Abstracts (first author)
How social structure affects gene flow in a wild passerine population
Gene flow is strongly affected by the spatial distribution of individuals, the variability of the physical environment and social processes such as dispersal, resource competition and territoriality. Here we investigate the contributions of those forces to gene flow in foraging flocks of great tits (Parus major) for three consecutive winters. We used a total of 85602 visits of flocks to 60 feeding tables which recorded the identity of 1711 birds by radio frequency identification technology. Of those birds 962 were genotyped based on 4701 autosomal single-nucleotide polymorphisms (SNPs). For 87% of the visits we were able to genotype at least one individual. We used asymmetric eigenvector maps (AEMs) to partition the contributions of space and social structure to the allele frequencies of all 4701 SNPS in the feeding flocks while taking the previous positions of individuals into account. We were able to explain 58% of the variance in allele frequencies with AEMs. This study shows that space and social structure have a substantial effect on the distribution of alleles over subpopulations and therefore on gene flow. To our knowledge this is the first study to investigate the effect of social structure and space to gene flow at such a fine scale while accounting for previous location of the individuals. Therefore it contributes to the understanding of how social behaviour affects evolution. Next we will extract Moran’s eigenvector maps (MEMs) for the spatial locations of feeding tables and use partial redundancy analysis (AEMs as explanatory variables while controlling for MEMs) to investigate how social structure affects allele frequencies, while controlling for space. We will use variance partitioning to quantify the relative contributions of space and social structure, determine which alleles have large effects on the AEMs and check whether those alleles are in linkage disequilibrium with candidate genes or are known to correlate to environmental variables.