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MES Webinar Series: Student Lightning Talks

Student Lightning Talks

Join us for a series of lightning talks by current students!

“Investigating Cross Disciplinary Tools for Improved Mosquito Detection, Control and Management” by Meg Schrierer. Brief overview of the ecological and behavioral science components of my dissertation related to mosquito ecology.

“Evolutionary Ecology of Associative Learning in the European Honey Bee” by Will Hallett, UMaine grad student. Associative learning is a kind of learning that maps or “associates” repeated sensory stimuli to some particular behavioral response through a positive or negative biasing of neural spike patterns with neuromodulators like octopamine (positive feedback) and dopamine (negative feedback). This presentation will discuss computer-based modeling approaches to understanding the evolutionary relationship between this trait and its environment within the context the European Honey Bee.

"Micro-CT offers new perspective on wing polyphenism in the red-shouldered bug (Jadera haematoloma)." by Asher Albert : Colby College undergrad.

Meg Schierer

PhD Candidate in Dr. Allie Gardner's lab and co-advised by social scientist Dr. Sandra De Urioste-Stone. PhD anticipated 5/27 (M.S. 2023 with same advisors).
Former member of the Ostfeld Lab at the Carry Institute of Ecosystem Studies
B.S. 2017 Integrative Biology University of Illinois at Urbana-Champaign

 

Will Hallett

is an MS student at the Ecology and Environmental Science program at the University of Maine. Under the advisement of Professor Leonard J. Kass, Will studies the neurobiology of associative learning in the European Honey Bee and related approaches to mathematical modeling and computer simulation. He holds an MPS in New Media Arts and Design from NYU and a BA in English from Bates College. Will has taught courses at The New School, Fordham University, Marymount Manhattan College, and the Estonia Academy of Art.

 
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March 29

Maple Syruping & Insects

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May 30

MES Field Day: Rosmarin and Saunders Family Forest