arXiv subjects

Maite Wilke Berenguer

Publications and source records attributed to Maite Wilke Berenguer.

3 recordsLinked to original sources

arXiv2022

The impact of dormancy on evolutionary branching

In this paper, we investigate the consequences of dormancy in the `rare mutation' and `large population' regime of stochastic adaptive dynamics. Starting from an individual-based micro-model, we first derive the polymorphic evolution sequence of the population, based on previous work by Baar and Bovier (2018). After passing to a second `small mutations' limit, we arrive at the canonical equation of adaptive dynamics, and state a corresponding criterion for evolutionary branching, extending a previous result of Champagnat and Méléard (2011). The criterion allows a quantitative and qualitative analysis of the effects of dormancy in the well-known model of Dieckmann and Doebeli (1999) for sympatric speciation. In fact, a quite intuitive picture merges: Dormancy enlarges the parameter range for evolutionary branching, increases the carrying capacity and niche width of the post-branching sub-populations, and, depending on the model parameters, can either increase or decrease the `speed of adaptation' of populations. Finally, dormancy increases diversity by increasing the genetic distance between subpopulations.

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arXiv2020

Statistical tools for seed bank detection

In this article, we derive statistical tools to analyze and distinguish the patterns of genetic variability produced by classical and recent population genetic models related to seed banks. In particular, we are concerned with models described by the Kingman coalescent (K), models exhibiting so-called weak seed banks described by a time-changed Kingman coalescent (W), models with so-called strong seed bank described by the seed bank coalescent (S) and the classical two-island model by Wright, described by the structured coalescent (TI). As the presence of a (strong) seed bank should stratify a population, we expect it to produce a signal roughly comparable to the presence of population structure. We begin with a brief analysis of Wright's $F_{ST}$, which is a classical but crude measure for population structure, followed by a derivation of the expected site frequency spectrum (SFS) in the infinite sites model based on 'phase-type distribution calculus' as recently discussed by Hobolth et al. (2019). Both the $F_{ST}$ and the SFS can be readily computed under various population models, they discard statistical signal. Hence we also derive exact likelihoods for the full sampling probabilities, which can be achieved via recursions and a Monte Carlo scheme both in the infinite alleles and the infinite sites model. We employ a pseudo-marginal Metropolis-Hastings algorithm of Andrieu and Roberts (2009) to provide a method for simultaneous model selection and parameter inference under the so-called infinitely-many sites model, which is the most relevant in real applications. It turns out that this full likelihood method can reliably distinguish among the model classes (K, W), (S) and (TI) on the basis of simulated data even from moderate sample sizes. It is also possible to infer mutation rates, and in particular determine whether mutation is taking place in the (strong) seed bank.

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arXiv2019

Robust model selection between population growth and multiple merger coalescents

We study the effect of biological confounders on the model selection problem between Kingman coalescents with population growth, and Xi-coalescents involving simultaneous multiple mergers. We use a low dimensional, computationally tractable summary statistic, dubbed the singleton-tail statistic, to carry out approximate likelihood ratio tests between these model classes. The singleton-tail statistic has been shown to distinguish between them with high power in the simple setting of neutrally evolving, panmictic populations without recombination. We extend this work by showing that cryptic recombination and selection do not diminish the power of the test, but that misspecifying population structure does. Furthermore, we demonstrate that the singleton-tail statistic can also solve the more challenging model selection problem between multiple mergers due to selective sweeps, and multiple mergers due to high fecundity with moderate power of up to 30%.

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