Implementation of mixture analysis on quantitative traits in studies of neutral versus selective divergence

Background: The spatial genetic structuring of natural populations is mostly studied using neutral markers. Recently, morphometric methods have also been used to to study genetic divergence through adaptive processes. These methods provide better insights into the conservation needs of focal populations. However, all morphometric methods assume that samples obtained in different localities represent distinct populations when, in fact, they may constitute a mixture of several populations due to cryptic population structure and/or environmental variability. This may lead to biased estimates of the adaptive divergence between populations. Mixture analysis makes no a priori assumption of the affiliation of samples. It can therefore be used to assign samples and detect population structure, allowing estimation of morphometric divergence. Methods: We perform mixture analyses on simulated data to estimate potential bias in adaptive population divergence measures due to a priori assumptions about the population structure. We present three examples illustrating the possible uses of mixture analyses for identification of distinct compartments (groups of individuals that are morphologically similar) between and within populations. Key assumptions: We assume that the presence of distinct compartments between populations can be attributed to different environmental conditions, the presence of barriers reducing gene flow, and phylogenetic signals and plasticity of the traits analysed. Conclusions: Certain cases of (cryptic) population structure may lead to substantial bias in the estimation of population morphometric divergence. This can have major implications for conservation guidelines and for the detection of evolutionarily distinct populations.