Knowledge of animal diets provides essential insights into their life history and ecology, although diet estimation is challenging and remains an active area of research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diet composition, especially for marine species. A primary assumption of QFASA is that constants called calibration coefficients, which account for the differential metabolism of individual fatty acids, are known. In practice, however, calibration coefficients are not known, but rather have been estimated in feeding trials with captive animals of a limited number of model species. The impossibility of verifying the accuracy of feeding trial derived calibration coefficients to estimate the diets of wild animals is a foundational problem with QFASA that has generated considerable criticism. We present a new model that allows simultaneous estimation of diet composition and calibration coefficients based only on fatty acid signature samples from wild predators and potential prey. Our model performed almost flawlessly in four tests with constructed examples, estimating both diet proportions and calibration coefficients with essentially no error. We also applied the model to data from Chukchi Sea polar bears, obtaining diet estimates that were more diverse than estimates conditioned on feeding trial calibration coefficients. Our model avoids bias in diet estimates caused by conditioning on inaccurate calibration coefficients, invalidates the primary criticism of QFASA, eliminates the need to conduct feeding trials solely for diet estimation, and consequently expands the utility of fatty acid data to investigate aspects of ecology linked to animal diets.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3179","usgsCitation":"Bromaghin, J.F., Budge, S.M., Thiemann, G.W., and Rode, K.D., 2017, Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data: Ecology and Evolution, v. 7, no. 16, p. 6103-6113, https://doi.org/10.1002/ece3.3179.","productDescription":"11 p.","startPage":"6103","endPage":"6113","ipdsId":"IP-082762","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469579,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3179","text":"Publisher Index Page"},{"id":345362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"16","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-28","publicationStatus":"PW","scienceBaseUri":"59a7ced1e4b0fd9b77d0929f","contributors":{"authors":[{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":709068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budge, Suzanne M.","contributorId":92168,"corporation":false,"usgs":false,"family":"Budge","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":709070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thiemann, Gregory W.","contributorId":83023,"corporation":false,"usgs":false,"family":"Thiemann","given":"Gregory","email":"","middleInitial":"W.","affiliations":[{"id":27291,"text":"York University, Toronto, ON","active":true,"usgs":false}],"preferred":false,"id":709071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":709069,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190406,"text":"70190406 - 2017 - Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest","interactions":[],"lastModifiedDate":"2017-08-30T14:09:12","indexId":"70190406","displayToPublicDate":"2017-08-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest","docAbstract":"Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12–54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37–98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.
","language":"English","publisher":"MDPI","doi":"10.3390/f8090322","usgsCitation":"Bright, B.C., Hudak, A.T., Meddens, A.J., Hawbaker, T., Briggs, J.S., and Kennedy, R.E., 2017, Prediction of forest canopy and surface fuels from Lidar and satellite time series data in a bark beetle-affected forest: Forests, v. 9, no. 8, p. 1-22, https://doi.org/10.3390/f8090322.","productDescription":"Article 322; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-086687","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469578,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f8090322","text":"Publisher Index Page"},{"id":345363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.04415893554688,\n 39.904469075530415\n ],\n [\n -105.70220947265625,\n 39.904469075530415\n ],\n [\n -105.70220947265625,\n 40.32246702124231\n ],\n [\n -106.04415893554688,\n 40.32246702124231\n ],\n [\n -106.04415893554688,\n 39.904469075530415\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-30","publicationStatus":"PW","scienceBaseUri":"59a7ced1e4b0fd9b77d092a6","contributors":{"authors":[{"text":"Bright, Benjamin C.","contributorId":196021,"corporation":false,"usgs":false,"family":"Bright","given":"Benjamin","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":709004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudak, Andrew T.","contributorId":196022,"corporation":false,"usgs":false,"family":"Hudak","given":"Andrew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":709005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meddens, Arjan J.H.","contributorId":140349,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","email":"","middleInitial":"J.H.","affiliations":[{"id":13466,"text":"Univ. of Idaho","active":true,"usgs":false}],"preferred":false,"id":709007,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Jenny S. 0000-0001-7454-6928 jsbriggs@usgs.gov","orcid":"https://orcid.org/0000-0001-7454-6928","contributorId":3087,"corporation":false,"usgs":true,"family":"Briggs","given":"Jenny","email":"jsbriggs@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709008,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kennedy, Robert E.","contributorId":196025,"corporation":false,"usgs":false,"family":"Kennedy","given":"Robert","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":709010,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190405,"text":"70190405 - 2017 - Trophic structure of mesopelagic fishes in the Gulf of Mexico revealed by gut content and stable isotope analyses","interactions":[],"lastModifiedDate":"2017-08-31T12:39:37","indexId":"70190405","displayToPublicDate":"2017-08-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5377,"text":"Marine Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Trophic structure of mesopelagic fishes in the Gulf of Mexico revealed by gut content and stable isotope analyses","docAbstract":"Mesopelagic fishes represent an important component of the marine food web due to their global distributions, high abundances and ability to transport organic material throughout a large part of the water column. This study combined stable isotope (SIAs) and gut content analyses (GCAs) to characterize the trophic structure of mesopelagic fishes in the North-Central Gulf of Mexico. Additionally, this study examined whether mesopelagic fishes utilized chemosynthetic energy from cold seeps. Specimens were collected (9–25 August 2007) over three deep (>1,000 m) cold seeps at discrete depths (surface to 1,503 m) over the diurnal cycle. GCA classified 31 species (five families) of mesopelagic fishes into five feeding guilds: piscivores, large crustacean consumers, copepod consumers, generalists and mixed zooplanktivores. However, these guilds were less clearly defined based on stable isotope mixing model (MixSIAR) results, suggesting diets may be more mixed over longer time periods (weeks–months) and across co-occurring species. Copepods were likely important for the majority of mesopelagic fishes, consistent with GCA (this study) and previous literature. MixSIAR results also identified non-crustacean prey items, including salps and pteropods, as potentially important prey items for mesopelagic fishes, including those fishes not analysed in GCA (Sternoptyx spp. and Melamphaidae). Salps and other soft-bodied species are often missed in GCAs. Mesopelagic fishes had δ13C results consistent with particulate organic matter serving as the baseline organic carbon source, fueling up to three trophic levels. Fishes that undergo diel vertical migration were depleted in 15N relative to weak migrators, consistent with depth-specific isotope trends in sources and consumers, and assimilation of 15N-depleted organic matter in surface waters. Linear correlations between fish size and δ15N values suggested ontogenetic changes in fish diets for several species. While there was no direct measure of mesopelagic fishes assimilating chemosynthetic material, detection of infrequent consumption of this food resource may be hindered by the assimilation of isotopically enriched photosynthetic organic matter. By utilizing multiple dietary metrics (e.g. GCA, δ13C, δ15N, MixSIAR), this study better defined the trophic structure of mesopelagic fishes and allowed for insights on feeding, ultimately providing useful baseline information from which to track mesopelagic trophodynamics over time and space.
","language":"English","publisher":"Wiley","doi":"10.1111/maec.12449","usgsCitation":"McClain-Counts, J.P., Demopoulos, A.W., and Ross, S., 2017, Trophic structure of mesopelagic fishes in the Gulf of Mexico revealed by gut content and stable isotope analyses: Marine Ecology, v. 38, no. 4, Article e12449; 23 p., https://doi.org/10.1111/maec.12449.","productDescription":"Article e12449; 23 p.","ipdsId":"IP-074617","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":438234,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7891407","text":"USGS data release","linkHelpText":"Trophic structure of mesopelagic fishes in the Gulf of Mexico revealed by gut content and stable isotope analyses"},{"id":345328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"4","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-20","publicationStatus":"PW","scienceBaseUri":"59a7ced2e4b0fd9b77d092ab","contributors":{"authors":[{"text":"McClain-Counts, Jennifer P. 0000-0002-3383-5472 jmcclaincounts@usgs.gov","orcid":"https://orcid.org/0000-0002-3383-5472","contributorId":4745,"corporation":false,"usgs":true,"family":"McClain-Counts","given":"Jennifer","email":"jmcclaincounts@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":708996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Demopoulos, Amanda W.J. 0000-0003-2096-4694 ademopoulos@usgs.gov","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":145681,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","email":"ademopoulos@usgs.gov","middleInitial":"W.J.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":708997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Steve W.","contributorId":41134,"corporation":false,"usgs":false,"family":"Ross","given":"Steve W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":708998,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190427,"text":"70190427 - 2017 - Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production","interactions":[],"lastModifiedDate":"2017-10-24T15:10:36","indexId":"70190427","displayToPublicDate":"2017-08-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3544,"text":"The Auk","onlineIssn":"1938-4254","printIssn":"0004-8038","active":true,"publicationSubtype":{"id":10}},"title":"Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production","docAbstract":"Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.
