A Lévy-flight diffusion model to predict transgenic pollen dispersal. Issue 126 (January 2017)

• Record Type: Journal Article
• Authors: Vallaeys, Valentin; Tyson, Rebecca C.; Lane, W. David; Deleersnijder, Eric; Hanert, Emmanuel
• Is Part Of: Journal of the Royal Society interface. Volume 14:Issue 126(2017)
• Publication Date: 2017-01
• Subjects: bee-mediated pollen dispersal -- genetically modified crops -- isolation distances -- truncated Lévy flights -- anomalous diffusion; Physical sciences -- Research -- Periodicals; Life sciences -- Research -- Periodicals; Interdisciplinary research -- Periodicals; 570.5
• DOI: 10.1098/rsif.2016.0889
• Languages: English
• ISSNs: 1742-5689

Abstract

Abstract : The containment of genetically modified (GM) pollen is an issue of significant concern for many countries. For crops that are bee-pollinated, model predictions of outcrossing rates depend on the movement hypothesis used for the pollinators. Previous work studying pollen spread by honeybees, the most important pollinator worldwide, was based on the assumption that honeybee movement can be well approximated by Brownian motion. A number of recent studies, however, suggest that pollinating insects such as bees perform Lévy flights in their search for food. Such flight patterns yield much larger rates of spread, and so the Brownian motion assumption might significantly underestimate the risk associated with GM pollen outcrossing in conventional crops. In this work, we propose a mechanistic model for pollen dispersal in which the bees perform truncated Lévy flights. This assumption leads to a fractional-order diffusion model for pollen that can be tuned to model motion ranging from pure Brownian to pure Lévy. We parametrize our new model by taking the same pollen dispersal dataset used in Brownian motion modelling studies. By numerically solving the model equations, we show that the isolation distances required to keep outcrossing levels below a certain threshold are substantially increased by comparison with the original predictions, suggesting that isolation distances may need to be much larger than originally thought.