As a postdoctoral researcher in the BatTrend project, I currently investigate how automated monitoring using infrared light barriers, camera traps, and acoustic methods can improve bat population monitoring. In parallel, my research increasingly focuses on long-term individual-based data to understand how seasonal behavioral decisions and environmental conditions shape individual survival. This work forms the basis for my developing research profile at the interface of long-term ecology and data science.

My research lies at the interface of behavioral ecology, conservation biology, and data science. I aim to understand how environmental change affects wildlife populations and to develop monitoring and analytical approaches that allow these changes to be studied over large temporal and spatial scales. My current work focuses primarily on automated bat monitoring, while long-term individual-based data are becoming an increasingly important direction of my research.

Automated monitoring is central to my work because it enables ecological processes to be studied continuously and over long periods. During my PhD, I combined infrared light barriers with camera traps to monitor bat populations at hibernation sites and developed reproducible workflows for processing and analyzing the resulting large datasets. In collaboration with computer scientists, I developed BatNet, a deep-learning approach for automated bat species identification from camera-trap images. As a postdoctoral researcher in the BatTrend project, I now build on these approaches to investigate how infrared light barriers, camera traps, and acoustic monitoring can improve the assessment of bat populations in Germany. More broadly, I am interested in how modern data science and AI-assisted approaches can help extract biologically meaningful information from complex ecological monitoring data.

Alongside this population-level research, I increasingly work with long-term RFID datasets of individually marked bats. These data provide a complementary perspective by allowing behavioral decisions to be linked across seasons and years at the individual level. This has motivated my growing interest in understanding how environmental conditions and individual state shape seasonal behavior, life histories, and survival. Developing reproducible and computational approaches for analyzing large ecological datasets provides a common thread connecting these different areas of my research.

Selected Awards

2024 – Promotionspreis, University of Greifswald
2024 – Lothar-Kämpfe Publication Award, Zoological Institute and Museum,  University of Greifswald
2023 – PhD in Biology, summa cum laude