The U.S. Geological Survey, in cooperation with the Montana Department of Transportation, developed regression equations based on channel width to estimate peak-flow frequencies at ungaged sites in Montana. The equations are based on peak-flow data at streamgages through September 2011 (end of water year 2011), and channel widths measured in the field and from aerial photographs.
Active-channel width and bankfull width (channel widths) were measured in the field at 64 sites across Montana in 2017. Channel widths also were measured near 515 streamgages from aerial photographs. These new channel-width data, along with more than 438 historical channel-width measurements, are published in a separate data release.
Regression equations were developed using generalized least squares regression or weighted least squares regression. The channel-width regression equations can be used to estimate peak-flow frequencies (peak-flow magnitudes associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites in each of the eight hydrologic regions in Montana. Methods are presented for weighting estimates from the channel-width equations with estimates from equations using basin characteristics. The weighting technique can be used to reduce the standard error of prediction relative to that obtained using a single method. Several example problems covering a range of estimation scenarios also are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205142","collaboration":"Prepared in cooperation with Montana Department of Transportation","usgsCitation":"Chase, K.J., Sando, R., Armstrong, D.W., and McCarthy, P., 2021, Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011 (ver. 1.1, September 2021): U.S. Geological Survey Scientific Investigations Report 2020–5142, 49 p., https://doi.org/10.3133/sir20205142.","productDescription":"Report: vi, 49 p.; Data Release; Dataset; Version History","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-102009","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":436235,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D7MEB1","text":"USGS data release","linkHelpText":"StreamStats Channel Width Weighting Services"},{"id":387993,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5142/coverthb2.jpg"},{"id":387995,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CCGJ0I","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Channel width measurements for selected streamgage sites in Montana"},{"id":387996,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":389479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5142/sir20205142.pdf","text":"Report","size":"4.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5142"},{"id":389480,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5142/versionHist.txt","text":"Version History","size":"2.05 kB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"}],"country":"United States","state":"Montana","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-105.038405,45.000345],[-105.848065,45.000396],[-105.913382,45.000941],[-105.914258,44.999986],[-105.928184,44.993647],[-106.263586,44.993788],[-107.050801,44.996424],[-107.074996,44.997004],[-107.084939,44.996599],[-107.105685,44.998734],[-107.125633,44.999388],[-107.13418,45.000109],[-107.351441,45.001407],[-107.49205,45.00148],[-107.608854,45.00086],[-107.750654,45.000778],[-107.997353,45.001565],[-108.000663,45.001223],[-108.14939,45.001062],[-108.218479,45.000541],[-108.238139,45.000206],[-108.249345,44.999458],[-108.271201,45.000251],[-108.500679,44.999691],[-108.565921,45.000578],[-109.08301,44.99961],[-109.103445,45.005904],[-109.375713,45.00461],[-109.386432,45.004887],[-109.574321,45.002631],[-109.663673,45.002536],[-109.75073,45.001605],[-109.875735,45.003275],[-109.99505,45.003174],[-110.026347,45.003665],[-110.110103,45.003905],[-110.199503,44.996188],[-110.28677,44.99685],[-110.324441,44.999156],[-110.342131,44.999053],[-110.362698,45.000593],[-110.402927,44.99381],[-110.48807,44.992361],[-110.552433,44.992237],[-110.705272,44.992324],[-110.750767,44.997948],[-110.761554,44.999934],[-110.785008,45.002952],[-111.055199,45.001321],[-111.056207,44.935901],[-111.055629,44.933578],[-111.056888,44.866658],[-111.056416,44.749928],[-111.055511,44.725343],[-111.055208,44.624927],[-111.048974,44.474072],[-111.062729,44.476073],[-111.122654,44.493659],[-111.131379,44.499925],[-111.139455,44.517112],[-111.143557,44.535732],[-111.15959,44.546376],[-111.166892,44.54722],[-111.175747,44.552219],[-111.182551,44.566874],[-111.189617,44.571062],[-111.201459,44.575696],[-111.225208,44.581006],[-111.23018,44.587025],[-111.231227,44.606915],[-111.224161,44.623402],[-111.25268,44.651092],[-111.262839,44.649658],[-111.276956,44.655626],[-111.26875,44.668279],[-111.29626,44.702271],[-111.323669,44.724474],[-111.341351,44.7293],[-111.348184,44.725459],[-111.355768,44.727602],[-111.366723,44.738361],[-111.36627,44.742234],[-111.37476,44.750295],[-111.385005,44.755128],[-111.393854,44.752549],[-111.397805,44.746738],[-111.394459,44.744578],[-111.398575,44.723343],[-111.414271,44.710741],[-111.424214,44.714024],[-111.429604,44.720149],[-111.438793,44.720546],[-111.486019,44.707654],[-111.489339,44.704946],[-111.490228,44.700221],[-111.484898,44.687578],[-111.47798,44.682393],[-111.468833,44.679335],[-111.473178,44.665479],[-111.50494,44.635746],[-111.521688,44.613371],[-111.525764,44.604883],[-111.524213,44.595585],[-111.519126,44.582916],[-111.492024,44.56081],[-111.469185,44.552044],[-111.467736,44.544521],[-111.471682,44.540824],[-111.500792,44.540062],[-111.518095,44.544177],[-111.524006,44.548385],[-111.546637,44.557099],[-111.556577,44.554495],[-111.562814,44.555209],[-111.585763,44.562843],[-111.591768,44.561502],[-111.601249,44.55421],[-111.614405,44.548991],[-111.681571,44.559864],[-111.704218,44.560205],[-111.709553,44.550206],[-111.715474,44.543543],[-111.737191,44.54306],[-111.746401,44.540766],[-111.758966,44.533766],[-111.761904,44.529841],[-111.806512,44.516264],[-111.807914,44.511716],[-111.821488,44.509286],[-111.842542,44.526069],[-111.849293,44.539837],[-111.870504,44.564033],[-111.887852,44.563413],[-111.903566,44.55723],[-111.947941,44.556776],[-111.951522,44.550062],[-111.980833,44.536682],[-111.995231,44.535444],[-112.01385,44.542348],[-112.032707,44.546642],[-112.035025,44.542691],[-112.034133,44.537716],[-112.036943,44.530323],[-112.053434,44.535089],[-112.069011,44.537104],[-112.093304,44.530002],[-112.096299,44.523212],[-112.101564,44.520847],[-112.106755,44.520829],[-112.125101,44.528527],[-112.129078,44.5363],[-112.136454,44.539911],[-112.164597,44.541666],[-112.179703,44.533021],[-112.183937,44.533067],[-112.221698,44.543519],[-112.229477,44.549494],[-112.226841,44.555239],[-112.230117,44.562759],[-112.242785,44.568091],[-112.258665,44.569516],[-112.286187,44.568472],[-112.307642,44.557651],[-112.312899,44.553536],[-112.315047,44.550049],[-112.315008,44.5419],[-112.319198,44.53911],[-112.348794,44.538691],[-112.35421,44.535638],[-112.358917,44.528847],[-112.3566,44.493127],[-112.358926,44.48628],[-112.368764,44.467153],[-112.387389,44.448058],[-112.435342,44.462216],[-112.460347,44.47571],[-112.473207,44.480027],[-112.50031,44.463051],[-112.511713,44.466445],[-112.512036,44.47042],[-112.518871,44.475784],[-112.541989,44.483971],[-112.550557,44.484928],[-112.573513,44.480983],[-112.584197,44.481368],[-112.601863,44.491015],[-112.660696,44.485756],[-112.664485,44.48645],[-112.671169,44.491265],[-112.707815,44.503023],[-112.71911,44.504344],[-112.735084,44.499159],[-112.749011,44.491233],[-112.781294,44.484888],[-112.797863,44.466112],[-112.828191,44.442472],[-112.836034,44.422653],[-112.821896,44.407436],[-112.812608,44.392275],[-112.81324,44.378103],[-112.820489,44.370946],[-112.844859,44.358221],[-112.855395,44.359975],[-112.881769,44.380315],[-112.886041,44.395874],[-112.915602,44.402699],[-112.951146,44.416699],[-112.981682,44.434279],[-113.003544,44.450814],[-113.020917,44.493827],[-113.018636,44.520064],[-113.019777,44.528505],[-113.032722,44.537137],[-113.04282,44.546757],[-113.042363,44.565237],[-113.061071,44.577329],[-113.083819,44.60222],[-113.07376,44.613928],[-113.065593,44.617391],[-113.05677,44.618657],[-113.053529,44.621187],[-113.049349,44.62938],[-113.051504,44.63695],[-113.065589,44.649371],[-113.068306,44.656374],[-113.07042,44.667844],[-113.067756,44.672807],[-113.06776,44.679474],[-113.081906,44.691392],[-113.098064,44.697477],[-113.101154,44.708578],[-113.102138,44.729027],[-113.116874,44.738104],[-113.134824,44.752763],[-113.137704,44.760109],[-113.131387,44.764738],[-113.131453,44.772837],