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Further support for the alignment of cattle along magnetic field lines: reply to Hert et al.


ABSTRACT: Hert et al. (J Comp Physiol A, 2011) challenged one part of the study by Begall et al. (PNAS 105:13451-13455, 2008) claiming that they could not replicate the finding of preferential magnetic alignment of cattle recorded in aerial images of Google Earth. However, Hert and co-authors used a different statistical approach and applied the statistics on a sample partly unsuitable to examine magnetic alignment. About 50% of their data represent noise (resolution of the images is too poor to enable unambiguous measurement of the direction of body axes, pastures are on slopes, near settlements or high voltage power-lines, etc.). Moreover, the authors have selected for their analysis only ~ 40% of cattle that were present on the pastures analyzed. Here, we reanalyze all usable data and show that cattle significantly align their body axes in North-South direction on pastures analyzed by Hert and co-authors. This finding thus supports our previous study. In addition, we show by using aerial Google Earth images with good resolution, that the magnetic alignment is more pronounced in resting than in standing cattle.

SUBMITTER: Begall S 

PROVIDER: S-EPMC3221857 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Further support for the alignment of cattle along magnetic field lines: reply to Hert et al.

Begall S S   Burda H H   Cervený J J   Gerter O O   Neef-Weisse J J   Němec P P  

Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology 20111022 12


Hert et al. (J Comp Physiol A, 2011) challenged one part of the study by Begall et al. (PNAS 105:13451-13455, 2008) claiming that they could not replicate the finding of preferential magnetic alignment of cattle recorded in aerial images of Google Earth. However, Hert and co-authors used a different statistical approach and applied the statistics on a sample partly unsuitable to examine magnetic alignment. About 50% of their data represent noise (resolution of the images is too poor to enable un  ...[more]

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