about
Gilles Colling
PhD Student
I am Gilles Colling, a PhD student in the Division of BioInvasions, Global Change & Macroecology at the University of Vienna. My research focuses on the spread of alien species and how they accumulate across spatial scales. I combine large biodiversity databases with statistical modelling to understand when increases in regional species pools translate into local establishment, and when ecological or environmental constraints hold them back.
Background
I originally trained in physics at the University of Innsbruck, where I specialized in numerical physics and astrophysics. My early work involved developing solvers for complex systems, and I became increasingly interested in the methods themselves: numerical modelling, simulation, and data analysis. That interest eventually led me to ecology, where I saw the same tools could be used to address urgent questions about global change and biodiversity.
Alongside my studies I gained experience in coding, open-source contributions, and data challenges, and worked on science communication projects such as interactive exhibits at the Luxembourg Museum of Natural History. These experiences shaped my approach to research.
PhD Project
Since 2022 I have been working on my PhD project ASAAS (Alien Species Accumulation Across Scales), supervised by Professors Franz Essl and Stefan Dullinger and funded by the Luxembourg National Research Fund (FNR). ASAAS investigates how alien species accumulate at regional and local scales, and whether observed mismatches reflect time lags or ecological limits. The project combines datasets such as GloNAF, Alien Species First Record Database, sPlot, EVA, BioTime, and AgriWeedClim, and spans plants, vertebrates, and selected invertebrates.
As part of ASAAS I co-authored my first publication on agricultural weed communities using the AgriWeedClim database, which became the first chapter of my thesis. I also co-supervised a master’s thesis on emerging agricultural weeds under climate change. Through these collaborations I have been able to link large-scale datasets with local-scale processes and develop new modelling approaches for understanding invasion dynamics.