Literature DB >> 10539960

Precise monitoring of porpoising behaviour of Adélie penguins determined using acceleration data loggers.

K Yoda1, K Sato, Y Niizuma, M Kurita, C Bost, Y Le Maho, Y Naito.   

Abstract

A new method using acceleration data loggers enabled us to measure the porpoising behaviour of Adélie penguins (Pygoscelis adeliae), defined as a continuous rapid swimming with rhythmic serial leaps. Previous hydrodynamic models suggested that leaping would be energetically cheaper when an animal swims continuously at depths of less than three maximum body diameters below the water surface. In the present study, free-ranging Adélie penguins leapt at a mean speed of 2.8 m s(-)(1) above the predicted threshold speed (0.18-1. 88 m s(-)(1)). Wild penguins reduced drag by swimming deeper (0.91 m) and did not swim continuously within the high-drag layer while submerged. This indicates that previous calculations may be incomplete. Moreover, leaps represented an average of only 3.8 % of the total distance travelled during the porpoising cycle, which would make energy savings marginal. Among the six penguins used in our study, two did not porpoise and three porpoised for less than 7 min, also indicating that this behaviour was not important during travel to and from foraging sites, as has been previously suggested. Birds mainly porpoised at the start and end of a trip. One explanation of porpoising might be an escape behaviour from predators.

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Year:  1999        PMID: 10539960     DOI: 10.1242/jeb.202.22.3121

Source DB:  PubMed          Journal:  J Exp Biol        ISSN: 0022-0949            Impact factor:   3.312


  21 in total

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Review 3.  Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures.

Authors:  Ran Nathan; Orr Spiegel; Scott Fortmann-Roe; Roi Harel; Martin Wikelski; Wayne M Getz
Journal:  J Exp Biol       Date:  2012-03-15       Impact factor: 3.312

4.  Development of flight performance in the brown booby.

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Journal:  Proc Biol Sci       Date:  2004-05-07       Impact factor: 5.349

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8.  Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus).

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Journal:  PLoS One       Date:  2012-11-19       Impact factor: 3.240

9.  Step by step: reconstruction of terrestrial animal movement paths by dead-reckoning.

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Journal:  Mov Ecol       Date:  2015-09-15       Impact factor: 3.600

10.  Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm.

Authors:  Owen R Bidder; Hamish A Campbell; Agustina Gómez-Laich; Patricia Urgé; James Walker; Yuzhi Cai; Lianli Gao; Flavio Quintana; Rory P Wilson
Journal:  PLoS One       Date:  2014-02-21       Impact factor: 3.240

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