Introgression through rare hybridization: A genetic study of a hybrid zone between red and sika deer (genus Cervus) in Argyll, Scotland

Genetics. 1999 May;152(1):355-71. doi: 10.1093/genetics/152.1.355.

Abstract

In this article we describe the structure of a hybrid zone in Argyll, Scotland, between native red deer (Cervus elaphus) and introduced Japanese sika deer (Cervus nippon), on the basis of a genetic analysis using 11 microsatellite markers and mitochondrial DNA. In contrast to the findings of a previous study of the same population, we conclude that the deer fall into two distinct genetic classes, corresponding to either a sika-like or red-like phenotype. Introgression is rare at any one locus, but where the taxa overlap up to 40% of deer carry apparently introgressed alleles. While most putative hybrids are heterozygous at only one locus, there are rare multiple heterozygotes, reflecting significant linkage disequilibrium within both sika- and red-like populations. The rate of backcrossing into the sika population is estimated as H = 0.002 per generation and into red, H = 0.001 per generation. On the basis of historical evidence that red deer entered Kintyre only recently, a diffusion model evaluated by maximum likelihood shows that sika have increased at approximately 9.2% yr-1 from low frequency and disperse at a rate of approximately 3.7 km yr-1. Introgression into the red-like population is greater in the south, while introgression into sika varies little along the transect. For both sika- and red-like populations, the degree of introgression is 30-40% of that predicted from the rates of current hybridization inferred from linkage disequilibria; however, in neither case is this statistically significant evidence for selection against introgression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles
  • Animals
  • DNA, Mitochondrial / metabolism
  • Deer / genetics*
  • Heterozygote
  • Hybridization, Genetic*
  • Linkage Disequilibrium
  • Microsatellite Repeats
  • Models, Statistical
  • Scotland

Substances

  • DNA, Mitochondrial