From May 2011 to August 2014, the U.S. Geological Survey (USGS) collected gravity data at more than 2,300 stations and physical property measurements on more than 640 rock samples from outcrops in the eastern Mojave Desert, California and Nevada. Gravity, magnetic, and physical-property data are used to study and locate regional crustal structures as an aid to understanding the geologic framework related to mineral resources of the eastern Mojave Desert.
The eastern Mojave Desert is host to a world-class rare earth element carbonatite deposit located at Mountain Pass, California. Carbonatites are typically defined as magmatic rocks with high modal abundances of primary carbonate minerals >50 weight percent and elevated abundances of rare earth elements (REEs) (Nelson and others, 1988; Woolley and Kempe, 1989). The “Sulphide Queen” carbonatite ore deposit is a composite, tabular body made up of sills and dikes of REE-bearing sovites and beforsites that occurs just south of the Clark Mountain Range along a north-northwest trending fault-bounded block that extends along the northeast edge of the Mescal Range and northwestern extent of Ivanpah Mountains. This early to middle Proterozoic block is composed of a 1.7 Ga metamorphic complex of gneiss and schist that underwent widespread metamorphism and associated plutonism during the Ivanpah orogeny (Miller and others, 2007). Subsequently, these rocks were intruded by a series of granitoids, which included the 1.4 Ga (DeWitt and others, 1987) ultrapotassic alkaline suite of intrusions that are spatially and temporally associated with hundreds of dikes, outcrops, and a carbonatite ore body. The relative age sequence of this intrusive suite of alkaline rocks from oldest to youngest includes shonkinite, mesosyenite, syenite, quartz syenite, potassic granite, carbonatite, and late shonkinite dikes (Olson and others, 1954; Wooden and Miller, 1990; Haxel, 2005; Miller and others, 2007).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161070","usgsCitation":"Denton, K.M., and Ponce, D.A., 2018, Gravity and magnetic studies of the eastern Mojave Desert, California and Nevada (ver 1.1, August 2018): U.S. Geological Survey Open-File Report 2016-1070, 20 p., https://doi.org/10.3133/ofr20161070.","productDescription":"Report: iv, 20 p.; 3 Tables; Metadata; version history","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-05-01","ipdsId":"IP-064300","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":323913,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_table01_gravity_data_v1.1.xlsx","text":"Table 1 version 1.1","size":"472 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1070 Table 1 ver. 1.1"},{"id":323914,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_table02_rock_property_data.xlsx","text":"Table 2","size":"128 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1070 Table 2"},{"id":323915,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_table03_rock_modifier_data.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1070 Table 3"},{"id":323910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1070/coverthb_.jpg"},{"id":323911,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_v1.1.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1070"},{"id":323912,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_metadata.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1070 Metadata"},{"id":356639,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1070/versionHist.txt"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.5,\n 35.0\n ],\n [\n -115.5,\n 35.5\n ],\n [\n -115.0,\n 35.5\n ],\n [\n -115.0,\n 35.0\n ],\n [\n -115.5,\n 35.0\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"ver. 1.1: August 2018; ver. 1: June 2016","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Tucson
U.S. Geological Survey, c/o University of Arizona
ENRB Bldg, 520 N. Park Ave, Rm 355
Tucson, AZ 85719-5035
http://geomaps.wr.usgs.gov/gmeg/
Resource managers rely on abundance or density metrics derived from beach seine surveys to make vital decisions that affect fish population dynamics and assemblage structure. However, abundance and density metrics may be biased by imperfect capture and lack of geographic closure during sampling. Currently, there is considerable uncertainty about the capture efficiency of juvenile Chinook salmon (Oncorhynchus tshawytscha) by beach seines. Heterogeneity in capture can occur through unrealistic assumptions of closure and from variation in the probability of capture caused by environmental conditions. We evaluated the assumptions of closure and the influence of environmental conditions on capture efficiency and abundance estimates of Chinook salmon from beach seining within the Sacramento–San Joaquin Delta and the San Francisco Bay. Beach seine capture efficiency was measured using a stratified random sampling design combined with open and closed replicate depletion sampling. A total of 56 samples were collected during the spring of 2014. To assess variability in capture probability and the absolute abundance of juvenile Chinook salmon, beach seine capture efficiency data were fitted to the paired depletion design using modified N-mixture models. These models allowed us to explicitly test the closure assumption and estimate environmental effects on the probability of capture. We determined that our updated method allowing for lack of closure between depletion samples drastically outperformed traditional data analysis that assumes closure among replicate samples. The best-fit model (lowest-valued Akaike Information Criterion model) included the probability of fish being available for capture (relaxed closure assumption), capture probability modeled as a function of water velocity and percent coverage of fine sediment, and abundance modeled as a function of sample area, temperature, and water velocity. Given that beach seining is a ubiquitous sampling technique for many species, our improved sampling design and analysis could provide significant improvements in density and abundance estimation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161099","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Perry, R.W., Kirsch, J.E., and Hendrix, A.N., 2016, Estimating juvenile Chinook salmon (Oncorhynchus tshawytscha) abundance from beach seine data collected in the Sacramento–San Joaquin Delta and San Francisco Bay, California: U.S. Geological Survey Open-File Report 2016–1099, 21 p., https://dx.doi.org/10.3133/ofr20161099.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074132","costCenters":[{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":323938,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1099/coverthb.jpg"},{"id":323939,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1099/ofr20161099.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1099"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5579833984375,\n 37.31338308990806\n ],\n [\n -122.5579833984375,\n 39.21523130910493\n ],\n [\n -121.1077880859375,\n 39.21523130910493\n ],\n [\n -121.1077880859375,\n 37.31338308990806\n ],\n [\n -122.5579833984375,\n 37.31338308990806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
Sewage contamination of urban waterways from sewer overflows and failing infrastructure is a major environmental and public health concern. Fecal coliforms (FC) are commonly employed as fecal indicator bacteria, but do not distinguish between human and non-human sources of fecal contamination. Human Bacteroides and humanLachnospiraceae, two genetic markers for human-associated indicator bacteria, were used to identify sewage signals in two urban rivers and the estuary that drains to Lake Michigan. Grab samples were collected from the rivers throughout 2012 and 2013 and hourly samples were collected in the estuary across the hydrograph during summer 2013. Human Bacteroides and human Lachnospiraceae were highly correlated with each other in river samples (Pearson’s r = 0.86), with average concentrations at most sites elevated during wet weather. These human indicators were found during baseflow, indicating that sewage contamination is chronic in these waterways. FC are used for determining total maximum daily loads (TMDLs) in management plans; however, FC concentrations alone failed to prioritize river reaches with potential health risks. While 84% of samples with >1000 CFU/100 ml FC had sewage contamination, 52% of samples with moderate (200–1000 CFU/100 ml) and 46% of samples with low (<200 CFU/100 ml) FC levels also had evidence of human sewage. Load calculations in the in the Milwaukee estuary revealed storm-driven sewage contamination varied greatly among events and was highest during an event with a short duration of intense rain. This work demonstrates urban areas have unrecognized sewage inputs that may not be adequately prioritized for remediation by the TMDL process. Further analysis using these approaches could determine relationships between land use, storm characteristics, and other factors that drive sewage contamination in urban waterways.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.watres.2016.05.056","usgsCitation":"Olds, H., Dila, D., Bootsma, M., Corsi, S., and McLellan, S., 2016, Quantification of human-associated fecal indicators reveal sewage from urban watersheds as a source of pollution to Lake Michigan: Water Research, v. 100, no. 1, p. 556-567, https://doi.org/10.1016/j.watres.2016.05.056.","productDescription":"12 p.","startPage":"556","endPage":"567","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073855","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":323742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Kinnickinnic River, Menomonee River, Milwaukee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.97147750854492,\n 42.99297119949901\n ],\n [\n -87.97147750854492,\n 43.05496631251752\n ],\n [\n -87.85989761352539,\n 43.05496631251752\n ],\n [\n -87.85989761352539,\n 42.99297119949901\n ],\n [\n -87.97147750854492,\n 42.99297119949901\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5763bf9ce4b07657d19b5bd4","contributors":{"authors":[{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":639198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dila, Deborah K.","contributorId":172000,"corporation":false,"usgs":false,"family":"Dila","given":"Deborah K.","affiliations":[{"id":26971,"text":"School of Freshwater Sciences, UW-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":639199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bootsma, Melinda J.","contributorId":172001,"corporation":false,"usgs":false,"family":"Bootsma","given":"Melinda J.","affiliations":[{"id":26971,"text":"School of Freshwater Sciences, UW-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":639200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":639201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLellan, Sandra L.","contributorId":172003,"corporation":false,"usgs":false,"family":"McLellan","given":"Sandra L.","affiliations":[{"id":26971,"text":"School of Freshwater Sciences, UW-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":639202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70171109,"text":"sir20165067 - 2016 - Geologic and geophysical models for Osage County, Oklahoma, with implications for groundwater resources","interactions":[],"lastModifiedDate":"2025-05-14T18:51:50.000213","indexId":"sir20165067","displayToPublicDate":"2016-06-16T10:00:00","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":"2016-5067","title":"Geologic and geophysical models for Osage County, Oklahoma, with implications for groundwater resources","docAbstract":"This report summarizes a three-dimensional (3-D) geologic model that was constructed to provide a framework to investigate groundwater resources of the Osage Nation in northeastern Oklahoma. This report also presents an analysis of an airborne electromagnetic (AEM) survey that assessed the spatial variation of electrical resistivity to depths as great as 300 meters in the subsurface. The report and model provide support for a countywide assessment of groundwater resources, emphasizing the Upper Pennsylvanian rock units in the shallow subsurface of central and eastern Osage County having electrical resistivity properties that may indicate aquifers.