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\"}}]}","contact":"Ecosystems Mission Area
https://www.usgs.gov/ask/
1-888-ASK-USGS (1-888-275-8747)
Natural resource monitoring involves repeated collections of resource condition data and analyses to detect possible changes and identify underlying causes of changes. For natural resource agencies, monitoring provides the foundation for management and science. Specifically, analyses of monitoring data allow managers to better understand effects of land-use and other changes on important natural resources and to achieve their conservation and management goals. Examples of natural resources monitored on public lands include wildlife habitats, plant productivity, animal movements and population trends, soil chemistry, and water quality and quantity. Broader definitions of monitoring also recognize the need for scientifically valid data to help support planning efforts and informed decisions, to develop adaptive management strategies, and to provide the means for evaluating management outcomes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20173030","usgsCitation":"Manier, D.J., Anderson, P.J., Assal, T.J., Chong, G.W., and Melcher, C.P., 2017, Monitoring the southwestern Wyoming landscape—A foundation for management and science: U.S. Geological Survey Fact Sheet 2017–3030, 6 p., https://doi.org/10.3133/fs20163030.","productDescription":"6 p.","onlineOnly":"N","ipdsId":"IP-081786","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":345007,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3030/fs20173030.pdf ","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3030"},{"id":345006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3030/coverthb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-106.0749,42.4325],[-106.0747,42.4179],[-106.0745,42.4038],[-106.0747,42.3748],[-106.0756,42.3189],[-106.076,42.3039],[-106.0752,42.2893],[-106.0756,42.2748],[-106.0753,42.2612],[-106.0734,42.1735],[-106.0738,42.1135],[-106.0744,41.9581],[-106.0748,41.9436],[-106.0746,41.9291],[-106.075,41.915],[-106.0741,41.9005],[-106.0739,41.8859],[-106.0743,41.8714],[-106.0747,41.8569],[-106.0745,41.8423],[-106.0743,41.8278],[-106.0735,41.8119],[-106.0745,41.7974],[-106.0747,41.7683],[-106.0745,41.7538],[-106.0728,41.6593],[-106.072,41.6407],[-106.0718,41.6257],[-106.071,41.5676],[-106.0708,41.3951],[-106.0977,41.3955],[-106.1155,41.3953],[-106.3243,41.3936],[-106.3251,41.2851],[-106.3241,41.2252],[-106.3237,41.2162],[-106.3233,41.1785],[-106.3227,41.1036],[-106.3227,41.075],[-106.3223,41.0446],[-106.3215,41.001],[-106.3257,41.0023],[-106.3263,41.0025],[-106.3318,41.0025],[-106.3519,41.0025],[-106.3793,41.0026],[-106.4481,41.0035],[-106.456,41.0035],[-106.4864,41.0033],[-106.5436,41.0038],[-106.5723,41.0038],[-106.582,41.0037],[-106.5911,41.0035],[-106.8639,41.0041],[-107.0021,41.0044],[-107.0259,41.0043],[-107.1355,41.0037],[-107.2299,41.0035],[-107.306,41.0034],[-107.3181,41.0035],[-107.3437,41.0033],[-107.3674,41.0032],[-107.3948,41.003],[-107.4137,41.0029],[-107.4575,41.0027],[-107.4947,41.0026],[-107.5093,41.0026],[-107.5136,41.0026],[-107.5288,41.0026],[-107.6049,41.0028],[-107.6767,41.0028],[-107.7078,41.0028],[-107.7845,41.0028],[-107.8131,41.0028],[-107.8206,41.0028],[-107.8326,41.0028],[-107.8391,41.0028],[-107.8521,41.0029],[-107.8801,41.0029],[-107.888,41.0029],[-107.9154,41.0029],[-107.966,41.0028],[-108.0007,41.0025],[-108.1808,41.001],[-108.2186,41.0007],[-108.263,41.0003],[-108.2923,41.0001],[-108.3118,41],[-108.3745,40.9997],[-108.3781,40.9997],[-108.5699,41.0003],[-108.6321,41.0005],[-108.6516,41.0005],[-108.746,41.0002],[-108.7655,41.0002],[-108.912,41.0001],[-108.9315,41.0001],[-108.9729,41.0002],[-109.049,41],[-109.0811,41.0006],[-109.1026,41.0006],[-109.1183,41.0007],[-109.1962,41.0015],[-109.2187,41.0017],[-109.2195,41.0017],[-109.2352,41.0016],[-109.4423,41],[-109.4685,40.9998],[-109.4708,40.9998],[-109.505,40.9997],[-109.5087,40.9997],[-109.5885,40.9995],[-109.5909,40.9995],[-109.6262,40.9996],[-109.6433,40.9996],[-109.6555,40.9997],[-109.6694,40.9997],[-109.6889,40.9997],[-109.7291,40.9996],[-109.8108,40.9995],[-109.8862,40.9994],[-109.942,40.9994],[-110.0001,40.9992],[-110.0485,40.9988],[-110.0573,40.9988],[-110.1608,40.9979],[-110.195,40.9976],[-110.218,40.9972],[-110.2724,40.9963],[-110.2764,40.9963],[-110.489,40.9969],[-110.4928,40.997],[-110.4937,40.997],[-110.5085,40.997],[-110.5273,40.9971],[-110.5462,40.9971],[-110.5471,40.9971],[-110.5651,40.9971],[-110.5736,40.9972],[-110.5785,40.9972],[-110.5834,40.9972],[-110.5836,40.9972],[-110.6035,40.9972],[-110.6224,40.9973],[-110.6419,40.9973],[-110.7752,40.9977],[-110.8173,40.9977],[-111.0456,40.9978],[-111.0458,41.2426],[-111.0458,41.2514],[-111.0459,41.3209],[-111.046,41.3349],[-111.046,41.3494],[-111.046,41.379],[-111.046,41.3944],[-111.0461,41.4085],[-111.0461,41.423],[-111.0461,41.4511],[-111.0463,41.5387],[-111.0463,41.5782],[-111.0466,41.7326],[-111.0465,41.8057],[-111.0465,41.8334],[-111.0465,41.8625],[-111.0464,41.9366],[-111.0463,42.0005],[-111.0463,42.0014],[-111.0463,42.0102],[-111.0467,42.0992],[-111.0465,42.14],[-111.0463,42.1701],[-111.0465,42.211],[-111.0467,42.2769],[-111.0468,42.2933],[-111.0472,42.426],[-111.0469,42.4387],[-111.0477,42.4469],[-111.0475,42.5136],[-111.0469,42.5137],[-111.0468,42.612],[-111.0465,42.6515],[-111.0459,42.7056],[-111.0455,42.7352],[-111.0446,42.7889],[-111.0446,42.818],[-111.0445,42.8412],[-111.0445,42.847],[-111.0445,42.9263],[-111.0441,43.0182],[-111.0441,43.02],[-111.0443,43.0519],[-111.0444,43.0837],[-111.0445,43.1411],[-111.0445,43.1442],[-111.0445,43.1701],[-111.0445,43.1979],[-111.0439,43.2817],[-111.0436,43.3136],[-110.9716,43.3148],[-110.9276,43.3148],[-110.9285,43.293],[-110.8964,43.2937],[-110.8178,43.2945],[-110.813,43.2357],[-110.5774,43.2353],[-110.5824,43.2939],[-110.3457,43.2938],[-110.3456,43.3252],[-110.3456,43.3807],[-110.1584,43.3798],[-110.1117,43.3797],[-110.0538,43.3797],[-110.0547,43.452],[-110.0545,43.4666],[-109.8172,43.4646],[-109.7775,43.4643],[-109.751,43.4642],[-109.7535,43.3895],[-109.7529,43.3672],[-109.7365,43.3638],[-109.7228,43.3735],[-109.704,43.3751],[-109.693,43.3666],[-109.6935,43.3571],[-109.6914,43.3498],[-109.6943,43.3397],[-109.6934,43.3293],[-109.6895,43.3257],[-109.6837,43.3212],[-109.6811,43.3158],[-109.6746,43.305],[-109.6769,43.2967],[-109.6775,43.2954],[-109.6797,43.2853],[-109.6834,43.2816],[-109.6826,43.2744],[-109.6794,43.2703],[-109.6792,43.2639],[-109.6809,43.2557],[-109.6789,43.2517],[-109.6743,43.2449],[-109.6729,43.2376],[-109.6747,43.2317],[-109.6769,43.2226],[-109.6749,43.2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Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
This report documents methodology and results of a study to evaluate groundwater discharge by evapotranspiration (GWET) in sparsely vegetated areas of Amargosa Desert and improve understanding of hydrologic-continuum processes controlling groundwater discharge. Evapotranspiration and GWET rates were computed and characterized at three sites over 2 years using a combination of micrometeorological, unsaturated zone, and stable-isotope measurements. One site (Amargosa Flat Shallow [AFS]) was in a sparse and isolated area of saltgrass (Distichlis spicata) where the depth to groundwater was 3.8 meters (m). The second site (Amargosa Flat Deep [AFD]) was in a sparse cover of predominantly shadscale (Atriplex confertifolia) where the depth to groundwater was 5.3 m. The third site (Amargosa Desert Research Site [ADRS]), selected as a control site where GWET is assumed to be zero, was located in sparse vegetation dominated by creosote bush (Larrea tridentata) where the depth to groundwater was 110 m.
