An intra-population analysis of the indris' song dissimilarity in the light of genetic distance

Abstract The increasing interest in the evolution of human language has led several fields of research to focus on primate vocal communication. The ‘singing primates’, which produce elaborated and complex sequences of vocalizations, are of particular interest for this topic. Indris (Indri indri) are the only singing lemurs and emit songs whose most distinctive portions are “descending phrases” consisting of 2-5 units. We examined how the structure of the indris’ phrases varied with genetic relatedness among individuals. We tested whether the acoustic structure could provide conspecifics with information about individual identity and group membership. When analyzing phrase dissimilarity and genetic distance of both sexes, we found significant results for males but not for females. We found that similarity of male song-phrases correlates with kin in both time and frequency parameters, while, for females, this information is encoded only in the frequency of a single type. Song phrases have consistent individual-specific features, but we did not find any potential for advertising group membership. We emphasize the fact that genetic and social factors may play a role in the acoustic plasticity of female indris. Altogether, these findings open a new perspective for future research on the possibility of vocal production learning in these primates

Finding Meanings in Low Dimensional Structures: Stochastic Neighbor Embedding Applied to the Analysis of <i>Indri indri</i> Vocal Repertoire

Although there is a growing number of researches focusing on acoustic communication, the lack of shared analytic approaches leads to inconsistency among studies. Here, we introduced a computational method used to examine 3360 calls recorded from wild indris (Indri indri) from 2005&#8211;2018. We split each sound into ten portions of equal length and, from each portion we extracted spectral coefficients, considering frequency values up to 15,000 Hz. We submitted the set of acoustic features first to a t-distributed stochastic neighbor embedding algorithm, then to a hard-clustering procedure using a k-means algorithm. The t-distributed stochastic neighbor embedding (t-SNE) mapping indicated the presence of eight different groups, consistent with the acoustic structure of the a priori identification of calls, while the cluster analysis revealed that an overlay between distinct call types might exist. Our results indicated that the t-distributed stochastic neighbor embedding (t-SNE), successfully been employed in several studies, showed a good performance also in the analysis of indris&#8217; repertoire and may open new perspectives towards the achievement of shared methodical techniques for the comparison of animal vocal repertoires