Keen, S. C. | Search bioRxiv

bioRxiv2026

Repertoire-wide contextual mapping reveals signal functions in cooperatively breeding crows

Communication structures society, and is likewise shaped by relationships and shared tasks; yet, for most socially complex species, we know little of their full vocal repertoire and its functions. We investigated how communication structures the who, what, and when of social interactions in cooperative carrion crows - group-living birds who rely on coordinated behaviors, as in chick care. Leveraging machine learning to integrate large-scale data from crow-borne audio-loggers and nest cameras, we charted the vocal repertoire across 24 cooperative groups and mapped all discovered call types to behaviors and social context. We found that crows used a rich repertoire across three domains of joint behavior - flocking, chick care, and territorial display. Relatively quiet call types were abundant and included close-range calls that may coordinate chick care by announcing nest visits. Our study demonstrates how combining continuous-capture data and machine learning can reveal a holistic understanding of how vocalizations function across contexts.

animal behavior and cognition↗

bioRxiv2023

The demographic drivers of cultural evolution in bird song: a multilevel study

Social learning within communities sometimes leads to behavioural patterns that persist over time, which we know as culture. Examples of culture include learned bird and whale songs, cetacean feeding techniques, and avian and mammalian migratory routes. Shaped by neutral and selective forces, animal cultures evolve dynamically and lead to cultural traditions that differ greatly in their diversity and stability. These cultural traits can influence individual and group survival, population structure, and even inform conservation efforts, underscoring the importance of understanding how other population processes interact with social learning to shape culture. Although the impact of social learning mechanisms and biases has been extensively explored, the role of demographic factors--such as population turnover, immigration, and age structure--on cultural evolution has received theoretical attention but rarely been subject to empirical investigation in natural populations. Doing so requires very complete trait sampling and detailed individual life history data, which are hard to acquire in combination. To this end, we built a multi-generational dataset containing over 109,000 songs from >400 individuals from a population of Great Tits (Parus major), which we study using a deep metric learning model to re-identify individuals and quantify song similarity. We show that demographic variation at the small spatial scales at which learning takes place has the potential to strongly impact the pace and outcome of animal cultural evolution. For example, age distributions skewed towards older individuals are associated with slower cultural change and increased diversity, while higher local population turnover leads to elevated rates of cultural change. Our analyses support theoretical expectations for a key role of demographic processes resulting from individual behaviour in determining cultural evolution, and emphasize that these processes interact with species-specific factors such as the timing of song acquisition. Implications extend to large-scale cultural dynamics and the formation of dialects or traditions.

animal behavior and cognition↗