Login / Signup

Paternity data and relative testes size as measures of level of sperm competition in the Cercopithecoidea.

R Robin BakerTodd K Shackelford
Published in: American journal of primatology (2018)
Historically, the empirical study of the role of sperm competition in the evolution of sexual traits has been problematic through an enforced reliance on indirect proxy measures. Recently, however, a procedure was developed that uses paternity data to measure sperm competition level directly in terms of males/conception (i.e., the number of males that have sperm present in a female's ampulla at conception). When tested on apes and humans (Hominoidea) this measure proved not only to correlate significantly with the traditionally used measure of relative testes size but also to offer a number of advantages. Here we provide a second test of the procedure, this time using paternity data for the Old World monkeys (Cercopithecoidea). We calculate sperm competition levels (males/conception) for 17 species of wild and free-ranging cercopithecoids and then analyze the data against measures of relative testes size. Calculated sperm competition levels correlate strongly with relative testes size both with and without phylogenetic control at both the species and generic levels. The signal-to-noise ratios inherent in both the past measure of relative testes size and the new measure of sperm competition level from paternity data are discussed. We conclude that although both measures are appropriate for the future study of the role of sperm competition in the evolution of sexual traits, when paternity data are available they provide the more direct and meaningful analytical tool. Not least, they potentially allow a first empirical analysis of the role of sperm competition in the evolution of relative testes size that could then be compared with the wealth of theoretical analyses that already exist.
Keyphrases
  • electronic health record
  • big data
  • minimally invasive
  • machine learning
  • gene expression
  • dna methylation
  • artificial intelligence
  • mass spectrometry