On 28th November OxTalks will move to the new Halo platform and will become 'Oxford Events' (full details are available on the Staff Gateway).
There will be an OxTalks freeze beginning on Friday 14th November. This means you will need to publish any of your known events to OxTalks by then as there will be no facility to publish or edit events in that fortnight. During the freeze, all events will be migrated to the new Oxford Events site. It will still be possible to view events on OxTalks during this time.
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Online seminar (via Zoom) followed by Q&A – all welcome. NB – all times given in UK time.
Tropical dry forests are characterized by lower rainfall compared to tropical rain forests and a dry season that can last for many months. In this talk, Jennifer will explore how rainfall regimes have shaped the diversity and function of plants in these forests, using plants from the liana lifeform and legume family as examples. She will then discuss how tropical dry forests are responding to climate change by focusing on drought, and the functional traits that underlie these responses.
Jennifer Powers is a Professor in the Departments of Ecology, Evolution & Behavior and Plant Biology & Microbial Biology at the University of Minnesota. She received her PhD in Biology from Duke University in 2001 and then completed postdoctoral studies at the State University of New York- Stony Brook and the University of Minnesota. Her research program focuses on understanding how land-use and climate change affect biogeochemical cycles, ecosystem processes and forest dynamics in tropical landscapes. Much of her work over the past twenty years has centered on seasonally dry tropical forests, which have received less attention but are more threatened than tropical rain forests. Together with her students and network of collaborators, they have investigated these questions from hierarchical scales—from the microbe to the biome, using a diverse toolkit that includes long-term observations, large-scale manipulative field experiments and simulation modeling.