4.6 Article

Modeling bee movement shows how a perceptual masking effect can influence flower discovery

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PLOS COMPUTATIONAL BIOLOGY
卷 19, 期 3, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1010558

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This study developed a model that incorporates realistic features of bee behavior to show that the probability of discovering a flower depends not only on its position but also on the position of other flowers around it. This "masking effect" may have important implications for plant pollination and terrestrial ecosystems.
Author summaryUnderstanding how pollinators move in space is key to understand plant reproduction and its consequences on terrestrial ecosystems. Current models assume simple movement rules that predict 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 bumblebee behaviour, and calibrated it with experimental data collected in naturalistic conditions. Our model shows that the probability to visit a flower does not only depend on its position, but also on the position of other flowers around that may mask it from the forager. This perceptual masking effect means that pollination efficiency depends on the density and spatial arrangement of flowers around the pollinators' nest, often in counter-intuitive ways. Taking these effects into account may be key for improving practical actions in precision pollination and pollinator conservation. Understanding 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 (homing), and a gaussian noise. Simulations revealed a << masking effect >> that reduces the detection of flowers close to another, with potential far reaching consequences on plant-pollinator interactions. At the plant level, flowers distant to the nest were more often discovered by bees in low density environments. At the bee colony level, foragers found more flowers when they were small and at medium densities. 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 bee foraging success and plant pollination.

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