We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We test a big data workflow for understanding and predicting plankton dynamics using monitoring data.
Deep neural networks (DNNs) have shown impressive empirical performance but they are still nevertheless a black-box function modeling data.
Activated dynamics is a very slow process that takes place on exponentially large time scales. Usually it is associated to barrier hopping.
We use machine learning methods to predict the effects of chemicals on aquatic species.
Community detection consists of extracting the affinity between agents of a system, which is extracted from quantities such as the frequency of interactions.