Dr. Alejandro Rico-Guevara, University of Washington
Behavioral ecophysics and evolutionary correlates of nectarivory
A central challenge of biological studies is to describe functional links between underlying architecture (e.g., genotype, phenotype) and emergent phenomena (e.g., performance, ecological patterns). To meet this challenge, it is necessary to identify and quantify causal relationships between variation in traits, such as wing and bill shape, and corresponding capabilities, such as cost of flight and feeding, of their possessors. Having this information allows us to unveil the connections between these capabilities and realized patterns (e.g., resource use, competition strategies), and thus their ecological and evolutionary implications. In this sense, nectar-feeding birds (e.g., hummingbirds) stand out as ideal study organisms because scientists have studied the details of their biology, ecology, and evolution for many years.
Dr. Alejandro Rico-Guevara is a Biologist from the Universidad Nacional de Colombia, where his mentor was Gary Stiles. For his PhD at the University of Connecticut, he worked with Margaret Rubega, and was supported as a Fulbright Scholar. He carried out postdoctoral research as a Miller Fellow at UC Berkeley, working with Robert Dudley. He firmly believes that as biologists, we have both the privilege to learn about the wonders of nature and the responsibility to share what we have learned with other scientists, and perhaps more importantly, with the wider community of non-scientists. His research has been featured by news media (e.g. New York Times, National Geographic Magazine), and in 6 nature documentaries / TV series. Pronouns: Any/No preference.
Dr. Alejandro Rico-Guevara will not be in person but will be presenting live via Zoom. People on campus are welcome to attend together in the Mitchell Center to watch. Coffee and cookies will be served.
If you can’t attend in person but would like to attend via Zoom: https://maine.zoom.us/j/97817337578?pwd=NWh3eHgzZnQyelN0bGxQQmNwL2R5UT09
Password: 886210.
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