}],"preferred":false,"id":747439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":747436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Balbi, Stefano 0000-0001-8190-5968","orcid":"https://orcid.org/0000-0001-8190-5968","contributorId":208481,"corporation":false,"usgs":false,"family":"Balbi","given":"Stefano","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":747440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marzo, Alessia","contributorId":208491,"corporation":false,"usgs":false,"family":"Marzo","given":"Alessia","email":"","affiliations":[{"id":37806,"text":"University of Catania","active":true,"usgs":false}],"preferred":false,"id":747441,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prato, Carlo","contributorId":208492,"corporation":false,"usgs":false,"family":"Prato","given":"Carlo","email":"","affiliations":[{"id":37806,"text":"University of Catania","active":true,"usgs":false}],"preferred":false,"id":747442,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sciandrello, Saverio 0000-0003-1132-5698","orcid":"https://orcid.org/0000-0003-1132-5698","contributorId":208493,"corporation":false,"usgs":false,"family":"Sciandrello","given":"Saverio","email":"","affiliations":[{"id":37806,"text":"University of Catania","active":true,"usgs":false}],"preferred":false,"id":747443,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Signorello, Giovanni 0000-0002-5140-4975","orcid":"https://orcid.org/0000-0002-5140-4975","contributorId":208494,"corporation":false,"usgs":false,"family":"Signorello","given":"Giovanni","email":"","affiliations":[{"id":37807,"text":"University of Cataria","active":true,"usgs":false}],"preferred":false,"id":747444,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Voigt, Brian","contributorId":208483,"corporation":false,"usgs":false,"family":"Voigt","given":"Brian","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":747445,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Villa, Ferdinando 0000-0002-5114-3007","orcid":"https://orcid.org/0000-0002-5114-3007","contributorId":208486,"corporation":false,"usgs":false,"family":"Villa","given":"Ferdinando","email":"","affiliations":[{"id":32916,"text":"Basque Centre for Climate Change","active":true,"usgs":false}],"preferred":false,"id":747446,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bullock, James M.","contributorId":208495,"corporation":false,"usgs":false,"family":"Bullock","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":37805,"text":"NERC Centre for Ecology and Hydrology","active":true,"usgs":false}],"preferred":false,"id":747447,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Athanasiadis, Ioannis 0000-0003-2764-0078","orcid":"https://orcid.org/0000-0003-2764-0078","contributorId":208484,"corporation":false,"usgs":false,"family":"Athanasiadis","given":"Ioannis","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":747448,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70197900,"text":"sir20185085 - 2018 - Historical eruptions and hazards at Bogoslof volcano, Alaska","interactions":[],"lastModifiedDate":"2018-10-09T11:20:05","indexId":"sir20185085","displayToPublicDate":"2018-10-05T12:05:38","publicationYear":"2018","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":"2018-5085","title":"Historical eruptions and hazards at Bogoslof volcano, Alaska","docAbstract":"Bogoslof volcano is a submarine volcano in the southern
Bering Sea (53.9272°N, 168.0344°W), located 100 kilometers
(km) west of Dutch Harbor/Unalaska, and 40 km north
of Umnak Island. The volcano has a relatively long history of
scientific investigation and several of its historical eruptions
have been documented during brief visits to the area since the
late 1700s. The purpose of this report is to provide a modern
volcanological perspective on past eruptions of Bogoslof and
to readdress some of the eruptive phenomena described in
historical documents and reports. We also present for the first
time a brief analysis of the hazards posed by Bogoslof eruptions.
While this report was being prepared, Bogoslof volcano
was in an ongoing state of eruptive activity that began in
mid-December 2016. Detectable eruptive activity ended in late
August 2017 and the volcano has remained quiet since then.
Because we have not yet visited Bogoslof Island and have
only a few distal tephra samples from two eruptive events,
we will not discuss in detail the 2016–17 eruptive sequence,
but will provide some information for comparative purposes.
When more detailed data has been collected, a more extensive
review of the 2016–17 Bogoslof eruption should be the subject
of future reports.
Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
Seafloor sediment flows (turbidity currents) are among the volumetrically most important yet least documented sediment transport processes on Earth. A scarcity of direct observations means that basic characteristics, such as whether flows are entirely dilute or driven by a dense basal layer, remain equivocal. Here we present the most detailed direct observations yet from oceanic turbidity currents. These powerful events in Monterey Canyon have frontal speeds of up to 7.2 m s−1, and carry heavy (800 kg) objects at speeds of ≥4 m s−1. We infer they consist of fast and dense near-bed layers, caused by remobilization of the seafloor, overlain by dilute clouds that outrun the dense layer. Seabed remobilization probably results from disturbance and liquefaction of loose-packed canyon-floor sand. Surprisingly, not all flows correlate with major perturbations such as storms, floods or earthquakes. We therefore provide a new view of sediment transport through submarine canyons into the deep-sea.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41467-018-06254-6","usgsCitation":"Paull, C.K., Talling, P., Maier, K.L., Parsons, D., Xu, J., Caress, D.W., Gwiazda, R., Lundsten, E., Anderson, K., Barry, J., Chaffey, M., O’Reilly, T., Rosenberger, K.J., Gales, J., Kieft, B., McGann, M., Simmons, S.E., McCann, M., Sumner, E., Clare, M.A., and Cartigny, M.J., 2018, Powerful turbidity currents driven by dense basal layers: Nature Communications, v. 9, no. 1, 4114, 9 p., https://doi.org/10.1038/s41467-018-06254-6.","productDescription":"4114, 9 p.","ipdsId":"IP-091840","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-018-06254-6","text":"Publisher Index Page"},{"id":395259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Monterey Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.30804443359375,\n 36.50301312197295\n ],\n [\n -121.73950195312499,\n 36.50301312197295\n ],\n [\n -121.73950195312499,\n 37.070519031125826\n ],\n [\n -122.30804443359375,\n 37.070519031125826\n ],\n [\n -122.30804443359375,\n 36.50301312197295\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Paull, C. K.","contributorId":200384,"corporation":false,"usgs":false,"family":"Paull","given":"C.","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":832511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talling, P. J.","contributorId":273043,"corporation":false,"usgs":false,"family":"Talling","given":"P. J.","affiliations":[{"id":56419,"text":"Departments of Geography and Earth Sciences, Durham University, Durham, DH1 3LE, UK","active":true,"usgs":false}],"preferred":false,"id":832512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maier, Katherine L. 0000-0003-2908-3340 kcoble@usgs.gov","orcid":"https://orcid.org/0000-0003-2908-3340","contributorId":4926,"corporation":false,"usgs":true,"family":"Maier","given":"Katherine","email":"kcoble@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":832513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parsons, Daniel","contributorId":216508,"corporation":false,"usgs":false,"family":"Parsons","given":"Daniel","affiliations":[{"id":39462,"text":"University of Hull, UK","active":true,"usgs":false}],"preferred":false,"id":832514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Xu, Jingping jpx@usgs.gov","contributorId":2574,"corporation":false,"usgs":true,"family":"Xu","given":"Jingping","email":"jpx@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":832540,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caress, D. W.","contributorId":200385,"corporation":false,"usgs":false,"family":"Caress","given":"D.