Visual obstruction, but not moderate traffic noise, increases reliance on heterospecific alarm calls
Ratnayake, Chaminda et al. (2021), Visual obstruction, but not moderate traffic noise, increases reliance on heterospecific alarm calls, Dryad, Dataset, https://doi.org/10.5061/dryad.05qfttf2t
Animals rely on both personal and social information about danger to minimise risk, yet environmental conditions constrain information. Both visual obstructions and background noise can reduce detectability of predators, which may increase reliance on social information, such as from alarm calls. Furthermore, a combination of visual and auditory constraints might greatly increase reliance on social information, because the loss of information from one source cannot be compensated by the other. Testing these possibilities requires manipulating personal information while broadcasting alarm calls. We therefore experimentally tested the effects of a visual barrier, traffic noise and their combination on the response of Australian magpies, Cracticus tibicen, to heterospecific alarm calls. The barrier blocked only visual cues, while playback of moderate traffic noise could mask subtle acoustic cues of danger, such as of a predator’s movement, but not alarm-call playback. We predicted that response to alarm calls would increase with either visual or acoustic constraint, and that there would be a disproportionate response when both were present. As predicted, individuals responded more strongly to alarm calls when there was a visual barrier. However, moderate traffic noise did not affect responses, and the effect of the visual barrier was not greater during traffic-noise playback. We conclude that a reduction of personal, visual information led to a greater reliance on social information from alarm calls, confirming indirect evidence from other species. The absence of a traffic-noise effect could be because in Australian magpies hearing subtle cues is less important than vision in detecting predators.
The methods of data collection are in the paper.
Please read the "README_for_Ratnayake_et_al 2021.txt" file. We have also added a pdf file with the R code to aid in interpretation of the data, and show how they were used in analyses.
Australian Research Council, Award: DP150102632