\nSurface outcrops and subsurface stratigraphic picks on wire-line geophysical logs of Upper Pennsylvanian–Lower Permian sedimentary rock were used to construct a 3-D model of the geologic subsurface as an aid for evaluating groundwater resources in Osage County. Quaternary alluvium and terraces along major streams and the Arkansas River are included in the geologic framework model. Data from the AEM survey were subjected to quality-control procedures, truncated at depth of investigation (DOI), and then used to build a 3-D electrical resistivity model making use of secondary and tertiary interpolation profiles between primary data profiles. The AEM data highlight westward-inclined resistivity gradients that parallel the shallow dip of bedrock strata; bodies have resistivity >30 ohm-meters, and extend as much as 10 kilometers (km) down the dip of host geologic units. Volume analysis and internal imaging of an integrated 3-D geology and electrical resistivity model give a proxy for likely aquifer units with large relative volumes of high resistivity: Quaternary alluvium, Elgin Sandstone Lentil in the upper part of the Vamoosa Group, Tallant Formation, and parts of a combined Wann-Iola-Chanute Formation. Less voluminous, high-resistivity bodies correspond to intervals in the lower part of the Vamoosa Group in the east-central part of the county and probable limestone intervals in the upper part of the Vanoss Group in the northwest part of the county. Northwestern and eastern troughs of potable water previously defined for central Osage County generally correspond to down-dip projections of high-resistivity bodies associated with the Elgin Sandstone Lentil of the Vamoosa Group and Tallant Formation, respectively.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165067","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Hudson, M.R., Smith, D.V., Pantea, M.P., and Becker, C.J., 2016, Geologic and geophysical models for Osage County, Oklahoma, and implications for groundwater resources: U.S. Geological Survey Scientific Investigations Report 2016–5067, 27 p., https://dx.doi.org/10.3133/sir20165067.","productDescription":"Report: vii, 27 p.; Companion Files; Datasets; Metadata; Readme File","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071283","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":323530,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2016/5067/Data.zip","text":"Data Files","size":"22.4 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5067 Data 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Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
Ulaanbaatar, the capital city of Mongolia (fig. 1), is dependent on groundwater for its municipal and industrial water supply. The population of Mongolia is about 3 million people, with about one-half the population residing in or near Ulaanbaatar (World Population Review, 2016). Groundwater is drawn from a network of shallow wells in an alluvial aquifer along the Tuul River. Evidence indicates that current water use may not be sustainable from existing water sources, especially when factoring the projected water demand from a rapidly growing urban population (Ministry of Environment and Green Development, 2013). In response, the Government of Mongolia Ministry of Environment, Green Development, and Tourism (MEGDT) and the Freshwater Institute, Mongolia, requested technical assistance on groundwater modeling through the U.S. Army Corps of Engineers (USACE) to the U.S. Geological Survey (USGS). Scientists from the USGS and USACE provided two workshops in 2015 to Mongolian hydrology experts on basic principles of groundwater modeling using the USGS groundwater modeling program MODFLOW-2005 (Harbaugh, 2005). The purpose of the workshops was to bring together representatives from the Government of Mongolia, local universities, technical experts, and other key stakeholders to build in-country capacity in hydrogeology and groundwater modeling.
A preliminary steady-state groundwater-flow model was developed as part of the workshops to demonstrate groundwater modeling techniques to simulate groundwater conditions in alluvial deposits along the Tuul River in the vicinity of Ulaanbaatar. ModelMuse (Winston, 2009) was used as the graphical user interface for MODFLOW for training purposes during the workshops. Basic and advanced groundwater modeling concepts included in the workshops were groundwater principles; estimating hydraulic properties; developing model grids, data sets, and MODFLOW input files; and viewing and evaluating MODFLOW output files. A key to success was developing in-country technical capacity and partnerships with the Mongolian University of Science and Technology; Freshwater Institute, Mongolia, a non-profit organization; United Nations Educational, Scientific and Cultural Organization (UNESCO); the Government of Mongolia; and the USACE.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161096","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers; U.S. Pacific Command; United Nations Educational, Scientific and Cultural Organization (UNESCO) and International Center for Integrated Water Resources Management under the auspices of UNESCO; Government of Mongolia Ministry of Environment, Green Development, and Tourism; and Freshwater Institute, Mongolia","usgsCitation":"Valder, J.F., Carter, J.M., Anderson, M.T., Davis, K.W., Haynes M.A., and Dechinlhundev, Dorjsuren, 2016, Building groundwater modeling capacity in Mongolia: U.S. Geological Survey Open-File Report 2016–1096, 1 sheet, https://dx.doi.org/10.3133/ofr20161096.","productDescription":"Sheet: 60.00 x 36.00 inches","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075136","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":323764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1096/ofr20161096.pdf","text":"Report","size":"8.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1096"},{"id":323763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1096/coverthb.jpg"}],"country":"Mongolia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[87.75126,49.2972],[88.80557,49.47052],[90.71367,50.33181],[92.23471,50.80217],[93.10422,50.49529],[94.14757,50.48054],[94.81595,50.01343],[95.81403,49.97747],[97.25973,49.72606],[98.23176,50.4224],[97.82574,51.011],[98.86149,52.04737],[99.98173,51.63401],[100.88948,51.51686],[102.06522,51.25992],[102.25591,50.51056],[103.67655,50.08997],[104.62155,50.27533],[105.88659,50.40602],[106.8888,50.2743],[107.86818,49.79371],[108.47517,49.28255],[109.40245,49.29296],[110.66201,49.13013],[111.58123,49.37797],[112.89774,49.54357],[114.36246,50.2483],[114.96211,50.14025],[115.4857,49.80518],[116.6788,49.88853],[116.1918,49.1346],[115.48528,48.13538],[115.74284,47.72654],[116.30895,47.85341],[117.29551,47.69771],[118.06414,48.06673],[118.86657,47.74706],[119.77282,47.04806],[119.66327,46.69268],[118.87433,46.80541],[117.4217,46.67273],[116.71787,46.3882],[115.9851,45.72724],[114.46033,45.33982],[113.46391,44.80889],[112.43606,45.01165],[111.87331,45.10208],[111.34838,44.45744],[111.66774,44.07318],[111.82959,43.74312],[111.12968,43.40683],[110.4121,42.87123],[109.2436,42.51945],[107.74477,42.48152],[106.12932,42.13433],[104.96499,41.59741],[104.52228,41.90835],[103.31228,41.90747],[101.83304,42.51487],[100.84587,42.6638],[99.51582,42.52469],[97.45176,42.74889],[96.3494,42.72564],[95.76245,43.31945],[95.30688,44.24133],[94.68893,44.35233],[93.48073,44.97547],[92.13389,45.11508],[90.94554,45.28607],[90.58577,45.71972],[90.97081,46.88815],[90.28083,47.69355],[88.8543,48.06908],[88.01383,48.59946],[87.75126,49.2972]]]},\"properties\":{\"name\":\"Mongolia\"}}]}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702
Or visit the South Dakota Water Science Center Web site at:
http://sd.water.usgs.gov/
Establishment of nonnative Northern Pike Esox lucius in Pactola Reservoir, South Dakota, has prompted concern among biologists about the influence of this species on the lake’s intensively managed salmonid fisheries. Ancedotal information suggests that catch rates of Rainbow Trout Oncorhynchus mykiss have declined while mean size and abundance of Northern Pike has increased, although quantitative information on diet and growth of the Northern Pike population is lacking. To address potential interactions between Northern Pike and Rainbow Trout, we assessed size-dependent predation by Northern Pike on Rainbow Trout and determined the relative energetic contribution of stocked Rainbow Trout to Northern Pike growth using bioenergetics modeling. Stable isotopes combined with traditional diet analyses revealed that smaller Northern Pike (<600 mm TL) consumed primarily centrarchids and Rainbow Smelt Osmerus mordax, and Rainbow Trout contributed less than 10% to their annual energy consumption. In contrast, larger Northern Pike (≥600 mm TL) consumed primarily Rainbow Trout, which accounted for 56% of their annual energy consumption. Combining estimates of Northern Pike predation with production costs of catchable-size Rainbow Trout revealed that annual economic losses ranged from US$15,259 to $24,801 per year. Over its lifespan, an age-10 Northern Pike was estimated to consume ~117 Rainbow Trout worth approximately $340. Thus, Northern Pike predation substantially influences salmonid management initiatives and is likely a primary factor contributing to reduced Rainbow Trout abundance and return to anglers in Pactola Reservoir. Strategies for reducing Northern Pike predation on Rainbow Trout include increasing the size of stocked fish or altering the timing and spatial distribution of stocking events.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2015.1116472","usgsCitation":"Scheibel, N.C., Dembkowski, D., Davis, J.L., and Chipps, S.R., 2016, Impacts of Northern Pike on stocked Rainbow Trout in Pactola Reservoir, South Dakota: North American Journal of Fisheries Management, v. 36, no. 2, p. 230-240, https://doi.org/10.1080/02755947.2015.1116472.","productDescription":"11 p.","startPage":"230","endPage":"240","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064502","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":323714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Pactola Reservoir","volume":"36","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-08","publicationStatus":"PW","scienceBaseUri":"57626e20e4b07657d199cd92","contributors":{"authors":[{"text":"Scheibel, Natalie C.","contributorId":171928,"corporation":false,"usgs":false,"family":"Scheibel","given":"Natalie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":639132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":78237,"corporation":false,"usgs":true,"family":"Dembkowski","given":"Daniel J.","affiliations":[],"preferred":false,"id":639133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Jacob L.","contributorId":171929,"corporation":false,"usgs":false,"family":"Davis","given":"Jacob","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":639134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":638863,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70139712,"text":"70139712 - 2016 - Movement patterns of Brook Trout in a restored coastal stream system in southern Massachusetts","interactions":[],"lastModifiedDate":"2016-06-15T15:56:40","indexId":"70139712","displayToPublicDate":"2016-06-15T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Movement patterns of Brook Trout in a restored coastal stream system in southern Massachusetts","docAbstract":"Coastal Brook Trout (Salvelinus fontinalis) populations are found from northern Canada to New England. The extent of anadromy generally decreases with latitude, but the ecology and movements of more southern populations are poorly understood. We conducted a 33-month acoustic telemetry study of Brook Trout in Red Brook, MA, and adjacent Buttermilk Bay (marine system) using 16 fixed acoustic receivers and surgically implanting acoustic transmitters in 84 individuals. Tagged Brook Trout used the stream, estuary (50% of individuals) and bay (10% of individuals). Movements into full sea water were brief when occurring. GAMM models revealed that transitions between habitat areas occurred most often in spring and fall. Environmental data suggest that use of the saline environment is limited by summer temperatures in the bay. Movements may also be related to moon phase. Compared to more northern coastal populations of Brook Trout, the Red Brook population appears to be less anadromous overall, yet the estuarine segment of the system may have considerable ecological importance as a food resource.