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Noise matters: elephants show risk-avoidance behaviour in response to human-generated seismic cues

Abstract:
African elephants (Loxodonta africana) use many sensory modes to gather information about their environment, including the detection of seismic, or ground-based, vibrations. Seismic information is known to include elephant-generated signals, but also potentially encompasses biotic cues that are commonly referred to as ‘noise’. To investigate seismic information transfer in elephants beyond communication, here we tested the hypothesis that wild elephants detect and discriminate between seismic vibrations that differ in their noise types, whether elephant- or human-generated. We played three types of seismic vibrations to elephants: seismic recordings of elephants (elephant-generated), white noise (human-generated), and a combined track (elephant- and human-generated). We found evidence of both detection of seismic noise and discrimination between the two treatments containing human-generated noise. In particular, we found evidence of retreat behaviour, where seismic tracks with human-generated noise caused elephants to move further away from the trial location. We conclude that seismic noise are cues that contain biologically-relevant information for elephants that they can associate with risk. This expands our understanding of how elephants use seismic information, with implications for elephant sensory ecology and conservation management.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rspb.2021.0774

Authors

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Institution:
University of Oxford
Department:
ZOOLOGY
Sub department:
Zoology
Oxford college:
Jesus College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Zoology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Zoology
Role:
Author


Publisher:
The Royal Society
Journal:
Proceedings of the Royal Society B: Biological Sciences More from this journal
Volume:
288
Issue:
1953
Article number:
20210774
Publication date:
2021-06-30
Acceptance date:
2021-06-08
DOI:
EISSN:
1471-2954
ISSN:
0962-8452


Language:
English
Keywords:
Pubs id:
1181059
Local pid:
pubs:1181059
Deposit date:
2021-06-08
ARK identifier:

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