Pam has been working on wild sheep and goat projects throughout British Columbia since 1997, beginning with herd classified inventories of bighorn sheep in Kootenay National Park. Since 2002, she has been working as project leader and liaison in multi-stakeholder collaborations focused on ecology and management strategies for Stone’s sheep, bighorn sheep, and mountain goats.
As project manager for the Sulphur 8 Mile Stone’s Sheep Project, Pam was responsible for funding proposals and budgeting, human resources, field implementation, data analyses, and reporting to two advisory groups representing 12 stakeholders and 15 funding organizations.
Her academic background includes graduate research on ecology and harvest management of bighorn sheep populations in BC, under the direction of renowned wild sheep and goat researcher Dr. Marco Festa-Bianchet. Her work evaluated population recovery after a pneumonia die-off nearly extirpated South Okanagan herds (Hengeveld 2008). As well, she investigated the link between bighorn sheep harvest regulations and the potential for undesirable artificial selection on horn growth (Hengeveld and Festa-Bianchet 2011).
Pam’s expertise has been applied in ground-based and aerial mark-resight population inventories; monitoring and analysis of habitat use by radio-collared sheep and goats; development of goat habitat supply models in collaboration with government and industry to inform forest development plans; development of Stone’s sheep management guidelines to inform oil and gas pre-tenure plans in the Muskwa-Kechika Management Area; review of Yukon government guidelines for management, harvest, and mitigation of human impacts on thinhorn sheep; and collaborative research on Stone’s sheep and bighorn sheep habitat use, health, survival, and population demographics.
PROFESSIONAL TRAINING AND CERTIFICATIONS
▪ M.Sc. Wildlife Ecology
▪ Chemical Immobilization and Handling of Wildlife; Wildlife Necropsy
▪ Backpack Electrofishing Certified Crew Supervisor
▪ Mixed-effect modelling of trends in large and complex data sets
▪ Graphics in R advanced statistical software
▪ Logistic regression statistical analyses
▪ Analyzing repeated measures in temporal data