Fully funded PhD position with Dr. Tal Avgar at the University of British Columbia Okanagan: From wildlife space-use patterns to population viability

Do you struggle to decide what is more exciting, ecology or statistics? Are you debating which is your favourite, moose or MCMC? Does a set of alternative mechanistic hypotheses, complete with their associated testable predictions, bring you true joy? You might have just found your dream position!

Start date: September 2024 (negotiable)

Annual stipend: Minimum $35,000 for four years

Location: This position will be based at the Department of Biology, Irving K. Barber Faculty of Science, the University of British Columbia, Okanagan campus (UBCO), located on the traditional, ancestral, and unceded Syilx Okanagan Nation Territory (Kelowna, BC, Canada). Kelowna, BC’s 3rd largest metropolitan area (20th largest in Canada), is situated on the shore of Okanagan Lake, surrounded by provincial parks, pine forests, vineyards, orchards, and mountains. The area offers excellent access to a variety of outdoor recreational activities, including hiking, climbing, water sports (motorized and non-motorized), biking (road, gravel, cross country, and downhill), and skiing (Nordic, Alpine, and Touring).

The advisor: Dr. Tal Avgar (he/him) is a quantitative wildlife ecologist with expertise in space-use ecology, wildlife population biology, animal conservation, predator-prey interactions, and statistical and mathematical modelling. Tal is an assistant professor at the Department of Biology at UBCO and a Senior Science Advisor at the Wildlife Science Centre, Biodiversity Pathways Ltd. Before moving to Kelowna, Tal was an assistant professor at Utah State University (2018-2022), a postdoctoral fellow at the University of Alberta (2014-2017), and a PhD student at the University of Guelph (2008-2013).

Minimum qualifications: BSc and research experience in ecology or related fields. Applicants must be admissible to the Biology PhD program at UBCO

Competitive applicants will also have:

  • MSc degree in ecology, wildlife biology, or conservation biology, with strong quantitative skills and training in statistics


    MSc degree in statistics, mathematics, or data science with a strong interest in animal ecology

  • Experience conducting statistical analysis of spatial and population data in R and/or Python
  • Experience in writing and publishing peer-reviewed scientific articles
  • Experience in presenting research to diverse audiences
  • Experience in working collaboratively with indigenous peoples, governments, and/or other stakeholders

Priority will be given to applicants who identify as belonging to equity-seeking groups, including (but not limited to) Indigenous peoples, LGBTQ+, and first-generation or otherwise non-traditional college students

Consideration of applications will begin on May 13th, 2024 and will continue until the position is filled. Qualified applicants should email the following materials, as a single PDF file, with the subject line “SUP2PVA PhD Position”, to tal.avgar@ubc.ca:

  1. Cover letter describing relevant experience, interests, and professional goals
  2. CV demonstrating their qualifications
  3. Contact information for three professional references

A bit about the project: Animal space-use patterns (SUPs; how, when, and where they move across geographical and environmental space) emerge from the interaction between the animal’s environment (the surrounding resources, risks, and conditions), and the animal’s ecological needs (to gain resources, avoid risks, and optimize conditions). As such, SUPs contain rich information about the animal’s needs, its reproductive performance, and its environment. Wildlife biologists routinely track individual animals using GPS tags and other sensors, and the resulting data are incredibly rich in information (SUPs), but are also severely underutilized. Instead, estimates of population density or growth are typically obtained using expensive and logistically challenging surveys with minimal integration of SUPs. The objective of this project is to study whether and how individual-based SUPs could be integrated with location-based population-level data to form a single cost-effective framework and inform an individual-based and spatially explicit population viability analysis (PVA).