The 2006-2009 Puget Sound Land-Use/Land-Cover Change Map

Land cover change is associated with human development is one of the most important indirect stressors in the Salish Sea Ecosystem, and is a Vital Sings indicator for the Puget Sound Partnership. In January 2014, the Washington Department of Fish and Wildlife completed the first iteration of the Puget Sound land cover change map covering the 2006-2009 time period. The map was created from 1-m National Agriculture Imagery Program aerial imagery using a hybrid data mining-photo interpretation process developed for mapping change with high resolution imagery. The map depicts over 36,000 individual change events covering over 85,000 acres throughout the basin, and represents one of the largest area, highest-resolution change maps ever created. The process we employed includes reviewing every change location to remove commission error while omission error was assessed using a more traditional sampling approach applied to non-change areas. Additionally, at each location the initial land cover, change agent (development, forestry, natural, etc.), total change area, canopy reduction and increase in impervious and semipervious surface were quantified during the photo interpretation step. The median change event was smaller than one acre, which is too small to be reliably assessed using intermediate resolution remote sensing data like LandSat. The spatial precision of the map provides a robust base layer for intersection analyses with other data sets such as riparian buffers, urban and planned growth area boundaries, mapped wetlands, mapped ownership, land-use, parcels. From these intersections, change rates can be calculated by area of interest. Sound-wide results will be presented along with information on obtaining the data and examples of potential analyses

Early marine survival of steelhead smolts in Puget Sound

Smolt-to adult survival rates for Puget Sound steelhead populations have declined substantially over the last 25 years and remain at or near historic lows. From 2006-2009, nearly 1,400 steelhead smolts from 9 watersheds within Puget Sound were tracked from river mouth to the Pacific Ocean using acoustic telemetry to: (1) estimate early marine survival through Puget Sound, (2) identify common areas of abnormally high mortality along the migration route, and (3) to identify factors that may influence survival. Cormac-Jolly-Seber mark-recapture models were used to jointly estimate survival and detection rate at telemetry arrays. Estimated survival rates from river mouths to near the Pacific Ocean ranged from 1.5% (Skokomish River hatchery smolts in 2009) to 34.0% (Big Beef Creek wild smolts in 2006), and averaged 14.9% for all populations. Factors influencing survival included population, migration segment, migration year, and rearing type (i.e., hatchery or wild), while geographic region, body length, and tag type (i.e., 7mm or 9mm) showed lesser effects. Comparison of survival rates between migration segments implicated central Puget Sound and Admiralty Inlet as potential areas of heightened mortality. Early marine survival rates estimated here are very low considering that steelhead smolts spend only about two to three weeks in Puget Sound before entering the Pacific Ocean. Mortality in Puget Sound may be a major driver behind low observed smolt-to adult survival rates. This study addresses a major gap in steelhead marine life history knowledge and can help to inform future Puget Sound steelhead recovery planning efforts

Seven years of development and change within 200\u27 of the shore in Puget Sound

The 1971 Washington Shoreline Management Act (SMA) defines Shorelands or shoreland areas as those lands extending landward for two hundred feet in all directions as measured on a horizontal plane from the ordinary high water mark. We analyzed land use change in the Shorelands of Puget Sound using WDFW’s High Resolution Change Detection project. We identified 2,960 individual change locations that intersected those shorelands. We found 73% of the locations exhibited anthropogenic change, 14% had no real change within the shoreland area, 5% of the locations were landslides, 3% were erroneously mapped as change and the remaining locations fell into other minor categories of changes, some difficult to interpret. The entire study area covered 230 km2 or a 61m wide strip about 3800 km in length. The total change area was about 1.4 km2 or about 0.09% per year. This change area included about 0.4 km2 new impervious surface with about 0.9 km2 of tree removal. While we do not quantify vegetation growth, tree and other vegetation growth likely outpaced loss during this time period. We will provide final statistics and examples of change events as part of this presentation. We believe this presents the first comprehensive Puget Sound wide assessment of change within the “Shorelands” defined by the SMA

Movements of sub-adult Chinook salmon, Oncorhynchus tshawytscha, in Puget Sound, Washington, as indicated by ultrasonic tracking