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12216","usgsCitation":"Snook, E., Letcher, B., Dubreuil, T.L., Zydlewski, J.D., O'Donnell, M., Whiteley, A.R., Hurley, S.T., and Danylchuk, A.J., 2016, Movement patterns of Brook Trout in a restored coastal stream system in southern Massachusetts: Ecology of Freshwater Fish, v. 25, no. 3, p. 360-375, https://doi.org/10.1111/eff.12216.","productDescription":"16 p.","startPage":"360","endPage":"375","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-056464","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":488240,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.7275/4727460","text":"External Repository"},{"id":323720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"25","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2015-03-06","publicationStatus":"PW","scienceBaseUri":"5524ffafe4b027f0aee3d47b","contributors":{"authors":[{"text":"Snook, Erin L.","contributorId":138978,"corporation":false,"usgs":false,"family":"Snook","given":"Erin L.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":539566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Letcher, Benjamin H. 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":2864,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin H.","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":539565,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dubreuil, Todd L. 0000-0003-0189-4336 tdubreuil@usgs.gov","orcid":"https://orcid.org/0000-0003-0189-4336","contributorId":5552,"corporation":false,"usgs":true,"family":"Dubreuil","given":"Todd","email":"tdubreuil@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":539567,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":539568,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":138979,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew J.","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":539569,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whiteley, Andrew R.","contributorId":52072,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":539570,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hurley, Stephen T.","contributorId":138980,"corporation":false,"usgs":false,"family":"Hurley","given":"Stephen","email":"","middleInitial":"T.","affiliations":[{"id":12605,"text":"Mass Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":539571,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Danylchuk, Andy J.","contributorId":138981,"corporation":false,"usgs":false,"family":"Danylchuk","given":"Andy","email":"","middleInitial":"J.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":539572,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70173936,"text":"70173936 - 2016 - Historical baselines and the future of shell calcification for a foundation species in a changing ocean","interactions":[],"lastModifiedDate":"2016-06-17T13:28:49","indexId":"70173936","displayToPublicDate":"2016-06-15T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3173,"text":"Proceedings of the Royal Society B","active":true,"publicationSubtype":{"id":10}},"title":"Historical baselines and the future of shell calcification for a foundation species in a changing ocean","docAbstract":"Seawater pH and the availability of carbonate ions are decreasing due to anthropogenic carbon dioxide emissions, posing challenges for calcifying marine species. Marine mussels are of particular concern given their role as foundation species worldwide. Here, we document shell growth and calcification patterns in Mytilus californianus, the California mussel, over millennial and decadal scales. By comparing shell thickness across the largest modern shells, the largest mussels collected in the 1960s–1970s and shells from two Native American midden sites (∼1000–2420 years BP), we found that modern shells are thinner overall, thinner per age category and thinner per unit length. Thus, the largest individuals of this species are calcifying less now than in the past. Comparisons of shell thickness in smaller individuals over the past 10–40 years, however, do not show significant shell thinning. Given our sampling strategy, these results are unlikely to simply reflect within-site variability or preservation effects. Review of environmental and biotic drivers known to affect shell calcification suggests declining ocean pH as a likely explanation for the observed shell thinning. Further future decreases in shell thickness could have significant negative impacts on M. californianus survival and, in turn, negatively impact the species-rich complex that occupies mussel beds..
","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2016.0392","usgsCitation":"Pfister, C.A., Roy, K., Wootton, T.J., McCoy, S.J., Paine, R.T., Suchanek, T., and Sanford, E., 2016, Historical baselines and the future of shell calcification for a foundation species in a changing ocean: Proceedings of the Royal Society B, v. 283, no. 1832, p. 1-8, https://doi.org/10.1098/rspb.2016.0392.","productDescription":"8 p.","startPage":"1","endPage":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075572","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470881,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2016.0392","text":"Publisher Index Page"},{"id":323897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"283","issue":"1832","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-15","publicationStatus":"PW","scienceBaseUri":"57651f35e4b07657d19c78ab","contributors":{"authors":[{"text":"Pfister, Catherine A.","contributorId":172095,"corporation":false,"usgs":false,"family":"Pfister","given":"Catherine","email":"","middleInitial":"A.","affiliations":[{"id":26978,"text":"Dep't of Ecology & Evolution, University of Chicago","active":true,"usgs":false}],"preferred":false,"id":639585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Kaustuv","contributorId":172096,"corporation":false,"usgs":false,"family":"Roy","given":"Kaustuv","email":"","affiliations":[{"id":26979,"text":"Section of Ecology, Behaviour & Evolution, U of C San Diego, CA","active":true,"usgs":false}],"preferred":false,"id":639586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wootton, Timothy J.","contributorId":172097,"corporation":false,"usgs":false,"family":"Wootton","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":26980,"text":"Dep't of Ecology & Evolution, U of Chicago","active":true,"usgs":false}],"preferred":false,"id":639587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCoy, Sophie J.","contributorId":172098,"corporation":false,"usgs":false,"family":"McCoy","given":"Sophie","email":"","middleInitial":"J.","affiliations":[{"id":26980,"text":"Dep't of Ecology & Evolution, U of Chicago","active":true,"usgs":false}],"preferred":false,"id":639588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paine, Robert T.","contributorId":172099,"corporation":false,"usgs":false,"family":"Paine","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":26981,"text":"Dep't of Biology, U of Washington","active":true,"usgs":false}],"preferred":false,"id":639589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suchanek, Tom tsuchanek@usgs.gov","contributorId":152285,"corporation":false,"usgs":true,"family":"Suchanek","given":"Tom","email":"tsuchanek@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":639584,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sanford, Eric","contributorId":172100,"corporation":false,"usgs":false,"family":"Sanford","given":"Eric","email":"","affiliations":[{"id":26982,"text":"UC Davis, Bodega Marine Lab and Dept of Evolution and Ecology","active":true,"usgs":false}],"preferred":false,"id":639590,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173888,"text":"70173888 - 2016 - Habitat use and growth of the western painted crayfish Orconectes palmeri longimanus","interactions":[],"lastModifiedDate":"2016-06-15T13:13:17","indexId":"70173888","displayToPublicDate":"2016-06-15T14:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2235,"text":"Journal of Crustacean Biology","active":true,"publicationSubtype":{"id":10}},"title":"Habitat use and growth of the western painted crayfish Orconectes palmeri longimanus","docAbstract":"Identifying ontogenetic shifts in habitat use by aquatic organisms is necessary for improving conservation strategies; however, our ability to designate life stages based on surrogate metrics (i.e., length) is questionable without validation. This study identified growth patterns of age-0 western painted crayfish Orconectes palmeri longimanus (Faxon, 1898) reared in the laboratory, provided support for field-based designations of age-0 lengths, and identified microhabitat factors important to adult and juvenile presence from field collections. Two growth periods of a laboratory crayfish population were described using a broken line model: a rapid, early-growth period (weeks 2-20, slope = 0.81 ± 0.03SE), and a slower, late-growth period (weeks 22-50, slope = 0.13 ± 0.03SE). A smoothed curve was generated to represent the size distribution of juveniles from our laboratory population to determine the probability that an age-0 crayfish from our laboratory population had a carapace length (CL) similar to that found in previous field studies using onset of maturity (22.4 mm CL). We determined that the probability of the age-0 crayfish in our summer laboratory population exceeding 22.4 mm CL was 0.06. The threshold between the lower 0.95 and upper 0.05 probabilities was 22.9 mm CL, confirming previous field observations of onset at maturity. We used this threshold to identify juveniles and adults from our field collections, and found that both life stages were positively associated with coarse substrate and negatively associated with water depth. Adults, however, were negatively related to gravel, whereas juveniles showed a positive relationship. This result is reflective of the relationship between crayfish body size and refuge use within the interstitial spaces of substrates, whereby adult crayfish are unable to seek refuge in the small interstitial spaces of gravel.