[-113.140618,44.776698],[-113.158206,44.780847],[-113.163806,44.778921],[-113.179366,44.787142],[-113.183395,44.793565],[-113.19436,44.802151],[-113.209624,44.80907],[-113.238729,44.814144],[-113.247166,44.82295],[-113.278382,44.812706],[-113.29683,44.803358],[-113.301508,44.798985],[-113.341704,44.784853],[-113.354034,44.791745],[-113.354763,44.795468],[-113.346692,44.798898],[-113.3461,44.800611],[-113.356062,44.819798],[-113.377153,44.834858],[-113.383984,44.8374],[-113.422376,44.842595],[-113.455071,44.865424],[-113.474573,44.910846],[-113.491121,44.927548],[-113.498745,44.942314],[-113.494446,44.948597],[-113.480836,44.95031],[-113.474781,44.948795],[-113.467467,44.948061],[-113.448958,44.953544],[-113.443782,44.95989],[-113.444862,44.976141],[-113.447013,44.984637],[-113.446884,44.998545],[-113.437726,45.006967],[-113.449909,45.035167],[-113.44912,45.046098],[-113.45197,45.059247],[-113.460578,45.064879],[-113.465073,45.062755],[-113.47377,45.0617],[-113.485278,45.063519],[-113.520134,45.093033],[-113.510819,45.099902],[-113.506638,45.107288],[-113.513342,45.115225],[-113.538037,45.11503],[-113.546488,45.112285],[-113.554744,45.112901],[-113.57467,45.128411],[-113.594632,45.166034],[-113.589891,45.176986],[-113.599506,45.191114],[-113.636889,45.212983],[-113.647399,45.228282],[-113.650064,45.23471],[-113.657027,45.241436],[-113.665633,45.246265],[-113.674409,45.249411],[-113.678749,45.24927],[-113.684946,45.253706],[-113.692039,45.265191],[-113.691557,45.270912],[-113.688077,45.276407],[-113.689359,45.28355],[-113.735601,45.325265],[-113.738729,45.329741],[-113.7402,45.34559],[-113.73553,45.364738],[-113.73239,45.385058],[-113.733092,45.390173],[-113.734402,45.392353],[-113.750546,45.40272],[-113.760924,45.406501],[-113.765203,45.410601],[-113.768058,45.418147],[-113.763368,45.427732],[-113.764591,45.431403],[-113.783272,45.451839],[-113.78416,45.454946],[-113.759986,45.480735],[-113.766022,45.520621],[-113.778361,45.523415],[-113.796579,45.523462],[-113.802849,45.523159],[-113.809144,45.519908],[-113.834555,45.520729],[-113.819868,45.566326],[-113.804796,45.580358],[-113.803261,45.584193],[-113.802955,45.592631],[-113.806729,45.602146],[-113.823068,45.612486],[-113.861404,45.62366],[-113.886006,45.61702],[-113.904691,45.622007],[-113.902539,45.636945],[-113.898883,45.644167],[-113.900588,45.648259],[-113.903582,45.651165],[-113.919752,45.658536],[-113.930403,45.671878],[-113.93422,45.682232],[-113.971565,45.700636],[-113.986656,45.704564],[-114.015633,45.696127],[-114.019315,45.692937],[-114.020533,45.681223],[-114.02007,45.670332],[-114.013786,45.658238],[-114.014973,45.654008],[-114.018731,45.648616],[-114.033456,45.648629],[-114.067619,45.627706],[-114.08179,45.611329],[-114.083149,45.603996],[-114.0821,45.596958],[-114.086584,45.59118],[-114.100308,45.586354],[-114.122322,45.58426],[-114.131469,45.574444],[-114.132359,45.572531],[-114.128601,45.568996],[-114.129099,45.565491],[-114.135249,45.557465],[-114.154837,45.552916],[-114.180043,45.551432],[-114.18647,45.545539],[-114.192802,45.536596],[-114.203665,45.53557],[-114.227942,45.546423],[-114.248121,45.545877],[-114.251836,45.537812],[-114.248183,45.533226],[-114.247824,45.524283],[-114.261616,45.495816],[-114.270717,45.486116],[-114.279217,45.480616],[-114.333218,45.459316],[-114.345019,45.459916],[-114.360719,45.474116],[-114.36562,45.490416],[-114.36852,45.492716],[-114.388618,45.502903],[-114.415804,45.509753],[-114.438991,45.536076],[-114.450863,45.54253],[-114.456764,45.543983],[-114.460542,45.561283],[-114.473759,45.563278],[-114.498176,45.555473],[-114.506341,45.559216],[-114.517761,45.568129],[-114.526075,45.570771],[-114.549508,45.56059],[-114.559038,45.565706],[-114.558253,45.585104],[-114.553999,45.591279],[-114.538132,45.606834],[-114.544905,45.616673],[-114.553937,45.619299],[-114.563305,45.631612],[-114.563652,45.637412],[-114.561046,45.639906],[-114.53577,45.650613],[-114.529678,45.65232],[-114.522142,45.64934],[-114.507645,45.658949],[-114.499637,45.669035],[-114.495421,45.703321],[-114.497553,45.710677],[-114.504869,45.722176],[-114.535634,45.739095],[-114.566172,45.773864],[-114.562509,45.779927],[-114.555487,45.786249],[-114.544692,45.791447],[-114.512973,45.828825],[-114.51704,45.833148],[-114.517143,45.835993],[-114.514596,45.840785],[-114.509303,45.845531],[-114.498809,45.850676],[-114.470296,45.851343],[-114.455532,45.855012],[-114.44868,45.858891],[-114.422963,45.855381],[-114.409477,45.85164],[-114.388243,45.88234],[-114.387166,45.889164],[-114.395059,45.901458],[-114.404314,45.903497],[-114.413168,45.911479],[-114.431159,45.935737],[-114.431328,45.938023],[-114.427717,45.939625],[-114.423681,45.9441],[-114.415977,45.947891],[-114.411933,45.952358],[-114.404708,45.9559],[-114.402261,45.961489],[-114.403712,45.967049],[-114.409353,45.97141],[-114.411892,45.977883],[-114.419899,45.981106],[-114.425843,45.984984],[-114.470965,45.995742],[-114.47729,46.000802],[-114.477922,46.009025],[-114.473811,46.016614],[-114.480241,46.030325],[-114.485793,46.030022],[-114.490572,46.032427],[-114.493418,46.03717],[-114.494683,46.042546],[-114.492153,46.04729],[-114.468529,46.062484],[-114.461864,46.078571],[-114.460049,46.097104],[-114.474415,46.112515],[-114.488303,46.113106],[-114.5213,46.125287],[-114.527096,46.146218],[-114.514706,46.167726],[-114.489254,46.167684],[-114.478333,46.160876],[-114.472643,46.162202],[-114.457549,46.170231],[-114.445928,46.173933],[-114.443215,46.202943],[-114.445497,46.220227],[-114.449819,46.237119],[-114.451912,46.241253],[-114.468254,46.248796],[-114.470479,46.26732],[-114.465024,46.273127],[-114.453257,46.270939],[-114.441326,46.2738],[-114.43544,46.27661],[-114.427309,46.283624],[-114.425587,46.287899],[-114.433478,46.305502],[-114.431708,46.310744],[-114.413758,46.335945],[-114.410682,46.360673],[-114.411592,46.366688],[-114.422458,46.387097],[-114.408974,46.400438],[-114.384756,46.411784],[-114.376413,46.442983],[-114.379338,46.460166],[-114.383051,46.466402],[-114.394447,46.469549],[-114.400068,46.47718],[-114.403019,46.498675],[-114.400257,46.502143],[-114.395204,46.503148],[-114.385871,46.50437],[-114.375348,46.501855],[-114.35874,46.505306],[-114.351655,46.508119],[-114.342072,46.519679],[-114.349208,46.529514],[-114.348733,46.533792],[-114.34534,46.548444],[-114.339533,46.564039],[-114.33175,46.571914],[-114.331338,46.577781],[-114.333931,46.582732],[-114.334992,46.588154],[-114.333931,46.592162],[-114.322519,46.611066],[-114.322912,46.642938],[-114.320665,46.646963],[-114.32456,46.653579],[-114.332887,46.660756],[-114.360709,46.669059],[-114.394514,46.664846],[-114.403383,46.659633],[-114.410907,46.657466],[-114.424424,46.660648],[-114.453239,46.649266],[-114.45425,46.640974],[-114.466902,46.631695],[-114.486218,46.632829],[-114.498007,46.637655],[-114.547321,46.644485],[-114.561582,46.642043],[-114.583385,46.633227],[-114.593292,46.632848],[-114.615036,46.639733],[-114.616354,46.643646],[-114.611676,46.647704],[-114.614716,46.655256],[-114.621483,46.658143],[-114.635713,46.659375],[-114.642713,46.673145],[-114.641745,46.679286],[-114.631898,46.68397],[-114.623198,46.691511],[-114.620859,46.707415],[-114.626695,46.712889],[-114.632954,46.715495],[-114.649388,46.73289],[-114.696656,46.740572],[-114.699008,46.740223],[-114.710425,46.717704],[-114.713516,46.715138],[-114.727445,46.714604],[-114.740115,46.711771],[-114.747758,46.702649],[-114.749257,46.699688],[-114.751921,46.697207],[-114.76689,46.696901],[-114.787065,46.711255],[-114.788656,46.714033],[-114.779668,46.730411],[-114.773765,46.731805],[-114.76718,46.738828],[-114.765127,46.745383],[-114.765106,46.758153],[-114.79004,46.778729],[-114.808587,46.78235],[-114.818161,46.781139],[-114.829117,46.782503],[-114.835917,46.791111],[-114.844794,46.794305],[-114.856874,46.801633],[-114.860067,46.804988],[-114.861376,46.81196],[-114.864342,46.813858],[-114.880588,46.811791],[-114.888146,46.808573],[-114.897857,46.813184],[-114.904505,46.822851],[-114.920459,46.827697],[-114.927837,46.83599],[-114.92845,46.843242],[-114.92349,46.847594],[-114.928615,46.854815],[-114.940398,46.85605],[-114.947413,46.859324],[-114.943281,46.867971],[-114.938713,46.869021],[-114.931608,46.876799],[-114.931058,46.882108],[-114.936805,46.897378],[-114.935035,46.901749],[-114.927948,46.909948],[-114.927432,46.914185],[-114.929997,46.919625],[-114.960597,46.93001],[-114.986539,46.952099],[-115.00091,46.967703],[-115.001274,46.971901],[-115.028386,46.975659],[-115.028994,46.973159],[-115.031651,46.971548],[-115.047857,46.969533],[-115.049538,46.970774],[-115.057098,46.986758],[-115.066223,46.996375],[-115.071254