The first plant names I learned in my mother’s garden in canton Uri. After college in Altdorf, I set out to start a BSc in Integrative Biology in Basel in 2003. Soon I experienced great botany excursions organized by the Botanical Institute in Basel where today I still feel home while giving lectures on plants.
2006 to 2007 I did a MSc study named "Biodiversity in Time and Space" at the University of Leiden in the Netherlands, my second home. There, I studied plant speciation in orchids and biodiversity conservation. But, the mountains brought me back to my official home country where I did my PhD thesis (2008-2011) at the University of Basel on the longevity and resilience of alpine clonal plants. On long field work tours through the Swiss Alps, the Carpathian Mountains as well as arctic Lapland and Svalbard, I investigated population structure and age of clonal plants. From 2011 to 2013, I supported digitalization projects of the Herbaria Basel. Then, I joined the Institute for Applied Plant Biology as a research associate contributing to forest ecosystem monitoring and studying changes in diversity and community composition of ectomycorrhizal symbionts of beech trees. Alongside, I rediscovered my passion for plants and improved my knowledge in botany and vegetation analysis with training e.g. at the Zürich University of Applied Sciences.
Since summer 2021, I work for the COMECO project in the group “Dynamic macroecology” at the Swiss Federal Research Institute (WSL). This project contributes to biodiversity monitoring and citizen science by improving image-based identification of plants. This project is a great challenge to deepen my knowledge on plant distribution and ecology across Switzerland while contributing to biodiversity conservation.
The COMECO project is a collaboration of WSL with Info Flora. We combine machine learning-based image recognition with spatially explicit ecological and morphological meta-information for the identification of the c. 4'000 Swiss plant species from georeferenced pictures. This combination is expected to considerably improve species identification as new images are not only classified according to visual features but also with regards to ecological and geographical plausibility. This will greatly aid data acquisition by citizen scientists.