Salmonids show a wide variety of migration patterns. Such variation is especially prevalent in Chinook salmon, Oncorhynchus tshawytscha. This species migrates to coastal and open ocean waters, and the tendency to use these different marine environments varies markedly among populations. For example, some Chinook salmon that enter Puget Sound do not migrate to the sea as juveniles in their first year but rather remain as “residents” through (at least) the following Spring. Known locally as blackmouth, these fish are the focus of extensive sport fisheries. In this study, we used acoustic telemetry to examine questions surrounding resident Chinook salmon in Puget Sound. The overall objective of this study was to determine the extent to resident and migratory behavior patterns are distinct or ends of a continuum of movement patterns, and then characterize the movements of resident fish. We first assessed the proportion of fish, caught and tagged as immature residents (inferred from the locations and dates of capture), that remained within Puget Sound and the proportion that moved to the coastal region, and tested the hypotheses that origin (wild or hatchery), location and season of tagging, fish size and condition factor would influence the tendency to remain resident. Second, we characterized the movements by resident fish with Puget Sound at a series of different spatial scales: movement among the major basins, travel rates, and areas of concentration within Puget Sound. Third, we tested the model of seasonal north-south movement patterns by examining the distribution of detections over the whole area and year. Because residents represent a significant portion of the Puget Sound Chinook salmon Evolutionarily Significant Unit, currently listed as Threatened under the U. S. Endangered Species Act, better understanding of their movements in Puget Sound will help identify critical habitat use patterns and evaluate fishery management objectives as the species crosses jurisdictional boundaries

Evidence for Geomagnetic Imprinting as a Homing Mechanism in Pacific Salmon

In the final phase of their spawning migration, Pacific salmon use chemical cues to identify their home river, but how they navigate from the open ocean to the correct coastal area has remained enigmatic [1]. To test the hypothesis that salmon imprint on the magnetic field that exists where they first enter the sea and later seek the same field upon return [2-4], we analyzed a 56-year fisheries data set on Fraser River sockeye salmon, which must detour around Vancouver Island to approach the river through either a northern or southern passageway [5, 6]. We found that the proportion of salmon using each route was predicted by geomagnetic field drift: the more the field at a passage entrance diverged from the field at the river mouth, the fewer fish used the passage. We also found that more fish used the northern passage in years with warmer sea surface temperature (presumably because fish were constrained to more northern latitudes). Field drift accounted for 16% of the variation in migratory route used, temperature 22%, and the interaction between these variables 28%. These results provide the first empirical evidence of geomagnetic imprinting in any species and imply that forecasting salmon movements is possible using geomagnetic models

Evidence for geomagnetic imprinting as a homing mechanism in Pacific salmon

In the final phase of their spawning migration, Pacific salmon use chemical cues to identify their home river, but how they navigate from the open ocean to the correct coastal area has remained enigmatic [1]. To test the hypothesis that salmon imprint on the magnetic field that exists where they first enter the sea and later seek the same field upon return [2, 3, 4], we analyzed a 56-year fisheries data set on Fraser River sockeye salmon, which must detour around Vancouver Island to approach the river through either a northern or southern passageway [5, 6]. We found that the proportion of salmon using each route was predicted by geomagnetic field drift: the more the field at a passage entrance diverged from the field at the river mouth, the fewer fish used the passage. We also found that more fish used the northern passage in years with warmer sea surface temperature (presumably because fish were constrained to more northern latitudes). Field drift accounted for 16% of the variation in migratory route used, temperature 22%, and the interaction between these variables 28%. These results provide the first empirical evidence of geomagnetic imprinting in any species and imply that forecasting salmon movements is possible using geomagnetic models.This is the author's final peer reviewed manuscript as accepted by the publisher. The official publication is copyrighted by Elsevier (Cell Press) and can be found at http://www.cell.com/current-biology/home.Keywords: geomagnetic secular variation, magnetic map, salmon, imprinting, magnetoreception, navigation, orientatio

Kepler-20: A Sun-like Star with Three Sub-Neptune Exoplanets and Two Earth-size Candidates

