This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2020, Research Using Biological Collections. The fellowship supports research and training of the Fellow that will utilize biological collections in innovative ways. The rapid rate of species extinction is one of the great crises of our time. Even when endangered species recover, though, population bottlenecks may have long-term effects on the species? survival. This project combines computer science, evolutionary genetics, and conservation biology to understand the impact of the extreme bottleneck and remarkable recovery of the northern elephant seal. This charismatic marine mammal was hunted to the verge of extinction in the 19th Century, but legal protections have allowed the species to rebound. Understanding the role of natural selection during the bottleneck and recovery of the northern elephant seal will help inform plans for the successful management of other species on the brink of extinction. In addition, the computational tools developed in this project can be applied to other species, including humans, for which genomes are available from before, during, and after a population bottleneck. The Fellow will collaborate with programming departments at the National and American Museums of Natural History to recruit diverse audiences to events designed as educational outreach opportunities focused on marine mammal evolution and conservation.
Decades of research in mathematical population genetics have led to many statistical approaches that aim to detect signals of natural selection and demographic history, but the patterns from these two forces can be difficult to disentangle. More recent developments in machine learning, however, show promise in deconvolving these signals. This project will focus on developing a new machine learning method to infer selection and demographic events, with increased sensitivity through the use of temporally sampled genomes. To test this method on empirical data, the Fellow will generate whole genome sequences from northern elephant seal specimens collected before, during, and after their extreme population bottleneck. This sampling strategy will track allele frequency changes through time, which can then be used by the machine learning algorithm to distinguish subtle differences in the patterns caused by selection and demography. In elephant seals, this method will be used specifically to examine how selection has shaped immune function and extreme sexual dimorphism. To achieve a widespread application of this method, the Fellow will work to create accessible documentation for these computational tools and will design workshops for researchers at various collections-based research institutions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
