Bayesian quantification of ecological determinants of outcrossing in natural plant populations: Computer simulations and the case study of biparental inbreeding in English yew

Mol Ecol. 2019 Sep;28(17):4077-4096. doi: 10.1111/mec.15195. Epub 2019 Aug 18.

Abstract

The mating system is a central parameter of plant biology because it shapes their ecological and evolutionary properties. Therefore, determining ecological variables that influence the mating system is important for a deeper understanding of the functioning of plant populations. Here, using old concepts and recent statistical developments, we propose a new statistical tool to make inferences about ecological determinants of outcrossing in natural plant populations. The method requires codominant genotypes of seeds collected from maternal plants within different locations. Using extensive computer simulations, we demonstrated that the method is robust to the issues expected for real-world data, including the Wahlund effect, inbreeding and genotyping errors such as allele dropout and allele misclassification. Furthermore, we showed that the estimates of ecological effects and outcrossing rates can be severely biased if genotyping errors and genetic differentiation are not treated explicitly. Application of the new method to the case study of a dioecious tree (Taxus baccata) allowed revealing that female trees that grow in lower local densities have a greater tendency towards mating with relatives. Moreover, we also demonstrated that biparental inbreeding is higher in populations that are characterized by a longer mean distance between trees and a smaller mean trunk perimeter. We found these results to agree with both the theoretical predictions and the history of English yew.

Keywords: ecological determinants; genetic markers; mating system; outcrossing; plants; regression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Computer Simulation*
  • Crosses, Genetic*
  • Ecosystem*
  • Gene Pool
  • Genetic Loci
  • Inbreeding*
  • Regression Analysis
  • Taxus / genetics*