Biological systems—from cells to tissues to individuals to societies—are hierarchically organised, which suggests there are multiple natural scale at which these systems should be studied. Across disciplines (e.g. neurobiology, genetics, evolutionary biology), biologists often focus on isolating a single scale and understanding the patterns observed at this level of the hierarchy. While the task of understanding observed patterns in nature requires revealing the mechanisms that underlie them, these mechanisms generally operate at a different scale than the observed pattern itself. In many cases, the patterns must be studied as emerging from interactions among a collection of smaller units. However, in other cases, they are imposed upon the system by larger scale constraints. For example, a compass-like representation of landmarks in the fruit fly brain is only observable at a larger scale by coarse-graining the neural firing rates; gradient tracking in fish schools emerges from individuals responding to local resource levels; natural selection on phenotypic traits is constrained by the species’ evolutionary history and other environmental factors. In all examples, spatially and temporally distributed information is captured at the system level and translated to some behavioural output. My lab explores the link between the different scales of biological organisation, and the idea that hierarchical organisation allows these systems to maximise information extraction across space and time.

Currently, we combine cutting-edge data acquisition techniques with modern advances in computer vision and machine learning to understanding the evolutionary and mechanistic underpinnings of mate-choice on leks.