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

• Published in: eLife Vol. 12
• Main Authors: Breen, Alexis J, Deffner, Dominik
• Format: Journal Article
• Language: English
• Published: England eLife Science Publications, Ltd 02-04-2024; eLife Sciences Publications Ltd; eLife Sciences Publications, Ltd
• Subjects: Bats; Bayesian analysis; Birds; cognition; Cognitive ability; dispersal; Ecology; Evolutionary Biology; Females; Food; Foraging behavior; Gender differences; great-tailed grackles; Learning; Learning strategies; Life history; Males; Movement ecology; Natural selection; Predation; Range extension; rapid range expansion; Reinforcement; reinforcement learning; Relearning; sex differences; Simulation methods; Stochasticity; Urban environments; ecology; evolutionary biology; cognition; dispersal; great-tailed grackles; rapid range expansion; sex differences; reinforcement learning
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.89315

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
cognition to shape invasion success in human-modified environments.