Twenty years of AFORO: New developments and connections enhancing otolith research

15 pages, 9 figures, 2 tables.-- Data availability: Data will be made available on request

Since 2003, with the aim of addressing different issues related to otoliths and the advancement of technologies, AFORO acronym of Anàlisi de FORmes d’Otòlits (otolith shape analysis), a computational environment with a set of tools including a website for this purpose, has gradually expanded its functionality. This implies different ways to measure otoliths in order to obtain different types of information of the same individual. The 2D otoliths shape description using different methods permits the comparison between classical methods and the Wavelet transform, used in the automatic classification system of AFORO. The 3D otoliths description in a few images opens the possibility of classifications using volumetric information, which they have performed well. The data of relationships between otolith and fish length in AFORO is used in predator-prey studies. Our website offers the possibility to display otolith georeferencing information interactively. Furthermore, the automatic classification system has been improved by incorporating geographic filters based on georeferenced data. The diverse information that AFORO offers on the otoliths opens the possibility to approach new studies or to improve the existing ones, combining this information or applying the most appropriate one to the specific problem. Classification cases with Wavelets that have been improved by introducing geographical area information will be presented. In addition, Deep Learning algorithms will be introduced by performing classifications with a small subset of otoliths of species which contain a sufficient number of specimens (>8)

JV was supported by the ‘Grants for the Viera y Clavijo training program for researchers’ of the Government of the Canary Islands (VIERA Y CLAVIJO-2022-CIENCIAS-1) and by the Portuguese national funds through Fundação para a Ciência e a Tecnologia I.P. (FCT) in the framework of MARE grant number UIDB/04292/2020 and Associate Laboratory ARNET grant number LA/P/0069/2020, the National Data Collection Framework, and the University of Las Palmas of Gran Canaria (ULPGC). This work acknowledges the accreditation of the ‘Severo Ochoa Centre of Excellence’ (CEX2019–000928-S)

Peer reviewed