Search bioRxiv⌕ Search

bioRxiv · 10.1101/2020.04.01.014514

A demogenetic agent based model for the evolution of traits and genome architecture under sexual selection

Abstract

Sexual selection has long been known to favor the evolution of mating behaviors such as mate preference and competitiveness, and to affect their genetic architecture, for instance by favoring genetic correlation between some traits. Reciprocally, genetic architecture can affect the expression and the evolvability of traits and mating behaviors. But sexual selection is highly context-dependent, making interactions between individuals a central process in evolution, governing the transmission of genotypes to the next generation. This loop between the genetic structure conditioning the expression and evolution of traits and behaviour, and the feedback of this phenotypic evolution on the architecture of the genome in the dynamic context of sexual selection, has yet to be thoroughly investigated. We argue that demogenetic agent-based models (DG-ABM) are especially suited to tackle such a challenge because they allow explicit modelling of both the genetic architecture of traits and the behavioural interactions in a dynamic population context. We here present a DG-ABM able to simultaneously track individual variation in traits (such as gametic investment, preference, competitiveness), fitness and genetic architecture throughout evolution. Using two simulation experiments, we compare various mating systems and show that behavioral interactions during mating triggered some complex feedback in our model, between fitness, population demography, and genetic architecture, placing interactions between individuals at the core of evolution through sexual selection. DG-ABMs can, therefore, relate to theoretical patterns expected at the population level from simpler analytical models in evolutionary biology, and at the same time provide a more comprehensive framework regarding individual trait and behaviour variation, that is usually envisioned separately from genome architecture in behavioural ecology.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chevalier, L., de Coligny, F., Labonne, J.. 2020-04-02. A demogenetic agent based model for the evolution of traits and genome architecture under sexual selection. https://doi.org/10.1101/2020.04.01.014514

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Ocean current models predict spatial structure for coastal biodiversity

Genomic diversity provides the raw material through which all evolutionary processes occur. Accurately describing how environmental features affect patterns of diversity shows promise in predicting the resilience of populations and entire communities to dynamic environments and novel stressors. Identifying the contribution of purely neutral responses to environmental effects can be difficult, especially across large spatial ranges with many covarying environmental features. Using an individual-based model of larvae carried by ocean currents, we predict patterns of neutral genomic diversity in a passively dispersed coastal species, suggesting large geographic regions of relative homogeneity with ancestry determined by the nearest "upstream" population with limited larval input. The genomic identity that dominates each region sharply transitions at the next such interruption, generating hierarchical structure and violating assumptions of isolation-by-distance. We call these upstream populations "lineage source regions" (LSRs), and we find strong concordance between LSR model predictions and an empirical population genomic survey of a keystone salt marsh mussel (Geukensia demissa). We quantitatively validate model performance and goodness-of-fit, updating baseline predictions of genomic diversity patterns from the classic isolation-by-distance model to this LSR framework. This spatially explicit coastal ancestry model has potential for generalization across passive dispersing species.

evolutionary biology↗

TaxaAudit: A statistical model of consensus in phylogenetics identifies non-monophyletic taxa

Interpreting variation in genes and traits across species requires understanding their evolutionary relationships. Taxonomic hierarchies provide a practical approximation of the tree of life, with the NCBI taxonomy linking this framework directly to public sequence data. Some named groups, however, are not evolutionary clades, and recognizing these exceptions requires knowledge scattered across the phylogenetics literature. Here we introduce TaxaAudit, a statistical model that identifies taxonomic groups whose monophyly is repeatedly contradicted by published phylogenies. The model accounts for background taxonomic conflict and the ability of each study's species sampling to expose non-monophyly. Applied to 5,865 molecular phylogenies from TimeTree, TaxaAudit flagged 516 NCBI taxa at family rank and below for significant recurrent contradiction of monophyly. Excluding these taxa's monophyly requirements improved the taxonomy's agreement with independent phylogenomic reference trees and phylogenies withheld from discovery. Flagged taxa were also 3.61 times as likely as matched controls to have their monophyly challenged in publications outside the discovery set. In tree-inference experiments, excluding these monophyly constraints improved agreement with reference phylogenies relative to enforcing the unmodified taxonomy. By identifying groups whose monophyly should not be assumed, TaxaAudit lets researchers draw on published phylogenetic knowledge without reviewing the specialist literature for every taxon. This supports broader use of the NCBI taxonomy as an evolutionary framework for biological comparisons.

evolutionary biology↗

Shared landscape, divergent genomes: Comparative population genomics of co-occurring flora in an isolated Australian mountain range

Understanding how landscape heterogeneity shapes genomic variation is key to conserving biodiversity and evolutionary potential. Yet conservation genomics is typically conducted at the single species level, limiting our ability to identify shared genomic patterns or understand how co-occurring taxa differ in their response to common environmental gradients. We assessed spatial patterns of genomic variation and their associations with geographic and environmental features among populations of nine co-existing plant species with contrasting life-histories and ecological traits across Gariwerd, a threatened biodiversity hotspot in south-eastern Australia. All species exhibited weak overall genetic structure, although the magnitude and spatial distribution of differentiation varied among species. Despite this variation, broadly consistent spatial trends were apparent, with higher genomic divergence largely concentrated in central zones and higher genomic diversity localised in southern regions of the landscape. Significant associations between genetic differentiation and geographic distance were observed in three species, while another species showed associations with environmental distance. These findings demonstrate that coexisting species can show overlapping spatial patterns in genomic variation while differing markedly in their associations with geographic and environmental variation across the same landscape. This comparative approach provides a basis for spatial prioritisation and conservation planning, while highlighting the complex eco-evolutionary processes operating across heterogeneous landscapes.

evolutionary biology↗