Literature DB >> 27457932

Flight paths of seabirds soaring over the ocean surface enable measurement of fine-scale wind speed and direction.

Yoshinari Yonehara1, Yusuke Goto2, Ken Yoda3, Yutaka Watanuki4, Lindsay C Young5, Henri Weimerskirch6, Charles-André Bost6, Katsufumi Sato2.   

Abstract

Ocean surface winds are an essential factor in understanding the physical interactions between the atmosphere and the ocean. Surface winds measured by satellite scatterometers and buoys cover most of the global ocean; however, there are still spatial and temporal gaps and finer-scale variations of wind that may be overlooked, particularly in coastal areas. Here, we show that flight paths of soaring seabirds can be used to estimate fine-scale (every 5 min, ∼5 km) ocean surface winds. Fine-scale global positioning system (GPS) positional data revealed that soaring seabirds flew tortuously and ground speed fluctuated presumably due to tail winds and head winds. Taking advantage of the ground speed difference in relation to flight direction, we reliably estimated wind speed and direction experienced by the birds. These bird-based wind velocities were significantly correlated with wind velocities estimated by satellite-borne scatterometers. Furthermore, extensive travel distances and flight duration of the seabirds enabled a wide range of high-resolution wind observations, especially in coastal areas. Our study suggests that seabirds provide a platform from which to measure ocean surface winds, potentially complementing conventional wind measurements by covering spatial and temporal measurement gaps.

Entities:  

Keywords:  GPS; biologging; dynamic soaring; meteorology; satellite scatterometer

Mesh:

Year:  2016        PMID: 27457932      PMCID: PMC4987799          DOI: 10.1073/pnas.1523853113

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  16 in total

1.  Fast and fuel efficient? Optimal use of wind by flying albatrosses.

Authors:  H Weimerskirch; T Guionnet; J Martin; S A Shaffer; D P Costa
Journal:  Proc Biol Sci       Date:  2000-09-22       Impact factor: 5.349

2.  Changes in wind pattern alter albatross distribution and life-history traits.

Authors:  Henri Weimerskirch; Maite Louzao; Sophie de Grissac; Karine Delord
Journal:  Science       Date:  2012-01-13       Impact factor: 47.728

3.  Global circumnavigations: tracking year-round ranges of nonbreeding albatrosses.

Authors:  John P Croxall; Janet R D Silk; Richard A Phillips; Vsevolod Afanasyev; Dirk R Briggs
Journal:  Science       Date:  2005-01-14       Impact factor: 47.728

4.  Experimental verification of dynamic soaring in albatrosses.

Authors:  G Sachs; J Traugott; A P Nesterova; F Bonadonna
Journal:  J Exp Biol       Date:  2013-11-15       Impact factor: 3.312

5.  Migratory shearwaters integrate oceanic resources across the Pacific Ocean in an endless summer.

Authors:  Scott A Shaffer; Yann Tremblay; Henri Weimerskirch; Darren Scott; David R Thompson; Paul M Sagar; Henrik Moller; Graeme A Taylor; David G Foley; Barbara A Block; Daniel P Costa
Journal:  Proc Natl Acad Sci U S A       Date:  2006-08-14       Impact factor: 11.205

6.  Southern Ocean frontal structure and sea-ice formation rates revealed by elephant seals.

Authors:  J-B Charrassin; M Hindell; S R Rintoul; F Roquet; S Sokolov; M Biuw; D Costa; L Boehme; P Lovell; R Coleman; R Timmermann; A Meijers; M Meredith; Y-H Park; F Bailleul; M Goebel; Y Tremblay; C-A Bost; C R McMahon; I C Field; M A Fedak; C Guinet
Journal:  Proc Natl Acad Sci U S A       Date:  2008-08-11       Impact factor: 11.205

7.  Flying at no mechanical energy cost: disclosing the secret of wandering albatrosses.

Authors:  Gottfried Sachs; Johannes Traugott; Anna P Nesterova; Giacomo Dell'Omo; Franz Kümmeth; Wolfgang Heidrich; Alexei L Vyssotski; Francesco Bonadonna
Journal:  PLoS One       Date:  2012-09-05       Impact factor: 3.240

8.  Toward the quantification of a conceptual framework for movement ecology using circular statistical modeling.

Authors:  Ichiro Ken Shimatani; Ken Yoda; Nobuhiro Katsumata; Katsufumi Sato
Journal:  PLoS One       Date:  2012-11-30       Impact factor: 3.240

9.  Can ethograms be automatically generated using body acceleration data from free-ranging birds?

Authors:  Kentaro Q Sakamoto; Katsufumi Sato; Mayumi Ishizuka; Yutaka Watanuki; Akinori Takahashi; Francis Daunt; Sarah Wanless
Journal:  PLoS One       Date:  2009-04-30       Impact factor: 3.240

10.  Assimilation of the seabird and ship drift data in the north-eastern sea of Japan into an operational ocean nowcast/forecast system.

Authors:  Yasumasa Miyazawa; Xinyu Guo; Sergey M Varlamov; Toru Miyama; Ken Yoda; Katsufumi Sato; Toshiyuki Kano; Keiji Sato
Journal:  Sci Rep       Date:  2015-12-03       Impact factor: 4.379

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  8 in total

1.  Optimal dynamic soaring consists of successive shallow arcs.

Authors:  Gabriel D Bousquet; Michael S Triantafyllou; Jean-Jacques E Slotine
Journal:  J R Soc Interface       Date:  2017-10       Impact factor: 4.118

2.  Pelagic seabirds reduce risk by flying into the eye of the storm.

Authors:  Emmanouil Lempidakis; Emily L C Shepard; Andrew N Ross; Sakiko Matsumoto; Shiho Koyama; Ichiro Takeuchi; Ken Yoda
Journal:  Proc Natl Acad Sci U S A       Date:  2022-10-04       Impact factor: 12.779

3.  Atmospheric conditions create freeways, detours and tailbacks for migrating birds.

Authors:  Judy Shamoun-Baranes; Felix Liechti; Wouter M G Vansteelant
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2017-05-15       Impact factor: 1.836

4.  Asymmetry hidden in birds' tracks reveals wind, heading, and orientation ability over the ocean.

Authors:  Yusuke Goto; Ken Yoda; Katsufumi Sato
Journal:  Sci Adv       Date:  2017-09-27       Impact factor: 14.136

5.  Benthic animal-borne sensors and citizen science combine to validate ocean modelling.

Authors:  Edward Lavender; Dmitry Aleynik; Jane Dodd; Janine Illian; Mark James; Sophie Smout; James Thorburn
Journal:  Sci Rep       Date:  2022-10-05       Impact factor: 4.996

6.  Flight speed and performance of the wandering albatross with respect to wind.

Authors:  Philip L Richardson; Ewan D Wakefield; Richard A Phillips
Journal:  Mov Ecol       Date:  2018-03-07       Impact factor: 3.600

7.  Machine learning enables improved runtime and precision for bio-loggers on seabirds.

Authors:  Joseph Korpela; Hirokazu Suzuki; Sakiko Matsumoto; Yuichi Mizutani; Masaki Samejima; Takuya Maekawa; Junichi Nakai; Ken Yoda
Journal:  Commun Biol       Date:  2020-10-30

8.  Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species.

Authors:  Melinda G Conners; Théo Michelot; Eleanor I Heywood; Rachael A Orben; Richard A Phillips; Alexei L Vyssotski; Scott A Shaffer; Lesley H Thorne
Journal:  Mov Ecol       Date:  2021-02-22       Impact factor: 3.600

  8 in total

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