The Cheshire cat reloaded: causes and consequences of the evolution of dormancy/quiescence in parasites.

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Quiescence and dormancy evolve as a bet-hedging evolutionary strategies in many plants, animals, and micro-organisms (including parasites or pathogens) in unpredictable environments. The central hypothesis of the project states that coevolution between hosts and their parasites promotes such conditions. This evolutionary scenario is referred to as the Cheshire Cat hypothesis (for either hosts or parasites). We have previously shown that 1) seed banking and dormancy can evolve in hosts due to coevolutionary dynamics, and 2) depending on the germination function, the seed bank itself can slow down or even damp off the coevolutionary oscillations. As allele frequency oscillations are critical to promote bet-hedging evolution, a complex eco-evolutionary interplay occurs between the evolution of dormancy and coevolutionary dynamics. The goals of this new phase of the project are twofold. First we want to understand and predict the conditions for parasites to evolve dormancy or quiescence as bet-hedging strategies in response to coevolution. Second, we want to develop simulation and statistical inference tools which allow to estimate the parameter of dormancy/quiescence using parasite full genome data. In the first objective, we study the conditions for bet-hedging to evolve under coevolutionary dynamics. Two dormancy models are developed: a short and a very long dormancy. Coevolution is generated within a continuous-time epidemiological model with two host and two parasite types with parasite dormancy/quiescence. We use analytical methods of adaptive dynamics to derive analytical solutions for the ESS of dormancy/quiescence under different conditions and separation of time scales for both dormancy models. In the second objective, we want to develop tools for detecting and estimating the existence of dormancy/quiescence using parasite full genome data. Therefore, forward in time full genome simulators of weak and strong dormancy/quiescence will be developed for sexually or asexually reproducing species. These simulators will be integrated into an Approximate Bayesian Computation (ABC) framework with Deep Learning (Convolutional Neural Networks) to perform statistical inference of the dormancy/quiescence parameters based on full genome data. In the third objective, we will apply our ABC methods to full genomes of several parasite species for which quiescence/dormancy is biologically suspected but untested: human parasites (Plasmodium vivax, P. falciparum, Bacillus sp., Mycobacter tuberculosis), the parasites of Daphnia magna, and the powdery mildewof Plantago lanceolata. Our general aim is thus to provide the first understanding of the causes and consequences of the evolution of dormancy or quiescence in parasite species due to coevolution, and to develop a novel population genomic inference method to reveal the existence and estimate the rate of dormancy in sexual and asexual parasite species.