IBRA, the world's longest established apicultural research publishers, promotes the value of bees by providing information on bee science and beekeeping worldwide. A not-for-profit organisation founded in 1949

Beekeeping activities, especially queen trading, have shaped the distribution of honey bee (Apis mellifera) subspecies in Europe, and have resulted in extensive introductions of two eastern European C-lineage subspecies (A. m. ligustica and A. m. carnica) into the native range of the M-lineage A. m. mellifera subspecies in Western Europe. As a consequence, replacement and gene flow between native and commercial populations have occurred at varying levels across western European populations. Genetic identification and introgression analysis using molecular markers is an important tool for management and conservation of honey bee subspecies. Previous studies have monitored introgression by using microsatellite, PCR-RFLP markers and most recently, high density assays using single nucleotide polymorphism (SNP) markers. While the latter are almost prohibitively expensive, the information gained to date can be exploited to create a reduced panel containing the most ancestry-informative markers (AIMs) for those purposes with very little loss of information. The objective of this study was to design reduced panels of AIMs to verify the origin of A. m. mellifera individuals and to provide accurate estimates of the level of C-lineage introgression into their genome. The discriminant power of the SNPs using a variety of metrics and approaches including the Weir & Cockerham’s F_ST, an F_ST-based outlier test, Delta, informativeness (I_n), and PCA was evaluated. This study shows that reduced AIMs panels assign individuals to the correct origin and calculates the admixture level with a high degree of accuracy. These panels provide an essential tool in Europe for genetic stock identification and estimation of admixture levels which can assist management strategies and monitor honey bee conservation programs.

The article by Harpur et al. (2012) ‘Management increases genetic diversity of honey bees via admixture’ concludes that ‘…honey bees do not suffer from reduced genetic diversity caused by management and, consequently, that reduced genetic diversity is probably not contributing to declines of managed Apis mellifera populations’. In the light of current honeybee and beekeeping declines and their consequences for honeybee conservation and the pollination services they provide, we would like to express our concern about the conclusions drawn from the results of Harpur et al. (2012). While many honeybee management practices do not imply admixture, we are convinced that the large‐scale genetic homogenization of admixed populations could drive the loss of valuable local adaptations. We also point out that the authors did not account for the extensive gene flow that occurs between managed and wild/feral honeybee populations and raise concerns about the data set used. Finally, we caution against underestimating the importance of genetic diversity for honeybee colonies and highlight the importance of promoting the use of endemic honeybee subspecies in apiculture.

The honeybee (Apis mellifera) has been threatened by multiple factors including pests and pathogens, pesticides and loss of locally adapted gene complexes due to replacement and introgression. In western Europe, the genetic integrity of the native A. m. mellifera (M‐lineage) is endangered due to trading and intensive queen breeding with commercial subspecies of eastern European ancestry (C‐lineage). Effective conservation actions require reliable molecular tools to identify pure‐bred A. m. mellifera colonies. Microsatellites have been preferred for identification of A. m. mellifera stocks across conservation centres. However, owing to high throughput, easy transferability between laboratories and low genotyping error, SNPs promise to become popular. Here, we compared the resolving power of a widely utilized microsatellite set to detect structure and introgression with that of different sets that combine a variable number of SNPs selected for their information content and genomic proximity to the microsatellite loci. Contrary to every SNP data set, microsatellites did not discriminate between the two lineages in the PCA space. Mean introgression proportions were identical across the two marker types, although at the individual level, microsatellites' performance was relatively poor at the upper range of Q‐values, a result reflected by their lower precision. Our results suggest that SNPs are more accurate and powerful than microsatellites for identification of A. m. mellifera colonies, especially when they are selected by information content.