Predicting the distribution of yellowfin tuna (Thunnus albacares) in the Indian Ocean using Bayesian probability: a species distribution modelling approach
2025
AL Naqeeb, Neran A. | Abdul Ameer, Safa Najah | Merza, Feryal Ameen | Hussein, Rasha Abed
The present study attempted to use Bayesian probability for predicting yellowfin tuna (Thunnus albacares) distribution in the FAO's 51 fishing grounds of the Indian Ocean based on mixed layer depth. Satellite remotely sensed mixed layer depth data from 2010-2017 were utilized. Bayesian probability was used to predict tuna fish distribution in the ocean, with the mixed layer depth serving as the prior probability. The northern Indian Ocean area was found to have minimal temporal change in mixed layer depth and therefore affects the T. albacares presence probability that is predicted. Variability in the predicted probability of T. albacares distribution was observed in Somali coastal waters and Madagascar western waters. The Bayesian probability method was not computationally intensive, largely because a single environmental variable was utilized in the study. Therefore, studies with limited environmental variables are recommended. Conversely, the application of numerous environmental factors has the potential to increase computational intensity and complexity of interpretation of the findings. In conclusion, the use of Bayesian probability can be another approach in modeling the distribution of marine fishes, particularly if presence data only is obtainable.
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