Ecology and evolution

In this post, I explore the benefits and drawbacks of using empirical (ML)-based models versus mechanistic models for predicting ecosystem responses to perturbations, and further develeop a hybrid approach combining their strengths.

In order to anticipate the responses of ecosystems to anthropogenic pressure and climate change, models that can extrapolate ecological dynamics beyond observations are required. While we know very well the laws of physics, we are very far from a predictive theory of life. Machine learning can help filling this huge gap! 🧠 🤖 🌱

Biodiversity results from differentiation mechanisms developing within biological populations. Such mechanisms are influenced by the properties of the landscape over which individuals interact, disperse and evolve. The documentation of high levels of species diversity in complex mountain region or riverine systems suggest that some peculiar landscape properties foster diversity. I use mathematical models to investigate how landscape connectivity and habitat heterogeneity foster diversity.