Biology subjects

Wierucka, K.

Publications and source records attributed to Wierucka, K..

12 recordsLinked to original sources

bioRxiv2026

WhaleSonic: Recordings of cetacean vocal behaviour with hydrophone arrays in Northern Norway

Killer whales, humpback whales, fin whales, and sperm whales converged in a fjord system in Northern Norway during the 2023-2024 winter, where whale watching and fishing vessels add to the acoustic scene. Cetaceans are intensely studied for their acoustic behaviour and communication, and the co-occurrence of four species in confined waters offers a unique opportunity to record them with hydrophone arrays. We present WhaleSonic, a raw acoustic dataset recorded with two synchronised array systems. Four drifting buoys, each carrying a tetrahedral hydrophone array, captured 78 hours of calibrated recordings; seven bottom-mounted hydrophones yielded 52 days of continuous data. Both systems are synchronised to UTC (<1 us and 3-7 ms) and positioned to 2 m accuracy. Transmission experiments and sound velocity measurements provide in-situ ground truth. All data are in physical units and a unified coordinate system, self-documented in HDF5 files, and published with open-source tools. WhaleSonic supports the development of multichannel algorithms for source separation, localisation, sound field analysis, and group communication in free-ranging cetaceans.

animal behavior and cognition↗

bioRxiv2026

Evidence for a cohesion-related function of alpaca hums: Linking automated call detection with GPS data

Understanding how animals communicate while moving helps explain social dynamics, but simultaneously tracking vocal and spatial behaviour in freely moving groups remains challenging. Animal-borne audio and GPS recordings can place naturally occurring vocalisations in their spatial context, but continuous recordings generate large datasets that require automated call detection.We used this approach to investigate whether alpaca hums, a poorly characterised vocalisation, contribute to group cohesion. We combined automated detection of hums from continuous collar-borne audio with synchronised GPS data from a partially collared herd to test whether humming is associated with changes in spacing and movement consistent with a contact-call function. We combined deep-learning-assisted detection with manual validation to build a large curated hum corpus from 2,011 h of continuous recordings from a partially collared herd. We then tested how humming was associated with spacing and movement at both individual and group levels. Following hum production, distance between focal alpaca and the group shifted toward closer contact relative to matched non-hum periods. Hums were also more likely to occur when the collared alpaca moved faster. These findings support a cohesion-related role of hums and suggest that this call type may help alpaca maintain social contact while moving. More broadly, our study shows how automated call detection combined with movement data can place poorly characterised vocalisations in a behavioural context, narrow their plausible functions, and generate targeted hypotheses even when caller identity remains uncertain and only part of a group can be collared. This approach extends functional research on vocal communication to species whose calls remain poorly understood.

animal behavior and cognition↗

bioRxiv2026

The effect of dominance rank on behavioural response to playback calls in ring-tailed lemurs (Lemur catta)

In group-living species, dominance hierarchies play a central role in shaping social dynamics, influencing both agonistic and affiliative interactions. Being able to recognise an individual's dominance rank at a distance, can reduce direct conflicts, facilitating group coordination, and mediating social cohesion. While the role of dominance in relation to aggressive or reproductive vocalizations has been well documented, its influence on affiliative contact calls, particularly in mediating group movement and social integration, remains underexplored. We conducted playback experiments with free-ranging adult ringtailed lemurs (Lemur catta) at the Affenwald Wildlife Park to investigate how dominance rank of the caller and rank differences between caller and listener affect behavioral responses. Using Generalized Linear Mixed Models (GLMMs), we analysed three response variables: vocal response, number of visual scans, and occurrence of movement. Our results revealed sex-specific effects: females exhibited increased movement following contact calls from high-ranking individuals, and tended to refrain from moving when their own rank exceeded that of the caller. In contrast, no significant effects of dominance rank were observed in males. These findings suggest that high-ranking females may play an important role in initiating group movement. This study provides insights into the role of dominance rank in mediating social coordination through contact calls and provides a foundation for further research into the role of vocal communication in maintaining group cohesion in primates.

animal behavior and cognition↗

bioRxiv2026

VesperFlow: An automated pipeline for decoding multisensor data from biologging tags

