| Project Detail |
Testing the hearing of elasmobranch fish
Sharks, skates and sawfish are all cartilaginous fish among the more than 1 000 species in the elasmobranch subclass. And like most fish, they possess highly developed sense organs. The EU-funded FISHEARS project will focus on their hearing: through novel bioimaging and computational tools, the project will investigate the elasmobranch fish ears. High-resolution 3D models of the inner ears will be developed using diffusible iodine-based contrast-enhanced computed tomography (diceCT). To understand the biomechanics of the fish ear’s structure, the project will create a digital replica. FISHEARS will also develop a statistical framework to incorporate factors that may shape the hearing system. The project will apply a machine learning algorithm to infer patterns and relationships between factors.
One of the predominant riddles of sensory biology is the diversity in fish auditory systems. It is widely
accepted that fishes are well adapted to utilising underwater sounds as sensory cues in key life-history
events. However, the functional significance and the driving force leading to the differences in fish
inner ear sizes and structures are unknown. A complex interplay of physical, evolutionary, functional
and ecological factors may shape the different elements: a multiscale environment too complicated
for human conceptualisation. I propose to address this question by applying novel bioimaging and
computational tools to investigate elasmobranch fish ears. Firstly, diffusible iodine-based contrast enhanced
computed tomography (diceCT) will be used, co-registered with MRI data, to build 3D high
resolution models of the inner ears. Secondly, a Finite Element (FE) model will be created to digitally
replicate a fish ear and understand the biomechanics of its structure. Finally, a statistical framework
will be developed to incorporate the factors that may shape the hearing system of elasmobranch
fishes, including the collected data, together with the available physiological, ecological and
biogeographical information on each species, as well as species’ acoustic environmental parameters. A
Machine Learning algorithm will be applied to infer patterns and relationships between the factors, to
perform both cluster and prediction analyses. Thus, a reliable model will be developed, which can
predict the hearing capability of any elasmobranch species based on the ear morphology and the first
evidence of the function of fish ear diversity. |