,47.022083],[-115.087806,47.045519],[-115.098136,47.048897],[-115.102681,47.047239],[-115.107132,47.049041],[-115.120917,47.061237],[-115.136671,47.078276],[-115.139515,47.08456],[-115.140375,47.093013],[-115.170436,47.106265],[-115.172938,47.112881],[-115.189451,47.131032],[-115.200547,47.139154],[-115.223246,47.148974],[-115.243707,47.150347],[-115.255146,47.162876],[-115.255786,47.174725],[-115.261885,47.181742],[-115.286353,47.18327],[-115.300504,47.188139],[-115.300805,47.19393],[-115.295986,47.205658],[-115.29211,47.209861],[-115.294785,47.220914],[-115.298794,47.225245],[-115.307239,47.229892],[-115.311875,47.229673],[-115.317124,47.233305],[-115.324832,47.244841],[-115.326903,47.255912],[-115.339201,47.261623],[-115.3593,47.259461],[-115.36628,47.261485],[-115.371825,47.265213],[-115.410685,47.264228],[-115.421645,47.271736],[-115.423173,47.276222],[-115.428359,47.278722],[-115.443566,47.277309],[-115.457077,47.277794],[-115.470959,47.284873],[-115.487314,47.286518],[-115.51186,47.295219],[-115.526751,47.303219],[-115.531971,47.314121],[-115.548658,47.332213],[-115.551079,47.349856],[-115.556318,47.353076],[-115.564766,47.353476],[-115.570887,47.356375],[-115.578619,47.367007],[-115.609492,47.380799],[-115.617247,47.382521],[-115.639186,47.378605],[-115.644341,47.381826],[-115.648479,47.390293],[-115.657681,47.400651],[-115.69057,47.415059],[-115.71034,47.417784],[-115.718934,47.420967],[-115.72148,47.424469],[-115.728801,47.428925],[-115.731348,47.433381],[-115.728801,47.445159],[-115.718247,47.45316],[-115.69293,47.457237],[-115.671188,47.45439],[-115.663867,47.456936],[-115.654318,47.468077],[-115.653044,47.476035],[-115.655272,47.477944],[-115.686704,47.485596],[-115.694106,47.498634],[-115.708748,47.51264],[-115.71034,47.52951],[-115.717024,47.532693],[-115.741371,47.538645],[-115.747263,47.543197],[-115.748536,47.549245],[-115.746945,47.555293],[-115.734674,47.567401],[-115.721207,47.576323],[-115.706473,47.577299],[-115.689404,47.595402],[-115.694284,47.62346],[-115.708537,47.635356],[-115.715193,47.63634],[-115.72993,47.642442],[-115.73627,47.654762],[-115.726613,47.672093],[-115.72377,47.696671],[-115.730764,47.704426],[-115.752349,47.716743],[-115.758623,47.719041],[-115.763424,47.717313],[-115.77177,47.717412],[-115.776219,47.719818],[-115.783504,47.729305],[-115.780441,47.743447],[-115.797299,47.75752],[-115.803917,47.75848],[-115.824597,47.752154],[-115.831755,47.755785],[-115.835365,47.760957],[-115.835069,47.77006],[-115.837438,47.774846],[-115.84044,47.780172],[-115.847487,47.785227],[-115.848509,47.809331],[-115.845474,47.814967],[-115.852291,47.827991],[-115.870861,47.834939],[-115.875262,47.843272],[-115.881522,47.849672],[-115.900934,47.843064],[-115.906409,47.846261],[-115.919291,47.857406],[-115.939993,47.883153],[-115.959946,47.898142],[-115.965153,47.910131],[-115.969076,47.914256],[-115.982791,47.915994],[-115.993678,47.926183],[-115.995121,47.933827],[-115.998236,47.938779],[-116.007246,47.950087],[-116.030751,47.973349],[-116.03834,47.971318],[-116.04882,47.97716],[-116.049153,47.999923],[-116.048739,48.060093],[-116.04932,48.066644],[-116.049415,48.07722],[-116.048911,48.12493],[-116.049977,48.237604],[-116.049353,48.249936],[-116.049735,48.274668],[-116.048948,48.309847],[-116.050003,48.413492],[-116.049155,48.481247],[-116.049193,49.000912],[-116.00103,49.00127],[-115.990685,49.000909],[-115.501016,49.000694],[-115.207912,48.999228],[-114.375977,49.00139],[-114.250971,49.000905],[-114.224912,48.999687],[-114.201107,48.999249],[-114.180211,48.999703],[-114.059188,48.998856],[-113.907487,48.998858],[-113.864127,48.998276],[-113.692982,48.997632],[-113.576118,48.998478],[-113.375925,48.998562],[-113.356874,48.998224],[-113.19474,48.998909],[-113.116356,48.998462],[-112.143769,48.998917],[-112.003866,48.99857],[-112.000878,48.998921],[-111.761679,48.997614],[-111.500812,48.996963],[-111.003916,48.997537],[-110.592465,48.999012],[-110.531615,48.99839],[-110.438151,48.999188],[-109.500737,49.00044],[-109.454023,49.001132],[-109.437397,49.000631],[-109.384068,49.000374],[-109.285975,49.000479],[-109.250722,49.000011],[-109.000708,48.999234],[-107.704696,48.999872],[-107.441017,48.999363],[-107.363582,49.000019],[-106.050543,48.999207],[-105.612577,48.999703],[-104.875527,48.998991],[-104.048736,48.999877],[-104.0489,48.847387],[-104.047582,48.633984],[-104.048212,48.599055],[-104.047876,48.530798],[-104.047513,48.525913],[-104.048054,48.500025],[-104.047555,48.49414],[-104.046969,48.390675],[-104.046371,48.374154],[-104.046039,48.256761],[-104.045645,48.246179],[-104.044162,47.992836],[-104.041662,47.862282],[-104.043242,47.747106],[-104.043742,47.625016],[-104.044241,47.612288],[-104.043912,47.603229],[-104.044109,47.523595],[-104.045333,47.343452],[-104.045057,47.266868],[-104.045517,47.215666],[-104.044788,47.12743],[-104.045018,47.081202],[-104.045354,47.078574],[-104.045052,47.040863],[-104.045901,46.83079],[-104.045045,46.509788],[-104.046103,46.383916],[-104.045481,46.366871],[-104.045237,46.125002],[-104.045759,46.123946],[-104.046822,46.000199],[-104.04403,45.881975],[-104.042597,45.749998],[-104.041937,45.557915],[-104.041145,45.503367],[-104.041764,45.490789],[-104.040816,45.462708],[-104.040114,45.374214],[-104.039977,45.124988],[-104.039563,45.124039],[-104.040128,44.999987],[-104.039681,44.998041],[-104.057698,44.997431],[-104.250145,44.99822],[-104.665171,44.998618],[-104.72637,44.999518],[-104.759855,44.999066],[-105.038405,45.000345]]]},\"properties\":{\"name\":\"Montana\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: August 19, 2021; Version 1.1: September 20, 2021","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
We created this guide to introduce the user to the San Francisco Volcanic Field as a terrestrial analog site for planetary volcanic processes. For decades, the San Francisco Volcanic Field has been used to teach scientists to recognize the products of common types of volcanic eruptions and associated volcanic features. The volcanic processes and products observed in this volcanic field are like those observed on lunar and Martian surfaces. As a result, this region has been a favored location for training National Aeronautics and Space Administration astronauts and engineers since the Apollo missions.
Though the San Francisco Volcanic Field has more than 600 volcanic vents and flows, this guide will focus on S P Mountain (known locally as S P Crater, located ~30 miles north of Flagstaff, Arizona), one of the best preserved and most accessible of the volcanic cones and lava flows. S P Mountain presents both major types of basaltic eruptions—explosive and effusive—as well as some commonly associated tectonic landforms.
We assume that the user has a basic understanding of geologic concepts and terminology. For more specialized terminology, we include tables showing the classification scheme for lava compositions, styles of eruptions, and tephra sizes (tables 1, 2, and 3). If a further introduction or refresher in volcanological terminology is desired, we suggest reviewing such terms on the U.S. Geological Survey Volcano Science Center’s online glossary (https://volcanoes.usgs.gov/vsc/glossary/).
One term requires clarification at the start of this guide—the term cinder. The terms cinder and cinder cone are widely used to describe the material and edifice produced by lava fountains. However, the term comes from the mining and construction industries and has no clear or formal definition. The international committees in geology and volcanology have chosen the term tephra to be the general term to describe pyroclasts (material ejected through a volcanic explosion or from a volcanic vent). Therefore, in this guide, we use the term tephra rather than cinder.
This guide is outlined as follows:
Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The Little Colorado River is a major tributary to the Colorado River with a confluence at the boundary between Marble and Grand Canyons within Grand Canyon National Park, Arizona. The bedrock gorge of the lower Little Colorado River is home to the largest known population of Gila cypha (humpback chub), an endangered fish endemic to the Colorado River Basin. Channel conditions might affect the spawning success of the humpback chub. Perennial base flow in the lower Little Colorado River deposits travertine, which forms dams and cascades. Geomorphic change in the lower Little Colorado River is controlled by the growth and collapse of travertine dams, debris flows from tributaries, and reworking of dams and debris fans by Little Colorado River floods.
