Individual vocal signatures show reduced complexity following invasion.
The manner in which vocal learning is used for social recognition may be sensitive to the social environment. Biological invaders capable of vocal learning are useful for testing this possibility, as invasion alters population size. Some vocal learning species use frequency modulation patterns of acoustic signals for individual recognition. In such species, frequency modulation patterns should be more complex in larger social groups, reflecting greater selection for individual distinctiveness. We used numbers of nests and nest densities as proxies of local population sizes of native range monk parakeets, Myiopsitta monachus, in Uruguay and invasive range populations in the United States. Flock sizes were obtained to estimate maximum social group sizes per range. Supervised machine learning and frequency contours were employed to compare contact call structure between native and invasive range populations, and the effect of urban habitats on call structure was also assessed. Invasive range populations exhibited fewer nests, lower nest densities and smaller maximum flock sizes, which is consistent with a reduction in population size following invasion. Parakeets at invasive range sites also produced contact calls with simpler frequency modulation patterns. Beecher's statistic (HS) revealed reduced individual identity content and fewer possible unique individual signatures in invasive range calls. Simpler individual signatures are consistent with relaxed selection on the complexity of learned calls likely used for individual vocal recognition in the smaller local populations that we identified post-invasion. Frequency modulation patterns were simpler in urban habitats in both ranges, indicating that urban habitats could also alter the social environment and in turn influence the complexity of learned individual signatures. These findings contribute to a growing literature on the use of vocal learning for individual recognition and indicate that vocal learning can be used to produce individual vocal signatures in a manner sensitive to local population size.