Data for: The central Alps comprise a major dispersal barrier between western and eastern populations of two butterfly species
Data files
Dec 11, 2022 version files 18.24 KB
Abstract
Aim: Environmental and species-specific factors shape spatial patterns in genetic diversity and population structure. Comparing different species within the same area helps to disentangle more general from species-specific factors affecting such geographic patterns. Here, we examined genetic diversity and population structuring through geographic features in two alpine butterfly species.
Location: European Alps.
Taxon: Copper butterflies (Lycaena spp.).
Methods: We sampled 21 Lycaena hippothoe and 14 L. virgaureae populations with 18 individuals per population. We analysed the genetic diversity and structure of these populations by using 14 and nine microsatellite markers for L. hippothoe and L. virgaureae , respectively.
Results: We found higher number of alleles, allelic richness, observed heterozygosity, FST values and more genetic clusters in L. hippothoe than in L. virgaureae. Both species displayed a major genetic barrier in the central Alps. Western and eastern L. hippothoe populations but central L. virgaureae populations showed the highest genetic diversity.
Main Conclusions: The population genetic structures of both Copper butterflies seemed to be strongly affected by population history and demography. Patterns indicate for both species a western and an eastern glacial refuge. The high genetic diversity and pronounced population structure found in L. hippothoe seems to be related to a low dispersal ability and closed populations with high local abundances as opposed to L. virgaureae. The higher dispersal of the latter likely caused hybridisation in the central alpine contact zone boosting genetic diversity, which was not the case in L. hippothoe. These findings suggest that different conservation strategies are needed for these closely related species.
Methods
The study area is located in the European Alps, including sampling sites in Austria (Salzburg, Tyrol, Vorarlberg), Italy (South Tirol, Lombardy), and Switzerland (Graubünden), spanning an altitudinal range from ca. 1260 to 2240 m a.s.l. (Figure 1, Table 1). We sampled 21 and 14 populations with 18 individuals each in 2017-19 for L. hippothoe and L. virgaureae, respectively. Butterflies were caught with an insect net in the field, one leg was removed and stored in 100% ethanol until DNA extraction. Afterwards, we released the butterflies immediately and marked all sampled individuals before release to avoid the recapture of individuals (Marschalek et al., 2013).
DNA was extracted with the E.Z.N.A. ® Tissue DNA Kit (Omega bio-tek, Germany) following the manufacturer’s instructions. In this study, we used 14 and nine microsatellite markers for L. hippothoe and L. virgaureae (for details see Trense et al., 2019; Supplementary Table S1). Genotyping the microsatellites was performed on an ABI 3130XL sequencer (Applied Biosystems, Germany) and the length of microsatellite loci was determined using the program GENEIOUS version 11.1.5 (Kearse et al. 2012; https://www.geneious.com).
The microsatellite loci were checked for potential null alleles using the program MICROCHECKER version 2.2.3 (van Oosterhout et al., 2004). Numbers of alleles, alleles per locus, polymorphic loci, gene diversity, inbreeding coefficient (FIS), FST and RST value, observed (HO) and expected heterozygosity (HE), and analyses of molecular variance (AMOVA) were computed with ARLEQUIN version 3.5 (Excoffier & Lischer, 2010). Number of private alleles, Nei’s genetic distance (Nei, 1972), and unbiased Nei’s genetic distance were calculated using GENALEX version 6.51 (Peakall & Smouse, 2006, 2012). FSTAT version 2.9.3 (Goudet, 1995) was used to calculate allelic richness and FREENA to control for the impact of null alleles on FST values (Chapuis & Estoup, 2007). Linkage disequilibrium and Hardy-Weinberg equilibrium (HWE) were tested with GENEPOP version 4.2 (Raymond & Rousset, 1995; Rousset, 2008). Resulting p-values for FST values were corrected for multiple testing using the Bonferroni Holm method (Holm, 1979).