A MACHINE LEARNING CLUSTERING ALGORITHM TO IDENTIFY GAPING BEHAVIOUR IN MYTILUS SPP UNDER CONTRASTING ENVIRONMENTAL CONDITIONS

Lagoons and deltas, are highly heterogenous transitional systems, subject to multiple pressures. Species inhabiting these areas have adapted to cope with the natural heterogeneity (Bertolini et al. 2021) but local and global anthropogenic pressures, including climate change, may increase stress and in some cases lead to mortality. Studying behavioural responses can be the key to identify sub-lethal stress, as behaviour can have physiological links. Mussel gaping, for example, is a highly dynamic process, linked to key functions of metabolism, such as feeding and respiring: changes in its temporal pattern can affect energy intake and allocation, ultimately influencing growth and reproduction. This can be important in the context of sustainable cultivation of these species, where resource utilisation should be optimised by maximising the return from the input of seed, thus avoiding periods of metabolic suppression or energetically costly processes. The aim of this study was therefore to (1) test the use of a machine learning algorithm to identify key behaviours and (2) understand whether consistent patterns of behaviour could be linked to specific environmental conditions.