Abstract

Dispersal is a key parameter of adaptation, invasion and persistence. Yet standard population genetics inference methods hardly distinguish it from drift and many species cannot be studied by direct mark-recapture methods. Here, we introduce a method using rates of change in cline shapes for neutral markers to estimate contemporary dispersal. We apply it to the devastating banana pest Mycosphaerella fijiensis, a wind-dispersed fungus for which a secondary contact zone had previously been detected using landscape genetics tools. By tracking the spatio-temporal frequency change of 15 microsatellite markers, we find that σ, the standard deviation of parent–offspring dispersal distances, is 1.2 km/generation1/2. The analysis is further shown robust to a large range of dispersal kernels. We conclude that combining landscape genetics approaches to detect breaks in allelic frequencies with analyses of changes in neutral genetic clines offers a powerful way to obtain ecologically relevant estimates of dispersal in many species.

Highlights

  • Every ecological and evolutionary process is influenced by dispersal

  • This occurs in models inferring dispersal from patterns of genetic variation expected at drift–dispersal equilibrium or selection–dispersal equilibrium

  • Our results show that combining genetic clustering approaches to detect breaks in allelic frequencies with neutral genetic cline analyses offers a convenient way to gain solid information on the rate of contemporary dispersal

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Summary

Introduction

Every ecological and evolutionary process is influenced by dispersal. From an ecological perspective, dispersal influences population dynamics and persistence, species distribution and abundance, and community structure (Dieckmann et al 1999). Only genetic-based inference methods are able to quantify effective gene (rather than individual) movements but contrary to direct methods, which rely on few assumptions, they critically depend on an appropriate modelling of a combination of underlying processes. This occurs in models inferring dispersal from patterns of genetic variation expected at drift–dispersal equilibrium (e.g. isolation by distance, Rousset 1997) or selection–dispersal equilibrium (e.g. tension zones, reviewed in Barton & Hewitt 1985). Models at drift–dispersal equilibrium present the specific drawback that they require independent information on effective population densities to infer dispersal rates

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