Pierre de Villemereuil | Search bioRxiv

bioRxiv2016

Population dynamics of Arabis alpina in the French Alps: evidence for demographic compensation?

O_LIDue to its genetic proximity with Arabidopsis thaliana, Arabis alpina (Brassicaceae) is increasingly used as a perennial model species in studies of molecular evolution and adaptation.\nC_LIO_LIWe studied the demography of A. alpina in six natural sites widely differing in their degree of disturbance, slope and vegetation, and encompassing the full altitudinal range of the species.\nC_LIO_LIWe estimated three vital rates (growth, reproductive effort and survival) for individually-marked plants, studied for six years (2008-2014). We characterized the thermic conditions of each site with different thermal variables obtained using in situ continuous-time data loggers.\nC_LIO_LIAlthough A. alpina is described as a perennial species, the average life expectancy was only 1.82 years and most plants died before setting seeds. Plant size was a strong predictor of all three vital rates.\nC_LIO_LIMean daily temperature showed a positive effect on growth and a negative effect on survival. Furthermore, reproductive effort covaried negatively with survival, suggesting a mechanism of demographic compensation acting on an elevational gradient.\nC_LIO_LISynthesis. These results are informative of the selective pressures experienced by A. alpina in natural conditions and will help design experimental and molecular studies of local adaptation in this species.\nC_LI

Ecology↗

bioRxiv2015

General methods for evolutionary quantitative genetic inference from generalised mixed models.

Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioural traits, have inherently non-normal distributions. The generalised linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for non-normal traits. However, whereas GLMMs provide inference on a statistically-convenient latent scale, it is sometimes desirable to express quantitative genetic parameters on the scale upon which traits are expressed. The parameters of a fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal for this effect by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulae for quantities such as heritability of traits with Binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation, and apply our approach to data from a wild pedigreed vertebrate population.

Evolutionary Biology↗