Despite population ecology is one of the most treated issues in ecological theory and its applications, there are still a large number of open questions. For instance, how common is the logistic growth model in nature and why we observe such a huge number of deviations in empirical studies? Or how can we anticipate non-linear responses in population dynamics varying over time and space? Similarly, movement ecology is currently experiencing a scientific revolution (driven by GPS and other tracking technologies), so it is expected that the increase in quantity and quality of movement data should help to conceptual synthesis. Despite this many questions, old and new, still remain open. It is only through combined data modelling and hypothesis-driven analyses that one can gain true knowledge on animal movement behaviour.

We contribute to the current scientific revolution in ecological data developing data mining techniques and new statistical and modelling tools, aiming to infer and understand behaviour from high-troughput ecological data. Our aim is to understand how individuals in populations interact with the biotic (e.g. competition, predation, parasitism) and abiotic (e.g. climate, deterministic human impacts) components of ecosystems, and and evaluate the impact of behaviourial variability as a main driver.