","language":"English","doi":"10.1163/1937240X-00002417","issn":"0278-0372","collaboration":"Oklahoma Cooperative Fish and Wildlife Research Unit","usgsCitation":"Dyer, J.J., Mouser, J., and Brewer, S.K., 2016, Habitat use and growth of the western painted crayfish Orconectes palmeri longimanus: Journal of Crustacean Biology, v. 36, no. 2, p. 172-179, https://doi.org/10.1163/1937240X-00002417.","productDescription":"8 p.","startPage":"172","endPage":"179","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069448","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":470882,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1163/1937240x-00002417","text":"Publisher Index Page"},{"id":323688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57626e1ee4b07657d199cd71","contributors":{"authors":[{"text":"Dyer, Joseph J.","contributorId":140681,"corporation":false,"usgs":false,"family":"Dyer","given":"Joseph","email":"","middleInitial":"J.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":639028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mouser, Joshua","contributorId":171894,"corporation":false,"usgs":false,"family":"Mouser","given":"Joshua","affiliations":[],"preferred":false,"id":639029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":638892,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173810,"text":"70173810 - 2016 - Demonstration of the Cascadia G‐FAST geodetic earthquake early warning system for the Nisqually, Washington, earthquake","interactions":[],"lastModifiedDate":"2016-07-01T12:52:03","indexId":"70173810","displayToPublicDate":"2016-06-15T06:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Demonstration of the Cascadia G‐FAST geodetic earthquake early warning system for the Nisqually, Washington, earthquake","docAbstract":"A prototype earthquake early warning (EEW) system is currently in development in the Pacific Northwest. We have taken a two‐stage approach to EEW: (1) detection and initial characterization using strong‐motion data with the Earthquake Alarm Systems (ElarmS) seismic early warning package and (2) the triggering of geodetic modeling modules using Global Navigation Satellite Systems data that help provide robust estimates of large‐magnitude earthquakes. In this article we demonstrate the performance of the latter, the Geodetic First Approximation of Size and Time (G‐FAST) geodetic early warning system, using simulated displacements for the 2001Mw 6.8 Nisqually earthquake. We test the timing and performance of the two G‐FAST source characterization modules, peak ground displacement scaling, and Centroid Moment Tensor‐driven finite‐fault‐slip modeling under ideal, latent, noisy, and incomplete data conditions. We show good agreement between source parameters computed by G‐FAST with previously published and postprocessed seismic and geodetic results for all test cases and modeling modules, and we discuss the challenges with integration into the U.S. Geological Survey’s ShakeAlert EEW system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220150255","usgsCitation":"Crowell, B., Schmidt, D., Bodin, P., Vidale, J., Gomberg, J.S., Hartog, J.R., Kress, V., Melbourne, T., Santillian, M., Minson, S.E., and Jamison, D., 2016, Demonstration of the Cascadia G‐FAST geodetic earthquake early warning system for the Nisqually, Washington, earthquake: Seismological Research Letters, v. 87, no. 4, p. 930-943, https://doi.org/10.1785/0220150255.","productDescription":"14 p.","startPage":"930","endPage":"943","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070674","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":470885,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.cwu.edu/geological_sciences/103","text":"External Repository"},{"id":324271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.0081787109375,\n 46.822616668804926\n ],\n [\n -124.0081787109375,\n 47.37603463349758\n ],\n [\n -122.37670898437499,\n 47.37603463349758\n ],\n [\n -122.37670898437499,\n 46.822616668804926\n ],\n [\n -124.0081787109375,\n 46.822616668804926\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"87","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-08","publicationStatus":"PW","scienceBaseUri":"576bb6b2e4b07657d1a22891","contributors":{"authors":[{"text":"Crowell, Brendan","contributorId":171723,"corporation":false,"usgs":false,"family":"Crowell","given":"Brendan","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":638423,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, David","contributorId":7596,"corporation":false,"usgs":true,"family":"Schmidt","given":"David","affiliations":[],"preferred":false,"id":638424,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodin, Paul","contributorId":104142,"corporation":false,"usgs":true,"family":"Bodin","given":"Paul","affiliations":[],"preferred":false,"id":638425,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vidale, John","contributorId":95804,"corporation":false,"usgs":true,"family":"Vidale","given":"John","affiliations":[],"preferred":false,"id":638426,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gomberg, Joan S. 0000-0002-0134-2606 gomberg@usgs.gov","orcid":"https://orcid.org/0000-0002-0134-2606","contributorId":1269,"corporation":false,"usgs":true,"family":"Gomberg","given":"Joan","email":"gomberg@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":638422,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartog, J. Renate","contributorId":171724,"corporation":false,"usgs":false,"family":"Hartog","given":"J.","email":"","middleInitial":"Renate","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":638427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kress, Victor","contributorId":171725,"corporation":false,"usgs":false,"family":"Kress","given":"Victor","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":638428,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Melbourne, Tim","contributorId":67800,"corporation":false,"usgs":true,"family":"Melbourne","given":"Tim","email":"","affiliations":[],"preferred":false,"id":638429,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Santillian, Marcelo","contributorId":171726,"corporation":false,"usgs":false,"family":"Santillian","given":"Marcelo","email":"","affiliations":[{"id":26935,"text":"Central Washington University","active":true,"usgs":false}],"preferred":false,"id":638430,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Minson, Sarah E. 0000-0001-5869-3477 sminson@usgs.gov","orcid":"https://orcid.org/0000-0001-5869-3477","contributorId":5357,"corporation":false,"usgs":true,"family":"Minson","given":"Sarah","email":"sminson@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":638431,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jamison, Dylan","contributorId":171727,"corporation":false,"usgs":false,"family":"Jamison","given":"Dylan","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":638432,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70173860,"text":"70173860 - 2016 - Identification of landscape features influencing gene flow: How useful are habitat selection models?","interactions":[],"lastModifiedDate":"2018-08-19T10:07:20","indexId":"70173860","displayToPublicDate":"2016-06-15T05:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1601,"text":"Evolutionary Applications","active":true,"publicationSubtype":{"id":10}},"title":"Identification of landscape features influencing gene flow: How useful are habitat selection models?","docAbstract":"Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid-range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long-term genetic connectivity.
