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PUBLIC TALK: Colleen Cassady St. Clair
2019-03-21 @ 19:00 - 21:00 MDT
Attractants, adaptation, and learning in bear-train collisions
Colleen Cassady St. Clair is a Professor of Biological Sciences at the University of Alberta. She and her students study the way wildlife use and move through human-dominated landscapes, which frequently involve human-wildlife conflict. They seek novel solutions to those problems by combining ideas and methods from animal behaviour, wildlife management, and conservation biology. Recent projects address coexistence between people and coyotes in urban areas, train strikes on grizzly bears, and bird mortality at industrial sites.
Roads and railways create foraging and travel opportunities that attract wildlife, as well as risk of mortality from collisions. Over the past two decades, train strikes appeared to threaten a vulnerable population of grizzly bears (Ursus arctos) in Banff National Park resulting in the Joint Initiative for Grizzly Bear Conservation by Canadian Pacific and Parks Canada. My team at UofA addressed its goals, to identify the root causes of this mortality and recommend potential mitigation, with core contributions by Jonathan Backs, Alyssa Friesen, Aditya Gangadharan, Jack Hopkins, Patrick Gilhooly, Maureen Murray, Scott Nielsen, Sonya Pollock, and Julia Put. We measured forage opportunities associated with the rail, monitored habitat and rail use of bears fitted with GPS collars, and examined spatial and temporal correlates of past mortality. We found that the rail generates substantial food attractants as well as mortality of many species. Rail use by bears was highly variable among individuals and appeared to have diverse motivations, but did not predict sites of past mortality. For bears, rail strikes may sometimes result from inadequate or maladaptive learning that might be mitigated with warning devices similar to those employed on railways for people. We support existing mitigation to minimize rail-side attractants and enhance alternative travel routes, but also emphasize the role of animal learning to reduce collision risk. This approach could increase the speed and reduce the costs of mitigation for railways around the world, relative to conventional mitigation used for roads based on exclusion fencing and crossing structures.