Risk-sensitive learning is a winning strategy for leading an urban invasion

Abstract

How do animals successfully invade urban environments? Sex-biased dispersal and learning arguably influence movement ecology, but their joint influence remains unexplored empirically, which might vary by space and time. We assayed reinforcement learning in wild-caught, temporarily-captive core-, middle- or edge-range inhabitants of great-tailed grackles-a bird species undergoing urban-tracking rapid range expansion, led by dispersing males. Across populations, Bayesian models revealed: both sexes initially learn at similar pace, but, when reward contingencies reverse, males-versus females-'relearn' faster via pronounced reward-payoff sensitivity, a risk-sensitive learning strategy. Confirming this mechanism, agent-based forward simulations of reinforcement learning replicate our sex-difference data. Separate evolutionary modelling revealed risk-sensitive learning is favoured by natural selection in stable but stochastic settings-characteristics typical of urban environments. Risk-sensitive learning, then, is a winning strategy for urban-invasion leaders, implying life history (sex-biased dispersal) and cognition (learning) interactively shape invasion success in the unpredictable Anthropocene. Our study sets the scene for future comparative research.