Acoustic Signals of Closely Related Bird Species Differ More in Sympatry Than in Allopatry - But Not If They Are Learned

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Authors
Munim, Samreen
Keyword
Sympatry , Acoustic signals , Character displacement , Species sorting , differential fusion
Abstract
Closely related species often have similar mating signals because of their shared recent ancestry, which can affect their reproductive isolation and ability to live together when they overlap in range. Similar mating signals can result in costs of co-occurrence (e.g., maladaptive hybridization, aggression), and we expect signals to diverge to reduce these costs where species overlap in range. However, other pressures (e.g., shared habitats) can lead to signal convergence instead, and it is unclear whether acoustic signals more often diverge or converge when species overlap in range. Here, we applied a comparative framework that controls for phylogeny and evolutionary time to determine whether acoustic signals of sympatric bird species are more different or similar compared to acoustic signals of allopatric species. Our holistic measures of signal differences suggest that acoustic signals of sympatric species differ more than those of allopatric species, but only if species do not learn their signals. We also found that, regardless of whether species learn their signals or not, sympatric species tended to differ more than allopatric species for three of eight frequency and time variables, with greater sympatric divergence occurring in different contexts for each variable. In contrast, sympatric species were more similar in frequency bandwidth compared to allopatric species, but only if signals were not learned, while four of eight variables showed no difference in divergence between sympatry and allopatry. Overall, our results suggest that learning may influence patterns of signal divergence in sympatry, that different selective pressures likely act on different aspects of acoustic signals, and that the methods we use to compare signals may impact which patterns we detect.
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