Results indicated that capillary rise brought groundwater to within 0.9 m (at AFS) and 3 m (at AFD) of land surface, and that GWET rates were largely controlled by the slow but relatively persistent upward flow of water through the unsaturated zone in response to atmospheric-evaporative demands. Greater GWET at AFS (50 ± 20 millimeters per year [mm/yr]) than at AFD (16 ± 15 mm/yr) corresponded with its shallower depth to the capillary fringe and constantly higher soil-water content. The stable-isotope dataset for hydrogen (δ2H) and oxygen (δ18O) illustrated a broad range of plant-water-uptake scenarios. The AFS saltgrass and AFD shadscale responded to changing environmental conditions and their opportunistic water use included the time- and depth-variable uptake of unsaturated-zone water derived from a combination of groundwater and precipitation. These results can be used to estimate GWET in other areas of Amargosa Desert where hydrologic conditions are similar.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175079","collaboration":"Prepared in cooperation with Nye County, Nevada, and the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office under Interagency Agreement DE-NA0001654","usgsCitation":"Moreo, M.T., Andraski, B.J., and Garcia, C.A., 2017, Groundwater discharge by evapotranspiration, flow of water in unsaturated soil, and stable isotope water sourcing in areas of sparse vegetation, Amargosa Desert, Nye County, Nevada: U.S. Geological Survey Scientific Investigations Report 2017–5079, 55 p., https://doi.org/10.3133/sir20175079.","productDescription":"Report: viii, 55 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-081689","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":438237,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZS2VQR","text":"USGS data release","linkHelpText":"Selected Evapotranspiration Data, Amargosa Desert Research Site, Nye County, Nevada, 7/5/2011-1/1/2017"},{"id":345331,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R49NZN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Evapotranspiration, groundwater, and unsaturated-zone data, Amargosa Desert, Nye County, Nevada, 2011-13"},{"id":345286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5079/sir20175079.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5079"},{"id":345285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5079/coverthb.jpg"}],"country":"United States","state":"Nevada","county":"Nye County","otherGeospatial":"Amargosa Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3774871826172,\n 36.3588219885685\n ],\n [\n -116.20548248291016,\n 36.3588219885685\n ],\n [\n -116.20548248291016,\n 36.50384103238002\n ],\n [\n -116.3774871826172,\n 36.50384103238002\n ],\n [\n -116.3774871826172,\n 36.3588219885685\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
This report documents a process used to estimate net infiltration from precipitation, evapotranspiration (ET), and soil data acquired at two sites on Rainier Mesa. Rainier Mesa is a groundwater recharge area within the Nevada National Security Site where recharged water flows through bedrock fractures to a deep (450 meters) water table. The U.S. Geological Survey operated two ET stations on Rainier Mesa from 2002 to 2005 at sites characterized by pinyon-juniper and scrub-brush vegetative cover. Precipitation and ET data were corrected to remove measurement biases and gap-filled to develop continuous datasets. Net infiltration (percolation below the root zone) and changes in root-zone water storage were estimated using a monthly water-balance model.
Site-scale water-budget results indicate that the heavily-fractured welded-tuff bedrock underlying thin (<40 centimeters) topsoil is a critical water source for vegetation during dry periods. Annual precipitation during the study period ranged from fourth lowest (182 millimeters [mm]) to second highest (708 mm) on record (record = 55 years). Annual ET exceeded precipitation during dry years, indicating that the fractured-bedrock reservoir capacity is sufficient to meet atmospheric-evaporative demands and to sustain vegetation through extended dry periods. Net infiltration (82 mm) was simulated during the wet year after the reservoir was rapidly filled to capacity. These results support previous conclusions that preferential fracture flow was induced, resulting in an episodic recharge pulse that was detected in nearby monitoring wells. The occurrence of net infiltration only during the wet year is consistent with detections of water-level rises in nearby monitoring wells that occur only following wet years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175078","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office under Interagency Agreement DE-NA0001654","usgsCitation":"Smith, D.W., Moreo, M.T., Garcia, C.A., Halford, K.J., and Fenelon, J.M., 2017, A process to estimate net infiltration using a site-scale water-budget approach, Rainier Mesa, Nevada National Security Site, Nevada, 2002–05: U.S. Geological Survey Scientific Investigations Report 2017-5078, 22 p., https://doi.org/10.3133/sir20175078.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-070070","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":345229,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5078/coverthb.jpg"},{"id":345230,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5078/sir20175078.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5078"},{"id":345231,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7222SP5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Supplemental data from—A process to estimate net infiltration using a site-scale water-budget approach, Rainier Mesa, Nevada National Security Site, 2002-05"}],"country":"United States","state":"Nevada","otherGeospatial":"Rainier Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.1333,\n 37.1\n ],\n [\n -116.2667,\n 37.1\n ],\n [\n -116.2667,\n 37.2667\n ],\n [\n -116.1333,\n 37.2667\n ],\n [\n -116.1333,\n 37.1\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, Nevada 89701
Understanding the habitat needs of fish and how these requirements may change seasonally over a 24-h period is important, especially for highly managed sport species. Consequently, we examined the diel and seasonal habitat use of four juvenile salmonid species in streams in the Lake Ontario watershed. For juvenile Atlantic salmon Salmo salarand juvenile rainbow trout Oncorhynchus mykiss, differences in day versus night habitat use were more profound than seasonal differences. Observed differences in day versus night habitat for all species and age classes were mainly due to the use of less object oriented cover at night and to a lesser extent to the use of slower velocities and smaller substrate at night. Seasonal differences in habitat use were also observed, likely due to increased fish size, and included movement to deeper and faster water and the use of larger substrate and more cover from summer to winter. Different habitat variables were important to individual species. Juvenile Atlantic salmon were associated with higher water velocities, juvenile rainbow trout with larger substrate and more cover, and subyearling Chinook salmon O. tshawytscha and subyearling coho salmon O. kisutch with small substrate and less cover. Our observations demonstrate that habitat partitioning occurs and likely reduces intraspecific and interspecific competition which may increase the potential production of all four species in sympatry. Consequently, these findings provide important information for resource managers charged with managing, protecting, and enhancing Great Lakes tributaries where all or some of these species occur.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.07.001","usgsCitation":"Johnson, J.H., and McKenna, J., 2017, Habitat use by juvenile salmonids in Lake Ontario tributaries-species, age, diel and seasonal effects: Journal of Great Lakes Research, v. 43, no. 5, p. 963-969, https://doi.org/10.1016/j.jglr.2017.07.001.","productDescription":"7 p.","startPage":"963","endPage":"969","ipdsId":"IP-088010","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":345247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77,\n 42.5\n ],\n [\n -75,\n 42.5\n ],\n [\n -75,\n 43.79092385423618\n ],\n [\n -77,\n 43.79092385423618\n ],\n [\n -77,\n 42.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a67d3fe4b0fd9b77ce477a","contributors":{"authors":[{"text":"Johnson, James H. 0000-0002-5619-3871 jhjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5619-3871","contributorId":389,"corporation":false,"usgs":true,"family":"Johnson","given":"James","email":"jhjohnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":190798,"corporation":false,"usgs":true,"family":"McKenna","given":"James E.","suffix":"Jr.","email":"jemckenna@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708608,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190394,"text":"70190394 - 2017 - Aftershocks driven by afterslip and fluid pressure sweeping through a fault-fracture mesh","interactions":[],"lastModifiedDate":"2017-09-18T15:28:24","indexId":"70190394","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Aftershocks driven by afterslip and fluid pressure sweeping through a fault-fracture mesh","docAbstract":"A variety of physical mechanisms are thought to be responsible for the triggering and spatiotemporal evolution of aftershocks. Here we analyze a vigorous aftershock sequence and postseismic geodetic strain that occurred in the Yuha Desert following the 2010 Mw 7.2 El Mayor-Cucapah earthquake. About 155,000 detected aftershocks occurred in a network of orthogonal faults and exhibit features of two distinct mechanisms for aftershock triggering. The earliest aftershocks were likely driven by afterslip that spread away from the main shock with the logarithm of time. A later pulse of aftershocks swept again across the Yuha Desert with square root time dependence and swarm-like behavior; together with local geological evidence for hydrothermalism, these features suggest that the events were driven by fluid diffusion. The observations illustrate how multiple driving mechanisms and the underlying fault structure jointly control the evolution of an aftershock sequence.