Bumblebees Learn to Forage like Bayesians

Abstract

Bayesian foraging in patchy environments requires that foragers have information about the distribution of resources among patches (prior information), either set by natural selection or learned from past experience. We test the hypothesis that bumblebee foragers can rapidly learn prior information from past experience in two very different experimental environments. In the high-variance environment (patches of low and high quality), stochastic optimality models predicted that finding rewards should sometimes sharply increase an optimal forager's tendency to stay in a patch (an incremental response), whereas in the uniform environment, finding rewards should always decrease the tendency to stay (a decremental response). We use Cox regression models to show that, in a matter of hours, bees learned to match both predicted responses, resulting in a reward intake rate that averaged 80% of the predicted maximum. Following training in either environment, bees' adaptive behavior carried over to a common test environment, thus confirming the influence of memorized prior information. Although Bayesian foraging by learning is often presumed, this study is the first to clearly isolate the adaptive use of a learned prior expectation. More generally, it highlights the remarkable adaptive plasticity of an important generalist pollinator and agent of selection.

Recognition and Avoidance of Contaminated Flowers by Foraging Bumblebees (Bombus terrestris)

“…Many theories on optimal foraging were tested in bumblebees and other pollinators, especially the marginal value theorem developed by Charnov in 1976 [35], [36], [37], [38], [39]. The results provided by these different experiments show that bumblebee foraging and patch departure follows a sub-optimal strategy [35], [36], [37], [38], [39]. To summarise briefly the strategy exhibited by bumblebees is to stay longer in large patches or patches providing a high reward.…”

Learning capabilities enhanced in harsh environments: a common garden approach

“…Rather than a fixed response to a given stimulus, behavioural flexibility allows for the expression of a variety of different behavioural outcomes under different contexts based on previous experiences (Dukas 1998;Reader 2003). Such flexibility seems to be adaptive and therefore has strong ecological and evolutionary relevance (Price et al 2003;Biernaskie et al 2009). For example, various aspects of learning or behavioural flexibility may play key roles in the success of biological invasions (e.g.…”

Sampling and tracking a changing environment: persistence and reward in the foraging decisions of bumblebees

“…For instance, the classic Krebs et al [63] paper on optimal sampling with great tits features a Bayesian-type decision rule. More recent work has suggested that bumblebees learn to forage in a Bayesian manner, demonstrating that bees used past experience about patchiness to inform later foraging [35]. However, this gives us little guidance about how past information is integrated with new information to determine choice, how the prior distribution is formed or about the actual Bayesian nature of that decision.…”

“…Finally, bumblebees appear to integrate many sources of information when making choices, as well as integrate information over multiple previous experiences with different outcomes (e.g. [34,35]). …”