A study was conducted by the U.S. Geological Survey, in cooperation with the Glen Canyon Dam Adaptive Management Program and the U.S. Fish and Wildlife Service, to document historical floods and geomorphic change in the lower Little Colorado River. For this study, we used historical and gaging records and hydraulic modeling of surveyed high-water marks from historical Little Colorado River floods to construct a peak-flow history of the lower Little Colorado River. We analyzed base-flow longitudinal profiles and historical photographs to determine changes in the longitudinal profile of the lower Little Colorado River from 1909 to 2019. The peak-flow magnitudes and the frequency of larger floods have declined since the late 1800s, and the longitudinal profile of the Little Colorado River has substantially changed between 1909 and 2019. Aggradation of as much as 6 meters in some reaches occurred between 1926 and 1992, mostly before the 1950s. This aggradation was caused largely by the documented growth of travertine dams continuing through at least 2013 at several locations. Other reaches were incised by as much as 10 meters between 1926 and 1992, but mostly before the 1950s, largely from the breaching of travertine dams. Travertine dams in the Little Colorado River have survived large flooding events and then later collapsed during floods of lower streamflow or even periods of base flow. The decline in peak-flow magnitude and frequency has changed the dominant geomorphic processes in this formerly dynamic reach. Large incision events have not been documented since the early 1950s; for this reason, the reach has only aggraded or remained stable since that time. This loss of geomorphic disturbance has likely affected, and will likely continue to affect, the spawning habitat of the endangered humpback chub in the lower Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215049","usgsCitation":"Unema, J.A., Topping, D.J., Kohl, K.A., Pillow, M.J., and Caster, J.J., 2021, Historical floods and geomorphic change in the lower Little Colorado River during the late 19th to early 21st centuries: U.S. Geological Survey Scientific Investigations Report 2021–5049, 34 p., https://doi.org/10.3133/sir20215049.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-113057","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5049/covrthb.jpg"},{"id":388148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5049/sir20215049.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VGWRV1","linkHelpText":"Topographic data, historical peak-stage data, and 2D flow models for the lowermost Little Colorado River, Arizona, USA, 2017"}],"country":"United States","state":"Arizona, Utah, New Mexico, Utah","otherGeospatial":"Lower Little Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.236328125,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 33.247875947924385\n ],\n [\n -107.666015625,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 37.33522435930639\n ],\n [\n -112.236328125,\n 33.247875947924385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The U.S. Geological Survey, with the support of the International Joint Commission, and in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation, is leading a project that should improve information available to apportion water between Canada and the United States in the St. Mary and Milk River Basins. One component of the water budget, consumptive use of irrigation water (the amount of supplemental water used by crops), can be estimated at 100-meter resolution almost every week using imagery recorded by satellites from 1985 to present (2021) and weather data, when conditions permit. Better estimates of consumptive water use should improve understanding of water availability and use in the basin and should assist with water apportionment procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213042","collaboration":"Prepared in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, R., Friedrichs, M., and Senay, G.B., 2021, Using satellite imagery to estimate consumptive water use from irrigated lands in the Milk River Basin, United States and Canada: U.S. Geological Survey Fact Sheet 2021–3042, 2 p., https://doi.org/10.3133/fs20213042.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-130575","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3042/coverthb.jpg"},{"id":388102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3042/fs20213042.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"fs 2021–3042"}],"country":"Canada, United States","state":"Alberta, Montana, Saskatchewan","otherGeospatial":"Milk River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.851318359375,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 48.07807894349862\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
U.S. Geological Survey
MS 303
12201 Sunrise Valley Drive
Reston, VA 20192
Recreational angling in the United States (US) is largely a personal hobby that scales up to a multibillion-dollar economic activity. Given dramatic changes to personal decisions and behaviors resulting from the COVID-19 pandemic, we surveyed recreational anglers across the US to understand how the pandemic may have affected their fishing motivations and subsequent activities. Nearly a quarter million anglers from 10 US states were invited to participate in the survey, and almost 18,000 responded. Anglers reported numerous effects of the pandemic, including fishing access restrictions. Despite these barriers, we found that the amount of fishing in the spring of 2020 was significantly greater—by about 0.2 trips per angler—than in non-pandemic springs. Increased fishing is likely associated with our result that most respondents considered recreational angling to be a COVID-19 safe activity. Nearly a third of anglers reported changing their motivation for fishing during the pandemic, with stress relief being more popular during the pandemic than before. Driven partly by the perceived safety of social fishtancing, recreational angling remained a popular activity for many US anglers during spring 2020.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0254652","usgsCitation":"Midway, S.R., Lynch, A.J., Peoples, B.K., Dance, M.A., and Caffey, R., 2021, COVID-19 influences on US recreational angler behavior: PLoS ONE, v. 16, no. 8, https://doi.org/10.1371/journal.pone.0254652.","productDescription":"e0254652, 16 p.","startPage":"e0254652","ipdsId":"IP-125141","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":451125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0254652","text":"Publisher Index Page"},{"id":388585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.2308349609375,\n 17.96305758238804\n ],\n [\n -67.2198486328125,\n 17.910795834978483\n ],\n [\n -66.5716552734375,\n 17.866361230891894\n ],\n [\n -66.16790771484375,\n 17.90556881196468\n ],\n [\n -65.85205078125,\n 17.973508079068797\n ],\n [\n -65.7861328125,\n 18.04142122189195\n ],\n [\n -65.50323486328125,\n 18.06231230454674\n ],\n [\n -65.2587890625,\n 18.114529138838503\n ],\n [\n -65.269775390625,\n 18.15629140283545\n ],\n [\n -65.4400634765625,\n 18.18238775108558\n ],\n [\n -65.51422119140625,\n 18.14324176648384\n ],\n [\n -65.5609130859375,\n 18.40665471391907\n ],\n [\n -65.64880371093749,\n 18.404048629104647\n ],\n [\n -65.77789306640625,\n 18.417078658661257\n ],\n [\n -65.9124755859375,\n 18.46918890441719\n ],\n [\n -66.24755859375,\n 18.510865709091377\n ],\n [\n -66.4837646484375,\n 18.503052080569763\n ],\n [\n -66.98638916015625,\n 18.51347017266187\n ],\n [\n -67.115478515625,\n 18.534304453676864\n ],\n [\n -67.181396484375,\n 18.48742375381096\n ],\n [\n -67.16217041015625,\n 18.432713391700858\n ],\n [\n -67.2637939453125,\n 18.375379094031825\n ],\n [\n -67.19238281249999,\n 18.2397859708389\n ],\n [\n -67.2308349609375,\n 17.96305758238804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Midway, Stephen R.","contributorId":172159,"corporation":false,"usgs":false,"family":"Midway","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":822089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail J. 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":204271,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":822090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peoples, Brandon K.","contributorId":177551,"corporation":false,"usgs":false,"family":"Peoples","given":"Brandon","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":822091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dance, Michael A.","contributorId":213049,"corporation":false,"usgs":false,"family":"Dance","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":822092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caffey, Rex","contributorId":264849,"corporation":false,"usgs":false,"family":"Caffey","given":"Rex","email":"","affiliations":[{"id":54570,"text":"Louisiana State U.","active":true,"usgs":false}],"preferred":false,"id":822093,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225617,"text":"70225617 - 2021 - Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species","interactions":[],"lastModifiedDate":"2021-10-28T13:38:24.352676","indexId":"70225617","displayToPublicDate":"2021-08-18T08:24:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species","docAbstract":"Salinity (sodium chloride [NaCl]) is a prevalent and persistent contaminant that negatively affects freshwater ecosystems. Although most studies focus on effects of salinity from road salts (primarily NaCl), high-salinity wastewaters from energy extraction (wastewaters) could be more harmful because they contain NaCl and other toxic components. Many amphibians are sensitive to salinity, and their eggs are thought to be the most sensitive life-history stage. However, there are few investigations with salinity that include eggs and larvae sequentially in long-term exposures. We investigated the relative effects of wastewaters from a large energy reserve, the Williston Basin (USA), and NaCl on northern leopard (Rana pipiens) and boreal chorus (Pseudacris maculata) frogs. We exposed eggs and tracked responses through larval stages (for 24 days). Wastewaters and NaCl caused similar reductions in hatching and larval survival, growth, development, and activity, while also increasing deformities. Chorus frog eggs and larvae were more sensitive to salinity than leopard frogs, suggesting species-specific responses. Contrary to previous studies, eggs of both species were less sensitive to salinity than larvae. Our ecologically relevant exposures suggest that accumulating effects can reduce survival relative to starting experiments with unexposed larvae. Alternatively, egg casings of some species may provide some protection against salinity. Notably, effects of wastewaters on amphibians were predominantly due to NaCl rather than other components. Therefore, findings from studies with other sources of increased salinity (e.g., road salts) could guide management of wastewater-contaminated ecosystems, and vice versa, to mitigate effects of salinization.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5193","usgsCitation":"Tornabene, B., Breuner, C., and Hossack, B., 2021, Comparative effects of energy-related saline wastewaters and sodium chloride on hatching, survival, and fitness-associated traits of two amphibian species: Environmental Science & Technology, v. 40, no. 11, p. 3137-3147, https://doi.org/10.1002/etc.5193.","productDescription":"11 p.","startPage":"3137","endPage":"3147","ipdsId":"IP-129251","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":391083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","city":"Dagmar, Moran","otherGeospatial":"Pary Waterfowl Production Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.600830078125,\n 48.29781249243716\n ],\n [\n -104.04739379882812,\n 48.29781249243716\n ],\n [\n -104.04739379882812,\n 48.67101262432597\n ],\n [\n -104.600830078125,\n 48.67101262432597\n ],\n [\n -104.600830078125,\n 48.29781249243716\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58151245117188,\n 43.717519867330765\n ],\n [\n -110.32882690429688,\n 43.717519867330765\n ],\n [\n -110.32882690429688,\n 43.89591323557617\n ],\n [\n -110.58151245117188,\n 43.89591323557617\n ],\n [\n -110.58151245117188,\n 43.717519867330765\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":825937,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breuner, Creagh","contributorId":268148,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":825938,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":825939,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223506,"text":"70223506 - 2021 - Genetic sequencing of Attwater's prairie chicken avian poxvirus and evaluation of its potential role in reticuloendotheliosis virus outbreaks","interactions":[],"lastModifiedDate":"2021-08-31T13:19:42.87042","indexId":"70223506","displayToPublicDate":"2021-08-18T08:15:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Genetic sequencing of Attwater's prairie chicken avian poxvirus and evaluation of its potential role in reticuloendotheliosis virus outbreaks","docAbstract":"Efforts to breed Attwater's prairie chickens (APC; Tympanuchus cupido attwateri) in captivity to supplement wild populations of this endangered bird have been negatively affected by infections with Avipoxvirus and reticuloendotheliosis virus (REV). Because REV can be integrated into the genome of fowlpox virus (FPV) and may be transmitted in that manner, identifying the source of avipox disease in APC is important to mitigate the impact of this virus. Tissue samples from APC were collected from breeding programs in Texas from 2016 to 2020. These samples consisted of 11 skin lesions and three internal organs from a total of 14 different birds that died of unknown causes or were euthanized. Avipoxvirus was detected by PCR and isolation in embryonating chicken eggs in all skin lesion samples but was not detected in internal organs. Using sequence analysis of FPV polymerase and 4b genes, we determined that 10 out of 11 Avipoxvirus detections resided within the fowlpox clade and a single sample resided within the canarypox clade. REV sequences were detected in all FPV positive samples and in all internal organ tissues but were not detected in the sample matching the canarypox clade. Analysis of REV sequences and PCR detection showed the REV infecting APC was consistent with REV-A and had little variability on analysis of the U3 region of the long terminal repeat. The results of this study indicate control of REV in APC breeding colonies may benefit by a vaccination program targeting FPV and REV. However, a commercially available vaccine for REV is not available at this time.
