Novel pathogen introduction rapidly alters the evolution of movement, restructuring animal societies

Animal social interactions are the outcomes of evolved strategies that integrate the costs and benefits of being sociable. Using a novel mechanistic, evolutionary, individual-based simulation model, we examine how animals balance the risk of pathogen transmission against the benefits of social information about resource patches, and how this determines the emergent structure of spatial social networks. We study a scenario in which a fitness-reducing infectious pathogen is introduced into a population which has initially evolved movement rules in its absence. Pathogen introduction leads to a rapid evolutionary shift, within only a few generations, in animal social-movement strategies. Generally, animals adopt a dynamic social distancing behaviour, trading more movement away from individuals (and less intake) for lower infection risk, but there is considerable individual variation in these social movement strategies. Pathogen-adapted populations are more widely dispersed over the landscape, and thus have lessclustered social networks than their pre-introduction, pathogen-naive ancestors. Running simple epidemiological models on these emergent social networks, we show that diseases do indeed spread more slowly through pathogen-adapted animal societies. The post-introduction, pathogen-adapted movement strategy mix is stongly influenced by a combination of landscape productivity and diseasecost. Our model suggests how the introduction of an infectious pathogen to a population rapidly changes social structure. While such events might make populations more resilient to future disease outbreaks, this is at the cost of social information benefits. Overall, we offer both a general modelling framework and initial predictions for the evolutionary consequences of wildlife pathogen spillovers

Ants show a leftward turning bias when exploring unknown nest sites

Behavioural lateralization in invertebrates is an important field of study because it may provide insights into the early origins of lateralization seen in a diversity of organisms. Here, we present evidence for a leftward turning bias in Temnothorax albipennis ants exploring nest cavities and in branching mazes, where the bias is initially obscured by thigmotaxis (wall-following) behaviour. Forward travel with a consistent turning bias in either direction is an effective nest exploration method, and a simple decision-making heuristic to employ when faced with multiple directional choices. Replication of the same bias at the colony level would also reduce individual predation risk through aggregation effects, and may lead to a faster attainment of a quorum threshold for nest migration. We suggest the turning bias may be the result of an evolutionary interplay between vision, exploration and migration factors, promoted by the ants' eusociality

Automated face recognition using deep neural networks produces robust primate social networks and sociality measures

Longitudinal video archives of behaviour are crucial for examining how sociality shifts over the lifespan in wild animals. New approaches adopting computer vision technology hold serious potential to capture interactions and associations between individuals in video at large scale; however, such approaches need a priori validation, as methods of sampling and defining edges for social networks can substantially impact results.Here, we apply a deep learning face recognition model to generate association networks of wild chimpanzees using 17 years of a video archive from Bossou, Guinea. Using 7 million detections from 100 h of video footage, we examined how varying the size of fixed temporal windows (i.e. aggregation rates) for defining edges impact individual-level gregariousness scores.The highest and lowest aggregation rates produced divergent values, indicating that different rates of aggregation capture different association patterns. To avoid any potential bias from false positives and negatives from automated detection, an intermediate aggregation rate should be used to reduce error across multiple variables. Individual-level network-derived traits were highly repeatable, indicating strong inter-individual variation in association patterns across years and highlighting the reliability of the method to capture consistent individual-level patterns of sociality over time. We found no reliable effects of age and sex on social behaviour and despite a significant drop in population size over the study period, individual estimates of gregariousness remained stable over time.We believe that our automated framework will be of broad utility to ethology and conservation, enabling the investigation of animal social behaviour from video footage at large scale, low cost and high reproducibility. We explore the implications of our findings for understanding variation in sociality patterns in wild ape populations. Furthermore, we examine the trade-offs involved in using face recognition technology to generate social networks and sociality measures. Finally, we outline the steps for the broader deployment of this technology for analysis of large-scale datasets in ecology and evolution.info:eu-repo/semantics/publishedVersio

Multiple spatial behaviours govern social network positions in a wild ungulate

The structure of wild animal social systems depends on a complex combination of intrinsic and extrinsic drivers. Population structuring and spatial behaviour are key determinants of individuals’ observed social behaviour, but quantifying these spatial components alongside multiple other drivers remains difficult due to data scarcity and analytical complexity. We used a 43‐year dataset detailing a wild red deer population to investigate how individuals’ spatial behaviours drive social network positioning, while simultaneously assessing other potential contributing factors. Using Integrated Nested Laplace Approximation (INLA) multi‐matrix animal models, we demonstrate that social network positions are shaped by two‐dimensional landscape locations, pairwise space sharing, individual range size, and spatial and temporal variation in population density, alongside smaller but detectable impacts of a selection of individual‐level phenotypic traits. These results indicate strong, multifaceted spatiotemporal structuring in this society, emphasising the importance of considering multiple spatial components when investigating the causes and consequences of sociality