The relationship between the annual catch of bigeye tuna and climate factors and its prediction

Introduction In order to explore the impact of climate factors on bigeye tuna catch, monthly data of nine climate factors, including El Niño-related indices (Niño1 + 2, Niño3, Niño4, and Niño3.4), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), North Pacific Index (NPI), and global sea–air temperature anomaly index (dT), were combined with the annual data of global bigeye tuna catch. Methods The relationship between low-frequency climate factors and bigeye tuna catch was studied using long short-term memory(LSTM) model, random forest (RF) model, BP neural network model, extreme gradient boosting tree (XGBoost) model, and Sparrow search optimization algorithm extreme gradient boosting tree (SSA-XGBoost) model. Results The results show that the optimal lag periods corresponding to the climate change characterization factors Niño1 + 2, dT, SOI, NPI, NAO, and PDO are 15 years,12 years, 12 years, 1 year, 14 years, and 4 years, respectively. The SSA-XGBoost model have the highest prediction accuracy, followed by XGBoost, BP, LSTM, and RF. The fitting degree between the predicted values and the actual values of the SSA-XGBoost model is 0.853, the mean absolute error is 0.104, the root mean square error is 0.124. Discussion The trend between the predicted values and the actual values of the SSA-XGBoost model is generally consistent, indicating good model fitting performance, which can provide a basis for the management of bigeye tuna fisheries.