From Data to Insight: Machine Learning Approaches for Fish Age Prediction in European Hake

Abstract

The European hake (Merluccius merluccius) is a highly sought after, overfished commercial species with a high ecological value. Otolith morphometric characteristics were employed from 150 individuals captured from the Central Aegean Sea (Eastern Mediterranean) using a commercial trawler. Age reading was independently performed by three readers. A multivariate methodology identified the morphometric factors that significantly affect age estimation, and easy to use equations using limited morphological otolith characteristics with a high degree of accuracy were produced as a practical tool for fisheries management. A second tool using ML algorithms produced a highly accurate ML model with the ability to further predict European hake’s age using limited otolith morphometric characteristics. Both tools are important for assessing fish population dynamics, managing sustainable fishing practices, and ensuring the long-term health of marine ecosystems. Practically, the models could be implemented by collecting fish otolith samples, measuring limited morphometric features using imaging techniques, and inputting these measurements into the machine learning model. Both model outputs will allow researchers and fisheries managers to obtain rapid and reliable age estimates without the need for labor-intensive traditional methods. By integrating these models into routine fisheries assessment workflows, stakeholders could make more informed decisions about fish stock assessments and conservation strategies.