Yushi Yang has recently completed his PhD project in Bristol focusing on the collective behaviour of a specific living organism: Zebrafish, a small, semi-transparent fish coming from tropical fresh waters employed extensively in biology as a model organism. Quantitative studies of the collective behaviour of animals have been performed in many different contexts, from midges to starlings and fish are not excluded. However, Yushi has been one of the first to concretely realise an experimental setup allowing for the three-dimensional tracking of the trajectories of a conspicuous number of individuals (about 50).
Together we looked into some simple models making physical sense of the emerging patterns of behaviour. This approach has serious epistemological challenges: what justifies the reduction of the social behaviour of an animal to a minimal physical model? what do we actually learn in the process of reduction of complexity? what predictive power is associated with such results?
In our approach, we pursued the identification of quantitative variables describing the evolving physical features of groups of fish: their average orientation, their relative distances, and their speed. We actually did not attempt to measure any specific form of interactions between the fish: these are certainly taking place by means that can be complex (vision, hydrodynamic feedback, maybe some form of signalling) but, in our description, they only appear as effective terms. In fact, what we seek is a small number of physical properties that allow us to organise the data: for example, we identify a scaled persistence length that appears to control the degree of polarisation of groups of fish of different ages: incoherent groups of older fish and well-coordinated groups of younger fish. It is indeed a key result of Yushi’s work that the behaviour of zebrafish changes markedly with age and that non-trivial correlations appear to be present only for the younger groups. We are able to map these different behaviours on a single master curve and model it with a simple physical model dominated by alignment interactions and delay (or inertia) in reorientation.
While the different physical ingredients of the simplified model do not have an immediate biological interpretation, they allow us to narrow down the spectrum of relevant variables controlling collective behaviour and justify further research aimed at providing a causal, biological link between the behaviour of the group and the phenotypical characteristics of the individuals.
This research has been published in PLoS Computational Biology
Francesco Turci 