Integrating multi-sensor data from biologging tags is key to understanding animal behavior, particularly in group-living species, where communication, movement, and spatial dynamics are closely linked. However, analyzing such data at scale is often hindered by proprietary binary formats or manual decoding workflows in existing software. We present VesperFlow, an open-source Python pipeline that automates the decoding of inertial measurement unit (IMU), audio, and GPS data from Vesper Wildlife tags. VesperFlow batch-processes large datasets from multiple individuals, synchronizes timestamps across sensors with sub-second precision, and removes hardware-induced artifacts such as spurious gyroscope spikes and audio pops. Outputs are exported as CSV and WAV files for direct use in common downstream analysis workflows. We validate the pipeline on field data from 10 ring-tailed lemurs (Lemur catta), showing that our pipeline reliably processes movement, vocal, and spatial data. By lowering technical barriers and automating processes, VesperFlow supports more scalable, multi-modal research on animal behavior. VesperFlow is freely available at https://github.com/dasilva98/vesperflow.

bioinformatics↗

bioRxiv2026

"Mine" or "yours"? Acoustic structure of food calls predicts sharing decisions in marmosets

Foraging and feeding are often associated with vocalisations across animal species. Food calls may function to claim ownership of resources or recruit others to feeding sites, yet their potential role in facilitating food sharing remains poorly understood. This study investigates the relationship between food calls and food-sharing behaviour in common marmosets (Callithrix jacchus), focusing on adult-infant interactions, where food transfers are most frequent. We analysed over 1000 food interactions across six groups, categorising events as non-sharing (without begging or despite begging) or sharing (proactive, resisted, or facilitated). We examined the spectro-temporal structure of food calls produced immediately before each interaction. Food transfers were more likely to occur when preceded by a food call, suggesting a functional link between food calls and sharing. Random forest models reliably classified food calls according to interaction type, while generalized linear mixed models identified acoustic variation associated with sharing outcomes. Our results suggest that food calls encode information to either encourage or deter conspecific (infant) approach, influencing food transfer outcomes. These findings highlight the role of food calls in modulating social interactions around food in marmosets, advancing our understanding of vocal communication and its integration with cooperative behaviours in primates.

animal behavior and cognition↗

bioRxiv2026

What is encoded in a marmoset phee call? Food context beyond arousal and valence

Animal vocalisations can convey information about external events, but whether this goes beyond reflecting the emotional state elicited by these events is debated. To explore this, we studied the acoustic structure of common marmoset (Callithrix jacchus) phee (long-distance contact) and ek (alert/mobbing) calls produced in five treatments varying in the emotional valence and arousal they elicit (internal state), as well as food and social context (external events). We measured changes in arousal via nasal temperature and analysed both basic acoustic parameters and Mel-frequency cepstral coefficients (MFCCs) of the calls. Support Vector Machines combined with Linear Mixed effect models revealed that phee calls encode both external events and internal states, while eks reflected predominantly arousal. Notably, an acoustic signature related to food context was present in phees both when provided (positive valence) and teased with highly preferred food items (negative valence), and even when food was not physically present (food call playback treatment). This suggests marmoset long-distant phee calls encode external information beyond emotional arousal and valence, and independently of the presence of an immediately triggering stimulus.

animal behavior and cognition↗

bioRxiv2026

Sequence-level vocal convergence in common marmosets

Vocal accommodation is the process by which individuals adjust their vocalizations to resemble those of social partners. This phenomenon is widespread in social animals and can reinforce affiliation, signal group identity, and facilitate coordination. Most studies of vocal accommodation have focused on convergence in the acoustic structure of individual calls. Whether social partners also converge in how calls are arranged into sequences remains largely unknown. We examined vocal convergence during pair formation in common marmosets (Callithrix jacchus) by recording phee sequences from nine dyads composed of three males and three females before pairing and again four months after, in two audience contexts: when individuals interacted vocally with their partner or with an opposite sex stranger. We quantified similarity between individuals in call sequence-structure using transition probabilities, bigram frequencies, repeat-length distributions, and local alignment, and quantified similarity in acoustic structure using spectral parameters, MFCCs, and dynamic time warping. We found vocal convergence on a sequence level. After pair formation, partners became more similar in sequence structure when calling to strangers, whereas no change was detected in partner directed sequences. In contrast, call acoustic structure did not change in either context. Because vocal repertoires are constrained by anatomy and physiology, reorganizing existing call types into different combinations may provide a flexible route for modifying signals without altering the acoustic structure of individual calls. Our results provide evidence that social bonds can drive sequence level vocal convergence in a non-human primate, suggesting that vocal flexibility may arise not only through changes in acoustic structures but also through changes in how calls are organized over time.

animal behavior and cognition↗

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↗