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL074634","usgsCitation":"Ross, Z.E., Rollins, C., Cochran, E.S., Hauksson, E., Avouac, J., and Ben-Zion, Y., 2017, Aftershocks driven by afterslip and fluid pressure sweeping through a fault-fracture mesh: Geophysical Research Letters, v. 44, no. 16, p. 8260-8267, https://doi.org/10.1002/2017GL074634.","productDescription":"8 p.","startPage":"8260","endPage":"8267","ipdsId":"IP-087136","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469581,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2017gl074634","text":"External Repository"},{"id":345278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"16","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-19","publicationStatus":"PW","scienceBaseUri":"59a67d3de4b0fd9b77ce475e","contributors":{"authors":[{"text":"Ross, Zachary E.","contributorId":196001,"corporation":false,"usgs":false,"family":"Ross","given":"Zachary","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":708910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rollins, Christopher","contributorId":196002,"corporation":false,"usgs":false,"family":"Rollins","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":708911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":708909,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hauksson, Egill","contributorId":48174,"corporation":false,"usgs":false,"family":"Hauksson","given":"Egill","affiliations":[{"id":27150,"text":"Seismological Laboratory, California Institute of Technology, Pasadena, CA, USA","active":true,"usgs":false}],"preferred":false,"id":708912,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Avouac, Jean-Philippe","contributorId":98195,"corporation":false,"usgs":true,"family":"Avouac","given":"Jean-Philippe","email":"","affiliations":[],"preferred":false,"id":708913,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ben-Zion, Yehuda","contributorId":195741,"corporation":false,"usgs":false,"family":"Ben-Zion","given":"Yehuda","email":"","affiliations":[{"id":16177,"text":"University of Southern California, Los Angeles, Ca.","active":true,"usgs":false}],"preferred":false,"id":708914,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70190372,"text":"70190372 - 2017 - Effects of environmental covariates and density on the catchability of fish populations and interpretation of catch per unit effort trends","interactions":[],"lastModifiedDate":"2017-08-29T11:37:39","indexId":"70190372","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Effects of environmental covariates and density on the catchability of fish populations and interpretation of catch per unit effort trends","docAbstract":"Article for outlet: Fisheries Research. Abstract: Quantifying temporal and spatial trends in abundance or relative abundance is required to evaluate effects of harvest and changes in habitat for exploited and endangered fish populations. In many cases, the proportion of the population or stock that is captured (catchability or capture probability) is unknown but is often assumed to be constant over space and time. We used data from a large-scale mark-recapture study to evaluate the extent of spatial and temporal variation, and the effects of fish density, fish size, and environmental covariates, on the capture probability of rainbow trout (Oncorhynchus mykiss) in the Colorado River, AZ. Estimates of capture probability for boat electrofishing varied 5-fold across five reaches, 2.8-fold across the range of fish densities that were encountered, 2.1-fold over 19 trips, and 1.6-fold over five fish size classes. Shoreline angle and turbidity were the best covariates explaining variation in capture probability across reaches and trips. Patterns in capture probability were driven by changes in gear efficiency and spatial aggregation, but the latter was more important. Failure to account for effects of fish density on capture probability when translating a historical catch per unit effort time series into a time series of abundance, led to 2.5-fold underestimation of the maximum extent of variation in abundance over the period of record, and resulted in unreliable estimates of relative change in critical years. Catch per unit effort surveys have utility for monitoring long-term trends in relative abundance, but are too imprecise and potentially biased to evaluate population response to habitat changes or to modest changes in fishing effort.","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2017.01.005","usgsCitation":"Korman, J., and Yard, M.D., 2017, Effects of environmental covariates and density on the catchability of fish populations and interpretation of catch per unit effort trends: Fisheries Research, v. 189, p. 18-34, https://doi.org/10.1016/j.fishres.2017.01.005.","productDescription":"17 p.","startPage":"18","endPage":"34","ipdsId":"IP-078953","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":461422,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fishres.2017.01.005","text":"Publisher Index Page"},{"id":345255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"189","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce476e","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":708761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":708760,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190371,"text":"70190371 - 2017 - A large-scale environmental flow experiment for riparian restoration in the Colorado River delta","interactions":[],"lastModifiedDate":"2018-03-21T12:58:49","indexId":"70190371","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"A large-scale environmental flow experiment for riparian restoration in the Colorado River delta","docAbstract":"Managing streamflow is a widely-advocated approach to provide conditions necessary for seed germination and seedling establishment of trees in the willow family (Salicaceae). Experimental flow releases to the Colorado River delta in 2014 had a primary objective of promoting seedling establishment of Fremont cottonwood (Populus fremontii) and Goodding's willow (Salix gooddingii). We assessed seed germination and seedling establishment of these taxa as well as the non-native tamarisk (Tamarix spp.) and native seepwillow shrubs (Baccharis spp.) in the context of seedling requirements and active land management (land grading, vegetation removal) at 23 study sites along 87 river km. In the absence of associated active land management, experimental flows to the Colorado River delta were minimally successful at promoting establishment of new woody riparian seedlings, except for non-native Tamarix. Our results suggest that the primary factors contributing to low seedling establishment varied across space, but included low or no seed availability in some locations for some taxa, insufficient soil moisture availability during the growing season indicated by deep groundwater tables, and competition from adjacent vegetation (and, conversely, availability of bare ground). Active land management to create bare ground and favorable land grades contributed to significantly higher rates of Salicaceae seedling establishment in a river reach with high groundwater tables. Our results provide insights that can inform future environmental flow deliveries to the Colorado River delta and its ecosystems and other similar efforts to restore Salicaceae taxa around the world.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2017.02.016","usgsCitation":"Shafroth, P.B., Schlatter, K., Gomez-Sapiens, M., Lundgren, E., Grabau, M.R., Ramirez-Hernandez, J., Rodriguez-Burgeueno, J.E., and Flessa, K.W., 2017, A large-scale environmental flow experiment for riparian restoration in the Colorado River delta: Ecological Engineering, v. 106, no. Part B, p. 645-660, https://doi.org/10.1016/j.ecoleng.2017.02.016.","productDescription":"16 p.","startPage":"645","endPage":"660","ipdsId":"IP-073728","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":345258,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.3509521484375,\n 32.19653293006282\n ],\n [\n -114.60937499999999,\n 32.19653293006282\n ],\n [\n -114.60937499999999,\n 32.84267363195431\n ],\n [\n -115.3509521484375,\n 32.84267363195431\n ],\n [\n -115.3509521484375,\n 32.19653293006282\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"Part B","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a67d3fe4b0fd9b77ce4772","contributors":{"authors":[{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":708752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlatter, Karen","contributorId":176222,"corporation":false,"usgs":false,"family":"Schlatter","given":"Karen","email":"","affiliations":[],"preferred":false,"id":708753,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gomez-Sapiens, Martha","contributorId":195954,"corporation":false,"usgs":false,"family":"Gomez-Sapiens","given":"Martha","email":"","affiliations":[],"preferred":false,"id":708754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lundgren, Erick","contributorId":195955,"corporation":false,"usgs":false,"family":"Lundgren","given":"Erick","email":"","affiliations":[],"preferred":false,"id":708755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grabau, Matthew