Project
My PhD is part of a project that develops an evidence-based framework for wild ungulate management in Switzerland, working with roe deer, red deer, chamois, and wild boar across three model cantons (St. Gallen, Fribourg, Basel).
Challenges in ungulate management include that data collection varies widely across cantons, population trends are estimated within political rather than ecologically meaningful boundaries, and key data, such as hunting effort or detection probability, are often missing or inconsistently recorded. To address these challenges, I combine simulation-based methods to evaluate data quality, integrated population models to estimate trends and demographic rates, and individual-based models to project population trajectories under different management scenarios.
I develop integrated population models (IPMs) to jointly analyse harvest and auxiliary data sources, estimating population trends and vital rates while explicitly accounting for imperfect detection. Through simulation studies, I evaluate how data quality, survey frequency, and missing data sources affect inference, with the aim of establishing minimum data standards for reliable population reconstruction. I then apply spatially explicit models to identify biologically meaningful management units and to quantify how environmental drivers (e.g. snow cover, habitat fragmentation, predator presence) shape population dynamics. Finally, I use individual-based models to project population trajectories under realistic management and environmental scenarios, providing evidence-based support for hunting planning.
CV
- 2025 – present, PhD Student in Ecology, Population Ecology Research Group, Department of Evolutionary Biology and Environmental Studies, University of Zurich
- 2024 – 2025, MSc Student in Ecology, Population Ecology Research Group, Department of Evolutionary Biology and Environmental Studies, University of Zurich
- 2018 – 2023, BSc Student in Biology, University of Zurich