Wheatears (genus Oenanthe) are an about 6.6 my old group of passerine birds mainly inhabiting arid and rocky ecosystems of Eurasia and Africa that exhibit striking patterns of phenotypic polymorphism: (i) High rates of character switching across the phylogeny suggests multiple convergent origins of numerous phenotypes, including melanin-based coloration and complex behavioral traits such as seasonal migration. (ii) Multiple interspecific differences segregate as polymorphisms within species. And finally, (iii) one of these polymorphisms found in black-eared wheatear (O. hispanica) and pied wheatear (O. pleschanka) supposedly arose by reciprocal introgression among these two pervasively hybridizing sister species. The convergent emergence of phenotypes across the phylogeny both within and between species points towards an involvement of a labile molecular switch between phenotypes.
We aim at addressing multiple questions relating to the evolution of phenotypic diversity within and between species, and to the evolution of species. Currently we settled out to characterize the genetic population structure across the hybrid zone of black-eared and pied wheatear using genome-wide polymorphism data. In future phenotypic, genetic, and methylomic polymorphism data from within these species, from three hybrid zones, and from across the wheatear genus will be used to identify the molecular bases of diverse color phenotypes and study their evolution, including the demographic and genomic constrains under which they evolve.
Current funding: Research Grant BU-3456/3-1 by the German Research Foundation (DFG), 2018-2021
People involved: Reto Burri
Adaptive evolutionary change occurs when selection is acting on heritable trait variation. But not all evolutionary responses are straightforward. Genetic covariation in particular may modify the speed and the direction of adaptive evolution. Genetic covariation arises from pleiotropy (the same genetic factors influence multiple traits) or from linkage disequilibrium (coinheritance) of multiple independent genetic factors. This can affect multiple traits of the same individual, but also traits expressed in different individuals, such as traits expressed in females and males. We have therefore studied the multivariate genetic architecture of trait variation in multiple species of grasshoppers to evaluated if evolution in grasshoppers is constraint or shaped by genetic covariation.
Sexual selection is a particularly potent force that can result in the evolution of extravagant ornaments and is therefore a driving force in generating biological diversity. We have therefore focused our research on the highly sexually dimorphic club-legged grasshopper. The species is unusual in that males possess swollen front legs (‘Popeye arms’). Neither females nor any related species show this feature and this begs the question about how these structures are used and how they have evolved. We have therefore studied the behavioral ecology of sexual selection in this intriguing species. It turns out that the courtship behavior of this species is highly peculiar.
Statistical Quantification of Individual Differences is the product of the SQuID working group. The package aims to help scholars who, like us, are interested in understanding patterns of phenotypic variance. Individual differences are the raw material for natural selection to act on and hence the basis of evolutionary adaptation. Understanding the sources of phenotypic variance is thus a most essential feature of biological investigation. Mixed effects models offer a great, albeit challenging tool in this context. Disseminating the properties, potentials and interpretational challenges in the research community is thus a foremost goal of SQuID.
The squid package has two main objectives: First, it provides an educational tool useful for students, teachers and researchers who want to learn to use mixed-effects models. Users can experience how the mixed-effects model framework can be used to understand biological phenomena by interactively exploring simulated multilevel data. Second, squid offers research opportunities to those who are already familiar with mixed-effects models, as it enables the generation of datasets that users may download and use for a range of simulation-based statistical analyses such as power and sensitivity analysis of multilevel and multivariate data.