","language":"English","doi":"10.1111/eva.12389","usgsCitation":"Roffler, G.H., Schwartz, M.K., Pilgrim, K.L., Talbot, S.L., Sage, G.K., Adams, L., and Luikart, G., 2016, Identification of landscape features influencing gene flow: How useful are habitat selection models?: Evolutionary Applications, v. 9, no. 6, p. 805-817, https://doi.org/10.1111/eva.12389.","productDescription":"13 p.","startPage":"805","endPage":"817","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063706","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":470887,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.12389","text":"Publisher Index Page"},{"id":323719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-03","publicationStatus":"PW","scienceBaseUri":"57626e1fe4b07657d199cd8a","chorus":{"doi":"10.1111/eva.12389","url":"http://dx.doi.org/10.1111/eva.12389","publisher":"Wiley-Blackwell","authors":"Roffler Gretchen H., Schwartz Michael K., Pilgrim Kristy L., Talbot Sandra L., Sage George K., Adams Layne G., Luikart Gordon","journalName":"Evolutionary Applications","publicationDate":"6/3/2016"},"contributors":{"authors":[{"text":"Roffler, Gretchen H. groffler@usgs.gov","contributorId":1946,"corporation":false,"usgs":true,"family":"Roffler","given":"Gretchen","email":"groffler@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":638828,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwartz, Michael K.","contributorId":102326,"corporation":false,"usgs":true,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":638831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilgrim, Kristy L.","contributorId":45222,"corporation":false,"usgs":true,"family":"Pilgrim","given":"Kristy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":638832,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":638829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":638830,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":638827,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luikart, Gordon","contributorId":97409,"corporation":false,"usgs":false,"family":"Luikart","given":"Gordon","affiliations":[{"id":6580,"text":"University of Montana, Flathead Lake Biological Station, Polson, Montana 59860, USA","active":true,"usgs":false}],"preferred":false,"id":638833,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173821,"text":"70173821 - 2016 - And the first one now will later be last: Time-reversal in cormack-jolly-seber models","interactions":[],"lastModifiedDate":"2016-06-15T16:33:05","indexId":"70173821","displayToPublicDate":"2016-06-15T00:25:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5001,"text":"Statistical Science","active":true,"publicationSubtype":{"id":10}},"title":"And the first one now will later be last: Time-reversal in cormack-jolly-seber models","docAbstract":"The models of Cormack, Jolly and Seber (CJS) are remarkable in providing a rich set of inferences about population survival, recruitment, abundance and even sampling probabilities from a seemingly limited data source: a matrix of 1's and 0's reflecting animal captures and recaptures at multiple sampling occasions. Survival and sampling probabilities are estimated directly in CJS models, whereas estimators for recruitment and abundance were initially obtained as derived quantities. Various investigators have noted that just as standard modeling provides direct inferences about survival, reversing the time order of capture history data permits direct modeling and inference about recruitment. Here we review the development of reverse-time modeling efforts, emphasizing the kinds of inferences and questions to which they seem well suited.","language":"English","publisher":"Project Euclid","doi":"10.1214/16-STS546","usgsCitation":"Nichols, J.D., 2016, And the first one now will later be last: Time-reversal in cormack-jolly-seber models: Statistical Science, v. 31, no. 2, p. 175-190, https://doi.org/10.1214/16-STS546.","productDescription":"16 p.","startPage":"175","endPage":"190","ipdsId":"IP-071767","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470888,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1214/16-sts546","text":"Publisher Index Page"},{"id":323723,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57626e1ce4b07657d199cd60","contributors":{"authors":[{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":638544,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188882,"text":"70188882 - 2016 - Surface slip during large Owens Valley earthquakes","interactions":[],"lastModifiedDate":"2017-06-27T09:52:37","indexId":"70188882","displayToPublicDate":"2016-06-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Surface slip during large Owens Valley earthquakes","docAbstract":"The 1872 Owens Valley earthquake is the third largest known historical earthquake in California. Relatively sparse field data and a complex rupture trace, however, inhibited attempts to fully resolve the slip distribution and reconcile the total moment release. We present a new, comprehensive record of surface slip based on lidar and field investigation, documenting 162 new measurements of laterally and vertically displaced landforms for 1872 and prehistoric Owens Valley earthquakes. Our lidar analysis uses a newly developed analytical tool to measure fault slip based on cross-correlation of sublinear topographic features and to produce a uniquely shaped probability density function (PDF) for each measurement. Stacking PDFs along strike to form cumulative offset probability distribution plots (COPDs) highlights common values corresponding to single and multiple-event displacements. Lateral offsets for 1872 vary systematically from ∼1.0 to 6.0 m and average 3.3 ± 1.1 m (2σ). Vertical offsets are predominantly east-down between ∼0.1 and 2.4 m, with a mean of 0.8 ± 0.5 m. The average lateral-to-vertical ratio compiled at specific sites is ∼6:1. Summing displacements across subparallel, overlapping rupture traces implies a maximum of 7–11 m and net average of 4.4 ± 1.5 m, corresponding to a geologic Mw ∼7.5 for the 1872 event. We attribute progressively higher-offset lateral COPD peaks at 7.1 ± 2.0 m, 12.8 ± 1.5 m, and 16.6 ± 1.4 m to three earlier large surface ruptures. Evaluating cumulative displacements in context with previously dated landforms in Owens Valley suggests relatively modest rates of fault slip, averaging between ∼0.6 and 1.6 mm/yr (1σ) over the late Quaternary.
","language":"English","publisher":"Geological Society of America","doi":"10.1002/2015GC006033","usgsCitation":"Haddon, E., Amos, C., Zielke, O., Jayko, A.S., and Burgmann, R., 2016, Surface slip during large Owens Valley earthquakes: Geochemistry, Geophysics, Geosystems, v. 17, no. 6, p. 2239-2269, https://doi.org/10.1002/2015GC006033.","productDescription":"31 p.","startPage":"2239","endPage":"2269","ipdsId":"IP-069700","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":470889,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015gc006033","text":"Publisher Index Page"},{"id":342937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Owens Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.8,\n 35.7\n ],\n [\n -117.4,\n 35.7\n ],\n [\n -117.4,\n 37.7\n ],\n [\n -118.8,\n 37.7\n ],\n [\n -118.8,\n 35.7\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-22","publicationStatus":"PW","scienceBaseUri":"59536ea9e4b062508e3c7a81","contributors":{"authors":[{"text":"Haddon, E.K.","contributorId":193553,"corporation":false,"usgs":false,"family":"Haddon","given":"E.K.","email":"","affiliations":[],"preferred":false,"id":700808,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Amos, C.B.","contributorId":193554,"corporation":false,"usgs":false,"family":"Amos","given":"C.B.","email":"","affiliations":[],"preferred":false,"id":700809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zielke, O.","contributorId":166853,"corporation":false,"usgs":false,"family":"Zielke","given":"O.","affiliations":[{"id":24561,"text":"KAUST","active":true,"usgs":false}],"preferred":false,"id":700810,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jayko, Angela S. 0000-0002-7378-0330 ajayko@usgs.gov","orcid":"https://orcid.org/0000-0002-7378-0330","contributorId":2531,"corporation":false,"usgs":true,"family":"Jayko","given":"Angela","email":"ajayko@usgs.gov","middleInitial":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":700807,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burgmann, R.","contributorId":193555,"corporation":false,"usgs":false,"family":"Burgmann","given":"R.","affiliations":[],"preferred":false,"id":700811,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173545,"text":"70173545 - 2016 - Influences of summer water temperatures on the movement, distribution, and resources use of fluvial Westslope Cutthroat Trout in the South Fork Clearwater River basin","interactions":[],"lastModifiedDate":"2016-06-14T15:02:00","indexId":"70173545","displayToPublicDate":"2016-06-14T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Influences of summer water temperatures on the movement, distribution, and resources use of fluvial Westslope Cutthroat Trout in the South Fork Clearwater River basin","docAbstract":"Although many Westslope Cutthroat Trout Oncorhynchus clarkii lewisi populations in Idaho are robust and stable, population densities in some systems remain below management objectives. In many of those systems, such as in the South Fork Clearwater River (SFCR) system, environmental conditions (e.g., summer temperatures) are hypothesized to limit populations of Westslope Cutthroat Trout. Radiotelemetry and snorkeling methods were used to describe seasonal movement patterns, distribution, and habitat use of Westslope Cutthroat Trout in the SFCR during the summers of 2013 and 2014. Sixty-six radio transmitters were surgically implanted into Westslope Cutthroat Trout (170–405 mm TL) from May 30–June 25, 2013, and June 20–July 6, 2014. Sedentary and mobile summer movement patterns by Westslope Cutthroat Trout were observed in the SFCR. Westslope Cutthroat Trout were generally absent from the lower SFCR. In the upper region of the SFCR, fish generally moved from the main-stem SFCR into tributaries as water temperatures increased during the summer. Fish remained in the middle region of the SFCR where water temperatures were cooler than in the upper or lower regions of the SFCR. A spatially explicit water temperature model indicated that the upper and lower regions of the SFCR exceeded thermal tolerance levels of Westslope Cutthroat Trout throughout the summer. During snorkeling, 23 Westslope Cutthroat Trout were observed in 13 sites along the SFCR and at low density (mean ± SD, 0.0003 ± 0.0001 fish/m2). The distribution of fish observed during snorkeling was consistent with the distribution of radio-tagged fish in the SFCR during the summer. Anthropogenic activities (i.e., grazing, mining, road construction, and timber harvest) in the SFCR basin likely altered the natural flow dynamics and temperature regime and thereby limited stream habitat in the SFCR system for Westslope Cutthroat Trout.