Dissertation Research: Molecular mechanisms underlying rapid adaptive divergence in the Swamp Sparrow

Cornell University, Ithaca NY

Investigators

Abstract

Populations of a single species that live in different geographic regions often diverge as natural selection drives adaptation to the local environment. Understanding the processes of divergence and local adaptation is of particular importance in this contemporary era of rapidly changing environments. The coastal salt marshes of North America provide excellent systems in which to study rapid adaptive divergence, given that these habitats have formed within the last 15,000 years and represent novel selective environments for their inhabitants. Within many independent lineages of birds, there is a strong pattern of evolving larger bills and darker plumage after colonizing coastal salt marshes, suggesting that these traits offer a selective advantage. This project will allow identification of the genetic basis of divergence in these traits, and provide a window into the evolutionary mechanisms underlying local adaptation. This project will also provide hands-on training for graduate and undergraduate students, and will provide useful information for the conservation of coastal bird populations. Two locally adapted subspecies of the Swamp Sparrow exist in the northeastern US: A brown inland form with a small bill, and a dark grey coastal form with a large bill. Preliminary DNA sequence (ddRAD) data reveal that the two forms are very similar, but several regions of the genome are highly differentiated between them. The natural history of coastal Swamp Sparrows suggests that strong directional selection may have influenced sequence variation in these highly differentiated regions. To test this hypothesis, patterns of genome-wide divergence will be surveyed by generating additional DNA sequence data for each differentiated region; these will be statistically examined for molecular signatures of selective sweeps. This project provides an important test of this emerging statistical methodology.