Potential directional asymmetry of the otolith shape tested on the red mullet (Mullus barbatus) in the Mediterranean Sea: comparative analysis of 2D and 3D otolith shape data
|Author(s)||Andrialovanirina Nicolas1, 2, Poisson Caillault Emilie1, Couette Sébastien3, Laffont Rémi3, Poloni Lauriane3, Lutet-Toti Camille3, Mahe Kelig2|
|Affiliation(s)||1 : Université du Littoral Côte d’Opale (ULCO), 62228 Calais, France
2 : Ifremer, Laboratoire Ressources Halieutiques, 62321 Boulogne-sur-Mer, France.
3 : Ecole Pratique des Hautes Etudes, PSL, Paris & UMR Biogéosciences, université de Bourgogne, 21000 Dijon, France.
|Meeting||vISC 2022 - Virtual International Sclerochronology Conference. 13-15 September 2022, Online|
A wide number of techniques were developed and applied to identify and discriminate stocks. Among them, otolith’s shape, which is affected by environment and genetic factors, can be used as a tool to identify the populations within the species. Before to identify the boundaries of stocks, the potential drivers, which control the otolith shape, must be analysed. In this study, Directional Asymmetry (DA; the effect of otolith’s location side, i.e., left versus right inner ear) was tested combining the approaches to otolith shape in 2D and 3D on 560 adults of the red mullet Mullus barbatus (Linnaeus, 1758) which is one of the most abundant demersal fish species in the Mediterranean Sea. Studied individual were sampled from 7 subunits for 2D analysis (476 individuals) and 3 subunits for 3D analysis (84 individuals) of geographical subareas (GSAs). To analyse otolith shape, the normalized Elliptical Fourier Descriptors (EFDs) computed from the two-dimensional outlines (extracted from otolithes 2D pictures) and Spherical Harmonic shape descriptors computed from three-dimensional surfaces/meshes (extracted from otolithes 3D scans) were analysed with principal component analysis (PCA) method. PCA’s scores were used in the multivariate mixed-effects model with side and subunits effects. From 3D surfaces/meshes, the univariate variables (i.e. otolithe’s surface and volume) were analysed with Redundancy analysis (RDA) too. The EFDs from 2D images showed that the side effect was significant on the otolith shape (p-value<0.00001). The reconstructed outlines of the mean Fourier harmonics of the left and right side were plotted and the percentage of non-overlapping surface was 1.010%. However, the interaction between side and geographical subareawas nosignificant from 2D images. The EFDs from 3D images showed that the side effect was signifant (p-value<0.00001). In additionnal, the interaction between side effect and geographical subarea was signifiant (p-value< 0.00001) from 3D images. The relationship between the fish length and the surface of 3D otolith shape was significant (p-value =0.001), this trend was not observed by the otolith volume (p-value=0.698). For these two univariate descriptors of 3D otolith shape, there were no significant difference between left and right otoliths. This comparative analysis of otolith shape in 2D and in 3D showed showed that the 3D approach, presents the data with more accuracy than those extracted from the regurlarly used 2D approach. This difference is very important because the directionnal asymmetry was significant on the relationship between the otolith 3D shape and the geographical area of sampling while this trend is not observable from the otolith 2D shape. This first study showing the difference between 2D and 3D approaches should be confirmed in the future.