Modeling bee movement shows how a perceptual masking effect can influence flower discovery, foraging efficiency and pollination

Abstract

AbstractUnderstanding how pollinators move across space is key to understanding plant mating patterns. Bees are typically assumed to search for flowers randomly or using simple movement rules, so that the probability of discovering a flower should primarily depend on its distance to the nest. However, experimental work shows this is not always the case. Here, we explored the influence of flower size and density on their probability of being discovered by bees by developing a movement model of central place foraging bees, based on experimental data collected on bumblebees. Our model produces realistic bee trajectories by taking into account the autocorrelation of the bee’s angular speed, the attraction to the nest, and a gaussian noise. Simulations revealed a « masking effect » that reduces the detection of flowers close to another, which may have critical consequences for pollination and foraging success. At the plant level, flowers distant to the nest were more often visited in low density environments, suggesting lower probabilities of pollination at high densities. At the bee colony level, foragers found more flowers when they were small and at medium densities, suggesting that there is an optimal flower size and density at which collective foraging efficiency is optimized. Our results indicate that the processes of search and discovery of resources are potentially more complex than usually assumed, and question the importance of resource distribution and abundance on plant-pollinator interactions.Author’s summaryUnderstanding how pollinators move in space is key to understanding plant reproduction, which in turn shapes entire ecosystems. Most current models assume simple movement rules that predict that flowers are more likely to be visited—and hence pollinated—the closer they are to the pollinators’ nest. Here we developed an explicit movement model that incorporates realistic features of bumblebees, including their flight characteristics and their tendency to return regularly to the nest, and calibrated it with experimental data collected in naturalistic conditions. This model revealed that the probability to visit a flower does not only depend on its position, but also on the position of other flowers that may mask it from the forager. This masking effect means that pollination efficiency depends on the density and spatial arrangement of flowers around the pollinator’s nest, often in counter-intuitive ways. Taking these effects into account will be key for improving precision pollination and pollinator conservation.