Dynamics of mixed-ploidy populations under demographic and environmental stochasticities

Abstract

The theoretical population dynamics of autopolyploids - organisms with more than two genome copies of a single ancestral species - and their diploid progenitors have been extensively studied. The acquisition of multiple genome copies, being in essence a stochastic process, strongly suggests a probabilistic approach to examine the long-term dynamics of a population with multiple cytotypes. Yet, our current understanding of empirical evidence on the dynamics of autopolyploid populations has not incorporated stochastic population dynamics. To investigate the factors contributing to the probability and stability of coexisting cytotypes, we designed a new population dynamics model with demographic and environmental stochasticities to simulate the formation, establishment, and persistence of diploids, triploids, and autotetraploids over time when gene flow is allowed among cytotypes. Contrary to previous research, increased selfing rates and pronounced reproductive isolation stabilized the long-run coexistence of multiple cytotypes. In stressful environments, these dynamics become much more complex, and our stochastic modeling approach helped reveal the resulting intricacies that give tetraploids competitive advantage over their diploid progenitors. Our work is fundamental to a better understanding of the dynamics of coexistence of multiple cytotypes and is a necessary step for further work modeling the dynamics between an autopolyploid and its diploid progenitor.

nQuack: An R package for predicting ploidal level from sequence data using site-based heterozygosity

“…The copyright holder for this preprint this version posted February 12, 2024. ; https://doi.org/10.1101/2024.02.12.579894 doi: bioRxiv preprint Gaynor et al -nQuack: Ploidal level prediction in many areas of biology (for instance, in phylogenetics), in this particular case it is extremely difficult to capture the complexities of nature in an analytical-based inference, and future model exploration utilizing data-based inference to classify ploidal levels is warranted. Alternatively, demographic models like the ones we proposed elsewhere (Gaynor et al, 2023) may provide the ecological and evolutionary framework necessary to design process-based predictions for mixed ploidy. These models, however, require rigorous coupling with evolutionary and genomic theory.…”