R.","contributorId":195953,"corporation":false,"usgs":false,"family":"Grabau","given":"Matthew","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":708756,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ramirez-Hernandez, Jorge","contributorId":176218,"corporation":false,"usgs":false,"family":"Ramirez-Hernandez","given":"Jorge","affiliations":[],"preferred":false,"id":708757,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rodriguez-Burgeueno, J. Eliana","contributorId":176219,"corporation":false,"usgs":false,"family":"Rodriguez-Burgeueno","given":"J.","email":"","middleInitial":"Eliana","affiliations":[],"preferred":false,"id":708758,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Flessa, Karl W.","contributorId":175308,"corporation":false,"usgs":false,"family":"Flessa","given":"Karl","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":708759,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70190370,"text":"70190370 - 2017 - Integrating active restoration with environmental flows to improve native riparian tree establishment in the Colorado River Delta","interactions":[],"lastModifiedDate":"2017-08-29T11:50:54","indexId":"70190370","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1454,"text":"Ecological Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Integrating active restoration with environmental flows to improve native riparian tree establishment in the Colorado River Delta","docAbstract":"Drastic alterations to river hydrology, land use change, and the spread of the nonnative shrub, tamarisk (Tamarix spp.), have led to the degradation of riparian habitat in the Colorado River Delta in Mexico. Delivery of environmental flows to promote native cottonwood (Populus spp.) and willow (Salix spp.) recruitment in human-impacted riparian systems can be unsuccessful due to flow-magnitude constraints and altered abiotic–biotic feedbacks. In 2014, an experimental pulse flow of water was delivered to the Colorado River in Mexico as part of the U.S.-Mexico binational agreement, Minute 319. We conducted a field experiment to assess the effects of vegetation removal, seed augmentation, and environmental flows, separately and in combination, on germination and first-year seedling establishment of cottonwood, willow, and tamarisk at five replicate sites along 5 river km. The relatively low-magnitude flow deliveries did not substantively restore natural fluvial processes of erosion, sediment deposition, and vegetation scour, but did provide wetted surface soils, shallow groundwater, and low soil salinity. Cottonwood and willow only established in wetted, cleared treatments, and establishment was variable in these treatments due to variable site conditions and inundation duration and timing. Wetted soils, bare surface availability, soil salinity, and seed availability were significant factors contributing to successful cottonwood and willow germination, while soil salinity and texture affected seedling persistence over the growing season. Tamarisk germinated and persisted in a wider range of environmental conditions than cottonwood and willow, including in un-cleared treatment areas. Our results suggest that site management can increase cottonwood and willow recruitment success from low-magnitude environmental flow events, an approach that can be applied in other portions of the Delta and to other human-impacted riparian systems across the world with similar ecological limitations.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoleng.2017.02.015","usgsCitation":"Schlatter, K., Grabau, M.R., Shafroth, P.B., and Zamora-Arroyo, F., 2017, Integrating active restoration with environmental flows to improve native riparian tree establishment in the Colorado River Delta: Ecological Engineering, v. 106, no. Part B, p. 661-674, https://doi.org/10.1016/j.ecoleng.2017.02.015.","productDescription":"14 p.","startPage":"661","endPage":"674","ipdsId":"IP-083963","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":345259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.3509521484375,\n 32.19653293006282\n ],\n [\n -114.60937499999999,\n 32.19653293006282\n ],\n [\n -114.60937499999999,\n 32.84267363195431\n ],\n [\n -115.3509521484375,\n 32.84267363195431\n ],\n [\n -115.3509521484375,\n 32.19653293006282\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"Part B","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a67d3fe4b0fd9b77ce4776","contributors":{"authors":[{"text":"Schlatter, Karen","contributorId":176222,"corporation":false,"usgs":false,"family":"Schlatter","given":"Karen","email":"","affiliations":[],"preferred":false,"id":708749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grabau, Matthew R.","contributorId":195953,"corporation":false,"usgs":false,"family":"Grabau","given":"Matthew","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":708750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":708748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zamora-Arroyo, Francisco","contributorId":75834,"corporation":false,"usgs":true,"family":"Zamora-Arroyo","given":"Francisco","email":"","affiliations":[],"preferred":false,"id":708751,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190343,"text":"70190343 - 2017 - Biotic and abiotic factors influencing zooplankton vertical distribution in Lake Huron","interactions":[],"lastModifiedDate":"2017-11-29T16:34:48","indexId":"70190343","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Biotic and abiotic factors influencing zooplankton vertical distribution in Lake Huron","docAbstract":"The vertical distribution of zooplankton can have substantial influence on trophic structure in freshwater systems, particularly by determining spatial overlap for predator/prey dynamics and influencing energy transfer. The zooplankton community in some of the Laurentian Great Lakes has undergone changes in composition and declines in total biomass, especially after 2003. Mechanisms underlying these zooplankton changes remain poorly understood, in part, because few studies have described their vertical distributions during daytime and nighttime conditions or evaluated the extent to which predation, resources, or environmental conditions could explain their distribution patterns. Within multiple 24-h periods during July through October 2012 in Lake Huron, we conducted daytime and nighttime sampling of zooplankton, and measured food (chlorophyll-a), temperature, light (Secchi disk depth), and planktivory (biomass of Bythotrephes longimanus and Mysis diluviana). We used linear mixed models to determine whether the densities for 22 zooplankton taxa varied between day and night in the epi-, meta-, and hypolimnion. For eight taxa, higher epilimnetic densities were observed at night than during the day; general linear models revealed these patterns were best explained by Mysis diluviana (four taxa), Secchi disk depth (three taxa), epilimnetic water temperature (three taxa), chlorophyll (one taxon), and biomass of Bythotrephes longimanus (one taxon). By investigating the potential effects of both biotic and abiotic variables on the vertical distribution of crustacean zooplankton and rotifers, we provide descriptions of the Lake Huron zooplankton community and discuss how future changes in food web dynamics or climate change may alter zooplankton distribution in freshwater environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.08.004","usgsCitation":"Nowicki, C.J., Bunnell, D., Dieter, P.M., Warner, D.M., Vanderploeg, H.A., Cavaletto, J.F., Mayer, C.M., and Adams, J.V., 2017, Biotic and abiotic factors influencing zooplankton vertical distribution in Lake Huron: Journal of Great Lakes Research, v. 43, no. 6, p. 1044-1054, https://doi.org/10.1016/j.jglr.2017.08.004.","productDescription":"11 p.","startPage":"1044","endPage":"1054","ipdsId":"IP-077143","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469582,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2017.08.004","text":"Publisher Index Page"},{"id":345246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a67d3fe4b0fd9b77ce477e","contributors":{"authors":[{"text":"Nowicki, Carly J.","contributorId":195889,"corporation":false,"usgs":false,"family":"Nowicki","given":"Carly","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":708600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David B. 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":3139,"corporation":false,"usgs":true,"family":"Bunnell","given":"David B.","email":"dbunnell@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":708599,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dieter, Patricia M. 0000-0003-1686-2679 parmenio@usgs.gov","orcid":"https://orcid.org/0000-0003-1686-2679","contributorId":5289,"corporation":false,"usgs":true,"family":"Dieter","given":"Patricia","email":"parmenio@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708601,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, David M. 0000-0003-4939-5368 dmwarner@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":2986,"corporation":false,"usgs":true,"family":"Warner","given":"David","email":"dmwarner@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vanderploeg, Henry A.","contributorId":195891,"corporation":false,"usgs":false,"family":"Vanderploeg","given":"Henry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":708603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cavaletto, Joann F.","contributorId":195892,"corporation":false,"usgs":false,"family":"Cavaletto","given":"Joann","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":708604,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mayer, Christine M.","contributorId":50814,"corporation":false,"usgs":true,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":708605,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708606,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70190395,"text":"70190395 - 2017 - Polar bears and sea ice habitat