Biology subjects

Wierucka, K.

Publications and source records attributed to Wierucka, K..

5 recordsLinked to original sources

bioRxiv2024

Robust encoding of acoustic identity in alpaca hums - a basis for individual recognition

Individual recognition is an important element of social interactions among animals. While the presence of individually distinct vocalisations (providing a basis for individual recognition) has been widely tested across species, information about which components of a call encode this information is lacking. We investigated whether female alpaca (Vicugna pacos) vocalisations, particularly their hums, encode information about individual identity and explored which parameters contribute to this encoding. We recorded vocalisations from 9 adult female alpaca and extracted both spectro-temporal features (frequencies and duration) and mel-frequency cepstral coefficients (MFCC). Random forest analyses revealed clear individual differences in both datasets, with the spectro-temporal features allowing for slightly more accurate classification than MFCC (71% and 66.5% accuracy for spectro-temporal features and MFCC, respectively). These robust acoustic identities have the potential to provide a basis for individual recognition in alpaca, which could have important flow on effects for alpaca communication, as it allows receivers to modulate their response to the callers identity. Alpaca, as herd-living and vocal animals, provide an excellent model system for better understanding the mechanisms, causes and consequences of recognition and inter-individual communication.

animal behavior and cognition↗

bioRxiv2024

Echoes of self: Understanding acoustic structure and informational content in common marmoset (Callithrix jacchus) phee sequences

Communication in social animals relies heavily on acoustic cues, yet many species possess limited vocal repertoires. To compensate, animals often produce vocalizations in sequences, potentially enhancing the diversity of transmitted information. However, the significance of repeated elements within these sequences remains poorly understood. Here, we investigated the spectro-temporal structure of elements within common marmoset (Callithrix jacchus) phees, a long-distance contact call, often produced in sequences. Employing machine learning techniques (random forests) and linear mixed effects models, we explored how elements varied based on their position within sequences and assessed their ability to encode identity and sex information. Additionally, we examined similarities between elements occupying the same position in different sequences. Our results reveal structural differences both within and between sequences, with variations observed in phees at different positions within the same sequence, yet similarities found between first elements of distinct sequences. Notably, all phees encoded caller identity and sex information, with varying accuracy across positions within sequences, indicating a higher encoding of sex information at the beginning of the sequence and a greater emphasis on identity in subsequent elements. These findings suggest that repeated sequences may be functionally diverse structures, enriching the complexity of animal communication systems.

animal behavior and cognition↗

bioRxiv2024

Same data, different results? Evaluating machine learning approaches for individual identification in animal vocalisations

Automated acoustic analysis is increasingly used in behavioural ecology, and determining caller identity is a key element for many investigations. However, variability in feature extraction and classification methods limits the comparability of results across species and studies, constraining conclusions we can draw about the ecology and evolution of the groups under study. We investigated the impact of using different feature extraction (spectro-temporal measurements, linear and Mel-frequency cepstral coefficients, as well as highly comparative time-series analysis) and classification methods (discriminant function analysis, neural networks, random forests, and support vector machines) on the consistency of caller identity classification accuracy across 16 mammalian datasets. We found that Mel-frequency cepstral coefficients and random forests yield consistently reliable results across datasets, facilitating a standardised approach across species that generates directly comparable data. These findings remained consistent across vocalisation sample sizes and number of individuals considered. We offer guidelines for processing and analysing mammalian vocalisations, fostering greater comparability, and advancing our understanding of the evolutionary significance of acoustic communication in diverse mammalian species.

animal behavior and cognition↗

bioRxiv2023

Dynamic vocal learning in adult marmoset monkeys

While vocal learning is vital to language acquisition in children, adults continue to adjust their speech while adapting to different social environments in the form of social vocal accommodation (SVA). Even though adult and infant vocal learning seemingly differ in their properties, whether the mechanisms underlying them differ remains unknown. The complex structure of language creates a challenge in quantifying vocal changes during SVA. Consequently, animals with simpler vocal communication systems are powerful tools for understanding the mechanisms underlying SVA. Here, we tracked acoustic changes in the vocalizations of adult common marmoset pairs, a highly vocal primate species known to show SVA, for up to 85 days after pairing with a new partner. We identified four properties of SVA in marmosets: (1) bidirectional learning, (2) exponential decrease in vocal distance with time, (3) sensitivity to initial vocal distance, and (4) dyadic acoustic feature synchrony. We developed a mathematical model that shows all four properties. The model suggests that marmosets continuously update the memory of their partners vocalizations and modify their own vocalizations to match them, a dynamic form of vocal learning. The model provides crucial insights into the mechanisms underlying SVA in adult animals and how they might differ from infant vocal learning.

animal behavior and cognition↗

bioRxiv2022

Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers

Marmosets, with their highly social nature and complex vocal communication system, are important models for comparative studies of vocal communication and, eventually, language evolution. However, our knowledge about marmoset vocalisations predominantly originates from playback studies or vocal interactions between dyads, and there is a need to move towards studying group-level communication dynamics. Efficient source identification from marmoset vocalisations is essential for this challenge, and machine learning algorithms (MLAs) can aid it. Here we built a pipeline capable of plentiful feature extraction, meaningful feature selection, and supervised classification of vocalisations of up to 18 marmosets. We optimised the classifier by building a hierarchical MLA that first learned to determine the sex of the source, narrowed down the possible source individuals based on their sex, and then determined the source identity. We were able to correctly identify the source individual with high precisions (87.21% - 94.42%, depending on call type, and up to 97.79% after the removal of twins from the dataset). We also examine the robustness of identification across varying sample sizes. Our pipeline is a promising tool not only for source identification from marmoset vocalisations but also for analysing vocalisations and tracking vocal learning trajectories of other species.

animal behavior and cognition↗