Related Experiment Video
Updated: Jun 8, 2026

Sperm Collection of Differential Quality Using Density Gradient Centrifugation
Published on: November 29, 2018
Fertilization success and the estimation of genetic variance in sperm competitiveness
Francisco Garcia-Gonzalez1, Jonathan P Evans
1Centre for Evolutionary Biology, School of Animal Biology, The University of Western Australia, Nedlands, WA 6009, Australia. pgarcia@cyllene.uwa.edu.au
Abstract:
A key question in sexual selection is whether the ability of males to fertilize eggs under sperm competition exhibits heritable genetic variation. Addressing this question poses a significant problem, however, because a male's ability to win fertilizations ultimately depends on the competitive ability of rival males. Attempts to partition genetic variance in sperm competitiveness, as estimated from measures of fertilization success, must therefore account for stochastic effects due to the random sampling of rival sperm competitors. In this contribution, we suggest a practical solution to this problem. We advocate the use of simple cross-classified breeding designs for partitioning sources of genetic variance in sperm competitiveness and fertilization success and show how these designs can be used to avoid stochastic effects due to the random sampling of rival sperm competitors. We illustrate the utility of these approaches by simulating various scenarios for estimating genetic parameters in sperm competitiveness, and show that the probability of detecting additive genetic variance in this trait is restored when stochastic effects due to the random sampling of rival sperm competitors are controlled. Our findings have important implications for the study of the evolutionary maintenance of polyandry.
Related Concept Videos
Fertilization
Spermatogenesis
Sperm Structure and Semen Composition
Testing a Claim about Mean: Known Population SD
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...

