On Non-Random Mating, Adaptive Evolution, and Information Theory.

Simple Summary: The evolutionary process can be seen as a process of acquisition, storage, and updating of information by a population about the environment in which it lives. In this paper, I propose a model that starts with the distribution of mating that occurs according to mutual mating fitness and ends with the distribution of viable adult genotypes obtained after this mating. The result of the evolutionary dynamics associated with each stage of the model can be described in terms of information. This informational description facilitates the connection between cause and effect, as well as the development of statistics to test the null model of zero information, i.e., random mating and/or no effect of natural selection. Incorporating the informational perspective into the mathematical formalism of population genetics/genomics contributes to clarifying, expanding, and deepening the mathematical description of evolutionary theory. Population genetics describes evolutionary processes, focusing on the variation within and between species and the forces shaping this diversity. Evolution reflects information accumulated in genomes, enhancing organisms' adaptation to their environment. In this paper, I propose a model that begins with the distribution of mating based on mutual fitness and progresses to viable adult genotype distribution. At each stage, the changes result in different measures of information. The evolutionary dynamics at each stage of the model correspond to certain aspects of interest, such as the type of mating, the distribution of genotypes in regard to mating, and the distribution of genotypes and haplotypes in the next generation. Changes to these distributions are caused by variations in fitness and result in Jeffrey's divergence values other than zero. As an example, a model of hybrid sterility is developed of a biallelic locus, comparing the information indices associated with each stage of the evolutionary process. In conclusion, the informational perspective seems to facilitate the connection between cause and effect and allows the development of statistical tests to perform hypothesis testing against zero-information null models (random mating, no selection, etc.). The informational perspective could contribute to clarify, deepen, and expand the mathematical foundations of evolutionary theory. [ABSTRACT FROM AUTHOR]