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":832516,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gwiazda, R.","contributorId":255044,"corporation":false,"usgs":false,"family":"Gwiazda","given":"R.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":832517,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lundsten, E.","contributorId":255047,"corporation":false,"usgs":false,"family":"Lundsten","given":"E.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":832518,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Anderson, K.","contributorId":255050,"corporation":false,"usgs":false,"family":"Anderson","given":"K.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":832519,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Barry, James P.","contributorId":140935,"corporation":false,"usgs":false,"family":"Barry","given":"James P.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":832520,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Chaffey, M.","contributorId":273044,"corporation":false,"usgs":false,"family":"Chaffey","given":"M.","email":"","affiliations":[{"id":56420,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California 95039, USA","active":true,"usgs":false}],"preferred":false,"id":832521,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"O’Reilly, T.","contributorId":273045,"corporation":false,"usgs":false,"family":"O’Reilly","given":"T.","email":"","affiliations":[{"id":56420,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California 95039, USA","active":true,"usgs":false}],"preferred":false,"id":832522,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":832523,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gales, Jenny","contributorId":216506,"corporation":false,"usgs":false,"family":"Gales","given":"Jenny","email":"","affiliations":[{"id":39461,"text":"University of Plymouth, UK","active":true,"usgs":false}],"preferred":false,"id":832524,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Kieft, Brian","contributorId":216923,"corporation":false,"usgs":false,"family":"Kieft","given":"Brian","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":832525,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":832526,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Simmons, Samantha E.","contributorId":156320,"corporation":false,"usgs":false,"family":"Simmons","given":"Samantha","email":"","middleInitial":"E.","affiliations":[{"id":20313,"text":"Marine Mammal Commission","active":true,"usgs":false}],"preferred":false,"id":832527,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"McCann, M.","contributorId":273046,"corporation":false,"usgs":false,"family":"McCann","given":"M.","email":"","affiliations":[{"id":56420,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California 95039, USA","active":true,"usgs":false}],"preferred":false,"id":832528,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Sumner, Esther J.","contributorId":147189,"corporation":false,"usgs":false,"family":"Sumner","given":"Esther J.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":832529,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Clare, M. A.","contributorId":273047,"corporation":false,"usgs":false,"family":"Clare","given":"M.","email":"","middleInitial":"A.","affiliations":[{"id":56421,"text":"Ocean and Earth Science, University of Southampton, European Way, Southampton, SO14 3ZH, UK","active":true,"usgs":false}],"preferred":false,"id":832530,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Cartigny, M. J.","contributorId":273048,"corporation":false,"usgs":false,"family":"Cartigny","given":"M.","email":"","middleInitial":"J.","affiliations":[{"id":56419,"text":"Departments of Geography and Earth Sciences, Durham University, Durham, DH1 3LE, UK","active":true,"usgs":false}],"preferred":false,"id":832531,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70199136,"text":"fs20183062 - 2018 - Assessment of environmental flows in the middle Verde River watershed, Arizona","interactions":[],"lastModifiedDate":"2018-10-09T11:12:25","indexId":"fs20183062","displayToPublicDate":"2018-10-05T09:04:31","publicationYear":"2018","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":"2018-3062","title":"Assessment of environmental flows in the middle Verde River watershed, Arizona","docAbstract":"This report summarizes analyses of middle Verde River watershed environmental flows detailed in U.S. Geological Survey Scientific Investigations Report 2017-5100, “Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona,\" by N.V. Paretti, A.M.D. Brasher, S.L. Pearlstein, D.M. Skow, B. Gungle, and B.D. Garner.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Large-scale climatic forcing is impacting oceanic biogeochemical cycles and is expected to influence the water-column distribution of trace gases, including methane and nitrous oxide. Our ability as a scientific community to evaluate changes in the water-column inventories of methane and nitrous oxide depends largely on our capacity to obtain robust and accurate concentration measurements that can be validated across different laboratory groups. This study represents the first formal international intercomparison of oceanic methane and nitrous oxide measurements whereby participating laboratories received batches of seawater samples from the subtropical Pacific Ocean and the Baltic Sea. Additionally, compressed gas standards from the same calibration scale were distributed to the majority of participating laboratories to improve the analytical accuracy of the gas measurements. The computations used by each laboratory to derive the dissolved gas concentrations were also evaluated for inconsistencies (e.g., pressure and temperature corrections, solubility constants). The results from the intercomparison and intercalibration provided invaluable insights into methane and nitrous oxide measurements. It was observed that analyses of seawater samples with the lowest concentrations of methane and nitrous oxide had the lowest precisions. In comparison, while the analytical precision for samples with the highest concentrations of trace gases was better, the variability between the different laboratories was higher: 36 % for methane and 27 % for nitrous oxide. In addition, the comparison of different batches of seawater samples with methane and nitrous oxide concentrations that ranged over an order of magnitude revealed the ramifications of different calibration procedures for each trace gas. Finally, this study builds upon the intercomparison results to develop recommendations for improving oceanic methane and nitrous oxide measurements, with the aim of precluding future analytical discrepancies between laboratories.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/bg-15-5891-2018","usgsCitation":"Wilson, S., Bange, H., Arevalo-Martinez, D., Barnes, J., Borges, A., Brown, I., Bullister, J., Burgos, M., Capelle, D., Casso, M., de la Paz, M., Farias, L., Fenwick, L., Ferron, S., Garcia, G., Glockzin, M., Karl, D., Kock, A., Laperriere, S., Law, C.S., Manning, C., Marriner, A., Myllykangas, J., Pohlman, J., Rees, A., Santoro, A., Tortll, P., Upstill-Goddard, R.C., Wisegarver, D., Zhang, G., and Rehder, G., 2018, An intercomparison of oceanic methane and nitrous oxide measurements: Biogeosciences, v. 15, no. 19, p. 5801-5907, https://doi.org/10.5194/bg-15-5891-2018.","productDescription":"7 p.","startPage":"5801","endPage":"5907","ipdsId":"IP-101291","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468335,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-15-5891-2018","text":"Publisher Index Page"},{"id":391572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"19","noUsgsAuthors":false,"publicationDate":"2018-10-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Samuel","contributorId":268439,"corporation":false,"usgs":false,"family":"Wilson","given":"Samuel","email":"","affiliations":[{"id":55644,"text":"University of Hawai'i, Daniel K. 