change","interactions":[],"lastModifiedDate":"2021-04-26T15:01:11.138007","indexId":"70190395","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Polar bears and sea ice habitat change","docAbstract":"The polar bear (Ursus maritimus) is an obligate apex predator of Arctic sea ice and as such can be affected by climate warming-induced changes in the extent and composition of pack ice and its impacts on their seal prey. Sea ice declines have negatively impacted some polar bear subpopulations through reduced energy input because of loss of hunting habitats, higher energy costs due to greater ice drift, ice fracturing and open water, and ultimately greater challenges to recruit young. Projections made from the output of global climate models suggest that polar bears in peripheral Arctic and sub-Arctic seas will be reduced in numbers or become extirpated by the end of the twenty-first century if the rate of climate warming continues on its present trajectory. The same projections also suggest that polar bears may persist in the high-latitude Arctic where heavy multiyear sea ice that has been typical in that region is being replaced by thinner annual ice. Underlying physical and biological oceanography provides clues as to why polar bear in some regions are negatively impacted, while bears in other regions have shown no apparent changes. However, continued declines in sea ice will eventually challenge the survival of polar bears and efforts to conserve them in all regions of the Arctic.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Marine mammal welfare","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-319-46994-2_23","usgsCitation":"Durner, G.M., and Atwood, T.C., 2017, Polar bears and sea ice habitat change, chap. of Marine mammal welfare, p. 419-443, https://doi.org/10.1007/978-3-319-46994-2_23.","productDescription":"25 p.","startPage":"419","endPage":"443","ipdsId":"IP-075153","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":345287,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-20","publicationStatus":"PW","scienceBaseUri":"59a67d3de4b0fd9b77ce475a","contributors":{"editors":[{"text":"Butterworth, Andy","contributorId":45100,"corporation":false,"usgs":false,"family":"Butterworth","given":"Andy","email":"","affiliations":[],"preferred":false,"id":708949,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":708915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":708916,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190261,"text":"fs20173065 - 2017 - Salish Kootenai College and U.S. Geological Survey partnership—Enhancing student opportunities and professional development","interactions":[],"lastModifiedDate":"2017-08-30T09:57:32","indexId":"fs20173065","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3065","title":"Salish Kootenai College and U.S. Geological Survey partnership—Enhancing student opportunities and professional development","docAbstract":"Salish Kootenai College (SKC), in the Flathead Reservation in the northwestern corner of Montana, is the largest of the seven Tribal colleges in the State. In 2011, U.S. Geological Survey (USGS) National Tribal Liaison Monique Fordham from the Office of Tribal Relations/Office of Science Quality and Integrity began discussions with SKC faculty to examine ways the USGS could assist with classes taught as part of the new hydrology program at the college. With funding provided by the USGS Office of Tribal Relations, Roy Sando from the Wyoming-Montana Water Science Center began collaborating with SKC. From 2012 to 2017, Sando and others have developed and taught eight educational workshops at SKC. Topics of the workshops have included classifying land cover using remote sensing, characterizing stream channel migration, estimating actual evapotranspiration, modeling groundwater contamination plumes, and building custom geographic information system tools. By contributing to the educational training of SKC students and establishing this high level of collaboration with a Tribal college, the USGS is demonstrating its commitment to helping build the next generation of Tribal scientists.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173065","collaboration":"Prepared in cooperation with Salish Kootenai College","usgsCitation":"Sando, Roy, and Fordham, Monique, 2017, Salish Kootenai College and U.S. Geological Survey partnership—Enhancing student opportunities and professional development: U.S. Geological Survey Fact Sheet 2017–3065, 2 p., https://doi.org/10.3133/fs20173065.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-084969","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":345201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3065/coverthb2.jpg"},{"id":345202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3065/fs20173065.pdf","text":"Report","size":"373 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3065"}],"contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The landscape of the Colorado River through Glen Canyon National Recreation Area formed over many thousands of years and was modified substantially after the completion of Glen Canyon Dam in 1963. Changes to river flow, sediment supply, channel base level, lateral extent of sedimentary terraces, and vegetation in the post-dam era have modified the river-corridor landscape and have altered the effects of geologic processes that continue to shape the landscape and its cultural resources. The Glen Canyon reach of the Colorado River downstream of Glen Canyon Dam hosts many archaeological sites that are prone to erosion in this changing landscape. This study uses field evaluations from 2016 and aerial photographs from 1952, 1973, 1984, and 1996 to characterize changes in potential windblown sand supply and drainage configuration that have occurred over more than six decades at 54 archaeological sites in Glen Canyon and uppermost Marble Canyon. To assess landscape change at these sites, we use two complementary geomorphic classification systems. The first evaluates the potential for aeolian (windblown) transport of river-derived sand from the active river channel to higher elevation archaeological sites. The second identifies whether rills, gullies, or arroyos (that is, overland drainages that erode the ground surface) exist at the archaeological sites as well as the geomorphic surface, and therefore the relative base level, to which those flow paths drain. Results of these assessments are intended to aid in the management of irreplaceable archaeological resources by the National Park Service and stakeholders of the Glen Canyon Dam Adaptive Management Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175082","usgsCitation":"East, A.E., Sankey, J.B., Fairley, H.C., Caster, J.J., and Kasprak, A., 2017, Modern landscape processes affecting archaeological sites along the Colorado River corridor downstream of Glen Canyon Dam, Glen Canyon National Recreation Area, Arizona: U.S. Geological Survey Scientific Investigations Report 2017–5082, 22 p., https://doi.org/10.3133/sir20175082.","productDescription":"iii, 22 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-086536","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":345264,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5082/sir20175082.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5082"},{"id":345263,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5082/coverthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Glen Canyon, Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.63551330566406,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 36.75594019674357\n ],\n [\n -111.14044189453124,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 37.020646433887805\n ],\n [\n -111.63551330566406,\n 36.75594019674357\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"On 16 September 2015, a Mw 8.3 earthquake ruptured the subduction zone offshore of Illapel, Chile, generating an aftershock sequence with 14 Mw 6.0–7.0 events. A double source W phase moment tensor inversion consists of a Mw 7.2 subevent and the main Mw 8.2 phase. We determine two slip models for the mainshock, one using teleseismic broadband waveforms and the other using static GPS and InSAR surface displacements, which indicate high slip north of the epicenter and west-northwest of the epicenter near the oceanic trench. These models and slip distributions published in other studies suggest spatial slip uncertainties of ~25 km and have peak slip values that vary by a factor of 2. We relocate aftershock hypocenters using a Bayesian multiple-event relocation algorithm, revealing a cluster of aftershocks under the Chilean coast associated with deep (20–45 km depth) mainshock slip. Less vigorous aftershock activity also occurred near the trench and along strike of the main aftershock region. Most aftershocks are thrust-faulting events, except for normal-faulting events near the trench. Coulomb failure stress change amplitudes and signs are uncertain for aftershocks collocated with deeper mainshock slip; other aftershocks are more clearly associated with loading from the mainshock. These observations reveal a frictionally heterogeneous interface that ruptured in patches at seismogenic depths (associated with many aftershocks) and with homogeneous slip (and few aftershocks) up to the trench. This event likely triggered seismicity separate from the main slip region, including along-strike events on the megathrust and intraplate extensional events.