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/02755947.2016.1141124","usgsCitation":"Dobos, M.E., Corsi, M., Schill, D.J., DuPont, J.M., and Quist, M.C., 2016, Influences of summer water temperatures on the movement, distribution, and resources use of fluvial Westslope Cutthroat Trout in the South Fork Clearwater River basin: North American Journal of Fisheries Management, v. 36, no. 3, p. 549-567, https://doi.org/10.1080/02755947.2016.1141124.","productDescription":"19 p.","startPage":"549","endPage":"567","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065451","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":323599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"South Fork Clearwater River,","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3067626953125,\n 45.794339630460705\n ],\n [\n -116.3067626953125,\n 46.524855311033434\n ],\n [\n -114.356689453125,\n 46.524855311033434\n ],\n [\n -114.356689453125,\n 45.794339630460705\n ],\n [\n -116.3067626953125,\n 45.794339630460705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-16","publicationStatus":"PW","scienceBaseUri":"57611c9ee4b04f417c2c32ff","contributors":{"authors":[{"text":"Dobos, Marika E.","contributorId":171810,"corporation":false,"usgs":false,"family":"Dobos","given":"Marika","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":638759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Matthew P.","contributorId":171811,"corporation":false,"usgs":false,"family":"Corsi","given":"Matthew P.","affiliations":[],"preferred":false,"id":638760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schill, Daniel J.","contributorId":66562,"corporation":false,"usgs":true,"family":"Schill","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":638761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuPont, Joseph M.","contributorId":171812,"corporation":false,"usgs":false,"family":"DuPont","given":"Joseph","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":638762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70157465,"text":"70157465 - 2016 - Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6","interactions":[],"lastModifiedDate":"2020-08-25T18:48:31.707401","indexId":"70157465","displayToPublicDate":"2016-06-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3.4.6","title":"Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6","docAbstract":"Mathematical models have been widely applied to surface waters to estimate rates of settling, resuspension, flow, dispersion, and advection in order to calculate movement of particles that influence water quality. Of particular interest are the movement, survival, and persistence of microbial pathogens or their surrogates, which may contaminate recreational water, drinking water, or shellfish. Most models devoted to microbial water quality have been focused on fecal indicator organisms (FIO), which act as a surrogate for pathogens and viruses. Process-based modeling and statistical modeling have been used to track contamination events to source and to predict future events. The use of these two types of models require different levels of expertise and input; process-based models rely on theoretical physical constructs to explain present conditions and biological distribution while data-based, statistical models use extant paired data to do the same. The selection of the appropriate model and interpretation of results is critical to proper use of these tools in microbial source tracking. Integration of the modeling approaches could provide insight for tracking and predicting contamination events in real time. A review of modeling efforts reveals that process-based modeling has great promise for microbial source tracking efforts; further, combining the understanding of physical processes influencing FIO contamination developed with process-based models and molecular characterization of the population by gene-based (i.e., biological) or chemical markers may be an effective approach for locating sources and remediating contamination in order to protect human health better.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Manual of Environmental Microbiology","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"ASM Press","publisherLocation":"Washington, D.C.","doi":"10.1128/9781555818821.ch3.4.6","usgsCitation":"Nevers, M., Byappanahalli, M., Phanikumar, M.S., and Whitman, R.L., 2016, Fecal indicator organism modeling and microbial source tracking in environmental waters: Chapter 3.4.6, chap. 3.4.6 of Manual of Environmental Microbiology, p. 3.4.6-1-3.4.6-16, https://doi.org/10.1128/9781555818821.ch3.4.6.","productDescription":"16 p.","startPage":"3.4.6-1","endPage":"3.4.6-16","ipdsId":"IP-049156","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":340175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2015-09-01","publicationStatus":"PW","scienceBaseUri":"58ff0e9de4b006455f2d61bc","contributors":{"authors":[{"text":"Nevers, Meredith 0000-0001-6963-6734 mnevers@usgs.gov","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":2013,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith","email":"mnevers@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":573242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":573243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phanikumar, Mantha S.","contributorId":147924,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":692598,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitman, Richard L. rwhitman@usgs.gov","contributorId":542,"corporation":false,"usgs":true,"family":"Whitman","given":"Richard","email":"rwhitman@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":573245,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170263,"text":"70170263 - 2016 - Recreation, values and stewardship: Rethinking why people engage in environmental behaviors in parks and protected areas","interactions":[],"lastModifiedDate":"2020-08-25T18:41:38.093294","indexId":"70170263","displayToPublicDate":"2016-06-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"19","title":"Recreation, values and stewardship: Rethinking why people engage in environmental behaviors in parks and protected areas","docAbstract":"Successfully promoting and encouraging the adoption of environmental stewardship behavior is an important responsibility for public land management agencies. Although people increasingly report high levels of concern about environmental issues, widespread patterns of stewardship behavior have not followed suit (Moore 2002). One concept that can be applied in social science research to explain behavior change is that of values. More specifically, held and assigned values lie at the heart of understanding why people around the world continue to live in unsustainable ways that impact parks and protected areas. A held value is an individual psychological orientation defined by Rokeach as “an enduring belief that a specific mode of conduct or endstate of existence is personally and socially preferable” (1973, 550). Held values are at the core of human cognition, and as such, influence attitudes and behavior. Assigned values on the other hand, according to Brown (1984), are the perceived qualities of an environment that are based on and deduced from held values. In other words, assigned values are considered the material and nonmaterial benefits that people believe they obtain from ecosystems. Held and assigned values predict stewardship behaviors (Figure 1).
During the 2013 George Wright Society Conference on Parks, Protected Areas, and Cultural Sites, we organized a session to improve our understanding of why individuals and groups choose to engage in stewardship behaviors that benefit the environment. We used held and assigned values as vehicles to explore what people cared about in diverse landscapes, review select case studies from across the globe, and question how best to incorporate visitor perspectives into protected area management decisions and policymaking. In addition to sharing project results, we also discussed the importance of accounting for multiple and often competing value perspectives, potential ways to integrate disciplinary perspectives on valuing nature, and future directions for social science research and practice.
In this paper, we present the results from our session to provide fodder for further contemplation about the timely question of how park and protected area managers can foster values that lead to environmental protection.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Engagement, education, and expectations—The future of parks and protected areas: Proceedings of the 2015 George Wright Society Conference on parks, protected areas, and cultural sites","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"George Wright Society Conference on Parks, Protected Areas, and Cultural Sites","conferenceDate":"March 19- April 3, 2015","conferenceLocation":"Oakland, CA","language":"English","publisher":"George Wright Society","publisherLocation":"Hancock, MI","usgsCitation":"van Riper, C.J., Sharp, R., Bagstad, K.J., Vagias, W.M., Kwenye, J., Depper, G., and Freimund, W., 2016, Recreation, values and stewardship: Rethinking why people engage in environmental behaviors in parks and protected areas, chap. 19 of Engagement, education, and expectations—The future of parks and protected areas: Proceedings of the 2015 George Wright Society Conference on parks, protected areas, and cultural sites, p. 117-122.","productDescription":"6 p.","startPage":"117","endPage":"122","ipdsId":"IP-065984","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":340079,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.georgewright.org/proceedings2015"},{"id":340080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4de4b0c3010a8087c1","contributors":{"authors":[{"text":"van Riper, Carena J.","contributorId":42827,"corporation":false,"usgs":false,"family":"van Riper","given":"Carena","email":"","middleInitial":"J.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":626685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sharp, Ryan","contributorId":168598,"corporation":false,"usgs":false,"family":"Sharp","given":"Ryan","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":626686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":626684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vagias, Wade M.","contributorId":98033,"corporation":false,"usgs":true,"family":"Vagias","given":"Wade","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":626687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kwenye, Jane","contributorId":168599,"corporation":false,"usgs":false,"family":"Kwenye","given":"Jane","email":"","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":626688,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Depper, Gina","contributorId":168600,"corporation":false,"usgs":false,"family":"Depper","given":"Gina","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":626689,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freimund, Wayne","contributorId":168601,"corporation":false,"usgs":false,"family":"Freimund","given":"Wayne","email":"","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":626690,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70158965,"text":"sir20155135 - 2016 - Modern (1992–2011) and projected (2012–99) peak snowpack and May–July runoff for the Fort Peck Lake and Lake Sakakawea watersheds in the Upper Missouri River Basin","interactions":[],"lastModifiedDate":"2017-10-12T19:57:38","indexId":"sir20155135","displayToPublicDate":"2016-06-14T00:00:00","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":"2015-5135","title":"Modern (1992–2011) and projected (2012–99) peak snowpack and May–July runoff for the Fort Peck Lake and Lake Sakakawea watersheds in the Upper Missouri River Basin","docAbstract":"Mountain snowpack is an important contributor to runoff in the Upper Missouri River Basin; for example, high amounts of winter and spring precipitation in the mountains and plains in 2010–11 were associated with the peak runoff of record in 2011 in the Upper Missouri River Basin. To project trends in peak mountain snowpack and runoff in the upcoming decades, multiple linear regression models of peak mountain snowpack and total May–July runoff were developed for the Fort Peck Lake (above Fort Peck Dam) and lower Lake Sakakawea watersheds (between Fort Peck and Garrison Dams) in the Upper Missouri River Basin. Input to regression models included seasonal estimates of precipitation, air temperature, and total reference evapotranspiration stratified by elevation. Calibration was based on records from 107 weather stations from 1991 to 2011. Regressed annual peak mountain snowpack was used as input to the transfer function of May–July runoff. Peak snowpack and May–July runoff were projected for 2012–99 on the basis of air temperature and precipitation from the Community Climate System Model (CCSM) output. Two estimates of projected peak snowpack and May–July runoff for 2012–99 were computed: one estimate was based on output from the CCSM, version 3.0 (CCSM3), and the second estimate was based on output from the CCSM, version 4.0 (CCSM4). The significance of projected trends was based on the Kendall’s tau nonparametric test.