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The diatom assemblages from fourteen sediment samples collected from marginal and profundal zones of seven lakes in the Ruby Mountains and East Humboldt Range of northeastern Nevada are characterized in order to identify the factors affecting controlling species diversity, equitability, and assemblage structure. Principle component analysis delineates three depth-controlled diatom assemblages: shallow (~1), medium (~11 m), and deep (>12 m). The shallowest samples are characterized by a diverse benthic assemblage, the medium depth sample is dominated by small fragilarioid taxa, and, the deepest samples, while not dominated by planktonic species, show an increase in their abundance. 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In 2015, we acquired high-resolution P- and S-wave seismic data near three strong-motion recording stations in Napa and Solano Counties where high peak ground accelerations (PGAs) were recorded during the South Napa earthquake. In this report, we present results from three sites—Lovall Valley Loop Road in Napa County (Northern California Seismic Network station, NCSN N019B) and Broadway Street and Sereno Drive (California Geological Survey station, CGS 68294) and Vallejo Fire Station (National Strong Motion Project station, NSMP 1759) in the City of Vallejo, California. To characterize the recording sites in terms of shallow-depth, shear-wave velocities (VS), we used both surface waves (Rayleigh and Love) and body waves (S-wave) to evaluate the time-averaged VS in the upper 30 meters of the subsurface (VS30). We used two-dimensional (2D) multichannel analysis of surface waves (MASW) to evaluate VS from surface waves, and a refraction tomography inversion algorithm, developed by Hole in 1992, to evaluate VS from the body waves. As determined by the tomography and MASW analysis for Love waves, we found VS30 near the strong-motion recording stations at Lovall Valley Loop Road, Broadway Street and Sereno Drive, and the Vallejo Fire Station to be from 711 meters per second (m/s) to 767 m/s, 455 to 673 m/s, and 490 to 583 m/s, respectively. We found that VS30 determined from Love waves were higher than those determined from Rayleigh waves at the Lovall Valley Loop Road recording site (221 m/s higher) and at the Vallejo Fire Station site (62 and 48 m/s higher); however, VS30 from Love waves was lower than those from Rayleigh waves at the Broadway Street and Sereno Drive site (78 m/s lower). We also found that VS30 varied depending on the number of shot points used in our MASW analysis for both Love and Rayleigh waves. Furthermore, VS30 values determined from S-wave refraction tomography are generally closer to those determined from MASW using Love waves than those determined using Rayleigh waves.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181162","usgsCitation":"Chan, J.H., Catchings, R.D., Goldman, M.R., and Criley, C.J., 2018, VS30 at three strong-motion recording stations in Napa and Solano Counties, California — Lovall Valley Road, Broadway Street and Sereno Drive in Vallejo, and Vallejo Fire Station — Calculations determined from S-wave refraction tomography and multichannel analysis of surface waves (Rayleigh and Love): U.S. Geological Survey Open-File Report 2018–1162, 62 p., https://doi.org/10.3133/ofr20181162.","productDescription":"Report: viii, 62 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-096908","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":358151,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F4IAAL","text":"USGS data release","description":"USGS data release","linkHelpText":"2015 high resolution seismic data recorded at six strong motion seismograph sites in Napa and Solano counties, California"},{"id":358150,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181161","text":"Open-File Report 2018-1161","linkHelpText":"- VS30 at Three Strong-Motion Recording Stations in Napa and Napa County, California—Main Street in Downtown Napa, Napa Fire Station Number 3, and Kreuzer Lane—Calculations Determined From S-wave Refraction Tomography and Multichannel Analysis of Surface Waves (Rayleigh and Love)"},{"id":358149,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1162/ofr20181162.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1162"},{"id":358148,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1162/coverthb.jpg"}],"country":"United States","state":"California","county":"Napa County, Solano County","city":"Vallejo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.41713722595051,\n 38.31876308218918\n ],\n [\n -122.41713722595051,\n 38.293376084062544\n ],\n [\n -122.3791235695102,\n 38.293376084062544\n ],\n [\n -122.3791235695102,\n 38.31876308218918\n ],\n [\n -122.41713722595051,\n 38.31876308218918\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27260163802902,\n 38.14045873726971\n ],\n [\n -122.27260163802902,\n 38.09368883459186\n ],\n [\n -122.23175717738528,\n 38.09368883459186\n ],\n [\n -122.23175717738528,\n 38.14045873726971\n ],\n [\n -122.27260163802902,\n 38.14045873726971\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The August 24, 2014, moment magnitude (Mw) 6.0 South Napa earthquake caused an estimated $400 million in structural damage to the City of Napa, California. In 2015, we acquired high-resolution P- and S-wave seismic data near three strong-motion recording stations in Napa County where high peak ground accelerations (PGAs) were recorded during the South Napa earthquake. In this report, we present results from three sites—Main Street in Downtown Napa (Northern California Seismic Network station, NCSN N016), Napa Fire Station Number 3 (National Strong Motion Project station, NSMP 1765), and Kreuzer Lane (station KRE, temporary deployment). To characterize the recording sites in terms of shallow-depth shear-wave velocities (VS), we used both surface waves (Rayleigh and Love) and body waves (S-wave) to evaluate the time-averaged VS in the upper 30 meters of the subsurface (VS30). We used two-dimensional multichannel analysis of surface waves (MASW) to evaluate VS from the surface waves, and a refraction tomography inversion algorithm, developed by Hole in 1992, to evaluate VS from the body waves. As determined by the various methods, we found VS30 near the strong-motion recording stations on Main Street in Downtown Napa, Napa Fire Station Number 3, and on Kreuzer Lane to be from 281 meters per second (m/s) to 286 m/s, 297 to 371 m/s, and 885 to 916 m/s, respectively. The VS30 calculated from Love waves were slightly lower (10 m/s) than those calculated from Rayleigh waves at the Downtown Napa location and at Napa Fire Station Number 3 (4 m/s); however, VS30 calculated from Love waves was higher (190 m/s) than those calculated from Rayleigh waves at Kreuzer Lane. We also found that VS30 determined from MASW for both Love and Rayleigh waves varied depending on the number of shots along the profiles, and VS30 was not systematic based on the number of shots used in the analysis. Furthermore, VS30 calculated from S-wave refraction tomography are closer to those determined from MASW calculated from Love waves than from using Rayleigh waves.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181161","usgsCitation":"Chan, J.H., Catchings, R.D., Goldman, M.R., and Criley, C.J., 2018, VS30 at three strong-motion recording stations in Napa and Napa County, California — Main Street in downtown Napa, Napa fire station number 3, and Kreuzer Lane — Calculations determined from s-wave refraction tomography and multichannel analysis of surface waves (Rayleigh and Love): U.S. Geological Survey Open-File Report 2018–1161, 47 p., https://doi.org/10.3133/ofr20181161.","productDescription":"Report: vii, 47 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091224","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":358146,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F4IAAL","text":"USGS data release","description":"USGS data release","linkHelpText":"2015 high resolution seismic data recorded at six strong motion seismograph sites in Napa and Solano counties, California"},{"id":358144,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1161/ofr20181161.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1161"},{"id":358143,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1161/coverthb.jpg"},{"id":358145,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181162","text":"Open-File Report 2018-1162","linkHelpText":"- VS30 at three strong-motion recording stations in Napa and Solano Counties, California—Lovall Valley Road, Broadway Street and Sereno Drive in Vallejo, and Vallejo Fire Station—Calculations determined from S-wave refraction tomography and multichannel analysis of surface waves (Rayleigh and Love)"}],"country":"United States","state":"California","county":"Napa County","city":"Napa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.33231191016716,\n 38.340761964883995\n ],\n [\n -122.33231191016716,\n 38.28175275852939\n ],\n [\n -122.22277030151793,\n 38.28175275852939\n ],\n [\n -122.22277030151793,\n 38.340761964883995\n ],\n [\n -122.33231191016716,\n 38.340761964883995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Contact Information,
