Biology subjects

Smith-Vidaurre, G.

Publications and source records attributed to Smith-Vidaurre, G..

3 recordsLinked to original sources

bioRxiv2025

Vocal convergence during formation of social relationships in vampire bats

In many group-living birds and mammals, the formation of affiliative relationships is hypothesized to cause vocal convergence (an increase in call similarity between individuals). However, testing this causal effect can be difficult, because it requires experimentally forming new relationships. Here, we demonstrate convergence in the contact calls of common vampire bats (Desmodus rotundus) that we introduced and experimentally housed together. To estimate and disentangle the roles of kinship, co-housing (familiarity), allogrooming, and food sharing in predicting call similarity, we first measured call similarity using 35 features of 693,494 contact calls from 95 bats, then fit a series of Bayesian generalized multi-membership models. We also measured changes in call similarity for a subset of individuals that were recorded before and after co-housing. We found that co-housing caused vocal convergence. Furthermore, food-sharing rates among familiar nonkin within the same co-housed group predicted contact call similarity. This finding suggests that the development of cooperative relationships causes further vocal convergence beyond the initial convergence caused by co-housing. Our findings have implications for the development of cooperative relationships and vocal learning.

animal behavior and cognition↗

bioRxiv2022

ohun: an R package for diagnosing and optimizing automatic sound event detection

Animal acoustic signals are widely used in diverse research areas due to the relative ease with which sounds can be registered across a wide range of taxonomic groups and research settings. However, bioacoustics research can quickly generate large data sets, which might prove challenging to analyze promptly. Although many tools are available for the automated detection of sounds, choosing the right approach can be difficult only a few tools provide a framework for evaluating detection performance. Here we present ohun, an R package intended to facilitate automated sound detection. ohun provides functions to diagnose and optimize detection routines and compare performance among different detection approaches. The package uses reference annotations containing the time position of target sounds in a training data set to evaluate detection routines performance using common signal detection theory indices. This can be done both with routine outputs imported from other software and detections run within the package. The package also provides functions to organize acoustic data sets in a format amenable to detection analyses. ohun also includes energy-based and template-based detection methods, two commonly used automatic approaches in bioacoustic research. We show how ohun automatically can be used to detect vocal signals with case studies of adult male zebra finch (Taenopygia gutata) songs and Spixs disc-winged bat (Thyroptera tricolor) ultrasonic social calls. We also include examples of how to evaluate the detection performance of ohun and external software. Finally, we provide some general suggestions to improve detection performance.

ecology↗

bioRxiv2020

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. If vocal learning is used for individual recognition, then individual identity should be encoded in frequency modulation patterns of acoustic signals. Furthermore, frequency modulation patterns should be more complex in larger social groups, reflecting greater selection for individual distinctiveness. We compared social group sizes and used supervised machine learning and frequency contours to compare contact call structure between native range monk parakeets (Myiopsitta monachus) in Uruguay and invasive range populations in the U.S. Invasive range sites exhibited fewer nests and simpler frequency modulation patterns. Beechers statistic revealed reduced individual identity content and fewer possible unique individual signatures in invasive range calls. Lower estimated social densities and simpler individual signatures are consistent with relaxed selection on the complexity of calls learned for individual recognition in smaller social groups. These findings run counter to the traditional view that vocal learning is used for imitation, and suggest that vocal learning can be employed to produce individual vocal signatures in a manner sensitive to local population size.

animal behavior and cognition↗