Inference with selection, varying population size and evolving population structure: Application of ABC to a forward-backward coalescent process with interactions

Abstract

9Genetic data are often used to infer history, demographic changes or detect genes under se-10 lection. Inferential methods are commonly based on models making various strong assumptions: 11 demography and population structures are supposed a priori known, the evolution of the genetic 12 composition of a population does not affect demography nor population structure, and there is no 13 selection nor interaction between and within genetic strains. In this paper, we present a stochastic 14 birth-death model with competitive interaction to describe an asexual population, and we develop 15 an inferential procedure for ecological, demographic and genetic parameters. We first show how genetic diversity and genealogies are related to birth and death rates, and to how individuals com-17 pete within and between strains. This leads us to propose an original model of phylogenies, with 18 trait structure and interactions, that allows multiple merging. Second, we develop an Approxi-19 mate Bayesian Computation framework to use our model for analyzing genetic data. We apply 20 our procedure to simulated and real data. We show that the procedure give accurate estimate of 21 the parameters of the model. We finally carry an illustration on real data and analyze the genetic 22 diversity of microsatellites on Y-chromosomes sampled from Central Asia populations in order to 23 test whether different social organizations show significantly different fertility. 24

Dynamics of lineages in adaptation to a gradual environmental change

“…Let us prove that under µ T,x t , the canonical process is an inhomogeneous Markov process with infinitesimal generator (55).…”

“…For directed selection, when the population at latter stages is issued from individuals at the tip of the wave, strongly asymmetric genealogical trees arise (see [12,11,24,63,72,7]). In [55], the genealogies in an adaptive dynamics time scale are described with a forward-backward coalescent. For structured populations with competition, other approaches include the look-down processes [25,26,30] or the tree-valued descriptions as in [5,38,51].…”

Eco-evolutionary model on spatial graphs reveals how habitat structure affects phenotypic differentiation

“…8a, b); (ii) characterising the landscape features with 〈l〉, h d and r Θ ; and (iii) relating the obtained metrics maps to observation data. More generally, the proposed eco-evolutionary model on spatial graphs could be combined with inference methods to estimate ecological, spatial, and evolutionary processes of real populations from observation data, similarly to 60 . This approach might improve current inferential techniques based on models that do not account for competition nor heterogeneous selection (see e.g.…”

Temporal matches and mismatches between monarch butterfly and milkweed population changes over the past 12,000 years

“…Despite the critical importance of understanding past population dynamics, especially for species of conservation concern, inferring demographic histories can be extremely challenging. Novel genomic methodologies based on sampling extant individuals and interpretation of genomic patterns of diversity have recently provided insight into the demographic histories of species ranging from protists to humans (Schwabl et al 2021; Lepers et al 2021). Over the past 25 years, conservationists have become increasingly alarmed by the decline of the monarch butterfly’s overwintering population (Thogmartin et al 2017; Pleasants et al 2017; Lincoln P. Brower et al 2012).…”