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
Aquatic invasive species (AIS) have spread throughout the United States via major rivers and tributaries. Locks and dams positioned along affected waterways, specifically lock chambers, are being evaluated as potential management sites to prevent further expansion into new areas. Recent research has shown that infusion of chemicals (e.g., carbon dioxide) into water can block or kill several invasive organisms and could be a viable option at navigational structures such as lock chambers because chemical infusion would not interfere with vessel passage or lock operation. Chemical treatments near lock structures will require large-scale fluid-mechanic systems and significant energy. Mixing must extend to all stagnation regions within a lock structure to prevent the passage of an invasive fish. This work describes the performance of both wall- and floor-based CO2-infused-water to water injection manifolds targeted for lock structures in terms of mixing time, mixing homogeneity, injection efficiency, and operational power requirements. Both systems have strengths and weaknesses so selection recommendations are given for applications such as open systems and closed systems.
","language":"English","publisher":"ASME","doi":"10.1115/1.4041361","usgsCitation":"Zolper, T.J., Cupp, A.R., and Smith, D.L., 2018, Investigating the mixing efficiencies of liquid-to-liquid chemical injection manifolds for aquatic invasive species management: Journal of Fluids Engineering, v. 141, no. 3, p. 1-14, https://doi.org/10.1115/1.4041361.","productDescription":"Article 031302; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-091100","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437722,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93J4EQ8","text":"USGS data release","linkHelpText":"Investigating the mixing efficiencies of liquid-to-liquid chemical injection manifolds for aquatic invasive species management:Data"},{"id":358968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-04","publicationStatus":"PW","scienceBaseUri":"5c10a92fe4b034bf6a7e5059","contributors":{"authors":[{"text":"Zolper, Thomas J.","contributorId":210258,"corporation":false,"usgs":false,"family":"Zolper","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":38093,"text":"University of Wisconsin - Platteville","active":true,"usgs":false}],"preferred":false,"id":750289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":750288,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David L.","contributorId":192711,"corporation":false,"usgs":false,"family":"Smith","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":750290,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228093,"text":"70228093 - 2018 - Effects of large-scale wetland loss on network connectivity of the Rainwater Basin, Nebraska","interactions":[],"lastModifiedDate":"2022-02-04T17:49:38.98144","indexId":"70228093","displayToPublicDate":"2018-10-04T11:43:51","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Effects of large-scale wetland loss on network connectivity of the Rainwater Basin, Nebraska","docAbstract":"Context
The Rainwater Basin region in south-central Nebraska supports a complex network of spatially-isolated wetlands that harbor diverse floral and faunal communities. Since European settlement, many wetlands have been lost from the network, which has increased distances among remaining wetlands. As a result, populations of wildlife species with limited dispersal capabilities may have become isolated and face greater local extinction risks.
Objectives
We compared the pre-European settlement and current extent of the Rainwater Basin network to assess the effects of wetland losses on network connectivity for a range of maximum dispersal distances.
Methods
We constructed network models for a range of maximum dispersal distances and calculated network metrics to assess changes in network connectivity and the relative importance of individual wetlands in regulating flow.
Results
Since European settlement, the number of wetlands in the Rainwater Basin has decreased by >90%. The average distance to the nearest neighboring wetland has increased by 150% to ~1.2 km, and the dispersal distance necessary to travel throughout the whole network has increased from 3.5 to 10.0 km. Last, relative importance of individual wetlands depended on the maximum dispersal distance. Which wetlands to preserve to maintain connectivity might therefore depend on the dispersal capabilities of the species or taxa of interest.
Conclusions
To preserve a broad range of biodiversity, conservation efforts should focus on preserving dense clusters of wetlands at fine spatial scales to maintain current levels of network connectivity, and restoring connections between clusters to facilitate long-range dispersal of species with limited dispersal capabilities.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-018-0721-1","usgsCitation":"Verheijen, B., Varner, D.M., and Haukos, D.A., 2018, Effects of large-scale wetland loss on network connectivity of the Rainwater Basin, Nebraska: Landscape Ecology, v. 33, p. 1939-1951, https://doi.org/10.1007/s10980-018-0721-1.","productDescription":"13 p.","startPage":"1939","endPage":"1951","ipdsId":"IP-097520","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Rainwater Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.77783203125,\n 40.04443758460856\n ],\n [\n -95.64697265625,\n 40.04443758460856\n ],\n [\n -95.64697265625,\n 41.376808565702355\n ],\n [\n -99.77783203125,\n 41.376808565702355\n ],\n [\n -99.77783203125,\n 40.04443758460856\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","noUsgsAuthors":false,"publicationDate":"2018-10-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Verheijen, Bram H. F.","contributorId":274514,"corporation":false,"usgs":false,"family":"Verheijen","given":"Bram H. F.","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":833103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varner, Dana M.","contributorId":271196,"corporation":false,"usgs":false,"family":"Varner","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":40582,"text":"Rainwater Basin Joint Venture","active":true,"usgs":false}],"preferred":false,"id":833104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833105,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199917,"text":"70199917 - 2018 - Artelle et al. (2018) miss the science underlying North American wildlife management","interactions":[],"lastModifiedDate":"2018-10-04T11:17:43","indexId":"70199917","displayToPublicDate":"2018-10-04T11:17:40","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Artelle et al. (2018) miss the science underlying North American wildlife management","docAbstract":"Artelle et al. (2018) conclude that “hallmarks of science” are largely missing from North American wildlife management based on a desk review of selected hunting management plans and related documents found through Internet searches and email requests to state and provincial wildlife agencies. We highlight three fundamental problems that compromise the validity of the conclusions posited: missing information to support selection of “hallmarks of science,” confusion about the roles and nature of science and management, and failure to engage effectively with the scientists and managers actively managing wildlife populations in North America.
