About
Research
I now contribute to several research projects in our lab, with a main focus on grassland biodiversity across different regions and spatial scales. My role is usually to lead or support the data analysis and coding, and also to discuss methods and results with the team.
Among others, I currently work on the following projects:
- sCaleGrassDiv: A synthesis project organised by the iDiv. In this project we look at biodiversity and its drivers at different spatial scales in temperate grasslands in Ukraine.
- LandUseMultBEF: A Biodiversity Exploratories project that looks at the effects of land use on the trophic structure and functioning of arthropod communities living in tree holes.
- Developing an R package called spatPatClassifyR to automatically classify landscape patterns in ecotones (in development)
Before that, during my PhD, I studied biological soil crusts (biocrusts) and how they affect dryland eco-hydrology. Biocrusts consist of algae, cyanobacteria, mosses, and lichens that bind to the soil surface and form a protective layer, especially on bare ground in drylands. Their composition depends on factors like climate and water availability. I looked at how climatic change affects biocrust lichens using both an experiment and a process-based eco-physiological model. I also examined how lichen-dominated biocrusts redistribute water across a patchy dryland hillslope using a process-based eco-hydrological model.
You can find my publications.
Teaching
I teach workshops for PhD students and researchers on scientific coding and research workflows like coding in R, note-taking with Obsidian, version control with Git, and more. The goal of my workshops is to help students and researchers to make their research more reproducible and efficient.
You can find an overview of the workshops I teach.
I also teach bachelor and master students together with other colleagues. The courses I am mainly involved in:
- Ecological modelling with NetLogo (teacher): The basics of individual-based modelling. The course teaches the entire modelling cycle from developing a research question, finding and building the right model, designing simulation experiments and evaluating and presenting the results. In the final study project, students develop their own models and present the results to the classroom.
- Introduction to statistics with R (teacher): A block course where students learn R from scratch and how to use it for statistical analyses. The course teaches the basics starting from descriptive statistics and statistical tests, to linear and generalised linear models. Students learn the methods, how they work, when to use them, how to implement them in R and how to interpret the results.
- Quantitative plant ecology (teaching assistant): A field course that combines lectures, field experiments and statistical analysis in R.
Interested in collaborating, or curious about coding and reproducible research? I’m always happy to hear from you, just send me an email.