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

Abstract

In the unpredictable Anthropocene, a particularly pressing open question is how certain species invade urban environments. Sex-biased dispersal and learning arguably influence movement ecology, but their joint influence remains unexplored empirically, and might vary by space and time. We assayed reinforcement learning in wild-caught, temporarily-captive core-, middle- or edge-range great-tailed grackles--a bird species undergoing urban-tracking rapid range expansion, led by dispersing males. We show: across populations, both sexes initially perform similarly when learning stimulus-reward pairings, but, when reward contingencies reverse, male--versus female--grackles finish relearning faster, making fewer choice-option switches. How do male grackles do this? Bayesian cognitive modelling revealed male grackles choice behaviour is governed more strongly by the weight of relative differences in recent foraging payoffs--i.e., they show more pronounced risk-sensitive learning. Confirming this mechanism, agent-based forward simulations of reinforcement learning--where we simulate birds based on empirical estimates of our grackles reinforcement learning--replicate our sex-difference behavioural data. Finally, evolutionary modelling revealed natural selection should favour risk-sensitive learning in hypothesised urban-like environments: stable but stochastic settings. Together, these results imply risk-sensitive learning is a winning strategy for urban-invasion leaders, underscoring the potential for life history and cognition to shape invasion success in human-modified environments.