Axis 3 : Biotic interactions - Understanding their importance, structure and dynamics

We search for patterns of community assembly rules ...
To measure the importance of biotic interactions at large spatial scales, we use large scale biodiversity data and test for a signal of biotic interactions in patterns, e.g. alpha and beta diversity patterns of plant communities across the Alps, combined distribution data, meta-web of vertebrates across Europe and repeated multi-trophic community samples in the observatory Orchamp.

We study the structure and dynamics of interaction networks ...
We aim at better understanding biotic interactions, their overall prevalence and importance, their nature (positive vs negative, intensity), their functional impact on the ecosystem and their temporal dynamics and context dependency. We build on combinations of experiments and long-term empirical observational data (plant-plant, plant-pollinator, plant-herbivore, herbivore-herbivore, herbivore-predator…) with Lotka-Volterra models and statistical network analyses to identify tipping points and changes of trajectories of bi-partite or tripartite interaction networks. We analyse the structure and spatial distribution of complex interaction networks and ask whether we can predict interactions from species traits and behavior, how past and current environment influence the spatial structure of networks, how and why some species are central in networks and finally how to quantify the flux of matter through the network. We build on our European vertebrate data that summarise the spatial distribution of all vertebrates together with their traits, their phylogenetic relationships and their known trophic interactions. We use artificial intelligence to reconstruct interaction networks from the environmental DNA collected for ORCHAMP (MIAI@GrenobleAlpes) and analyse their distribution and diversity along the environmental gradients.