We present the discovery of the Kepler-20 planetary system, which we initially identified through the detection of five distinct periodic transit signals in the Kepler light curve of the host star 2MASSJ19104752+4220194. We find a stellar effective temperature Teff=5455+-100K, a metallicity of [Fe/H]=0.01+-0.04, and a surface gravity of log(g)=4.4+-0.1. Combined with an estimate of the stellar density from the transit light curves we deduce a stellar mass of Mstar=0.912+-0.034 Msun and a stellar radius of Rstar=0.944^{+0.060}_{-0.095} Rsun. For three of the transit signals, our results strongly disfavor the possibility that these result from astrophysical false positives. We conclude that the planetary scenario is more likely than that of an astrophysical false positive by a factor of 2e5 (Kepler-20b), 1e5 (Kepler-20c), and 1.1e3 (Kepler-20d), sufficient to validate these objects as planetary companions. For Kepler-20c and Kepler-20d, the blend scenario is independently disfavored by the achromaticity of the transit: From Spitzer data gathered at 4.5um, we infer a ratio of the planetary to stellar radii of 0.075+-0.015 (Kepler-20c) and 0.065+-0.011 (Kepler-20d), consistent with each of the depths measured in the Kepler optical bandpass. We determine the orbital periods and physical radii of the three confirmed planets to be 3.70d and 1.91^{+0.12}_{-0.21} Rearth for Kepler-20b, 10.85 d and 3.07^{+0.20}_{-0.31} Rearth for Kepelr-20c, and 77.61 d and 2.75^{+0.17}_{-0.30} Rearth for Kepler-20d. From multi-epoch radial velocities, we determine the masses of Kepler-20b and Kepler-20c to be 8.7\+-2.2 Mearth and 16.1+-3.5 Mearth, respectively, and we place an upper limit on the mass of Kepler-20d of 20.1 Mearth (2 sigma).Comment: accepted by ApJ, 58 pages, 12 figures revised Jan 2012 to correct table 2 and clarify planet parameter extractio

The Formation of Compact Elliptical Galaxies in The Vicinity of A Massive Galaxy: The Role of Ram-pressure Confinement

Compact ellipticals (cEs) are outliers from the scaling relations of early-type galaxies, particularly the mass-metallicity relation which is an important outcome of feedback. The formation of such low-mass, but metal-rich and compact, objects is a long-standing puzzle. Using a pair of high-resolution N-body+gas simulations, we investigate the evolution of a gas-rich low-mass galaxy on a highly radial orbit around a massive host galaxy. As the infalling low-mass galaxy passes through the host's corona at supersonic speeds, its diffuse gas outskirts are stripped by ram pressure, as expected. However, the compactness increases rapidly because of bursty star formation in the gas tidally driven to the centre. The metal-rich gas produced by supernovae and stellar winds is confined by the ram pressure from the surrounding environment, leading to subsequent generations of stars being more metal-rich. After the gas is depleted, tidal interactions enhance the metallicity further via the stripping of weakly bound, old, and metal-poor stars, while the size of the satellite is changed only modestly. The outcome is a metal-rich cE that is consistent with observations. These results argue that classical cEs are neither the stripped remnants of much more massive galaxies nor the merger remnants of normal dwarfs. We present observable predictions that can be used to test our model

Performance of the Gemini Planet Imager Non-Redundant Mask and spectroscopy of two close-separation binaries HR 2690 and HD 142527