\nAnnual peak snowpack was projected to have a downward trend for the Fort Peck Lake watershed and an upward trend for the lower Lake Sakakawea watershed. Projections of May–July runoff had a significant downward trend for the Fort Peck Lake, lower Lake Sakakawea, and Lake Sakakawea (combination of Fort Peck Lake and lower Lake Sakakawea) watersheds. Downward trends in projected May–July runoff indicated that power production at Fort Peck Dam might be affected particularly in the later part of the simulation (2061–99); however, confidence in projected May–July runoff for the later part of the simulation was less certain because bias-corrected air temperatures from CCSM3 and CCSM4 commonly fell outside of the observed range used for calibration. Projected May–July runoff combined for the Fort Peck Lake and lower Lake Sakakawea watersheds were on the order of magnitude of the 2011 flood for 1 simulation year for each of the CCSM-based simulations. High peak snowpack and precipitation in April, May, and June in the plains was associated with large May–July runoff events; therefore, high precipitation at lower elevations in the Fort Peck Lake and lower Lake Sakakawea watersheds was a factor in the simulation of extreme runoff events at the magnitude of the 2011 flood.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155135","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Stamm, J.F.; Todey, Dennis; Mayes Boustead, Barbara; Rossi, Shawn; Norton, P.A.; and Carter, J.M., 2016, Modern (1992–2011) and projected (2012–99) peak snowpack and May–July runoff for the Fort Peck Lake and Lake Sakakawea watersheds in the Upper Missouri River Basin (ver. 1.2, June 2016): U.S. Geological Survey Scientific Investigations Report 2015–5135, 44 p., https://dx.doi.org/10.3133/sir20155135.","productDescription":"Report: vii, 44 p.; 6 Companion Files","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-063643","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science 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U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702
Or visit the South Dakota Water Science Center Web site at:
http://sd.water.usgs.gov/
The U.S. Geological Survey, in cooperation with the Tennessee Duck River Development Agency, monitored water quality at several locations in the upper Duck River watershed between October 2007 and September 2010. Discrete water samples collected at 24 sites in the watershed were analyzed for water quality, and Escherichia coli (E. coli) and enterococci concentrations. Additional analyses, including the determination of anthropogenic-organic compounds, bacterial concentration of resuspended sediment, and bacterial-source tracking, were performed at a subset of sites. Continuous monitoring of streamflow, turbidity, and specific conductance was conducted at seven sites; a subset of sites also was monitored for water temperature and dissolved oxygen concentration. Multiple-regression models were developed to predict instantaneous E. coli concentrations and loads at sites with continuous monitoring. This data collection effort, along with the E. coli models and predictions, support analyses of the relations among land use, bacteria source and transport, and basin hydrology in the upper Duck River watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1003","collaboration":"Prepared in cooperation with the Tennessee Duck River Development Agency","usgsCitation":"Murphy, Jennifer, Farmer, James, and Layton, Alice, 2016, Water-quality data and Escherichia coli predictions for selected karst catchments of the upper Duck River watershed in central Tennessee, 2007–10:\nU.S. Geological Survey Data Series 1003, 17 p., https://dx.doi.org/10.3133/ds1003.","productDescription":"Report: v, 17 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","temporalStart":"2007-10-01","ipdsId":"IP-059652","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":438613,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7445JKC","text":"USGS data release","linkHelpText":"Water-quality datasets and E. coli predictions for selected streams in the Upper Duck River Watershed, central Tennessee, 2007-2010"},{"id":323294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1003/coverthb.jpg"},{"id":323295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1003/ds1003.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":" Data Series 1003"},{"id":323304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7445JKC","text":"USGS data release - Water-quality datasets and E. coli predictions for selected streams in the Upper Duck River Watershed, central Tennessee, 2007–10","description":"USGS Data Release"}],"country":"United States","state":"Tennessee","otherGeospatial":"Duck River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.66908264160156,\n 35.45395828344931\n ],\n [\n -86.39579772949219,\n 35.45395828344931\n ],\n [\n -86.39579772949219,\n 35.66566448946006\n ],\n [\n -86.66908264160156,\n 35.66566448946006\n ],\n [\n -86.66908264160156,\n 35.45395828344931\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Ste 100
Nashville, TN 37211
Predicting changes in species distributions under a changing climate is becoming widespread with the use of species distribution models (SDMs). The resulting predictions of future potential habitat can be cast in light of planned land use changes, such as urban expansion and energy development to identify areas with potential conflict. However, SDMs rarely incorporate an understanding of dispersal capacity, and therefore assume unlimited dispersal in potential range shifts under uncertain climate futures. We use SDMs to predict future distributions of the Mojave ground squirrel, Xerospermophilus mohavensis Merriam, and incorporate partial dispersal models informed by field data on juvenile dispersal to assess projected impact of climate change and energy development on future distributions of X. mohavensis. Our models predict loss of extant habitat, but also concurrent gains of new habitat under two scenarios of future climate change. Under the B1 emissions scenario- a storyline describing a convergent world with emphasis on curbing greenhouse gas emissions- our models predicted losses of up to 64% of extant habitat by 2080, while under the increased greenhouse gas emissions of the A2 scenario, we suggest losses of 56%. New potential habitat may become available to X. mohavensis, thereby offsetting as much as 6330 km2 (50%) of the current habitat lost. Habitat lost due to planned energy development was marginal compared to habitat lost from changing climates, but disproportionately affected current habitat. Future areas of overlap in potential habitat between the two climate change scenarios are identified and discussed in context of proposed energy development.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2016.05.033","usgsCitation":"Inman, R.D., Esque, T., Nussear, K.E., Leitner, P., Matocq, M.D., Weisberg, P.J., and Dilts, T.E., 2016, Impacts of climate change and renewable energy development on habitat of an endemic squirrel, Xerospermophilus mohavensis, in the Mojave Desert, USA: Biological Conservation, v. 200, p. 112-121, https://doi.org/10.1016/j.biocon.2016.05.033.","productDescription":"10 p.","startPage":"112","endPage":"121","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071079","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":323498,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 34\n ],\n [\n -119,\n 38\n ],\n [\n -116,\n 38\n ],\n [\n -116,\n 34\n ],\n [\n -119,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"200","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"575fcb1ee4b04f417c2b2673","contributors":{"authors":[{"text":"Inman, Richard D. rdinman@usgs.gov","contributorId":3316,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":638567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esque, Todd C. 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":168763,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":638566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nussear, Kenneth E. knussear@usgs.gov","contributorId":2695,"corporation":false,"usgs":true,"family":"Nussear","given":"Kenneth","email":"knussear@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":638568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leitner, Philip","contributorId":31319,"corporation":false,"usgs":true,"family":"Leitner","given":"Philip","email":"","affiliations":[],"preferred":false,"id":638569,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matocq, Marjorie D.","contributorId":25482,"corporation":false,"usgs":true,"family":"Matocq","given":"Marjorie","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":638570,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weisberg, Peter J.","contributorId":33631,"corporation":false,"usgs":true,"family":"Weisberg","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":638571,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dilts, Thomas E.","contributorId":36833,"corporation":false,"usgs":true,"family":"Dilts","given":"Thomas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":638572,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173559,"text":"70173559 - 2016 - Anthropogenic disturbance and environmental associations with fish assemblage structure in two nonwadeable rivers","interactions":[],"lastModifiedDate":"2019-12-14T06:55:00","indexId":"70173559","displayToPublicDate":"2016-06-13T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Anthropogenic disturbance and environmental associations with fish assemblage structure in two nonwadeable rivers","docAbstract":"Nonwadeable rivers are unique ecosystems that support high levels of aquatic biodiversity, yet they have been greatly altered by human activities. Although riverine fish assemblages have been studied in the past, we still have an incomplete understanding of how fish assemblages respond to both natural and anthropogenic influences in large rivers. The purpose of this study was to evaluate associations between fish assemblage structure and reach-scale habitat, dam, and watershed land use characteristics. In the summers of 2011 and 2012, comprehensive fish and environmental data were collected from 33 reaches in the Iowa and Cedar rivers of eastern-central Iowa. Canonical correspondence analysis (CCA) was used to evaluate environmental relationships with species relative abundance, functional trait abundance (e.g. catch rate of tolerant species), and functional trait composition (e.g. percentage of tolerant species). On the basis of partial CCAs, reach-scale habitat, dam characteristics, and watershed land use features explained 25.0–81.1%, 6.2–25.1%, and 5.8–47.2% of fish assemblage variation, respectively. Although reach-scale, dam, and land use factors contributed to overall assemblage structure, the majority of fish assemblage variation was constrained by reach-scale habitat factors. Specifically, mean annual discharge was consistently selected in nine of the 11 CCA models and accounted for the majority of explained fish assemblage variance by reach-scale habitat. This study provides important insight on the influence of anthropogenic disturbances across multiple spatial scales on fish assemblages in large river systems.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/rra.2844","usgsCitation":"Parks, T.P., Quist, M.C., and Pierce, C., 2016, Anthropogenic disturbance and environmental associations with fish assemblage structure in two nonwadeable rivers: River Research and Applications, v. 32, no. 1, p. 66-84, https://doi.org/10.1002/rra.2844.","productDescription":"19 p.","startPage":"66","endPage":"84","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-043810","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":470901,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/97","text":"External Repository"},{"id":323527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Cedar River, Iowa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.758056640625,\n 41.566141964768384\n ],\n [\n -93.251953125,\n 43.866218006556394\n ],\n [\n -93.779296875,\n 43.691707903073805\n ],\n [\n -92.74658203125,\n 42.47209690919285\n ],\n [\n -91.395263671875,\n 41.376808565702355\n ],\n [\n -91.07666015625,\n 41.31082388091818\n ],\n [\n -90.758056640625,\n 41.566141964768384\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2014-10-08","publicationStatus":"PW","scienceBaseUri":"575fcb1be4b04f417c2b2665","contributors":{"authors":[{"text":"Parks, T. P.","contributorId":171776,"corporation":false,"usgs":false,"family":"Parks","given":"T.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":638611,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":637298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pierce, C.L. 0000-0001-5088-5431","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":93606,"corporation":false,"usgs":true,"family":"Pierce","given":"C.L.","affiliations":[],"preferred":false,"id":638612,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173783,"text":"fs20163020 - 2016 - Earthquake outlook for the San Francisco Bay region 2014–2043","interactions":[],"lastModifiedDate":"2017-11-27T12:56:24","indexId":"fs20163020","displayToPublicDate":"2016-06-13T00:00:00","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":"2016-3020","title":"Earthquake outlook for the San Francisco Bay region 2014–2043","docAbstract":"Using information from recent earthquakes, improved mapping of active faults, and a new model for estimating earthquake probabilities, the 2014 Working Group on California Earthquake Probabilities updated the 30-year earthquake forecast for California. They concluded that there is a 72 percent probability (or likelihood) of at least one earthquake of magnitude 6.7 or greater striking somewhere in the San Francisco Bay region before 2043. Earthquakes this large are capable of causing widespread damage; therefore, communities in the region should take simple steps to help reduce injuries, damage, and disruption, as well as accelerate recovery from these earthquakes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163020","usgsCitation":"Aagaard, B.T., Blair, J.L., Boatwright, J., Garcia, S.H., Harris, R.A., Michael, A.J., Schwartz, D.P., and DiLeo, J.S., 2016, Earthquake outlook for the San Francisco Bay region 2014–2043 (ver. 1.1, August 2016): U.S. Geological Survey Fact Sheet 2016–3020, 6 p., https://dx.doi.org/10.3133/fs20163020.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071104","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true}],"links":[{"id":326609,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2016/3020/versionHist.txt"},{"id":323400,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3020/fs20163020.pdf","text":"Report","size":"3.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3020"},{"id":323399,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3020/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46984863281249,\n 36.491973470593685\n ],\n [\n -123.46984863281249,\n 38.852542390364235\n ],\n [\n -121.59667968749999,\n 38.852542390364235\n ],\n [\n -121.59667968749999,\n 36.491973470593685\n ],\n [\n -123.46984863281249,\n 36.491973470593685\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
http://earthquake.usgs.gov/
Lead (Pb) and calcium (Ca) concentrations were measured in fillet samples of longear sunfish (Lepomis megalotis) and redhorse suckers (Moxostoma spp.) collected in 2005–2012 from the Big River, which drains a historical mining area in southeastern Missouri and where a consumption advisory is in effect due to elevated Pb concentrations in fish. Lead tends to accumulated in Ca-rich tissues such as bone and scale. Concentrations of Pb in fish muscle are typically low, but can become elevated in fillets from Pb-contaminated sites depending in part on how much bone, scale, and skin is included in the sample. We used analysis-of-covariance to normalize Pb concentration to the geometric mean Ca concentration (415 ug/g wet weight, ww), which reduced variation between taxa, sites, and years, as was the number of samples that exceeded Missouri consumption advisory threshold (300 ng/g ww). Concentrations of Pb in 2005–2012 were lower than in the past, especially after Ca-normalization, but the consumption advisory is still warranted because concentrations were >300 ng/g ww in samples of both taxa from contaminated sites. For monitoring purposes, a simple linear regression model is proposed for estimating Ca-normalized Pb concentrations in fillets from Pb:Ca molar ratios as a way of reducing the effects of differing preparation methods on fillet Pb variation.
