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

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.Peer reviewe

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.NSF BII 2021909; the University of Toronto EEB Fellowship; the Wellcome Trust; the National Institute of Allergy and Infectious Diseases of the National Institutes of Health and the Defense Threat Reduction Agency.http://rstb.royalsocietypublishing.orgam2022Medical Virolog