Inferring dispersal across a fragmented landscape using reconstructed families in the Glanville fritillary butterfly

Toby Fountain,Michelle F Dileo,Janne H Korhonen,Torsti Schulz,Marjo Saastamoinen,Arild Husby,Ilkka Hanski,Pasi Rastas,Etsuko Nonaka

doi:10.1111/eva.12552

Abstract

AbstractDispersal is important for determining both species ecological processes, such as population viability, and its evolutionary processes, like gene flow and local adaptation. Yet obtaining accurate estimates in the wild through direct observation can be challenging or even impossible, particularly over large spatial and temporal scales. Genotyping many individuals from wild populations can provide detailed inferences about dispersal. We therefore utilized genomewide marker data to estimate dispersal in the classic metapopulation of the Glanville fritillary butterfly (Melitaea cinxia L.), in the Åland Islands in SW Finland. This is an ideal system to test the effectiveness of this approach due to the wealth of information already available covering dispersal across small spatial and temporal scales, but lack of information at larger spatial and temporal scales. We sampled three larvae per larval family group from 3732 groups over a six‐year period and genotyped for 272 SNPs across the genome. We used this empirical data set to reconstruct cases where full‐sibs were detected in different local populations to infer female effective dispersal distance, that is, dispersal events directly contributing to gene flow. On average this was one kilometre, closely matching previous dispersal estimates made using direct observation. To evaluate our power to detect full‐sib families, we performed forward simulations using an individual‐based model constructed and parameterized for the Glanville fritillary metapopulation. Using these simulations, 100% of predicted full‐sibs were correct and over 98% of all true full‐sib pairs were detected. We therefore demonstrate that even in a highly dynamic system with a relatively small number of markers, we can accurately reconstruct full‐sib families and for the first time make inferences on female effective dispersal. This highlights the utility of this approach in systems where it has previously been impossible to obtain accurate estimates of dispersal over both ecological and evolutionary scales.

Highlights

The increasing availability of genomic information continues to transform the way we study natural populations
We sampled three larvae per larval family group from 3732 groups over a six- year period and genotyped for 272 SNPs across the genome. We used this empirical data set to reconstruct cases where full-sibs were detected in different local populations to infer female effective dispersal distance, that is, dispersal events directly contributing to gene flow
The use of molecular data makes it possible to examine the role of dispersal and gene flow in influencing the long-term and large-scale spatial genetic structure of populations in such species (Slatkin, 1985), providing an opportunity to investigate dispersal at previously unobtainable ecological and evolutionary scales, as well as in organisms where no dispersal data has previously been available

Summary

Introduction

The increasing availability of genomic information continues to transform the way we study natural populations. It is possible to accurately and efficiently measure a wide range of important parameters that directly influence the fitness and survival of wild populations such as effective population size (Gilbert & Whitlock, 2015; Palstra & Fraser, 2012), effective number of breeders (Ackerman et al, 2017), extra pair paternity (Firth, Hadfield, Santure, Slate, & Sheldon, 2015; Griffith, Owens, & Thuman, 2002), heterozygosity (Fountain et al, 2016; Saccheri et al, 1998), inbreeding depression (Huisman, Kruuk, Ellis, Clutton-B rock, & Pemberton, 2016) and reproductive success (Coltman et al, 1999) Another key ecological parameter is dispersal, the ecological and evolutionary causes and consequences of which have been studied for decades. This approach allows the estimation of effective dispersal, that is, dispersal directly associated with reproductive fitness, often a more biologically relevant parameter than general dispersal

Results

Discussion

Conclusion