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JB013617","usgsCitation":"Herman, M.W., Nealy, J., Yeck, W.L., Barnhart, W., Hayes, G.P., Furlong, K.P., and Benz, H.M., 2017, Integrated geophysical characteristics of the 2015 Illapel, Chile, earthquake: Journal of Geophysical Research B: Solid Earth, v. 122, no. 6, p. 4691-4711, https://doi.org/10.1002/2016JB013617.","productDescription":"21","startPage":"4691","endPage":"4711","ipdsId":"IP-084811","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":345253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","city":"Illapel","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.19449615478516,\n -31.640229241591086\n ],\n [\n -71.18900299072266,\n -31.652212394297177\n ],\n [\n -71.16153717041016,\n -31.639352364896208\n ],\n [\n -71.14248275756836,\n -31.627513720252146\n ],\n [\n -71.1390495300293,\n -31.62385951329282\n ],\n [\n -71.14660263061523,\n -31.61815866386382\n ],\n [\n -71.18247985839844,\n -31.616989215685837\n ],\n [\n -71.19449615478516,\n -31.640229241591086\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-29","publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce476a","contributors":{"authors":[{"text":"Herman, Matthew W.","contributorId":195964,"corporation":false,"usgs":false,"family":"Herman","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":708774,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nealy, Jennifer 0000-0002-6743-2487 jnealy@usgs.gov","orcid":"https://orcid.org/0000-0002-6743-2487","contributorId":147559,"corporation":false,"usgs":true,"family":"Nealy","given":"Jennifer","email":"jnealy@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":708775,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":708776,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, William D. 0000-0003-0498-1697","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":192730,"corporation":false,"usgs":false,"family":"Barnhart","given":"William D.","affiliations":[],"preferred":false,"id":708777,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":708778,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Furlong, Kevin P. 0000-0002-2674-5110","orcid":"https://orcid.org/0000-0002-2674-5110","contributorId":19576,"corporation":false,"usgs":false,"family":"Furlong","given":"Kevin","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":708779,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":708780,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70190391,"text":"70190391 - 2017 - Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification","interactions":[],"lastModifiedDate":"2017-09-25T13:43:35","indexId":"70190391","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification","docAbstract":"Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.
","language":"English","publisher":"AGU","doi":"10.1002/2016WR020197","usgsCitation":"Tesoriero, A., Gronberg, J.A., Juckem, P.F., Miller, M.P., and Austin, B.P., 2017, Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification: Water Resources Research, v. 53, no. 8, p. 7316-7331, https://doi.org/10.1002/2016WR020197.","productDescription":"16 p.","startPage":"7316","endPage":"7331","ipdsId":"IP-080660","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":469580,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016wr020197","text":"Publisher Index Page"},{"id":438235,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9VM1","text":"USGS data release","linkHelpText":"Source and model output layers used for the prediction and display of the probability of elevated concentrations of redox-sensitive constituents in groundwater in the Fox-Wolf-Peshtigo watershed in Wisconsin and Michigan"},{"id":345270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-23","publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce4764","contributors":{"authors":[{"text":"Tesoriero, Anthony J.","contributorId":40207,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":708857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gronberg, Jo Ann M.","contributorId":192232,"corporation":false,"usgs":false,"family":"Gronberg","given":"Jo","email":"","middleInitial":"Ann M.","affiliations":[],"preferred":false,"id":708858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":708859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":708860,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Austin, Brian P.","contributorId":195992,"corporation":false,"usgs":false,"family":"Austin","given":"Brian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":708861,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190340,"text":"70190340 - 2017 - Simulated effects of YY-male stocking and manual suppression for eradicating nonnative Brook Trout populations","interactions":[],"lastModifiedDate":"2017-08-29T10:19:25","indexId":"70190340","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Simulated effects of YY-male stocking and manual suppression for eradicating nonnative Brook Trout populations","docAbstract":"Eradication of nonnative Brook Trout Salvelinus fontinalis populations is difficult to achieve with standard techniques, such as electrofishing removal or piscicides; new approaches are needed. A novel concept is to stock “supermale” hatchery fish with wild conspecifics. Supermales (MYY) have two Y-chromosomes, resulting in offspring that are all males; over time, successful supermale reproduction could eradicate the wild population. We constructed an age-structured stochastic model to investigate the effects of manually suppressing wild fish and stocking MYY fingerlings on the long-term viability of hypothetical nonnative Brook Trout populations. In streams, an annual stocking rate of supermales equivalent to 50% of wild age-0 Brook Trout density combined with an annual selective suppression rate equivalent to 50% of wild Brook Trout density resulted in a time to extirpation of only 2–4 years if supermale fitness was equivalent to wild male fitness. However, time to extirpation in streams was 5–15 years if supermale fitness was 80% lower than wild male fitness. In alpine lakes, higher supermale stocking rates and nonselective gillnetting were required to eradicate Brook Trout populations. If supermales were assumed to be as fit as wild males, however, any supermale stocking rate greater than 49% in alpine lakes or 60% in streams achieved eradication in 10 years or less, regardless of the suppression rate. Because manual suppression and the stocking of MYY fingerlings can readily be conducted at the levels assumed in our simulations, use of such an integrated pest management (IPM) approach could extirpate undesirable Brook Trout populations within reasonably short periods of time. Given the recent successful development of an MYY Brook Trout broodstock capable of producing large numbers of MYY fingerlings and given the positive results of the present simulations for both streams and alpine lakes, field testing of MYY stocking is warranted within an IPM program that includes manual suppression for eradicating undesirable Brook Trout populations.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2017.1342720","usgsCitation":"Schill, D.J., Meyer, K., and Hansen, M.J., 2017, Simulated effects of YY-male stocking and manual suppression for eradicating nonnative Brook Trout populations: North American Journal of Fisheries Management, v. 37, no. 5, p. 1054-1066, https://doi.org/10.1080/02755947.2017.1342720.","productDescription":"13 p.","startPage":"1054","endPage":"1066","ipdsId":"IP-085808","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469583,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02755947.2017.1342720","text":"Publisher Index Page"},{"id":345244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"5","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-16","publicationStatus":"PW","scienceBaseUri":"59a67d3fe4b0fd9b77ce4780","contributors":{"authors":[{"text":"Schill, Daniel J.","contributorId":195886,"corporation":false,"usgs":false,"family":"Schill","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":708591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Kevin A.","contributorId":195887,"corporation":false,"usgs":false,"family":"Meyer","given":"Kevin A.","affiliations":[],"preferred":false,"id":708592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":708590,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190392,"text":"70190392 - 2017 - Predictive framework for estimating exposure of birds to pharmaceuticals","interactions":[],"lastModifiedDate":"2017-08-30T10:19:54","indexId":"70190392","displayToPublicDate":"2017-08-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Predictive framework for estimating exposure of birds to pharmaceuticals","docAbstract":"We present and evaluate a framework for estimating concentrations of pharmaceuticals over time in wildlife feeding at wastewater treatment plants (WWTPs). The framework is composed of a series of predictive steps involving the estimation of pharmaceutical concentration in wastewater, accumulation into wildlife food items, and uptake by wildlife with subsequent distribution into, and elimination from, tissues. Because many pharmacokinetic parameters for wildlife are unavailable for the majority of drugs in use, a read-across approach was employed using either rodent or human data on absorption, distribution, metabolism, and excretion. Comparison of the different steps in the framework against experimental data for the scenario where birds are feeding on a WWTP contaminated with fluoxetine showed that estimated concentrations in wastewater treatment works were lower than measured concentrations; concentrations in food could be reasonably estimated if experimental bioaccumulation data are available; and read-across from rodent data worked better than human to bird read-across. The framework provides adequate predictions of plasma concentrations and of elimination behavior in birds but yields poor predictions of distribution in tissues. The approach holds promise, but it is important that we improve our understanding of the physiological similarities and differences between wild birds and domesticated laboratory mammals used in pharmaceutical efficacy/safety trials, so that the wealth of data available can be applied more effectively in ecological risk assessments.