The Gemini Planet Imager (GPI) contains a 10-hole non-redundant mask (NRM), enabling interferometric resolution in complement to its coronagraphic capabilities. The NRM operates both in spectroscopic (integral field spectrograph, henceforth IFS) and polarimetric configurations. NRM observations were taken between 2013 and 2016 to characterize its performance. Most observations were taken in spectroscopic mode with the goal of obtaining precise astrometry and spectroscopy of faint companions to bright stars. We find a clear correlation between residual wavefront error measured by the AO system and the contrast sensitivity by comparing phase errors in observations of the same source, taken on different dates. We find a typical 5-$\sigma$ contrast sensitivity of $2-3~\times~10^{-3}$ at $\sim\lambda/D$. We explore the accuracy of spectral extraction of secondary components of binary systems by recovering the signal from a simulated source injected into several datasets. We outline data reduction procedures unique to GPI's IFS and describe a newly public data pipeline used for the presented analyses. We demonstrate recovery of astrometry and spectroscopy of two known companions to HR 2690 and HD 142527. NRM+polarimetry observations achieve differential visibility precision of $\sigma\sim0.4\%$ in the best case. We discuss its limitations on Gemini-S/GPI for resolving inner regions of protoplanetary disks and prospects for future upgrades. We summarize lessons learned in observing with NRM in spectroscopic and polarimetric modes.Comment: Accepted to AJ, 22 pages, 14 figure

Sex-Specific Genetic Associations for Barrett's Esophagus and Esophageal Adenocarcinoma

