Dear colleagues.
Biology Thursday seminar continues on zoom on thu 16th Nov, 12:15, with Dr. Cecilia di Bernardi from Grimsö Wildlife Research Station, Department of Ecology, Swedish University of Agricultural Sciences on
"Wolf feeding ecology in a multi-ungulate system" Predation patterns by large carnivores are influenced by a combination of physical, behavioral, and environmental factors. With the recolonization of these apex predators, there is a need to better understand carnivores' ecology in human dominated landscapes. Data on individual traits of large carnivores are difficult to retrieve and even more to associate with individual feeding behavior. Alongside cluster checks of GPS-collared wolves, the growing field of faecal DNA-based diet analysis in combination with individual genotyping has the potential to increase the feasibility of large-scale analyses of food use related to individual predator traits. In this research, the development and empirical validation of a molecular method for prey DNA detection was followed by the analysis of intrinsic and extrinsic factors affecting feeding ecology of wolves in Sweden. The method used nanofluidic array technology and a set of 80 multiple species-specific markers to detect DNA of 17 target prey from wolf scats. By taking advantage of the long-term wolf monitoring, we showed that wolf feeding patterns (i.e. prey use with DNA-data and extent of scavenging with GPS-data) were affected by social status, sex, and level of inbreeding. These innovative patterns underline the relevance of considering predator individual traits when studying carnivore feeding ecology. More on Cecilia's work at: https://www.slu.se/en/ew-cv/cecilia-di-bernardi/
See you online on Thursday,
cheers,
Heikki
Heikki Helanterä is inviting you to a scheduled Zoom meeting.
Topic: Biology Thursday seminar Time: This is a recurring meeting Meet anytime
Join Zoom Meeting https://oulu.zoom.us/j/62917763539?pwd=WENZRy9Bb3NlSzlyeVlVamJ4RHR5Zz09
Meeting ID: 629 1776 3539 Passcode: 166151
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Meeting ID: 629 1776 3539 Passcode: 166151