"Natural death rate drives star graphs from amplifiers to suppressors of natural selection"Slyeptsova, NatalyaEvolutionary graph theory (EGT) considers evolutionary dynamics in a structured population that is represented by a graph. Fixation probability is a measure of probability that a mutation takes over the resident population. One of the major questions EGT tries to answer is how the graph structure impacts the fixation probability. It has been found that certain graphs can act as amplifiers or suppressors of selection. However, the type of update rule used in the model can impact this result. For example, the star graph is an amplifier for birth-death with fitness on birth (Bd) dynamics and is a suppressor for death-birth with fitness on birth (dB) dynamics. Typically, EGT has focused on discrete time models, which can be hard to link with realistic population dynamics. Recently, these discrete time models have been generalized to a continuous time Markov-process model based on eco-evolutionary dynamics, where the results for dB and Bd dynamics can be recreated by suppressing the ecological dynamics. This work shows that within this continuous time framework, there exists a continuous transition from Bd to dB results. Therefore, the interplay between the underlying biological parameters of birth rate, death rate, and competition, will drive the qualitative shift from amplifier (under Bd) to suppressor (under dB). Using the star graph as an example, we prove that the transition from Bd to dB depends on the magnitude of the natural death rate. By increasing the natural death rate of individuals, population structures that typically amplify selection under Bd dynamics can be driven to suppressing fixation. Exploring the fundamental drivers behind this qualitative shift will provide further insights into whether population structures will amplify or suppress selection under realistic population dynamics. If not selected for a talk, I would love for my application to be considered for a poster, if possible. Thank you |
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