Biology subjects

Chimento, M.

Publications and source records attributed to Chimento, M..

10 recordsLinked to original sources

bioRxiv2025

Rapid Social Transmission of Predator Location via Gaze Following in Pigeons

Social information transmission is a key advantage of group living, particularly in uncertain or high-stakes contexts. During collective predator vigilance, individuals often initiate evasive action based on social cues--such as the escape behaviour of others--before detecting threats themselves. Yet these cues can be ambiguous or misleading, leading to costly false alarms. Gaze following, a key socio-cognitive skill widespread across species, may reduce this ambiguity by allowing individuals to localize threats with minimal effort and avoid unnecessary escapes. Although this vigilance function has long been hypothesized, direct evidence is lacking. Here we show that pigeons foraging in flocks detect a predator and socially transmit its location rapidly via gaze following, before any escape behaviour occurs. In experiments simulating predator attacks, we reconstructed predator-oriented gaze using high-resolution posture tracking and modelled social transmission with dynamic multi-network Bayesian models. Comparing models based on distinct social cues, we found that pigeons responded selectively to conspecific gaze toward the predator, rather than locomotion, head-up vigilance, or gaze elsewhere. Analyses of network structure further revealed that this transmission occurs through visually connected networks, particularly via peripheral rather than foveal vision. While individuals occasionally followed gaze to non-threatening locations, flocks reliably converged on the actual threat. These findings uncover a previously undocumented, cognition-based mechanism of collective detection. To our knowledge, this is the first evidence that gaze following serves an adaptive function in survival contexts, illustrating how individual-level cognition and group-level dynamics interact to the adaptive value of group living.

animal behavior and cognition↗

bioRxiv2025

STbayes: An R package for creating, fitting and understanding Bayesian models of social transmission

O_LIA critical consequence of joining social groups is the possibility of social transmission of information related to novel behaviours or resources. Network-based diffusion analysis (NBDA) has emerged as a leading frequentist framework for inferring and quantifying social transmission, particularly in non-human animal populations. C_LIO_LINBDA has been extended several times to account for multiple diffusions, multiple networks, individual-level variables, and complex transmission functions. Bayesian versions of NBDA have been proposed before, although these implementations have seen limited usage and have not kept pace with the evolving ecosystem of Bayesian methods. There is not yet a user-friendly package to implement a Bayesian NBDA. C_LIO_LIHere, we present a unified framework for performing Bayesian analysis of social transmission using NBDA-type models, implemented in the widely used Stan programming language. We provide a user-friendly R package "STbayes" (ST: social transmission) for other researchers to easily use this framework. STbayes accepts user-formatted data, but can also import data directly from the existing NBDA R package. Based on the data users provide, STbayes automatically generates multi-network, multi-diffusion models that allow for covariates that may influence transmission, and varying (random) effects. C_LIO_LIUsing simulated data, we demonstrate that this model can accurately differentiate the relative contribution of individual and social learning in the spread of information through networked populations. We illustrate how incorporating upstream uncertainty about the relationships between individuals can improve model fit. Our framework can be used to infer complex transmission rules, and we describe a numerically stable parameterization of frequency-dependent transmission. Finally, we introduce support for dynamic transmission weights and a "high-resolution" data mode, which allows users to make use of fine-scale data collected by contemporary automated tracking methods. These extensions increase the set of contexts that this type of model may be used for. C_LI

animal behavior and cognition↗

bioRxiv2025

Timing of independence is explained by movement ability, but depends on how independence is defined for a long-lived raptor

Many young animals must gain independence from parental care by acquiring the necessary skills for survival. Thus, the rate of skill acquisition can predict how long juveniles remain with their parents before emigrating from the natal territory. Yet in some systems, young animals acquire adult-like skills and the capacity to be independent long before they emigrate. This raises the question of why the timing of emigration decouples from the rate of skill acquisition, resulting in extended parental care. We addressed this by leveraging GPS tracking data from juvenile golden eagles (Aquila chrysaetos), which learn to fly and to hunt while remaining with their parents for 61-283 days after fledging. Young golden eagles often go on excursions without their parents outside of their natal territories before emigration. We expected flight performance to predict the timing of excursions better than the timing of emigration, which should be more influenced by external factors. In a set of time-to-event models, we predicted the rate of excursions and of emigration using metrics of soaring flight performance or of daily movement. Contrary to our prediction, we found that soaring performance failed to predict the rate of either excursions or emigration, whereas daily movement predicted both rates, but inconsistently. This was likely because those coarser metrics captured motivation and weather in addition to flight skills. Thus external factors seem more likely to explain the timing of independence and internal development--in this case of flight skills--may be completed quickly. Juveniles may delay emigration not due to lack of skill, but because the costs of leaving parental care outweigh the benefits. The difference we discovered between when individuals were capable of independence and when they committed to it may obscure the more general link between skill acquisition and the end of parental care in other systems as well.