Strandlines of peak-stage indicators (such as driftwood logs, woody debris, and trash) provide valuable data for understanding the maximum stage and extent of inundation during floods. A series of seven strandlines have been preserved along the Colorado River in Grand Canyon National Park, Arizona, USA. A survey and analysis of these strandlines was completed from the Colorado River at Lees Ferry, Ariz., gaging station to the Colorado River near Grand Canyon, Ariz., gaging station. Owing to the longitudinally discontinuous nature of the strandlines, several lines of evidence were used to determine the year of the flood associated with each strandline segment. This evidence included strandline relative vertical position, degree of peak-stage indicator weathering, datable trash drift, and map-view location. The seven distinct strandlines identified were deposited during floods with the following peak discharges (in cubic feet per second [ft3/s]) at the Colorado River at Lees Ferry, Ariz., gaging station (year of flood in parentheses): 210,000 ft3/s (1884), 170,000 ft3/s (1921), 125,000 ft3/s (1957), 108,000 ft3/s (1958), 97,000 ft3/s (1983), 52,500 ft3/s (1986), and 45,000 ft3/s (multiple events between 1996 and 2012). Stage-discharge relations were developed in areas where all, or most of the strandlines were present, and were compared to predicted stage-discharge relations from a one-dimensional flow model. River width exerted a strong control on these relations, with much greater stage change occurring for a given discharge change in narrower bedrock-dominated reaches than in wider reaches with more extensive channel-margin alluvium. This comprehensive dataset allows for the verification of model-predicted flood stage along the Colorado River in Grand Canyon National Park.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215048","usgsCitation":"Sabol, T.A., Griffiths, R.E., Topping, D.J., Mueller, E.R., Tusso, R.B., and Hazel, J.E., Jr., 2021, Strandlines from large floods on the Colorado River in Grand Canyon National Park, Arizona: U.S. Geological Survey Scientific Investigations Report 2021-5048, 41 p., https://doi.org/10.3133/sir20215048.","productDescription":"Report: vi, 41 p.; Data Release; Version History","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-118687","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388103,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GIQ9ZN","linkHelpText":"Surveyed peak-stage elevations, coordinates, and indicator data of strandlines from large floods on the Colorado River in Grand Canyon National Park, Arizona"},{"id":388754,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5048/versionhist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":387949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5048/covrthb.jpg"},{"id":388753,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5048/sir20215048.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.027099609375,\n 35.78662688467009\n ],\n [\n -111.37390136718749,\n 35.78662688467009\n ],\n [\n -111.37390136718749,\n 36.98500309285596\n ],\n [\n -114.027099609375,\n 36.98500309285596\n ],\n [\n -114.027099609375,\n 35.78662688467009\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Various ecomorphs of shallow-water Cisco Coregonus artedi were the dominant fish planktivores in each of the Great Lakes until invasive species and over fishing resulted in extirpations and extinctions. In this paper we describe the present morphological diversity and distribution of shallow-water Ciscoes in each of Lake Huron’s three basins: the main basin, Georgian Bay, and North Channel. Typical artedi, a formerly widespread ecomorph, which had supported the lake’s largest fishery, appears to have been extirpated from all three basins. Three types of shorthead ciscoes, a recently described and variable ecomorph, were extant. One type was morphologically robust and abundant along the north rim of the lake. The second type was large bodied, terete, short finned, and collected at only one location in the main basin. The third type consisted of putative shorthead cisco × typical artedi hybrids, which were widespread in Georgian Bay and the North Channel. Only the putative hybrids were regularly collected in midwater trawls, suggesting they were more-pelagic, which we attribute to an inferred partial ancestry with typical artedi. The putative shorthead cisco × typical artedi hybrids of Georgian Bay and the North Channel have replaced typical artedi to some degree, while shorthead ciscoes in the main basin, though possibly more abundant now than in the past, have not measurably replaced typical artedi. Even with the apparent extirpation of typical artedi, Lake Huron has a greater diversity of shallow-water Ciscoes than any of the other Great Lakes, which we attribute to its more-complex topography.
Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.
Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–750 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.
Noble gases record fluid interactions in multiphase subsurface environments through fractionation processes during fluid equilibration. Water in the presence of hydrocarbons at the subsurface acquires a distinct elemental signature due to the difference in solubility between these two fluids. We find the atmospheric noble gas signature in produced water is partially preserved after hydrocarbons production and water disposal to unlined ponds at the surface. This signature is distinct from meteoric water and can be used to trace oil-field water seepage into groundwater aquifers. We analyse groundwater (n = 30) and fluid disposal pond (n = 2) samples from areas overlying or adjacent to the Fruitvale, Lost Hills, and South Belridge Oil Fields in the San Joaquin Basin, California, USA. Methane (2.8 × 10−7 to 3 × 10−2 cm3 STP/cm3) was detected in 27 of 30 groundwater samples. Using atmospheric noble gas signatures, the presence of oil-field water was identified in 3 samples, which had equilibrated with thermogenic hydrocarbons in the reservoir. Two (of the three) samples also had a shallow microbial methane component, acquired when produced water was deposited in a disposal pond at the surface. An additional 6 samples contained benzene and toluene, indicative of interaction with oil-field water; however, the noble gas signatures of these samples are not anomalous. Based on low tritium and 14C contents (≤ 0.3 TU and 0.87–6.9 pcm, respectively), the source of oil-field water is likely deep, which could include both anthropogenic and natural processes. Incorporating noble gas analytical techniques into the groundwater monitoring programme allows us to 1) differentiate between thermogenic and microbial hydrocarbon gas sources in instances when methane isotope data are unavailable, 2) identify the carrier phase of oil-field constituents in the aquifer (gas, oil-field water, or a combination), and 3) differentiate between leakage from a surface source (disposal ponds) and from the hydrocarbon reservoir (either along natural or anthropogenic pathways such as faulty wells).
Here we respond to Baveye and colleagues' recent critique of our PROMISE model, describing how this new framework significantly advances our understanding of soil spatial heterogeneity and its influence on organic matter transformations.
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, collects data pertaining to the surface-water resources of Missouri. Established in 1964, the Ambient Water-Quality Monitoring Network (AWQMN) consisted of 69 sites in 2017. Two additional sites from the National Water-Quality Program are included with the AWQMN sites for the analyses in this report. The sites are sampled typically from 2 to 12 times per year for physical properties, total suspended solids, nutrients, fecal indicator bacteria, and trace elements.
The period of analysis for this study was from 1993 through 2017 and data analysis included 71 sites and 15 water-quality constituents plus discharge. Data analysis involved retrieving the data, conditioning the data for analysis, analyzing the data for trends, and analyzing the monitoring network to determine if potential data gaps or data redundancies exist in the network. Results from these analyses can be used to help manage the monitoring network into the future.
Water-quality data were analyzed using several software packages to provide graphical and statistical information for interpretation of trends in the data at selected sites. Discharge data at selected sites were analyzed to determine the general trends during the analysis period and how the water-quality samples represented the range of daily mean discharges at each site. Water-quality data also were analyzed at selected sites to determine the relative sensitivity of selected sites and constituents to changes in data collection frequency. Trend analysis at selected sites using a simulated reduction in sampling frequency was completed to compare to trends obtained using monthly data to determine the potential degradation in the ability of determining trends from a reduced sampling frequency. The viability of using estimated discharge to evaluate long-term trends for sites with no continuous discharge was investigated. Data from sites were statistically compared in groups to determine the relative similarity (or difference) between sites for each water-quality constituent to identify potentially redundant sites in the monitoring network.
Discharge-weighted long-term trends during 1993 through 2017 were analyzed for 15 water-quality constituents at 58 sites and results indicated there were significant single- or two-period trends in about 17 percent of the analyses. Some trends indicated improvement and some trends indicated deterioration of the general water quality at some sites in the AWQMN. No trend was indicated in about 31 percent of the analyses. The constituents pH, specific conductance, and total phosphorus showed the most frequent significant trends, and each of the 15 constituents examined had a significant trend at one or more sites. A total of 42 sites indicated at least 1 constituent with a significant single- or two-period trend, and 10 sites indicated 6 or more significant trends.
Potential data gaps identified for computing discharge-weighted long-term trends in the monitoring network included the lack of collection of continuous discharge at 23 sites, insufficient sampling frequency for some constituents (dissolved chloride and total and dissolved lead and zinc) at some sites, insufficient temporal sample distribution (lack of at least one sample in each season per year) at some sites, and insufficient sampling frequency for some highly censored constituents (nutrients and total and dissolved lead and zinc) at some sites. Potential data gaps based on site spatial distribution were identified in 7 basins greater than 800 square miles.
