Postdoctoral Fellowship quantifying American black duck reproductive metrics and Canadian boreal forest environmental covariates, University of Saskatchewan

University of Saskatchewan
Saskatoon, Saskatchewan
Job Category
Post Doctoral Appointments
$65,000 Canadian per year plus benefits
Start Date
Last Date to Apply
The Department of Biology at the University of Saskatchewan is seeking a two-year postdoctoral fellow to lead a project focused on quantifying reproductive metrics in American black ducks, with hypothesis tests of environmental drivers on breeding areas. We anticipate the postdoctoral fellow will use machine learning algorithms to retrospectively assess egg-laying, full-term incubation and brood-rearing in black ducks, using GPS and acceleration (ACC) data from tracking devices fitted to individual females. Black ducks nest primarily in the eastern Canadian boreal forest, a large remote region where assessing reproductive success with field crews is not practical. While machine learning algorithms have been widely used to classify behaviours from ACC data, they have not been customized for reproductive metrics. The postdoctoral fellow also will determine feasibility of environmental covariates in the Canadian boreal forest (e.g., spatial layers for beaver ponds, commercial logging) for hypothesis tests about the reproductive period. We have deployed 200 devices and anticipate another 300 devices will be deployed in the next two years to collect information about the reproductive period. The devices collect GPS information every hour and ACC information every 10 minutes. The postdoctoral fellow will work closely with a PhD student studying the full annual cycle for black ducks. There are other projects in our group using similar GPS-ACC devices on Atlantic brant and greater white-fronted geese to assess the reproductive period in the context of annual cycle movements, behaviour and habitat use. The postdoctoral fellow will work with graduate students on those projects to develop best practices for using machine learning to identify reproductive metrics. We anticipate broad applicability of results for studying migratory birds that are cryptic or nest in inaccessible areas. In addition, there will be opportunities to collaborate on and lead other projects linking population and individual processes in migratory birds in the Weegman lab.
Minimum qualifications: Ph.D. in statistics, wildlife ecology or closely related field Skills in Program R Demonstrated excellence in verbal and written communication Ability to work independently and as part of a research team Preferred qualifications: Skills in JAGS Experience forming and running machine learning algorithms, demographic and animal movement models Knowledge and experience in avian ecology To be considered for this position, please send a cover letter, curriculum vitae, research statement and contact information for three references to Dr. Mitch Weegman (
Contact Person
Mitch Weegman
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