","language":"English","publisher":"Science Advances","doi":"10.1126/sciadv.aat8281","usgsCitation":"Mawdsley, J.R., Organ, J.F., Decker, D.J., Forstchen, A., Regan, R.J., Riley, S.J., Boyce, M.S., Mcdonald, J.E., Dwyer, C., and Mahoney, S.P., 2018, Artelle et al. (2018) miss the science underlying North American wildlife management: Science Advances, v. 4, no. 10, p. 1-2, https://doi.org/10.1126/sciadv.aat8281.","productDescription":"eaat8281; 2 p.","startPage":"1","endPage":"2","ipdsId":"IP-096267","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":468336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aat8281","text":"Publisher Index Page"},{"id":358135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f7ae4b0fc368eb5384f","contributors":{"authors":[{"text":"Mawdsley, Jonathan R.","contributorId":208479,"corporation":false,"usgs":false,"family":"Mawdsley","given":"Jonathan","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":747370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Organ, John F. 0000-0002-0959-0639 jorgan@usgs.gov","orcid":"https://orcid.org/0000-0002-0959-0639","contributorId":189047,"corporation":false,"usgs":true,"family":"Organ","given":"John","email":"jorgan@usgs.gov","middleInitial":"F.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":747268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Decker, Daniel J.","contributorId":166906,"corporation":false,"usgs":false,"family":"Decker","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":747371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forstchen, Ann","contributorId":166904,"corporation":false,"usgs":false,"family":"Forstchen","given":"Ann","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":747372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Regan, Ronald J.","contributorId":113871,"corporation":false,"usgs":true,"family":"Regan","given":"Ronald","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":747373,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riley, Shawn J.","contributorId":202177,"corporation":false,"usgs":false,"family":"Riley","given":"Shawn","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":747374,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyce, Mark S.","contributorId":113205,"corporation":false,"usgs":false,"family":"Boyce","given":"Mark","email":"","middleInitial":"S.","affiliations":[{"id":12980,"text":"Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":747375,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mcdonald, John E. Jr.","contributorId":171604,"corporation":false,"usgs":false,"family":"Mcdonald","given":"John","suffix":"Jr.","email":"","middleInitial":"E.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":747376,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dwyer, Chris","contributorId":177908,"corporation":false,"usgs":false,"family":"Dwyer","given":"Chris","affiliations":[],"preferred":false,"id":747377,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mahoney, Shane P.","contributorId":199084,"corporation":false,"usgs":false,"family":"Mahoney","given":"Shane","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":747378,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70199921,"text":"70199921 - 2018 - Impact of sylvatic plague vaccine on non-target small rodents in grassland ecosystems","interactions":[],"lastModifiedDate":"2023-06-23T14:34:21.980434","indexId":"70199921","displayToPublicDate":"2018-10-04T10:54:38","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1443,"text":"EcoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Impact of sylvatic plague vaccine on non-target small rodents in grassland ecosystems","docAbstract":"Oral vaccination is an emerging management strategy to reduce the prevalence of high impact infectious diseases within wild animal populations. Plague is a flea-borne zoonosis of rodents that often decimates prairie dog (Cynomys spp.) colonies in the western USA. Recently, an oral sylvatic plague vaccine (SPV) was developed to protect prairie dogs from plague and aid recovery of the endangered black-footed ferret (Mustela nigripes). Although oral vaccination programs are targeted toward specific species, field distribution of vaccine-laden baits can result in vaccine uptake by non-target animals and unintended indirect effects. We assessed the impact of SPV on non-target rodents at paired vaccine and placebo-treated prairie dog colonies in four US states from 2013 to 2015. Bait consumption by non-target rodents was high (70.8%, n = 3113), but anti-plague antibody development on vaccine plots was low (23.7%, n = 266). In addition, no significant differences were noted in combined deer mice (Peromyscus maniculatus) and western harvest mouse (Reithrodontomys megalotis) abundance or community evenness and richness of non-target rodents between vaccine-treated and placebo plots. In our 3-year field study, we could not detect a significant positive or negative effect of SPV application on non-target rodents.
","language":"English","publisher":"Springer","doi":"10.1007/s10393-018-1334-5","usgsCitation":"Bron, G.M., Richgels, K.L., D., S.M., Poje, J.E., Lorenzsonn, F., Matteson, J.P., Boulerice, J.T., Osorio, J.E., and Rocke, T.E., 2018, Impact of sylvatic plague vaccine on non-target small rodents in grassland ecosystems: EcoHealth, v. 15, no. 3, p. 555-565, https://doi.org/10.1007/s10393-018-1334-5.","productDescription":"11 p.","startPage":"555","endPage":"565","ipdsId":"IP-093242","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":500025,"rank":3,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research.wur.nl/en/publications/impact-of-sylvatic-plague-vaccine-on-non-target-small-rodents-in-","text":"External Repository"},{"id":358134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418362,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WCSCDK","text":"USGS data release","description":"USGS data release","linkHelpText":"Impact of the oral Sylvatic Plague Vaccine on Non-target Small Rodents: Data"}],"country":"United States","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-111.048974,44.474072],[-111.323669,44.724474],[-111.50494,44.635746],[-111.469185,44.552044],[-112.258665,44.569516],[-112.387389,44.448058],[-112.749011,44.491233],[-112.844859,44.358221],[-113.134824,44.752763],[-113.455071,44.865424],[-113.802955,45.592631],[-114.015633,45.696127],[-114.345019,45.459916],[-114.559038,45.565706],[-114.422963,45.855381],[-114.527096,46.146218],[-114.322912,46.642938],[-114.76689,46.696901],[-115.294785,47.220914],[-115.731348,47.433381],[-115.72377,47.696671],[-116.049153,47.999923],[-116.049193,49.000912],[-97.229039,49.000687],[-97.116185,48.709348],[-97.145243,48.174046],[-96.854812,47.606328],[-96.774763,46.607461],[-96.557952,46.102442],[-96.612512,45.794442],[-96.82616,45.654164],[-96.452315,45.208986],[-96.453049,43.500415],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.41932,40.048442],[-94.916918,39.836138],[-95.113077,39.559133],[-94.615834,39.160003],[-94.617919,36.499414],[-94.431822,35.397652],[-94.485528,33.663388],[-94.386086,33.544923],[-94.070395,33.574561],[-94.0427,32.056012],[-93.523248,31.037842],[-93.765822,30.333318],[-93.702436,30.112721],[-93.922744,29.818808],[-93.852868,29.675885],[-94.731047,29.369141],[-94.532348,29.5178],[-94.767246,29.525523],[-94.724616,29.774766],[-94.965963,29.70033],[-94.894234,29.338],[-95.16525,29.113566],[-94.73132,29.338066],[-94.803695,29.279237],[-96.341617,28.417334],[-95.983106,28.641942],[-96.221784,28.580364],[-96.287942,28.683164],[-96.473694,28.57324],[-96.664534,28.696904],[-96.481836,28.407844],[-96.790235,28.383926],[-96.898123,28.152881],[-97.21535,28.076575],[-97.040618,28.028708],[-97.183455,27.833231],[-97.354614,27.849572],[-97.296598,27.613947],[-97.399398,27.344735],[-97.640111,27.270943],[-97.485149,27.250841],[-97.552325,26.867633],[-97.145567,25.971132],[-97.445113,25.850026],[-97.711145,26.033043],[-98.20496,26.066419],[-99.110855,26.426278],[-99.452316,27.062669],[-99.556812,27.614336],[-99.841708,27.766464],[-100.280518,28.267969],[-100.785521,29.228137],[-101.441059,29.753451],[-102.341033,29.869305],[-102.698347,29.695591],[-102.944911,29.18882],[-103.227801,28.991532],[-104.46652,29.609296],[-104.924796,30.604832],[-106.158218,31.438885],[-106.381039,31.73211],[-108.208394,31.783599],[-108.208573,31.333395],[-109.050044,31.332502],[-109.050076,41.000659],[-111.046723,40.997959],[-111.048974,44.474072]]],[[[-97.240849,26.411504],[-97.383531,26.875521],[-97.366771,27.333276],[-96.946988,28.026522],[-96.403206,28.371475],[-96.929053,27.99044],[-97.276091,27.472145],[-97.370731,26.909706],[-97.161471,26.088705],[-97.240849,26.411504]]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"15","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-09","publicationStatus":"PW","scienceBaseUri":"5bc02f7ae4b0fc368eb53851","contributors":{"authors":[{"text":"Bron, Gebbiena