Data from: Variation in the ecstatic display call of the Gentoo Penguin (Pygoscelis papua) across regional geographic scales
Lynch, Maureen A.; Lynch, Heather J. (2018), Data from: Variation in the ecstatic display call of the Gentoo Penguin (Pygoscelis papua) across regional geographic scales, Dryad, Dataset, https://doi.org/10.5061/dryad.rm228
Geographic variation in bird vocalizations is common and has been associated with genetic differences and speciation, as well as with short-term changes in response to anthropogenic noise. Because vocalizations are used for individual recognition in many species, geographic variation in these traits may affect mate choice, pair bonding, and territory defense. Anecdotal evidence suggests the existence of geographic variation in vocalizations between isolated populations of Gentoo Penguins (Pygoscelis papua), but there have been no comprehensive studies of Gentoo Penguin vocalizations across a broad geographic range. We used acoustic recordings of ambient colony sound at 22 breeding colonies in the Antarctic Peninsula and South Shetland Islands, South Georgia, the Falkland Islands, and Argentina to address 2 main questions regarding Gentoo Penguin vocalizations: (1) How do ecstatic display calls vary both within and between individuals, colonies, and regions? (2) Can ecstatic display calls be used to distinguish subspecies? We found high levels of variation between individuals and between colonies, but little additional variation between regions or subspecies. We found no trends to suggest a latitudinal gradient in vocal characteristics, although we did find that some measures varied with relative distance between colonies. Although we found significant differences at the colony level, unknown calls could not easily be categorized to colony or region by machine learning. We conclude that the vocal soundscape of each colony is driven by variation between individuals within a colony and, developing independently from neighboring colonies, becomes differentiated from other colonies through a process of drift. Although individual calls could, in most cases, be identified to subspecies by machine learning, our analysis suggests that subspecies differences may be driven by variation among colonies and that subspecies identification may be unreliable using acoustics alone.