Acknowledgments We thank Dr Stuart MacGregor for his input on the study proposal and review of prior versions of this manuscript. We also thank all patients and controls for participating in this study. The MD Anderson controls were drawn from dbGaP (study accession: phs000187.v1.p1). Genotyping of these controls were done through the University of Texas MD Anderson Cancer Center (UTMDACC) and the Johns Hopkins University Center for Inherited Disease Research (CIDR). We acknowledge the principal investigators of this study: Christopher Amos, Qingyi Wei, and Jeffrey E. Lee. Controls from the Genome-Wide Association Study of Parkinson Disease were obtained from dbGaP (study accession: phs000196.v2.p1). This work, in part, used data from the National Institute of Neurological Disorders and Stroke (NINDS) dbGaP database from the CIDR: NeuroGenetics Research Consortium Parkinson’s disease study. We acknowledge the principal investigators and coinvestigators of this study: Haydeh Payami, John Nutt, Cyrus Zabetian, Stewart Factor, Eric Molho, and Donald Higgins. Controls from the Chronic Renal Insufficiency Cohort (CRIC) were drawn from dbGaP (study accession: phs000524.v1.p1). The CRIC study was done by the CRIC investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Data and samples from CRIC reported here were supplied by NIDDK Central Repositories. This report was not prepared in collaboration with investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repositories, or the NIDDK. We acknowledge the principal investigators and the project officer of this study: Harold I Feldman, Raymond R Townsend, Lawrence J. Appel, Mahboob Rahman, Akinlolu Ojo, James P. Lash, Jiang He, Alan S Go, and John W. Kusek. The following UK hospitals participated in sample collection through the Stomach and Oesophageal Cancer Study (SOCS) collaboration network: Addenbrooke’s Hospital, University College London, Bedford Hinchingbrooke Hospital, Peterborough City Hospital, West Suffolk Norfolk and Norwich University Hospital, Churchill Hospital, John Hospital, Velindre Hospital, St Bartholomew’s Hospital, Queen’s Burton, Queen Elisabeth Hospital, Diana Princess of Wales, Scunthorpe General Hospital, Royal Devon & Exeter Hospital, New Cross Hospital, Belfast City Hospital, Good Hope Hospital, Heartlands Hospital, South Tyneside District General Hospital, Cumberland Infirmary, West Cumberland Hospital, Withybush General Hospital, Stoke Mandeville Hospital, Wycombe General Hospital, Wexham Park Hospital, Southend Hospital, Guy’s Hospital, Southampton General Hospital, Bronglais General Hospital, Aberdeen Royal Infirmary, Manor Hospital, Clatterbridge Centre for Oncology, Lincoln County Hospital, Pilgrim Hospital, Grantham & District Hospital, St Mary’s Hospital London, Croydon University Hospital, Whipps Cross University Hospital, Wansbeck General Hospital, Hillingdon Hospital, Milton Keynes General Hospital, Royal Gwent Hospital, Tameside General Hospital, Castle Hill Hospital, St Richard’s Hospital, Ipswich Hospital, St Helens Hospital, Whiston Hospital, Countess of Chester Hospital, St Mary’s Hospital IOW, Queen Alexandra Hospital, Glan Clwyd Hospital, Wrexham Maelor Hospital, Darent Valley Hospital, Royal Derby Hospital, Derbyshire Royal Infirmary, Scarborough General Hospital, Kettering General Hospital, Kidderminster General Hospital, Royal Lancaster Infirmary, Furness General Hospital, Westmorland General Hospital, James Cook University Hospital, Friarage Hospital, Stepping Hill Hospital, St George’s Hospital London, Doncaster Royal Infirmary, Maidstone Hospital, Tunbridge Hospital, Prince Charles Hospital, Hartlepool Hospital, University Hospital of North Tees, Ysbyty Gwynedd, St. Jame’s University Hospital, Leeds General Infirmary, North Hampshire Hospital, Royal Preston Hospital, Chorley and District General, Airedale General Hospital, Huddersfield Royal Infirmary, Calderdale Royal Hospital, Torbay District General Hospital, Leighton Hospital, Royal Albert Edward Infirmary, Royal Surrey County Hospital, Bradford Royal Infirmary, Burnley General Hospital, Royal Blackburn Hospital, Royal Sussex County Hospital, Freeman Hospital, Royal Victoria Infirmary, Victoria Hospital Blackpool, Weston Park Hospital, Royal Hampshire County Hospital, Conquest Hospital, Royal Bournemouth General Hospital, Mount Vernon Hospital, Lister Hospital, William Harvey Hospital, Kent and Canterbury Hospital, Great Western Hospital, Dumfries and Galloway Royal Infirmary, Poole General Hospital, St Hellier Hospital, North Devon District Hospital, Salisbury District Hospital, Weston General Hospital, University Hospital Coventry, Warwick Hospital, George Eliot Hospital, Alexandra Hospital, Nottingham University Hospital, Royal Chesterfield Hospital, Yeovil District Hospital, Darlington Memorial Hospital, University Hospital of North Durham, Bishop Auckland General Hospital, Musgrove Park Hospital, Rochdale Infirmary, North Manchester General, Altnagelvin Area Hospital, Dorset County Hospital, James Paget Hospital, Derriford Hospital, Newham General Hospital, Ealing Hospital, Pinderfields General Hospital, Clayton Hospital, Dewsbury & District Hospital, Pontefract General Infirmary, Worthing Hospital, Macclesfield Hospital, University Hospital of North Staffordshire, Salford Royal Hospital, Royal Shrewsbury Hospital, and Manchester Royal Infirmary. Conflict of interest The authors disclose no conflicts. Funding This work was primarily funded by the National Institutes of Health (NIH) (R01CA136725). The funders of the study had no role in the design, analysis, or interpretation of the data, nor in writing or publication decisions related to this article. Jing Dong was supported by a Research Training Grant from the Cancer Prevention and Research Institute of Texas (CPRIT; RP160097) and the Research and Education Program Fund, a component of the Advancing a Healthier Wisconsin endowment at the Medical College of Wisconsin (AHW). Quinn T. Ostrom was supported by RP160097. Puya Gharahkhani was supported by a grant from National Health and Medical Research Council of Australia (1123248). Geoffrey Liu was supported by the Alan B. Brown Chair in Molecular Genomics and by the CCO Chair in Experimental Therapeutics and Population Studies. The University of Cambridge received salary support for Paul D. Pharoah from the NHS in the East of England through the Clinical Academic Reserve. Brian J. Reid was supported by a grant (P01CA91955) from the NIH/National Cancer Institute (NCI). Nicholas J. Shaheen was supported by a grant (P30 DK034987) from NIH. Thomas L. Vaughan was supported by NIH Established Investigator Award K05CA124911. Michael B. Cook was supported by the Intramural Research Program of the NCI, NIH, Department of Health and Human Services. Douglas A. Corley was supported by the NIH grants R03 KD 58294, R21DK077742, and RO1 DK63616 and NCI grant R01CA136725. Carlo Maj was supported by the BONFOR-program of the Medical Faculty, University of Bonn (O-147.0002). Jesper Lagergren was supported by the United European Gastroenterology (UEG) Research Prize. David C. Whiteman was supported by fellowships from the National Health and Medical Research Council of Australia (1058522, 1155413).Peer reviewedPostprin