M. 0000-0002-4431-2482","orcid":"https://orcid.org/0000-0002-4431-2482","contributorId":206593,"corporation":false,"usgs":false,"family":"Bron","given":"Gebbiena","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":747288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richgels, Katherine L. 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Treatments were administered by egg injection to compare embryonic effects of an environmentally relevant PCB congener mixture in the domestic chicken over a range of doses. Chicken eggs were injected with the PCB mixture with a profile similar to that found in avian eggs collected on the upper Hudson River, New York, USA, at doses that spanned 0 to 98 μg/g egg. Eggs were hatched in the laboratory to ascertain hatching success. In the domestic chicken, the median lethal dose was 0.3 μg/g. These data demonstrate adverse effects of an environmentally relevant PCB mixture and provide the basis for further work using in vitro and other models to characterize the potential risk to avian populations.
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We evaluated the chronic (28 days) toxicity of 4-nonylphenol (4-NP) to two commonly tested species of mussels: fatmucket (Lampsilis siliquoidea) and rainbow mussel (Villosa iris). By the end of the 28 days chronic exposures, mean survival was ≥93% in all treatments, but the mean dry weight and biomass of mussels at the highest exposure concentrations were significantly reduced relative to the control. The 20% effect concentrations were similar between the two species. When compared to all other tested species, fatmucket and rainbow mussels are among the top four most sensitive species to 4-NP. However, U.S. Environmental Protection Agency chronic water quality criterion of 6.6 μg 4-NP/L should protect the two mussel species.
","language":"English","publisher":"Springer","doi":"10.1007/s00128-018-2422-5","usgsCitation":"Ivey, C.D., Wang, N., Alvarez, D., Hammer, E.J., and Bauer, C.R., 2018, Chronic toxicity of 4-nonylphenol to two unionid mussels in water-only exposures: Bulletin of Environmental Contamination and Toxicology, v. 101, no. 4, p. 423-427, https://doi.org/10.1007/s00128-018-2422-5.","productDescription":"5 p.","startPage":"423","endPage":"427","ipdsId":"IP-098145","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":437723,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R5MQJO","text":"USGS data release","linkHelpText":"Chronic toxicity of 4-Nonylphenol to two unionid mussels in water-only exposures-metadata"},{"id":358132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","issue":"4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-21","publicationStatus":"PW","scienceBaseUri":"5bc02f7be4b0fc368eb53855","contributors":{"authors":[{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747310,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alvarez, David 0000-0002-6918-2709 dalvarez@usgs.gov","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":150499,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","email":"dalvarez@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":747312,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. 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Examples include processing geospatial data, analyzing -omics data, and running large-scale simulations. Conventional desktop computing cannot handle these tasks when they are large, and high-performance computing is not always available nor the most appropriate solution for all computationally intense problems. High-throughput computing (HTC) is one method for handling computationally intense research. In contrast to high-performance computing, which uses a single \"supercomputer,\" HTC can distribute tasks over many computers (e.g., idle desktop computers, dedicated servers, or cloud-based resources). HTC facilities exist at many academic and government institutes and are relatively easy to create from commodity hardware. Additionally, consortia such as Open Science Grid facilitate HTC, and commercial entities sell cloud-based solutions for researchers who lack HTC at their institution. We provide an introduction to HTC for biologists and environmental scientists. Our examples from biology and the environmental sciences use HTCondor, an open source HTC system.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pcbi.1006468","usgsCitation":"Erickson, R.A., Fienen, M.N., McCalla, S.G., Weiser, E.L., Bower, M.L., Knudson, J.M., and Thain, G., 2018, Wrangling distributed computing for high-throughput environmental science: An introduction to HTCondor: PLOS Computational Biology, v. 14, no. 10, p. 1-8, https://doi.org/10.1371/journal.pcbi.1006468.","productDescription":"e1006468; 8 p.","startPage":"1","endPage":"8","ipdsId":"IP-087169","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468338,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pcbi.1006468","text":"Publisher Index Page"},{"id":358128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"10","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-03","publicationStatus":"PW","scienceBaseUri":"5bc02f7ce4b0fc368eb53859","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":747337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCalla, S. Grace 0000-0003-4292-8694 smccalla@usgs.gov","orcid":"https://orcid.org/0000-0003-4292-8694","contributorId":168436,"corporation":false,"usgs":true,"family":"McCalla","given":"S.","email":"smccalla@usgs.gov","middleInitial":"Grace","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":206605,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":747335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bower, Melvin L. 0000-0002-4408-3771","orcid":"https://orcid.org/0000-0002-4408-3771","contributorId":208457,"corporation":false,"usgs":true,"family":"Bower","given":"Melvin","email":"","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747339,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knudson, Jonathan M. 0000-0003-4985-988X","orcid":"https://orcid.org/0000-0003-4985-988X","contributorId":208458,"corporation":false,"usgs":true,"family":"Knudson","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":747340,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thain, Greg","contributorId":208459,"corporation":false,"usgs":false,"family":"Thain","given":"Greg","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":747341,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70199930,"text":"70199930 - 2018 - Regional patterns in the geochemistry of oil-field water, southern San Joaquin Valley, California, USA","interactions":[],"lastModifiedDate":"2018-10-04T10:31:11","indexId":"70199930","displayToPublicDate":"2018-10-04T10:31:04","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Regional patterns in the geochemistry of oil-field water, southern San Joaquin Valley, California, USA","docAbstract":"Chemical and isotopic data for water co-extracted with hydrocarbons in oil and gas fields are commonly used to examine the source of the formation water and possible impacts on groundwater in areas of oil and gas development. Understanding the geochemical variability of oil-field water could help to evaluate its origin and delineate possible contamination of shallow aquifers in cases where oil-field water is released to the environment. Here we report geochemical and multiple isotope (H, C, O, Sr, Ra) data from 22 oil wells, three sources of produced water that are disposed of in injection wells, and two surface disposal ponds in four oil fields in the southern San Joaquin Valley, California (Fruitvale, Lost Hills, North and South Belridge). Correlations between Cl and δ18O, as well as other ions, and gradual increases in salinity with depth, indicate dilution of one or more saline end-members by meteoric water. The saline end-members, represented by deep samples (610 m–2621 m) in three oil-bearing zones, are characterized by NaCl composition, near-seawater Cl concentrations (median 20,000 mg/L), enriched δ18O
