Wing Geometric Morphometrics of Workers and Drones and Single Nucleotide Polymorphisms Provide Similar Genetic Structure in the Iberian Honey Bee (<i>Apis mellifera iberiensis</i>)

• Main Authors: Dora Henriques, Julio Chávez-Galarza, Juliana S. G. Teixeira, Helena Ferreira, Cátia J. Neves, Tiago M. Francoy, M. Alice Pinto
• Format: Article
• Language: English
• Published: MDPI AG 2020-01-01
• Series: Insects
• Subjects: iberian honey bee; spatial population structure; spatial principal component analysis (spca); snps
• Online Access: https://www.mdpi.com/2075-4450/11/2/89

Wing geometric morphometrics has been applied to honey bees (<i>Apis mellifera</i>) in identification of evolutionary lineages or subspecies and, to a lesser extent, in assessing genetic structure within subspecies. Due to bias in the production of sterile females (workers) in a colony, most studies have used workers leaving the males (drones) as a neglected group. However, considering their importance as reproductive individuals, the use of drones should be incorporated in these analyses in order to better understand diversity patterns and underlying evolutionary processes. Here, we assessed the usefulness of drone wings, as well as the power of wing geometric morphometrics, in capturing the signature of complex evolutionary processes by examining wing shape data, integrated with geographical information, from 711 colonies sampled across the entire distributional range of <i>Apis mellifera iberiensis</i> in Iberia. We compared the genetic patterns reconstructed from spatially-explicit shape variation extracted from wings of both sexes with that previously reported using 383 genome-wide SNPs (single nucleotide polymorphisms). Our results indicate that the spatial structure retrieved from wings of drones and workers was similar (r = 0.93) and congruent with that inferred from SNPs (r = 0.90 for drones; r = 0.87 for workers), corroborating the clinal pattern that has been described for <i>A. m. iberiensis</i> using other genetic markers. In addition to showing that drone wings carry valuable genetic information, this study highlights the capability of wing geometric morphometrics in capturing complex genetic patterns, offering a reliable and low-cost alternative for preliminary estimation of population structure.