Search

Secondary contact between species often results in the formation of a hybrid zone, with the eventual fates of the hybridizing species dependent on evolutionary and ecological forces. We examine this process in the Amazon Basin by conducting the first genomic and phenotypic characterization of the hybrid zone formed after secondary contact between two obligate army‐ant‐followers: the White‐breasted Antbird (Rhegmatorhina hoffmannsi) and the Harlequin Antbird (Rhegmatorhina berlepschi). We found a major geographic displacement (∼120 km) between the mitochondrial and nuclear clines, and we explore potential hypotheses for the displacement, including sampling error, genetic drift, and asymmetric cytonuclear incompatibilities. We cannot exclude roles for sampling error and genetic drift in contributing to the discordance; however, the data suggest expansion and unidirectional introgression of hoffmannsi into the distribution of berlepschi. [ABSTRACT FROM AUTHOR]

The adoption of a multivariate perspective of selection implies the existence of multivariate adaptive peaks and pervasive correlational selection that promotes co‐adaptation between traits. However, to test for the ubiquity of correlational selection in nature, we must first have a sense of how well can we estimate multivariate nonlinear selection (i.e., the γ‐matrix) in the face of sampling error. To explore the sampling properties of estimated γ‐matrices, we simulated inidividual traits and fitness under a wide range of sample sizes, using different strengths of correlational selection and of stabilizing selection, combined with different number of traits under selection, different amounts of residual variance in fitness, and distinct patterns of selection. We then ran nonlinear regressions with these simulated datasets to simulate γ‐matrices after adding random error to individual fitness. To test how well could we detect the imposed pattern of correlational selection at different sample sizes, we measured the similarity between simulated and imposed γ‐matrices. We show that detection of the pattern of correlational selection is highly dependent on the total strength of selection on traits and on the amount of residual variance in fitness. Minimum sample size needs to be at least 500 to precisely estimate the pattern of correlational selection. Furthermore, a pattern of selection in which different sets of traits contribute to different functions is the easiest to diagnose, even when using a large number of traits (10 traits), but with sample sizes in the order of 1000 individuals. Consequently, we recommend working with sets of traits from distinct functional complexes and fitness proxies less prone to effects of environmental and demographic stochasticity to test for correlational selection with lower sample sizes. [ABSTRACT FROM AUTHOR]