Integrating eco-evolutionary data from islands to infer biodiversity dynamics

Lecture

Speaker(s)

Rosemary Gillespie

Professor and Chair in Systematic Entomology; Environmental Science, Policy, and Management; UC Berkeley, Assistant Director, Essig Museum of Entomology, UC Berkeley, BIDS Faculty Council Member

Rosemary G. Gillespie is a professor and the Schlinger Chair in Systematic Entomology in UC Berkeley's Department of Environmental Science, Policy, and Management. She is president elect of the American Genetics Association, past president of the International Biogeography Society, trustee and fellow of the California Academy of Sciences, senior editor for Molecular Ecology, and associate editor for Journal of Biogeography. Her primary research uses islands as model systems to understand ecological and evolutionary processes. Hotspot archipelagoes, such as Hawaii, provide a temporal framework of islands that allows one to synthesize ecological and evolutionary perspectives. She uses this framework to integrate macroecological (interaction networks and maximum entropy inference) and evolutionary (population genetics and phylogenetics) approaches to build a predictive understanding of the dynamic interplay between ecology and evolution in shaping the ecology of complex ecosystems.

Tuesday, November 13, 2018

This lecture was presented as part of the Berkeley Distinguished Lectures in Data Science on Tuesday, November 13, 2018.

A central challenge in understanding the origins of biodiversity is that, while we can observe and test local ecological phenomena, we must usually infer the longer-term outcomes of these ecological forces indirectly. My colleagues and I have been developing inferential models at the interface between macroecology and population-level processes, and applying them to data from geological chronosequences that present communities of different ages. Inferences from these “snapshots in time” provide a link between direct observational methods for local communities and models that make indirect inferences underlying community history. We use data from multiple insular systems, each comprising replicated sites that range from <500 to >5 million years. In this way we can directly link ecological theories and models of community composition within a temporal framework so as to understand the history underlying patterns of species diversity. The approach bridges ecological and evolutionary theory to provide a framework for making predictions about biodiversity dynamics.

The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features Berkeley faculty doing visionary research that illustrates the character of the ongoing data revolution.  This lecture series is offered to engage our diverse campus community and enrich active connections among colleagues.  All campus community members are welcome and encouraged to attend. Arrive at 3:30 PM for light refreshments and discussion prior to the formal presentation.

Share this video