Potential site redundancies were identified in 4 basins that had an area greater than 500 square miles with a site density greater than 2 sites per 1,000 square miles. Potential site redundancies also were identified for nine site pairs by observing statistical similarities in the constituent data distributions. Sampling frequency was investigated to determine if reducing the sampling frequency of select constituents could provide a statistically similar data distribution. At 28 of 71 sites, 11 constituents had sufficient data collection frequency (approximately monthly) to allow for the creation of simulated datasets of various reduced data collection frequency. For the selected monitoring network sites analyzed, the data distribution of a simulated sampling frequency of four times per year or greater, roughly evenly distributed over the year, was not significantly different than the data distribution of the original monthly sampling frequency. Sites analyzed using varying simulated sampling frequencies tended to be more sensitive to sampling frequency changes if they were in basins classified as large or very large size and tended to be least sensitive in basins classified as small and medium size in the Ozark Plateaus Province. Simulated reduced frequency sampling analysis indicated that the constituents and measurements most sensitive to changes in sampling frequencies were water temperature, dissolved oxygen, discharge, and dissolved nitrate, and least sensitive were pH, total suspended solids, dissolved phosphorus, and total phosphorus. Discharge-weighted long-term trend analysis was repeated at 22 sites for 11 constituents using a simulated quarterly sampling frequency, and matched about 46 percent of the significant single-period trends identified using monthly data and about 65 percent of the analyses that indicated no trend using the monthly data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215079","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079.","productDescription":"Report: xi, 75 p.; Data Release; Dataset; 11 Tables","numberOfPages":"91","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123674","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":388009,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5079/downloads/xlsx/","text":"Tables 1, 2, 4, 5, 6, 15, 16, 17, 18, 20, and 22 in .xlsx format","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2021–5079 Tables"},{"id":388008,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2021/5079/downloads/csv/","text":"Tables 1, 2, 4, 5, 6, 15, 16, 17, 18, 20, and 22 in .csv format","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2021–5079 Tables"},{"id":388002,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R2R9DF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supporting data for analysis of general water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017"},{"id":388000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5079/coverthb.jpg"},{"id":388001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5079/sir20215079.pdf","text":"Report","size":"7.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5079"},{"id":388003,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Missouri","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.545006,36.336809],[-89.605668,36.342234],[-89.615841,36.336085],[-89.620255,36.323006],[-89.611819,36.309088],[-89.578492,36.288317],[-89.554289,36.277751],[-89.539487,36.277368],[-89.534507,36.261802],[-89.539229,36.248821],[-89.562206,36.250909],[-89.577544,36.242262],[-89.602374,36.238106],[-89.642182,36.249486],[-89.678046,36.248284],[-89.695235,36.252766],[-89.705328,36.239898],[-89.69263,36.224959],[-89.607004,36.171179],[-89.591605,36.144096],[-89.59307,36.129699],[-89.601936,36.11947],[-89.666598,36.095802],[-89.678821,36.084636],[-89.688577,36.029238],[-89.706932,36.000981],[-90.37789,35.995683],[-90.351732,36.025347],[-90.34909,36.040131],[-90.339343,36.047112],[-90.333261,36.067504],[-90.320746,36.071326],[-90.320662,36.087138],[-90.29991,36.098236],[-90.294492,36.112949],[-90.266256,36.120559],[-90.235585,36.139474],[-90.231386,36.147348],[-90.23537,36.159153],[-90.220425,36.184764],[-90.21128,36.183392],[-90.188189,36.20536],[-90.152497,36.215582],[-90.14224,36.227522],[-90.126366,36.229367],[-90.130114,36.240307],[-90.118219,36.253491],[-90.114922,36.265595],[-90.086471,36.271531],[-90.06398,36.303038],[-90.081961,36.322097],[-90.074074,36.342895],[-90.077695,36.348478],[-90.066297,36.3593],[-90.064514,36.382085],[-90.078671,36.399116],[-90.138512,36.413952],[-90.134231,36.422827],[-90.143743,36.424433],[-90.143798,36.428483],[-90.134136,36.436602],[-90.137323,36.455411],[-90.141101,36.461791],[-90.155804,36.463555],[-90.152888,36.47093],[-90.142222,36.470554],[-90.143683,36.476029],[-90.158838,36.479558],[-90.159305,36.492446],[-90.152481,36.497952],[-94.617919,36.499414],[-94.617975,37.722176],[-94.607354,39.113444],[-94.589933,39.140403],[-94.591933,39.155003],[-94.608834,39.160503],[-94.640035,39.153103],[-94.662435,39.157603],[-94.663835,39.179103],[-94.680336,39.184303],[-94.714137,39.170403],[-94.741938,39.170203],[-94.763138,39.179903],[-94.781518,39.206146],[-94.811663,39.206594],[-94.831679,39.215938],[-94.835056,39.220658],[-94.825663,39.241729],[-94.831471,39.256273],[-94.84632,39.268481],[-94.887056,39.28648],[-94.905329,39.311952],[-94.910017,39.352543],[-94.88136,39.370383],[-94.879281,39.37978],[-94.885026,39.389801],[-94.901823,39.392798],[-94.92311,39.384492],[-94.942039,39.389499],[-94.946293,39.405646],[-94.972952,39.421705],[-94.982144,39.440552],[-95.0375,39.463689],[-95.045716,39.472459],[-95.052177,39.499996],[-95.082714,39.516712],[-95.109304,39.542285],[-95.113077,39.559133],[-95.103228,39.577783],[-95.089515,39.581028],[-95.064519,39.577115],[-95.049277,39.589583],[-95.046361,39.599557],[-95.055152,39.621657],[-95.053367,39.630347],[-95.027644,39.665454],[-95.018318,39.672869],[-94.984149,39.67785],[-94.971317,39.68641],[-94.971206,39.729305],[-94.965318,39.739065],[-94.948726,39.745593],[-94.902612,39.724202],[-94.875643,39.730494],[-94.862943,39.742994],[-94.860743,39.763094],[-94.869644,39.772894],[-94.912293,39.759338],[-94.934262,39.773642],[-94.935206,39.78313],[-94.929654,39.788282],[-94.884084,39.794234],[-94.875944,39.813294],[-94.878677,39.826522],[-94.886933,39.833098],[-94.916918,39.836138],[-94.942567,39.856602],[-94.928466,39.876344],[-94.929574,39.888754],[-94.95154,39.900533],[-94.986975,39.89667],[-95.00844,39.900596],[-95.024389,39.891202],[-95.027931,39.871522],[-95.037767,39.865542],[-95.085003,39.861883],[-95.128166,39.874165],[-95.140601,39.881688],[-95.143802,39.901918],[-95.149657,39.905948],[-95.179453,39.900062],[-95.199347,39.902709],[-95.206326,39.912121],[-95.20069,39.928155],[-95.204428,39.938949],[-95.250254,39.948644],[-95.269886,39.969396],[-95.302507,39.984357],[-95.315271,40.01207],[-95.356876,40.031522],[-95.387195,40.02677],[-95.40726,40.033112],[-95.416824,40.043235],[-95.42164,40.058952],[-95.409856,40.07432],[-95.407591,40.09803],[-95.394216,40.108263],[-95.39284,40.115887],[-95.398667,40.126419],[-95.428749,40.135577],[-95.436348,40.15872],[-95.460746,40.169173],[-95.479193,40.185652],[-95.482757,40.197346],[-95.469718,40.227908],[-95.477501,40.24272],[-95.490333,40.248966],[-95.521925,40.24947],[-95.552473,40.261904],[-95.556325,40.267714],[-95.550966,40.285947],[-95.562157,40.297359],[-95.581787,40.29958],[-95.610439,40.31397],[-95.642262,40.306025],[-95.657328,40.310856],[-95.653729,40.322582],[-95.625204,40.334288],[-95.623728,40.346567],[-95.641027,40.366399],[-95.643934,40.386849],[-95.659134,40.40869],[-95.65819,40.44188],[-95.693133,40.469396],[-95.699969,40.505275],[-95.661687,40.517309],[-95.652262,40.538114],[-95.655848,40.546609],[-95.671754,40.562626],[-95.678718,40.56256],[-95.694147,40.556942],[-95.69505,40.533124],[-95.708591,40.521551],[-95.722444,40.528118],[-95.75711,40.52599],[-95.769281,40.536656],[-95.763366,40.550797],[-95.773549,40.578205],[-95.765645,40.585208],[-94.632035,40.571186],[-94.080463,40.572899],[-92.689854,40.589884],[-91.729115,40.61364],[-91.716769,40.59853],[-91.686357,40.580875],[-91.690804,40.559893],[-91.681714,40.553035],[-91.6219,40.542292],[-91.618028,40.53403],[-91.621353,40.510072],[-91.590817,40.492292],[-91.574746,40.465664],[-91.52509,40.457845],[-91.524053,40.448437],[-91.533623,40.43832],[-91.519935,40.433673],[-91.526555,40.419872],[-91.522333,40.409648],[-91.498093,40.401926],[-91.489816,40.404317],[-91.484507,40.3839],[-91.465116,40.385257],[-91.465009,40.376223],[-91.452458,40.375501],[-91.441243,40.386255],[-91.419422,40.378264],[-91.444833,40.36317],[-91.46214,40.342414],[-91.492727,40.278217],[-91.490524,40.259498],[-91.505828,40.238839],[-91.505495,40.195606],[-91.512974,40.181062],[-91.508224,40.157665],[-91.510322,40.127994],[-91.489606,40.057435],[-91.494878,40.036453],[-91.465315,39.983995],[-91.41936,39.927717],[-91.41988,39.916533],[-91.443513,39.893583],[-91.446922,39.883034],[-91.436051,39.84551],[-91.377971,39.811273],[-91.361571,39.787548],[-91.370009,39.732524],[-91.3453,39.709402],[-91.27614,39.665759],[-91.229317,39.620853],[-91.181936,39.602677],[-91.174651,39.593313],[-91.168419,39.564928],[-91.153628,39.548248],[-91.100307,39.538695],[-91.079769,39.507728],[-91.064305,39.494643],[-91.059439,39.46886],[-91.03827,39.448436],[-90.993789,39.422959],[-90.940766,39.403984],[-90.928745,39.387544],[-90.904862,39.379403],[-90.893777,39.367343],[-90.8475,39.345272],[-90.816851,39.320496],[-90.793461,39.309498],[-90.751599,39.265432],[-90.72996,39.255894],[-90.717113,39.213912],[-90.707902,39.15086],[-90.686051,39.117785],[-90.681086,39.10059],[-90.681994,39.090066],[-90.712541,39.057064],[-90.71158,39.046798],[-90.678193,38.991851],[-90.675949,38.96214],[-90.657254,38.92027],[-90.639917,38.908272],[-90.625122,38.888654],[-90.583388,38.86903],[-90.555693,38.870785],[-90.500117,38.910408],[-90.486974,38.925982],[-90.482419,38.94446],[-90.472122,38.958838],[-90.440078,38.967364],[-90.395816,38.960037],[-90.309454,38.92412],[-90.250248,38.919344],[-90.109407,38.843548],[-90.123107,38.798048],[-90.166409,38.772649],[-90.176309,38.754449],[-90.20991,38.72605],[-90.20921,38.70275],[-90.18641,38.67475],[-90.181325,38.660381],[-90.17801,38.63375],[-90.18451,38.611551],[-90.196011,38.594451],[-90.222112,38.576451],[-90.260314,38.528352],[-90.285215,38.443453],[-90.295316,38.426753],[-90.349743,38.377609],[-90.368219,38.340254],[-90.373929,38.281853],[-90.353902,38.213855],[-90.331554,38.18758],[-90.290765,38.170453],[-90.274928,38.157615],[-90.243116,38.112669],[-90.218708,38.094365],[-90.17222,38.069636],[-90.158533,38.074735],[-90.130788,38.062341],[-90.126612,38.043981],[-90.11052,38.026547],[-90.08826,38.015772],[-90.059367,38.015543],[-90.051357,38.003584],[-90.03241,37.995258],[-90.00011,37.964563],[-89.978919,37.962791],[-89.942099,37.970121],[-89.933797,37.959143],[-89.925085,37.960021],[-89.932467,37.947497],[-89.959646,37.940196],[-89.974918,37.926719],[-89.952499,37.883218],[-89.923185,37.870672],[-89.901832,37.869822],[-89.844786,37.905572],[-89.799333,37.881517],[-89.796087,37.859505],[-89.786369,37.851734],[-89.782035,37.855092],[-89.739873,37.84693],[-89.71748,37.825724],[-89.669644,37.799922],[-89.660227,37.781032],[-89.667993,37.759484],[-89.665546,37.752095],[-89.64953,37.745498],[-89.617278,37.74972],[-89.612478,37.740036],[-89.596566,37.732886],[-89.583316,37.713261],[-89.516685,37.692762],[-89.51204,37.680985],[-89.517718,37.641217],[-89.478399,37.598869],[-89.47603,37.590226],[-89.486062,37.580853],[-89.519808,37.582748],[-89.521925,37.560735],[-89.517051,37.537278],[-89.475525,37.471388],[-89.439769,37.4372],[-89.421054,37.387668],[-89.432836,37.347056],[-89.489005,37.333368],[-89.511842,37.310825],[-89.51834,37.285497],[-89.489915,37.251315],[-89.470525,37.253357],[-89.458827,37.248661],[-89.467631,37.2182],[-89.456105,37.18812],[-89.42558,37.138235],[-89.37871,37.094586],[-89.375712,37.080505],[-89.384681,37.048251],[-89.362397,37.030156],[-89.322982,37.01609],[-89.29213,36.992189],[-89.278628,36.98867],[-89.263527,37.00005],[-89.257608,37.015496],[-89.260003,37.023288],[-89.304752,37.047565],[-89.310819,37.057897],[-89.30829,37.068371],[-89.259936,37.064071],[-89.25493,37.072014],[-89.234053,37.037277],[-89.200793,37.016164],[-89.192097,36.979995],[-89.185491,36.973518],[-89.170008,36.970298],[-89.125069,36.983499],[-89.109498,36.976563],[-89.099594,36.964543],[-89.100762,36.944002],[-89.117567,36.887356],[-89.131944,36.857437],[-89.137969,36.847349],[-89.1704,36.841522],[-89.178888,36.831368],[-89.179229,36.812915],[-89.171069,36.798119],[-89.155891,36.789126],[-89.12353,36.785309],[-89.116563,36.767557],[-89.126134,36.751735],[-89.166888,36.759633],[-89.184523,36.753638],[-89.197808,36.739412],[-89.19948,36.716045],[-89.169522,36.688878],[-89.169467,36.674596],[-89.15908,36.666352],[-89.197654,36.628936],[-89.202607,36.601576],[-89.217447,36.576159],[-89.236542,36.566824],[-89.258318,36.564948],[-89.278935,36.577699],[-89.326731,36.632186],[-89.365548,36.625059],[-89.375453,36.615719],[-89.382762,36.583603],[-89.41977,36.493896],[-89.448468,36.46442],[-89.464153,36.457189],[-89.486215,36.46162],[-89.494248,36.475972],[-89.465888,36.529946],[-89.467761,36.546847],[-89.479093,36.568206],[-89.500076,36.576305],[-89.542459,36.580566],[-89.566817,36.564216],[-89.571241,36.547343],[-89.560344,36.525436],[-89.519501,36.475419],[-89.523427,36.456572],[-89.543406,36.43877],[-89.545255,36.427079],[-89.509722,36.373626],[-89.519,36.3486],[-89.545006,36.336809]]]},\"properties\":{\"name\":\"Missouri\",\"nation\":\"USA \"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Many of the world's rivers are ice-covered during winter months but increasing evidence indicates that the extent of river ice will shift substantially as winters warm. However, our knowledge of rivers during winter lags far behind that of the growing season, limiting our understanding of how ice loss will affect rivers. Physical, chemical, and biological processes change from headwaters to large rivers; thus, we expect ice processes and resulting effects on the ecology of rivers could also vary with river size, as a result of the associated changes in geomorphology, temperature regimes, and connectivity. To conceptualize these relationships, we review typically disparate literature on ice processes and winter ecology and compare what is known in the smallest and largest rivers. In doing so, we show that our ability to link ice with ecology across river networks is made difficult by a primary focus on ice processes in larger rivers and a lack of study of ecosystem processes during winter. To address some of these gaps, we provide new scenarios of river ice loss and analyses of how the annual importance of winter gross primary productivity (GPP) varies with river size. We show projected ice loss varied with large-scale watershed characteristics such as north-south orientation and that the importance of winter to annual GPP was greatest in the smallest rivers. Finally, we highlight information needed to fill knowledge gaps on winter across river networks and improve our understanding of how rivers may change as climate and ice regimes shift.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006275","usgsCitation":"Thellman, A., Jankowski, K.J., Hayden, B., Yang, X., Dolan, W., Smits, A.P., and O’Sullivan, A.M., 2021, The ecology of river ice: JGR Biogeosciences, v. 126, no. 9, e2021JG006275, 28 p., https://doi.org/10.1029/2021JG006275.","productDescription":"e2021JG006275, 28 p.","ipdsId":"IP-126569","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Thellman, Audrey 0000-0003-3716-6664","orcid":"https://orcid.org/0000-0003-3716-6664","contributorId":265349,"corporation":false,"usgs":false,"family":"Thellman","given":"Audrey","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":822601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":822602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayden, Brian","contributorId":190917,"corporation":false,"usgs":false,"family":"Hayden","given":"Brian","email":"","affiliations":[],"preferred":false,"id":822603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yang, Xiao","contributorId":149701,"corporation":false,"usgs":false,"family":"Yang","given":"Xiao","affiliations":[],"preferred":false,"id":822604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dolan, Wayana 0000-0001-8405-4302","orcid":"https://orcid.org/0000-0001-8405-4302","contributorId":265350,"corporation":false,"usgs":false,"family":"Dolan","given":"Wayana","email":"","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":822605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smits, Adrianne P 0000-0001-9967-5419","orcid":"https://orcid.org/0000-0001-9967-5419","contributorId":217759,"corporation":false,"usgs":false,"family":"Smits","given":"Adrianne","email":"","middleInitial":"P","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":822606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"O’Sullivan, Antoin M 0000-0003-0599-887X","orcid":"https://orcid.org/0000-0003-0599-887X","contributorId":265351,"corporation":false,"usgs":false,"family":"O’Sullivan","given":"Antoin","email":"","middleInitial":"M","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":822607,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229104,"text":"70229104 - 2021 - The roles of antimicrobial resistance, phage diversity, isolation source, and selection in shaping the genomic architecture of Bacillus anthracis","interactions":[],"lastModifiedDate":"2022-03-01T15:17:43.049137","indexId":"70229104","displayToPublicDate":"2021-08-17T09:10:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10142,"text":"Microbial Genomics","onlineIssn":"2057-5858","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The roles of antimicrobial resistance, phage diversity, isolation source, and selection in shaping the genomic architecture of Bacillus anthracis ","title":"The roles of antimicrobial resistance, phage diversity, isolation source, and selection in shaping the genomic architecture of Bacillus anthracis","docAbstract":"Bacillus anthracis, the causative agent of anthrax disease, is a worldwide threat to livestock, wildlife and public health. While analyses of genetic data from across the globe have increased our understanding of this bacterium’s population genomic structure, the influence of selective pressures on this successful pathogen is not well understood. In this study, we investigate the effects of antimicrobial resistance, phage diversity, geography and isolation source in shaping population genomic structure. We also identify a suite of candidate genes potentially under selection, driving patterns of diversity across 356 globally extant B. anthracis genomes. We report ten antimicrobial resistance genes and 11 different prophage sequences, resulting in the first large-scale documentation of these genetic anomalies for this pathogen. Results of random forest classification suggest genomic structure may be driven by a combination of antimicrobial resistance, geography and isolation source, specific to the population cluster examined. We found strong evidence that a recombination event linked to a gene involved in protein synthesis may be responsible for phenotypic differences between comparatively disparate populations. We also offer a list of genes for further examination of B. anthracis evolution, based on high-impact single nucleotide polymorphisms (SNPs) and clustered mutations. The information presented here sheds new light on the factors driving genomic structure in this notorious pathogen and may act as a road map for future studies aimed at understanding functional differences in terms of B. anthracis biogeography, virulence and evolution.
","language":"English","doi":"10.1099/mgen.0.000616","usgsCitation":"Bruce, S., Huang, Y., Kamath, P., van Heerden, H., and Turner, W.C., 2021, The roles of antimicrobial resistance, phage diversity, isolation source, and selection in shaping the genomic architecture of Bacillus anthracis: Microbial Genomics, v. 7, no. 8, 000616, 12 p., https://doi.org/10.1099/mgen.0.000616.","productDescription":"000616, 12 p.","ipdsId":"IP-122314","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451152,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1099/mgen.0.000616","text":"Publisher Index Page"},{"id":396603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bruce, Spencer A.","contributorId":287146,"corporation":false,"usgs":false,"family":"Bruce","given":"Spencer A.","affiliations":[{"id":61494,"text":"University of Albany - SUNY","active":true,"usgs":false}],"preferred":false,"id":836522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huang, Yen-Hua","contributorId":287147,"corporation":false,"usgs":false,"family":"Huang","given":"Yen-Hua","affiliations":[{"id":61495,"text":"University of Albany -SUNY","active":true,"usgs":false}],"preferred":false,"id":836523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kamath, Pauline L.","contributorId":287148,"corporation":false,"usgs":false,"family":"Kamath","given":"Pauline L.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":836524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van Heerden, Henriette","contributorId":287149,"corporation":false,"usgs":false,"family":"van Heerden","given":"Henriette","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":836525,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836521,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224929,"text":"70224929 - 2021 - Warmer winters increase the biomass of phytoplankton in a large floodplain river","interactions":[],"lastModifiedDate":"2021-10-06T12:59:41.249846","indexId":"70224929","displayToPublicDate":"2021-08-17T07:53:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Warmer winters increase the biomass of phytoplankton in a large floodplain river","docAbstract":"Winters are changing rapidly across the globe but the implications for aquatic productivity and food webs are not well understood. In addition, the degree to which winter dynamics in aquatic systems respond to large-scale climate versus ecosystem-level factors is unclear but important for understanding and managing potential changes. We used a unique winter data set from the Upper Mississippi River System to explore spatial and temporal patterns in phytoplankton biomass (chlorophyll a, CHL) and associated environmental covariates across 25 years and ∼1,500 river km. To assess the role of regional climate versus site-specific drivers of winter CHL, we evaluated whether there were coherent long-term CHL dynamics from north to south and across lotic-lentic areas. We then estimated the degree to which these patterns were associated with climate variability (i.e., the Multivariate El Nino-Southern Oscillation Index), winter severity (freezing degree days), river discharge, or site-specific environmental variables (ice depth, snow depth, and nutrient concentrations). We found that winter CHL was typically highest in ice-free reaches and backwater lakes, occasionally exceeding summer values. We did not find highly synchronous CHL dynamics across the basin, but instead show that temporal trends were independent among river reaches and lotic-lentic areas of the river. Moreover, after accounting for these spatial dynamics, we found that CHL was most responsive to winter air temperature, being consistently higher in years with warmer winters across the basin. These results indicate that although productivity dynamics are highly dynamic within large river ecosystems, changes in the duration and severity of winter may uniformly increase wintertime productivity.
Flathead catfish are rapidly expanding into nonnative waterways throughout the United States. Once established, flathead catfish may cause disruptions to the local ecosystem through consumption and competition with native fishes, including species of conservation concern. Flathead catfish often become a popular sport fish in their introduced range, and so management strategies must frequently balance the need to protect native and naturalized fauna while meeting the desire to maintain or enhance fisheries. However, there are currently few tools available to inform management of invasive flathead catfish (Pylodictis olivaris). We describe a suite of microsatellite loci that can be used to characterize population structure, predict invasion history, and assess potential mitigation strategies for flathead catfish.