","language":"English","publisher":"Springer","doi":"10.1007/s00128-016-1850-3","usgsCitation":"Schmitt, C.J., and McKee, M., 2016, Concentration trends for lead and calcium-normalized lead in fish fillets from the Big River, a mining-contaminated stream in southeastern Missouri USA: Bulletin of Environmental Contamination and Toxicology, v. 97, no. 5, p. 593-600, https://doi.org/10.1007/s00128-016-1850-3.","productDescription":"8 p.","startPage":"593","endPage":"600","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074165","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":324406,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big River, Flat River, Meramec River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.8333,\n 37.75\n ],\n [\n -90.8333,\n 38.5833\n ],\n [\n -90.25,\n 38.5833\n ],\n [\n -90.25,\n 37.75\n ],\n [\n -90.8333,\n 37.75\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"97","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-11","publicationStatus":"PW","scienceBaseUri":"57724e2de4b07657d1a8194c","chorus":{"doi":"10.1007/s00128-016-1850-3","url":"http://dx.doi.org/10.1007/s00128-016-1850-3","publisher":"Springer Nature","authors":"Schmitt Christopher J., McKee Michael J.","journalName":"Bulletin of Environmental Contamination and Toxicology","publicationDate":"6/11/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"6/11/2016"},"contributors":{"authors":[{"text":"Schmitt, Christopher J. 0000-0001-6804-2360 cjschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6804-2360","contributorId":491,"corporation":false,"usgs":true,"family":"Schmitt","given":"Christopher","email":"cjschmitt@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":640763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKee, Michael J.","contributorId":59527,"corporation":false,"usgs":true,"family":"McKee","given":"Michael J.","affiliations":[],"preferred":false,"id":640764,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70173787,"text":"ofr20161093 - 2016 - Bathymetric survey and digital elevation model of Little Holland Tract, Sacramento-San Joaquin Delta, California","interactions":[],"lastModifiedDate":"2017-06-23T12:35:48","indexId":"ofr20161093","displayToPublicDate":"2016-06-10T00:00:00","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":"2016-1093","title":"Bathymetric survey and digital elevation model of Little Holland Tract, Sacramento-San Joaquin Delta, California","docAbstract":"The U.S. Geological Survey conducted a bathymetric survey in Little Holland Tract, a flooded agricultural tract, in the northern Sacramento-San Joaquin Delta (the “Delta”) during the summer of 2015. The new bathymetric data were combined with existing data to generate a digital elevation model (DEM) at 1-meter resolution. Little Holland Tract (LHT) was historically diked off for agricultural uses and has been tidally inundated since an accidental levee breach in 1983. Shallow tidal regions such as LHT have the potential to improve habitat quality in the Delta. The DEM of LHT was developed to support ongoing studies of habitat quality in the area and to provide a baseline for evaluating future geomorphic change. The new data comprise 138,407 linear meters of real-time-kinematic (RTK) Global Positioning System (GPS) elevation data, including both bathymetric data collected from personal watercraft and topographic elevations collected on foot at low tide. A benchmark (LHT15_b1) was established for geodetic control of the survey. Data quality was evaluated both by comparing results among surveying platforms, which showed systematic offsets of 1.6 centimeters (cm) or less, and by error propagation, which yielded a mean vertical uncertainty of 6.7 cm. Based on the DEM and time-series measurements of water depth, the mean tidal prism of LHT was determined to be 2,826,000 cubic meters. The bathymetric data and DEM are available at http://dx.doi.org/10.5066/F7RX9954.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161093","usgsCitation":"Snyder, A.G., Lacy, J.R., Stevens, A.W., and Carlson, E.M., 2016, Bathymetric survey and digital elevation model of Little Holland Tract, Sacramento-San Joaquin Delta, California: U.S. Geological Survey Open-File Report 2016‒1093, 14 p., https://dx.doi.org/10.3133/ofr20161093. ","productDescription":"iv, 14 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-071752","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":323417,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1093/ofr20161093.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1093"},{"id":323416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1093/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.69349670410158,\n 38.27632714009116\n ],\n [\n -121.69349670410158,\n 38.34556060133404\n ],\n [\n -121.64148330688475,\n 38.34556060133404\n ],\n [\n -121.64148330688475,\n 38.27632714009116\n ],\n [\n -121.69349670410158,\n 38.27632714009116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Pacific Coastal and Marine Science Center
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Pacific Science Center
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Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
In order for mark–recapture models to provide unbiased estimates of population parameters, it is critical that uniquely identifying tags or marks are not lost. We double-banded male and female wild turkeys with aluminum rivet bands and estimated the probability that a bird would be recovered with both bands <1–225 wk since banding (mean = 51.2 wk, SD = 44.0). We found that 100% of females (n = 37) were recovered with both bands. For males, we recovered 6 of 188 turkeys missing a rivet band for a retention probability of 0.984 (95% CI = 0.96–0.99). If male turkeys are double-banded with rivet bands the probability of recovering a turkey without any marks is <0.001. We failed to detect a change in band retention over time or differences between adults and juveniles. Given the low cost and high retention rates of rivet aluminum bands, we believe they are an effective marking technique for wild turkeys and, for most studies, will minimize any concern about the assumption that marks are not lost.
","language":"English","publisher":"FWS Publications","doi":"10.3996/072015-JFWM-064","usgsCitation":"Diefenbach, D.R., Vreeland, W.C., Casalena, M.J., and Schiavone, M.V., 2016, Retention of riveted aluminum leg bands by wild turkeys: Journal of Fish and Wildlife Management, v. 7, no. 1, p. 162-164, https://doi.org/10.3996/072015-JFWM-064.","productDescription":"3 p.","startPage":"162","endPage":"164","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066695","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":490011,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/072015-jfwm-064","text":"Publisher Index Page"},{"id":323384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-01","publicationStatus":"PW","scienceBaseUri":"575a8523e4b04f417c271091","contributors":{"authors":[{"text":"Diefenbach, Duane R. 0000-0001-5111-1147 drd11@usgs.gov","orcid":"https://orcid.org/0000-0001-5111-1147","contributorId":5235,"corporation":false,"usgs":true,"family":"Diefenbach","given":"Duane","email":"drd11@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":638063,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vreeland, Wendy C.","contributorId":171658,"corporation":false,"usgs":false,"family":"Vreeland","given":"Wendy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":638227,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casalena, Mary Jo","contributorId":98965,"corporation":false,"usgs":false,"family":"Casalena","given":"Mary","email":"","middleInitial":"Jo","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":638228,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schiavone, Michael V.","contributorId":30064,"corporation":false,"usgs":false,"family":"Schiavone","given":"Michael","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":638229,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}