Regularities in animal behaviour offer insight into the underlying
organisational and functional principles of nervous systems and automated tracking provides the opportunity to extract features
of behaviour directly from large-scale video data. Yet how to effectively analyse such behavioural data remains an open question. Here we explore whether a minimum description length principle can be
exploited to identify meaningful behaviours and phenotypes. We apply
a dictionary compression algorithm to behavioural sequences from
the nematode worm Caenorhabditis elegans freely crawling on an
agar plate both with and without food and during chemotaxis. We find that the motifs identified by the compression algorithm are rare but
relevant for comparisons between worms in different environments, suggesting that hierarchical compression can be a useful step in behaviour analysis. We also use compressibility as a new quantitative phenotype and find that the behaviour of wild-isolated strains of C.
elegans is more compressible than that of the laboratory strain N2 as well as the majority of mutant strains examined. Importantly, in distinction to more conventional phenotypes such as overall motor activity or aggregation behaviour, the increased compressibility of wild isolates is not explained by the loss of function of the gene npr-1, which suggests that erratic locomotion is a laboratory-derived trait with a novel genetic basis. Because hierarchical compression can be applied to any sequence, we anticipate that compressibility can offer insight into the organisation of behaviour in other animals including humans.