H2O (median 3.4‰), high ammonium(up to 460 mg-N/L), and relatively high radium activity (226Ra+228Ra = 12.3 Bq/L). The deepest sample has low Na/Cl (0.74), high Ca/Mg (5.0), and low 87Sr/86Sr (0.7063), whereas the shallower samples have higher Na/Cl (0.86–1.2), Ca/Mg near 1, and higher 87Sr/86Sr (∼0.7083). The data are consistent with an original seawater source being modified by various depth and lithology dependent diagenetic processes. Dilution by meteoric water occurs naturally on the east side of the valley, and in association with water-injectionactivities on the west side. Meteoric-water flushing, particularly on the east side, results in lower solute concentrations (minimum total dissolved solids 2730 mg/L) and total radium (minimum 0.27 Bq/L) in oil-field water, and promotes biodegradation of dissolved organic carbon and hydrocarbon gases like propane. Acetate concentrations and δ13C of dissolved inorganic carbon indicate biogenic methane production occurs in some shallow oil zones. Natural and human processes produce substantial variability in the geochemistry of oil-field water that should be considered when evaluating mixing between oil-field waters and groundwater. The variability could result in uncertainty as to detecting the potential source and impact of oil-field water on groundwater.
Raman spectroscopy was studied as a thermal maturity probe in a series of Upper Devonian Ohio Shale samples from the Appalachian Basin spanning from immature to dry gas conditions. Raman spectroscopy also was applied to samples spanning a similar thermal range created from 72-h hydrous pyrolysis (HP) experiments of the Ohio Shale at temperatures from 300 to 360 °C and isothermal HP experiments lasting up to 100 days of similar Devonian–Mississippian New Albany Shale. Raman spectra were treated by automated evaluation software based on iterative and simultaneous modeling of signal and baseline functions to decrease subjectivity. Spectra show robust correlation to measured solid bitumen reflectance (BRo) values and were therefore used to construct logarithmic regression relationships for calculation of BRo equivalent values. Raman spectra show considerable differences between natural samples and HP residues with similar measured BRo values, indicating as-yet undetermined differences in carbon chemistry. We speculate this result may be due to differences in the sampling interactions of Raman vs reflectance measurements, and the incomplete nature of maturation reactions in the time-limited hydrous pyrolysis residues. Samples used in this study are similar in organic assemblage (dominantly solid bitumen) to other commonly exploited North American shale petroleum systems, i.e., Bakken, Barnett, Duvernay, Fayetteville, and Woodford shales. Therefore, results presented herein may be broadly applicable to other important shale plays. However, caution is suggested and Raman spectroscopy as a thermal probe may need individual calibration in each shale play due to differences in solid bitumen carbon chemistry.
The Fuels Guide and Database (FGD) is intended to provide fuel loading and vegetation information for big sagebrush (Artemisia tridentata) ecological sites in the Morley Nelson Snake River Birds of Prey National Conservation Area (hereinafter the NCA) in southern Idaho. Sagebrush ecosystems in the NCA and throughout much of the Great Basin are highly influenced by non-native plants that alter successional trajectories and promote frequent wildfires, especially due to fine-fuel loadings that are highly variable over time and space. These dynamic fuel conditions can increase uncertainty when attempting to project fire risk and fire behavior. The FGD was developed to help quantify and assess these dynamic fuel loadings, and it provides access to fuels data across a range of conditions, from relatively intact sagebrush-bunchgrass communities to degraded communities dominated by nonnative annual grasses and forbs. The FGD can be queried for a variety of environmental conditions, and it provides tabular data, reports, and photographic records of fuels based on user queries. This report describes the FGD, including overall data content and data-collection methods, as well as instructions for installing and using the database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1048","collaboration":"Prepared in cooperation with the Joint Fire Science Program","usgsCitation":"Shinneman, D.J., Welty, J.L., Arkle, R.S., Pilliod, D.S., Glenn, N.F., McIlroy, S.K., and Halford, A.S., 2018, Fuels guide and database for intact and invaded big sagebrush (Artemisia tridentata) ecological sites—User manual: U.S. Geological Survey Data Series 1048, 9 p., https://doi.org/10.3133/ds1048.","productDescription":"Report: iv, 9 p.; Data release","onlineOnly":"Y","ipdsId":"IP-083880","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":358027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1048/ds1048.pdf","text":"Report","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1048"},{"id":358026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1048/coverthb.jpg"},{"id":358028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC31P4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Fuels database for intact and invaded big sagebrush (Artemisia tridentata) ecological sites"}],"contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Floods in the Mississippi basin can have large negative societal, natural, and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-yr-return lower Mississippi River floods using a global coupled climate model with an integrated surface water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land surface data, transient climate). In the months preceding the model’s 100-yr floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the buildup of snowpack, making the river system prone to these major flooding events. The meltwater from snowpack in the northern Missouri and upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-yr floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully coupled land–atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-18-0018.1","usgsCitation":"van der Wiel, K., Kapnick, S.B., Vecchi, G.A., Smith, J.A., Milly, P.C., and Jia, L., 2018, 100-year lower Mississippi floods in a global climate model: Characteristics and future changes: Journal of Hydrometeorology, v. 19, p. 1547-1563, https://doi.org/10.1175/JHM-D-18-0018.1.","productDescription":"17 p.","startPage":"1547","endPage":"1563","ipdsId":"IP-092375","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":358547,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-03","publicationStatus":"PW","scienceBaseUri":"5c10a92fe4b034bf6a7e505b","contributors":{"authors":[{"text":"van der Wiel, Karin","contributorId":209883,"corporation":false,"usgs":false,"family":"van der Wiel","given":"Karin","email":"","affiliations":[{"id":16158,"text":"Royal Netherlands Meteorological