Day Troy
Department of Mathematics and Statistics, Jeffery Hall, Queen's University, Kingston, ON, K7L 3N6, Canada; Department of Biology, Queen's University, Kingston, ON, K7L 3N6, Canada.
Mol Ecol. 2015 May;24(9):2073-83. doi: 10.1111/mec.13082. Epub 2015 Feb 16.
In recent years, several studies have examined the relationship between genetic diversity and establishment success in colonizing species. Many of these studies have shown that genetic diversity enhances establishment success. There are several hypotheses that might explain this pattern, and here I focus on the possibility that greater genetic diversity results in greater evolvability during colonization. Evaluating the importance of this mechanism first requires that we quantify evolvability. Currently, most measures of evolvability have been developed for quantitative traits whereas many studies of colonization success deal with discrete molecular markers or phenotypes. The purpose of this study is to derive a suitable measure of evolvability for such discrete data. I show that under certain assumptions, Shannon's information entropy of the allelic distribution provides a natural measure of evolvability. This helps to alleviate previous concerns about the interpretation of information entropy for genetic data. I also suggest that information entropy provides a natural generalization to previous measures of evolvability for quantitative traits when the trait distributions are not necessarily multivariate normal.
近年来,多项研究探讨了遗传多样性与定殖物种定殖成功之间的关系。其中许多研究表明,遗传多样性会提高定殖成功率。有几种假说或许可以解释这种模式,在此我关注的是更大的遗传多样性在定殖过程中导致更高进化能力的可能性。评估这一机制的重要性首先需要我们对进化能力进行量化。目前,大多数进化能力的衡量方法是针对数量性状开发的,而定殖成功的许多研究涉及离散的分子标记或表型。本研究的目的是为这类离散数据推导一种合适的进化能力衡量方法。我表明,在某些假设下,等位基因分布的香农信息熵提供了一种自然的进化能力衡量方法。这有助于缓解以往对遗传数据信息熵解释的担忧。我还提出,当性状分布不一定是多元正态分布时,信息熵是对以往数量性状进化能力衡量方法的自然推广。