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Biology Seminar Series: Dr. Patrick James

March 31, 2023 at 2:00 PM to 3:30 PM

Location:4440Q Carleton Technology and Training Centre
Cost:Free
Audience:Anyone
Contact Email:biology@carleton.ca

Title: Population genetic consequences of insect outbreaks

Dr. Patrick James

University of Toronto

Friday March 31st, 2023 at 2:30pm; CTTC 4440Q

Coffee & Refreshments will be available at 2:00 p.m.

Everyone is welcome!

Abstract: Population outbreaks are common across many taxa, including several species of forest insect pest that affect Canada’s northern forests. Understanding the processes underlying spatial outbreak dynamics is essential to developing effective forest management and mitigation strategies. Population and landscape genetics are often used to infer the role of dispersal and population connectivity in species of conservation concern. However, species that exhibit outbreaking population dynamics can confound our ability to make meaningful genetic inferences regarding these processes due to artefacts introduced by geographic and demographic expansions. In this talk I will discuss some of the work in the James Lab aimed at using population and landscape genetics to better understand outbreaking population dynamics in economically important forest insect pests. I will also describe further opportunities and remaining challenges in using population genetics to understand outbreaking systems.

Bio: Dr. Patrick James is Associate Professor in the Institute of Forestry and Conservation at the University of Toronto. Previously, he was Associate Professor at the Université de Montréal and a Killam post-doctoral research fellow at the University of Alberta. He holds PhD in forest ecology from the University of Toronto as well as a BSc in Zoology, also from U of T. Dr. James is originally from Paris, Ontario, where he developed a deep curiosity about forest landscapes and how they change through time. Current research in the James Lab focuses on landscape- and stand-scale forest disturbance dynamics using a range of statistical, simulation, and empirical approaches.