Date:February 8, 2016
Source:University of Cambridge
Largest quantitative study of howling, and first to use machine learning, defines different howl types and finds that wolves use these types more or less depending on their species, resembling a howling dialect. Researchers say findings could help conservation efforts and shed light on the earliest evolution of our own use of language.
The largest ever study of howling in the 'canid' family of species -- which includes wolves, jackals and domestic dogs -- has shown that the various species and subspecies have distinguishing repertoires of howling, or "vocal fingerprints": different types of howls are used with varying regularity depending on the canid species.
Researchers used computer algorithms for the first time to analyse howling, distilling over 2,000 different howls into 21 howl types based on pitch and fluctuation, and then matching up patterns of howling.
They found that the frequency with which types of howls are used -- from flat to highly modulated -- corresponded to the species of canid, whether dog or coyote, as well as to the subspecies of wolf.
For example, the howling repertoire of the timber wolf is heavy with low, flat howls but doesn't feature the high, looping vocal that is the most frequently used in the range of howls deployed by critically-endangered red wolves.
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