Ecological Research

, Volume 23, Issue 1, pp 235–240

Parasitism and breeding system variation in North American populations of Daphnia pulex

  • Stuart C. Killick
  • Darren J. Obbard
  • Stuart A. West
  • Tom J. Little
Original Article

DOI: 10.1007/s11284-007-0368-x

Cite this article as:
Killick, S.C., Obbard, D.J., West, S.A. et al. Ecol Res (2008) 23: 235. doi:10.1007/s11284-007-0368-x

Abstract

The Red Queen hypothesis proposes that frequency-dependent selection by parasites may be responsible for the evolutionary maintenance of sexual reproduction. We sought to determine whether parasites could be responsible for variation in the occurrence of sexual reproduction in 21 populations of Daphnia pulex (Crustacea; Cladocera) that previous studies have shown to consist of either cyclical parthenogens, obligate parthenogens, or a mixture of both. We measured parasite prevalence over a four-week period (which essentially encompasses an entire season for the temporary snow-melt habitats we sampled) and regressed three different measures of sexuality against mean levels of parasite prevalence. Levels of parasitism were low and we found no relationship between levels of sexuality and mean parasite prevalence. Genetic variation with infection level was detected in 2 of the 21 populations, with several different clones showing signs of overparasitism or underparasitism. Overall, however, our results suggest that parasites are not a major source of selection in these populations and it thus seems unlikely they are responsible for maintaining breeding system variation across the study region.

Keywords

Geographical parthenogenesis Red Queen hypothesis Evolution of sex Parthenogenesis Natural selection Infection 

Copyright information

© The Ecological Society of Japan 2007

Authors and Affiliations

  • Stuart C. Killick
    • 1
  • Darren J. Obbard
    • 1
  • Stuart A. West
    • 1
  • Tom J. Little
    • 1
  1. 1.Institute of Evolutionary Biology, School of Biological SciencesUniversity of EdinburghEdinburghUK

Personalised recommendations