Institute","active":true,"usgs":false}],"preferred":false,"id":749019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kapnick, Sarah B.","contributorId":189908,"corporation":false,"usgs":false,"family":"Kapnick","given":"Sarah","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":749020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vecchi, Gabriel A.","contributorId":209884,"corporation":false,"usgs":false,"family":"Vecchi","given":"Gabriel","email":"","middleInitial":"A.","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":749021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, James A.","contributorId":209885,"corporation":false,"usgs":false,"family":"Smith","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":749022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":749018,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jia, Liwei","contributorId":209886,"corporation":false,"usgs":false,"family":"Jia","given":"Liwei","email":"","affiliations":[{"id":38020,"text":"NOAA/NWS/NCEP Climate Prediction Center","active":true,"usgs":false}],"preferred":false,"id":749023,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199369,"text":"fs20183063 - 2018 - 2018 hurricane and wildfire supplemental funding: USGS recovery activities","interactions":[],"lastModifiedDate":"2018-11-15T12:27:01","indexId":"fs20183063","displayToPublicDate":"2018-10-02T18:15:00","publicationYear":"2018","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":"2018-3063","displayTitle":"2018 Hurricane and Wildfire Supplemental Funding: USGS Recovery Activities","title":"2018 hurricane and wildfire supplemental funding: USGS recovery activities","docAbstract":"The Additional Supplemental Appropriations for Disaster Relief Requirements Act, 2018 (P.L. 115-123), was signed by the President on February 9, 2018. This funding provided $42.2 million to the U.S. Geological Survey (USGS) for equipment repair and replacement, high-resolution elevation data collection in both hurricane- and wildfire-impacted areas, and scientific studies and assessments that will support recovery and rebuilding decisions in the wake of Hurricanes Harvey, Irma, and Maria and the California Wildfires.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183063","usgsCitation":"Hinck, J.E., and Stachyra, J., 2018, 2018 hurricane and wildfire supplemental funding—USGS recovery activities: U.S. Geological Survey Fact Sheet 2018–3063, 4 p., https://doi.org/10.3133/fs20183063.","productDescription":"4 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101229","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":358115,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3063/fs20183063_spread.pdf","text":"Report - Spread","size":"3.40 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":358025,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3063/fs20183063.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018-3063"},{"id":358024,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3063/coverthb2.jpg"}],"contact":"Associate Director, Natural Hazards Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Director, Lower Mississippi-Gulf Water Science Center—Tennessee
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The U.S. Geological Survey (USGS), along with partner organizations, has developed an earthquake early warning (EEW) system called ShakeAlert for the highest risk areas of the United States: namely, California, Oregon, and Washington. The purpose of the system is to reduce the impact of earthquakes and save lives and property by providing alerts to institutional users and the public. Using networks of ground-motion sensors and sophisticated computer algorithms, ShakeAlert can detect an earthquake seconds after it begins, calculate its location and magnitude, and estimate the resulting intensity of shaking. Alerts can then be sent to people and systems that may experience damaging shaking, allowing them to take appropriate protective actions. Depending on the user’s distance from the earthquake, alerts may be delivered before, during, or after the arrival of strong shaking.
ShakeAlert is built on the foundation of the sensor networks and data processing infrastructure of the USGS-led Advanced National Seismic System. However, these networks were not originally designed for EEW; old equipment needs to be updated and new stations must be added to construct EEW-capable networks. The ShakeAlert data-processing infrastructure includes redundant servers that are geographically distributed at monitoring centers in Seattle, Washington, as well as Menlo Park, Berkeley, and Pasadena in California. Three data-processing layers collect raw ground-motion data from field stations (data layer), analyze these data to estimate the area and intensity of the resulting shaking (production layer), and publish alert products as appropriate for end users (alert layer). The alert layer can support thousands of institutional users and alert redistributors, but the USGS does not have the mission, infrastructure, or expertise to perform public notifications and is therefore recruiting technology enablers from the private sector. Additionally, ShakeAlert will coordinate with both public and private partners to accomplish consistent and ongoing public communication, education, and outreach.
The estimated cost of completing the ShakeAlert infrastructure and sensor networks is \\$39.4 million and has an estimated annual operation and maintenance cost of \\$28.6 million per year. Building a highly reliable data telemetry infrastructure would cost another \\$20.5 million and operating this telemetry system would add \\$49.8 million per year; however, these costs could be reduced if project partners provide bandwidth on existing systems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181155","usgsCitation":"Given, D.D., Allen, R.M., Baltay, A.S., Bodin, P., Cochran, E.S., Creager, K., de Groot, R.M., Gee, L.S., Hauksson, E., Heaton, T.H., Hellweg, M., Murray, J.R., Thomas, V.I., Toomey, D., and Yelin, T.S., 2018, Revised technical implementation plan for the ShakeAlert system—An earthquake early warning system for the West Coast of the United States: U.S. Geological Survey Open-File Report 2018–1155, 42 p., https://doi.org/10.3133/ofr20181155. [Supersedes USGS Open-File Report 2014–1097.]","productDescription":"vii, 42 p.","ipdsId":"IP-098251","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":358030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1155/ofr20181155.pdf","text":"Report","size":"16.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2018-1155"},{"id":358029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1155/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Earthquake Science Center-Pasadena Field Office
U.S. Geological Survey
525 South Wilson Ave.
Pasadena, CA 91106-3212
To improve ability to detect white sharks without the need for tags, or visual census, we developed a species-specific environmental DNA (eDNA) assay that targets a 163 bp fragment of the white shark (Carcharodon carcharias) mitochondrial cytochrome B gene on a digital droplet PCR (ddPCR) platform. We used this marker to detect white shark DNA in 250 ml water samples taken from across two sites in Santa Barbara, California (United States) frequented by juvenile white sharks. We did not detect white shark DNA in samples from two neighboring sites where sharks are presumably absent, suggesting that eDNA can indicate nearby white sharks. This marker development, testing, and opportunistic application in a region with known distributions of white sharks indicates that eDNA could be developed further to monitor white sharks, thereby informing conservation planning and public safety. With the potential increase in white shark populations due to decades of protection, there is a need for fishery independent methods for assessing white shark distributions, and eDNA may provide an ideal, non-intrusive tool for coastal assessments.
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