","language":"English","publisher":"Springer","doi":"10.1186/s13104-021-05725-2","usgsCitation":"White, S.L., Eackles, M.S., Wagner, T., Schall, M.K., Smith, G., Avery, J., and Kazyak, D., 2021, Optimization of a suite of flathead catfish (Pylodictis olivaris) microsatellite markers for understanding the population genetics of introduced populations in the northeast United States: BMC Research Notes, 341, 14 p., https://doi.org/10.1186/s13104-021-05725-2.","productDescription":"341, 14 p.","ipdsId":"IP-129433","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-021-05725-2","text":"Publisher Index Page"},{"id":388393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","noUsgsAuthors":false,"publicationDate":"2021-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":821770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schall, Megan K.","contributorId":115964,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":17758,"text":"Pennsylvania State Univ.","active":true,"usgs":false}],"preferred":false,"id":821771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Geoffrey","contributorId":199064,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","affiliations":[],"preferred":false,"id":821772,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Avery, Julian","contributorId":264623,"corporation":false,"usgs":false,"family":"Avery","given":"Julian","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":821773,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":821774,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223173,"text":"ofr20211076 - 2021 - An integrated population model for southern sea otters","interactions":[],"lastModifiedDate":"2021-08-17T12:12:45.270165","indexId":"ofr20211076","displayToPublicDate":"2021-08-16T13:30:04","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1076","displayTitle":"An Integrated Population Model for Southern Sea Otters","title":"An integrated population model for southern sea otters","docAbstract":"Southern sea otters (Enhydra lutris nereis) have recovered slowly from their near extinction a century ago, and their continued recovery has been challenged by multiple natural and anthropogenic factors. Development of an integrated population model (IPM) for southern sea otters has been identified as a management priority, to help in evaluating the relative impacts of known threats and guide best management options for species recovery. An IPM represents an analytical modeling framework where various types of data relevant to animal health, population trends, and survival can be evaluated collectively to project future population dynamics under different resource management scenarios. Here, we describe the development of a spatially explicit IPM for southern sea otters that is fit by using Bayesian methods to multiple datasets including a time series of range-wide survey counts, estimated survival rates of tagged animals from telemetry-based population studies, and cause-of-death data from comprehensive necropsies of beach-cast carcasses. The core of the model is a stage-structured matrix, in which survival rates for a given life history stage, year, and location are computed as the outcome of multiple ‘competing risks,’ or hazards, allowing for spatiotemporal variation in each hazard, density-dependence, and stochasticity. The parameterized IPM was used to (1) examine how age and sex-specific hazards vary over space and time, (2) gain insights into density-dependent variation in specific hazards, (3) assess population-level effects of known mortality hazards in the past and in future projections, and (4) evaluate the relative benefits of various potential management actions to address these hazards.
Our results indicated that different types of hazards have variable impacts at different life history stages of sea otters; for example, shark-bite mortality had a strong impact on mortality of subadult females but relatively low impacts on aged adult female survival, whereas End Lactation Syndrome showed just the opposite age-based pattern. There also was spatial and temporal variation in exposure to different hazards; for example, shark-bite mortality generally was highest at the north and south ends of the sea otter range, End Lactation Syndrome and cardiac disease were highest in the center part of the range, and harmful algal bloom intoxication and protozoal infection mortalities were highest around Morro Bay. The relative impacts of hazards depended on population density; for example, shark-bite mortality had the greatest effect on male survival when population abundance was low, but as densities increased the impacts of cardiac disease (for aged adults) and acanthocephalan peritonitis (for subadults) exceeded the effects of shark-bite mortality. Sensitivity analyses showed that modifying certain hazard rates can have substantial impacts on future population growth; for example, if the shark-bite hazard rate were to decrease by 20 percent, projected abundance after 50 years is predicted to be 18-percent higher, on average, than under baseline conditions. We used the IPM to evaluate the possible impacts of a potential management action: the reintroduction of sea otters to currently unoccupied parts of their historical range. We found that there were large increases in expected growth potential associated with reintroduction programs to various locations to the north and south of the currently occupied range, although a reintroduction to San Francisco Bay was projected to have the greatest potential impacts on future population growth.
The IPM for southern sea otters presented here provides resource managers with a useful tool for evaluating the impacts of specific hazards, forecasting future population dynamics and range expansion, and evaluating alternative management scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211076","programNote":"Wildlife Program","usgsCitation":"Tinker, M.T., Carswell, L.P., Tomoleoni, J.A., Hatfield, B.B., Harris, M.D., Miller, M.A., Moriarty, M.E., Johnson, C.K., Young, C., Henkel, L.A., Staedler, M.M., Miles, A.K., and Yee, J.L., 2021, An integrated population model for southern sea otters: U.S. Geological Survey Open-File Report 2021–1076, 50 p., https://doi.org/10.3133/ofr20211076.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-126237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387937,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1076/images"},{"id":387936,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.xml"},{"id":387935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1076/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23388671874999,\n 37.125286284966805\n ],\n [\n -121.59667968749999,\n 37.37015718405753\n ],\n [\n -121.55273437499999,\n 37.666429212090605\n ],\n [\n -122.1240234375,\n 38.61687046392973\n ],\n [\n -122.84912109375,\n 39.30029918615029\n ],\n [\n -123.37646484374999,\n 40.329795743702064\n ],\n [\n -123.37646484374999,\n 40.84706035607122\n ],\n [\n -123.3544921875,\n 41.705728515237524\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -124.49707031249999,\n 42.01665183556825\n ],\n [\n -124.98046874999999,\n 40.94671366508002\n ],\n [\n -124.67285156250001,\n 39.90973623453719\n ],\n [\n -124.18945312500001,\n 38.92522904714054\n ],\n [\n -123.3544921875,\n 37.579412513438385\n ],\n [\n -122.9150390625,\n 37.23032838760387\n ],\n [\n -122.23388671874999,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Salinity-driven density stratification of the upper Arctic Ocean isolates sea-ice cover and cold, nutrient-poor surface waters from underlying warmer, nutrient-rich waters. Recently, stratification has strengthened in the western Arctic but has weakened in the eastern Arctic; it is unknown if these trends will continue. Here we present foraminifera-bound nitrogen isotopes from Arctic Ocean sediments since 35,000 years ago to reconstruct past changes in nutrient sources and the degree of nutrient consumption in surface waters, the latter reflecting stratification. During the last ice age and early deglaciation, the Arctic was dominated by Atlantic-sourced nitrate and incomplete nitrate consumption, indicating weaker stratification. Starting at 11,000 years ago in the western Arctic, there is a clear isotopic signal of Pacific-sourced nitrate and complete nitrate consumption associated with the flooding of the Bering Strait. These changes reveal that the strong stratification of the western Arctic relies on low-salinity inflow through the Bering Strait. In the central Arctic, nitrate consumption was complete during the early Holocene, then declined after 5,000 years ago as summer insolation decreased. This sequence suggests that precipitation and riverine freshwater fluxes control the stratification of the central Arctic Ocean. Based on these findings, ongoing warming will cause strong stratification to expand into the central Arctic, slowing the nutrient supply to surface waters and thus limiting future phytoplankton productivity.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41561-021-00789-y","usgsCitation":"Farmer, J.R., Sigman, D., Granger, J., Underwood, O.M., Frapiat, F., Cronin, T.M., Martinez-Garcia, A., and Haug, G.H., 2021, Arctic Ocean stratification set by sea level and freshwater inputs since the last ice age: Nature Geoscience, v. 14, p. 684-689, https://doi.org/10.1038/s41561-021-00789-y.","productDescription":"6 p.","startPage":"684","endPage":"689","ipdsId":"IP-118860","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":451157,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41561-021-00789-y","text":"Publisher Index Page"},{"id":396117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic Ocean","volume":"14","noUsgsAuthors":false,"publicationDate":"2021-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, Jesse R.","contributorId":279531,"corporation":false,"usgs":false,"family":"Farmer","given":"Jesse","email":"","middleInitial":"R.","affiliations":[{"id":57270,"text":"1Department of Geosciences, Princeton University","active":true,"usgs":false}],"preferred":false,"id":835099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sigman, Daniel","contributorId":279532,"corporation":false,"usgs":false,"family":"Sigman","given":"Daniel","email":"","affiliations":[{"id":57270,"text":"1Department of Geosciences, Princeton University","active":true,"usgs":false}],"preferred":false,"id":835100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Granger, Julie","contributorId":279533,"corporation":false,"usgs":false,"family":"Granger","given":"Julie","affiliations":[{"id":57272,"text":"3Department of Marine Sciences, University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":835101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Underwood, Ona M.","contributorId":279660,"corporation":false,"usgs":false,"family":"Underwood","given":"Ona","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":835307,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frapiat, Francois","contributorId":279534,"corporation":false,"usgs":false,"family":"Frapiat","given":"Francois","email":"","affiliations":[{"id":57273,"text":"2Max-Planck Institute for Chemistry","active":true,"usgs":false}],"preferred":false,"id":835102,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cronin, Thomas M. 0000-0002-2643-0979 tcronin@usgs.gov","orcid":"https://orcid.org/0000-0002-2643-0979","contributorId":2579,"corporation":false,"usgs":true,"family":"Cronin","given":"Thomas","email":"tcronin@usgs.gov","middleInitial":"M.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":835103,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Martinez-Garcia, Alfredo","contributorId":279535,"corporation":false,"usgs":false,"family":"Martinez-Garcia","given":"Alfredo","email":"","affiliations":[{"id":57273,"text":"2Max-Planck Institute for Chemistry","active":true,"usgs":false}],"preferred":false,"id":835104,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haug, Gerald H.","contributorId":279536,"corporation":false,"usgs":false,"family":"Haug","given":"Gerald","email":"","middleInitial":"H.","affiliations":[{"id":57274,"text":"Max-Planck Institute for Chemistry","active":true,"usgs":false}],"preferred":false,"id":835105,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224933,"text":"70224933 - 2021 - Multiple coping strategies maintain stability of a small mammal population in a resource-restricted environment","interactions":[],"lastModifiedDate":"2021-10-06T12:31:28.063054","indexId":"70224933","displayToPublicDate":"2021-08-16T07:26:08","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Multiple coping strategies maintain stability of a small mammal population in a resource-restricted environment","docAbstract":"In semi-arid environments, aperiodic rainfall pulses determine plant production and resource availability for higher trophic levels, creating strong bottom-up regulation. The influence of climatic factors on population vital rates often shapes the dynamics of small mammal populations in such resource-restricted environments. Using a 21-year biannual capture–recapture dataset (1993 to 2014), we examined the impacts of climatic factors on the population dynamics of the brush mouse (Peromyscus boylii) in semi-arid oak woodland of coastal-central California. We applied Pradel's temporal symmetry model to estimate capture probability (p), apparent survival (φ), recruitment (f), and realized population growth rate (λ) of the brush mouse and examined the effects of temperature, rainfall, and El Niño on these demographic parameters. The population was stable during the study period with a monthly realized population growth rate of 0.993 ± SE 0.032, but growth varied over time from 0.680 ± 0.054 to 1.450 ± 0.083. Monthly survival estimates averaged 0.789 ± 0.005 and monthly recruitment estimates averaged 0.175 ± 0.038. Survival probability and realized population growth rate were positively correlated with rainfall and negatively correlated with temperature. In contrast, recruitment was negatively correlated with rainfall and positively correlated with temperature. Brush mice maintained their population through multiple coping strategies, with high recruitment during warmer and drier periods and higher survival during cooler and wetter conditions. Although climatic change in coastal-central California will likely favor recruitment over survival, varying strategies may serve as a mechanism by which brush mice maintain resilience in the face of climate change. Our results indicate that rainfall and temperature are both important drivers of brush mouse population dynamics and will play a significant role in predicting the future viability of brush mice under a changing climate.