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.3771","usgsCitation":"Bean, T., Arnold, K.E., Lane, J.M., Bergstrom, E., Thomas-Oates, J., Rattner, B.A., and Boxall, A.B., 2017, Predictive framework for estimating exposure of birds to pharmaceuticals: Environmental Toxicology and Chemistry, v. 36, no. 9, p. 2335-2344, https://doi.org/10.1002/etc.3771.","productDescription":"10 p.","startPage":"2335","endPage":"2344","ipdsId":"IP-073187","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469584,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://eprints.whiterose.ac.uk/112481/1/Bean_et_al_2017_Environmental_Toxicology_and_Chemistry.pdf","text":"External Repository"},{"id":345269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"9","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-15","publicationStatus":"PW","scienceBaseUri":"59a67d3ee4b0fd9b77ce4761","contributors":{"authors":[{"text":"Bean, Thomas G. 0000-0002-3577-1994 tbean@usgs.gov","orcid":"https://orcid.org/0000-0002-3577-1994","contributorId":195993,"corporation":false,"usgs":true,"family":"Bean","given":"Thomas G.","email":"tbean@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":708864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arnold, Kathryn E.","contributorId":195994,"corporation":false,"usgs":false,"family":"Arnold","given":"Kathryn","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":708865,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lane, Julie M.","contributorId":195995,"corporation":false,"usgs":false,"family":"Lane","given":"Julie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":708866,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergstrom, Ed","contributorId":195996,"corporation":false,"usgs":false,"family":"Bergstrom","given":"Ed","email":"","affiliations":[],"preferred":false,"id":708867,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thomas-Oates, Jane","contributorId":195997,"corporation":false,"usgs":false,"family":"Thomas-Oates","given":"Jane","email":"","affiliations":[],"preferred":false,"id":708868,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":708863,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boxall, Allistair B.A.","contributorId":195998,"corporation":false,"usgs":false,"family":"Boxall","given":"Allistair","email":"","middleInitial":"B.A.","affiliations":[],"preferred":false,"id":708869,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70189971,"text":"sir20175083 - 2017 - Low-flow frequency and flow-duration characteristics of selected streams in Alabama through March 2014","interactions":[],"lastModifiedDate":"2017-08-29T09:02:57","indexId":"sir20175083","displayToPublicDate":"2017-08-28T15:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5083","title":"Low-flow frequency and flow-duration characteristics of selected streams in Alabama through March 2014","docAbstract":"Low-flow statistics are needed by water-resource engineers, planners, and managers to protect and manage the water resources of Alabama. The accuracy of these statistics is influenced by such factors as length of record and specific hydrologic conditions measured in those records. As such, it is generally recommended that flow statistics be updated about every 10 years to provide improved and representative low-flow characteristics. The previous investigation of low-flow characteristics for Alabama included data through September 1990. Since that time, Alabama has experienced several historic droughts highlighting the need to update the low-flow characteristics at U.S. Geological Survey streamgaging stations. Consequently, this investigation was undertaken in cooperation with a number of State and local agencies to update low-flow frequency and flow-duration statistics at 210 continuous-record streamgaging stations in Alabama and 67 stations from basins that are shared with surrounding States. The flow characteristics were computed on the basis of available data through March 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175083","collaboration":"Prepared in cooperation with Alabama Association of Conservation Districts; Alabama Association of Resource Conservation and Development Councils; Alabama Department of Agriculture and Industries; Alabama Department of Conservation and Natural Resources—Division of Wildlife and Freshwater Fisheries; Alabama Department of Economic and Community Affairs—Office of Water Resources Division; Alabama Farmers Federation; Alabama Power; Choctawhatchee, Pea and Yellow Rivers Watershed Management Authority; Geological Survey of Alabama; and The University of Alabama—Water Policy and Law Institute","usgsCitation":"Feaster, T.D., and Lee, K.G., 2017, Low-flow frequency and flow-duration characteristics of selected streams in Alabama through March 2014: U.S. Geological Survey Scientific Investigations Report 2017–5083, 371 p., https://doi.org/10.3133/sir20175083.","productDescription":"viii, 371 p.","numberOfPages":"383","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-078394","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":345130,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5083/sir20175083.pdf","text":"Report","size":"8.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5083"},{"id":345129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5083/coverthb.jpg"}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.956298828125,\n 30.259067203213018\n ],\n [\n -84.517822265625,\n 30.259067203213018\n ],\n [\n -84.517822265625,\n 35.33529320309328\n ],\n [\n -88.956298828125,\n 35.33529320309328\n ],\n [\n -88.956298828125,\n 30.259067203213018\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park
Nashville, TN 37211
On the Ground
The 1989 Exxon Valdez oil spill damaged a wide range of natural resources, including intertidal communities, and post-spill studies demonstrated acute and chronic exposure and injury to an array of species. Standard toxicological methods to evaluate petroleum contaminants have assessed tissue burdens, with fewer assays providing indicators of health or physiology, particularly when contaminant levels are low and chronic. Marine mussels are a ubiquitous and crucial component of the nearshore environment, and new genomic technologies exist to quantify molecular responses of individual mussels to stimuli, including exposure to polycyclic aromatic hydrocarbons (PAHs). We used gene-based assays of exposure and physiological function to assess chronic oil contamination using the Pacific blue mussel, Mytilus trossulus. We developed a diagnostic gene transcription panel to investigate exposure to PAHs and other contaminants and its effects on mussel physiology and health. During 2012–2015, we analyzed mussels from five field sites in western Prince William Sound, Alaska, with varying oil histories from the 1989 Exxon Valdez oil spill, and from three boat harbors in the area. Gene transcription patterns of mussels from harbors were consistent with elevated exposure to PAHs or other contaminants, whereas transcription patterns of mussels sampled from shorelines in areas affected by the oil spill indicated no PAH exposure.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2017.08.007","usgsCitation":"Bowen, L., Miles, A.K., Ballachey, B.E., Waters-Dynes, S.C., Bodkin, J.L., Lindeberg, M., and Esler, D., 2017, Gene transcription patterns in response to low level petroleum contaminants in Mytilus trossulus from field sites and harbors in southcentral Alaska: Deep-Sea Research Part II: Topical Studies in Oceanography, v. 147, p. 27-35, https://doi.org/10.1016/j.dsr2.2017.08.007.","productDescription":"9 p.","startPage":"27","endPage":"35","ipdsId":"IP-085357","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":469586,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2017.08.007","text":"Publisher Index Page"},{"id":438238,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70P0XHD","text":"USGS data release","linkHelpText":"Data for Gene Transcription Patterns in Response to Low Level Petroleum Contaminants in Mytilus trossulus from Field Sites and Harbors in Southcentral Alaska"},{"id":345219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.875,\n 58.6769376725869\n ],\n [\n -143.08593749999997,\n 58.6769376725869\n ],\n [\n -143.08593749999997,\n 62.30879369102805\n ],\n [\n -151.875,\n 62.30879369102805\n ],\n [\n -151.875,\n 58.6769376725869\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"147","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59a52bd1e4b0fa5ae7c7481e","contributors":{"authors":[{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":708716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miles, A. Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":708717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballachey, Brenda E. 0000-0003-1855-9171 bballachey@usgs.gov","orcid":"https://orcid.org/0000-0003-1855-9171","contributorId":2966,"corporation":false,"usgs":true,"family":"Ballachey","given":"Brenda","email":"bballachey@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":708718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waters-Dynes, Shannon C. 0000-0002-9707-4684 swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":708719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":708720,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindeberg, Mandy","contributorId":195895,"corporation":false,"usgs":false,"family":"Lindeberg","given":"Mandy","email":"","affiliations":[],"preferred":false,"id":708721,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":true,"id":708722,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}