animal behavior and cognition↗

bioRxiv2025

Temporal dynamics and policy implications in an innovation arms race

Human-modified environments offer novel resources, but their exploitation can be a source of human-wildlife conflict. Residents of Sydney have reported increasing cases of bin-opening behavior by sulphur-crested cockatoos (Cacatua galerita), with evidence this behavior is socially learned between birds. Households protected their bins, yet cockatoos have learned to defeat these defences. In response, residents increased their defence-level, setting the stage for a behavioral "arms race". Here, we investigate this arms race by combining field observations with agent-based modeling. We systematically document protections in a suburban locality over two years, revealing spatial clustering of protections indicative of social learning among residents. We find that protections have decreased since 2019, and overly-costly protection is dis-preferred. With a controlled assay, we characterize the knowledge of local cockatoos, finding differential proficiency in defeating protections. Surprisingly, we identified several cockatoos that can defeat high-efficacy protections, such as locks. Finally, we simulate interactions between two populations of learning agents representing households and cockatoos. We find that social learning accelerates adaptation in both species, while coordination reduces costs associated with defensive escalation. However, policy interventions can have unintended consequences, accelerating cockatoos skill acquisition and shifting conflict to neighboring areas. Our findings highlight the dynamic nature of human-wildlife conflict and the importance of considering behavioral feedback loops in urban wildlife management.

animal behavior and cognition↗

bioRxiv2024

Peering into the world of wild passerines with 3D-SOCS: synchronized video capture and posture estimation

O_LICollection of large behavioural data-sets on wild animals in natural habitats is vital in ecology and evolution studies. Recent progress in machine learning and computer vision, combined with inexpensive microcomputers, have unlocked a new frontier of fine-scale markerless measurements. C_LIO_LIHere, we leverage these advancements to develop a 3D Synchronized Outdoor Camera System (3D-SOCS): an inexpensive, mobile and automated method for collecting behavioural data on wild animals using synchronized video frames from Raspberry Pi controlled cameras. Accuracy tests demonstrate 3D-SOCS markerless tracking can estimate postures with a 3mm tolerance. C_LIO_LITo illustrate its research potential, we place 3D-SOCS in the field and conduct a stimulus presentation experiment. We estimate 3D postures and trajectories for multiple individuals of different bird species, and use this data to characterize the visual field configuration of wild great tits (Parus major), a model species in behavioural ecology. We find their optic axes at approximately {+/-}60{degrees} azimuth and -5{degrees} elevation. Furthermore, birds exhibit functional lateralization in their use of the right eye with conspecific stimulus, and show individual differences in lateralization. We also show that birds convex hulls predicts body weight, highlighting 3D-SOCS potential for non-invasive population monitoring. C_LIO_LI3D-SOCS is a first-of-its-kind camera system for wild research, presenting exciting potential to measure fine-scaled behaviour and morphology in wild birds. C_LI

animal behavior and cognition↗

bioRxiv2024

Immigrant birds use payoff biased social learning in spatially variable environments

Longstanding theory predicts that strategic flexibility in when and how to use social information can help individuals make adaptive decisions, especially when environments are temporally or spatially variable. A short-term increase in reliance on social information under these conditions has been experimentally shown in primates, including humans, but whether this occurs in other taxa is unknown. We asked whether migration between spatially variable environments affected social information use with a large-scale cultural diffusion experiment with wild great tits (Parus major) in captivity, a small passerine bird that can socially learn novel behaviors. We simulated an immigration event where knowledgeable birds were exchanged between groups with opposing preferences for a socially learned foraging puzzle, living in similar or different environments. We found evidence that both immigrants and residents were influenced by social information and attended to the rewards that others received. Our analysis supported the use of a payoff-biased social learning by immigrants when both resources and habitat features were spatially variable. In contrast, immigrants relied more-so on individual learning when payoffs or the environment were unchanged. In summary, our results suggest that great tits assess the payoffs others receive, and are more influenced by socially observed differences in payoffs when environmental cues differ in their new environment. Our results provide experimental support for the hypothesis that spatial variability is a strong driver for the evolution of social learning strategies.

