do O, I. | bioRxiv Science

bioRxiv2026

LAVA: a method for identifying local and global adaptation in structured populations

Demonstrating that local adaptation drives phenotypic divergence in quantitative traits requires distinguishing selection from neutral differentiation. Existing methods for detecting selection on quantitative traits, particularly QST - FST comparisons, rely on simplified assumptions about population structure. These methods assume equal relatedness among all subpopulations, which is not necessarily true in a real metapopulation. When assumptions are violated, it has been shown that QST - FST leads to elevated false positive rates. Here we present MONET, a model for the neutral evolution of traits that creates a robust baseline against which hypotheses on trait evolution can be tested. We show how the null model provided by MONET can be the basis of two tests: one based on the log-ratio of ancestral variances (logAV) statistics we previously introduced, and another, based on testing the influence of environmental variables from the populations of origin on the phenotypic traits measured in a common garden. MONET is implemented in an R-package as a Bayesian linear mixed-effect framework modeling population structure through relatedness matrices. We show through extensive simulations across different population structures and selective scenarios how our two hypothesis tests based on MONET maintain proper calibration while maintaining or exceeding the power of alternative methods.

evolutionary biology↗

bioRxiv2025

A method for identifying spatially divergent selection in structured populations.

Species occupy diverse, heterogeneous environments, which expose populations to spatially varied selective pressures. Populations in different environments can diverge due to local adaptation. However, neutral evolution can also drive population divergence. Thus, testing for local adaptation requires a neutral baseline for population differentiation. The classical QST -FST comparison was developed for this purpose. Yet, QST -FST frequently fails to account for the complexities of population structure because the theory underlying this comparison assumes that all subpopulations are equally related, resulting in inflated false positive rates in metapopulations that deviate from the island model. To address this limitation we use estimates of between- and within-population relatedness to model population structure. Using those relatedness matrices, we infer the between- and within-population ancestral additive genetic variances under a mixed-effects model. Under neutrality, these inferred variances are expected to be equal. We propose here a test to detect selection based on the comparison of these two estimates of the ancestral variance and we compare its performance with earlier solutions. We find our method is well calibrated across various population structures and has high power to detect adaptive divergence. Author summaryPopulations of the same species often face different environmental pressures, driving them to adapt locally. However, even in the absence of adaptation, subpopulations can diverge due to random genetic drift and limited migration. Distinguishing between adaptive evolution and random divergence is a central challenge in evolutionary biology. Traditional methods, such as QST -FST comparison, assume equal relatedness among subpopulations--a simplification that rarely holds in complex real-world scenarios, leading to flawed conclusions. To overcome this limitation, we developed a novel method that incorporates genetic relatedness among subpopulations, leveraging quantitative genetic theory to estimate ancestral additive genetic variances. Our approach provides a powerful tool for testing local adaptation, reliably distinguishing adaptive divergence from drift across a variety of population structures.

evolutionary biology↗