BGC Member Spotlight: Shubhi Sharma

December 5, 2021

Background: My research interests are broadly centered on building statistical models to predict and study the impact of climate change on biodiversity. A predictive understanding of ecological trends and underlying processes is vital for management of our natural resources. I studied Natural Sciences for my BSc at Durham University, United Kingdom. My undergraduate thesis used Dynamic Vegetation Models to predict the response of forests in the Western Ghats region of India to changing precipitation cycles. During my masters at Duke University, I combined questions of forest response to global environmental change with statistical tools. My master's thesis modelled abundance, recruitment and mast in North America to infer direction of migration of tree populations at the continent scale. Combining the expansive seed production database housed at Clark Lab (Nicholas School of Environment) and forest inventory data from Forest Inventory Analysis (FIA), National Ecological Observatory Network (NEON), Canadian Forest Inventory (CFI), we model seed production and recruitment in a single Bayesian framework. This methodology enabled us to make continent-wide predictions about demographic processes that have been studied mainly at an individual or plot scale.

At the BGC Center: My current research interests follow my undergrad and masters' themes of investigating biodiversity response to global environmental change, but now with an evolutionary perspective. I am keen to add an evolutionary dimension to the study of geographic distributions of species. Specifically, to understand how processes that take place on evolutionary timescales intersect with ecological interactions to form individual, population and community responses to rapid environmental change. During the first year of my PhD, my primary project focused on utilizing phylogenetic information to better characterize species’ niches, particularly for data-deficient species. Without a sufficient number of occurrence records, it is difficult to characterize a species environmental niche, predict it’s geographical distribution and forecast how this distribution will respond to environmental change. The premise of this project is to leverage the huge boom in genetic and phylogenetic data, supplement cases where species have few occurrence records and most importantly, test and utilize knowledge of niche conservatism to inform distributions of data-deficient or missing species.