animal behavior and cognition↗

bioRxiv2024

The contribution of movement to social network structure and spreading dynamics under simple and complex transmission

The structure of social networks fundamentally influences spreading dynamics. In general, the more contact between individuals, the more opportunity there is for the transmission of information or disease to take place. Yet, contact between individuals, and any resulting transmission events, are determined by a combination of spatial (where individuals choose to move) and social rules (who they choose to interact with or learn from). Here we examine the effect of the social-spatial interface on spreading dynamics using a simulation model. We quantify the relative effects of different movement rules (localized, semi-localized, nomadic, and resource-based movement) and social transmission rules (simple transmission, anti-conformity, proportional, conformity, and threshold rules) to both the structure of social networks and spread of a novel behaviour. Localized movement created weakly connected sparse networks, nomadic movement created weakly connected dense networks, and resource-based movement generated strongly connected modular networks. The resulting rate of spreading varied with different combinations of movement and transmission rules, but-- importantly--the relative rankings of transmission rules changed when running simulations on static versus dynamic representations of networks. Our results emphasize that individual-level social and spatial behaviours influence emergent network structure, and are of particular consequence for the spread of information under complex transmission rules.

animal behavior and cognition↗

bioRxiv2022

Optimal population turnover regimes for cultural evolution depend on network size, density and behavioral transmissibility

A change to a populations social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover-- or the repeated replacement of individuals. Experimental data has led to the hypothesis that naive learners can drive cultural evolution by being better samplers, although this hypothesis has only been expressed verbally. We conduct a formal exploration of this hypothesis using a generative model that concurrently simulates its two key ingredients: social transmission and reinforcement learning. We explore how variation in turnover influences changes in the distributions of cultural behaviors over long and short time-scales. We simulate competition between a high and low reward behavior, while varying turnover magnitude and tempo. We find optimal turnover regimes that amplify the production of higher reward behaviors. We also find that these optimal regimes result in a new population composition, where fewer agents which know both behaviors, and more agents know only the high reward behavior. These two effects depend on network size, density, behavioral transmissibility, and characteristics of the learners. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability, identified by previous experimental studies. By moving from experimental data to theory, we illuminate an under-discussed generative process arising from an interaction between social dynamics and learning that can lead to changes in cultural behavior.

animal behavior and cognition↗

bioRxiv2021

Cultural diffusion dynamics depend on behavioural production rules

Culture is an outcome of both the acquisition of knowledge about behaviour through social transmission, and its subsequent production by individuals. Acquisition and production are often discussed interchangeably or modeled separately, yet to date, no study has accounted for both processes and explored their interaction. We present a generative model that integrates the two to explore how variation in production rules might shape cultural diffusion dynamics. Agents make behavioural choices that change as they learn from their productions. Their repertoires also change over time, and the social transmission of behaviours depends on their frequency. We diffuse a novel behaviour through social networks across a large parameter space to demonstrate how individual-level behavioural production rules influence population-level diffusion dynamics. We then investigate how linking transmission and production might affect the performance of two commonly used inferential models for social learning; Network-based Diffusion Analysis, and Experienced Weighted Attraction models. Clarifying the distinction between acquisition and production yields predictions for how production influences diffusion that are generalisable across species, and has consequences for how inferential methods are applied to empirical data. Our model illuminates the differences between social learning and social influence, demonstrates the overlooked role of reinforcement learning in cultural diffusions, and allows for clearer discussions about social learning strategies.

animal behavior and cognition↗

bioRxiv2020

Social network architecture and the tempo of cumulative cultural evolution

The ability to build upon previous knowledge--cumulative cultural evolution--is a hallmark of human societies. While cumulative cultural evolution depends on the interaction between social systems, cognition and the environment, there is increasing evidence that cumulative cultural evolution is facilitated by larger and more structured societies. However, such effects may be interlinked with patterns of social wiring, thus the relative importance of social network architecture as an additional factor shaping cumulative cultural evolution remains unclear. By simulating innovation and diffusion of cultural traits in populations with stereotyped social structures, we disentangle the relative contributions of network architecture from those of population size and connectivity. We demonstrate that while more structured networks, such as those found in multilevel societies, can promote the recombination of cultural traits into high-value products, they also hinder spread and make products more likely to go extinct. We find that transmission mechanisms are therefore critical in determining the outcomes of cumulative cultural evolution. Our results highlight the complex interaction between population size, structure and transmission mechanisms, with important implications for future research.

evolutionary biology↗