Relationship between Resource Distribution and Vertical Structure of Water Temperature of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean Based on GAM and GBT Models

The Northwest Indian Ocean is a key fishing ground for China’s pelagic fisheries, with the purpleback flying squid being significant target. This study uses commercial logs of between 2015 and 2021, alongside seawater temperature its vertical difference within 0–200 m depth range, to construct generalized additive models (GAMs) gradient boosting tree (GBTs). These two are evaluated using cross-validation assess their ability predict distribution squid. findings show that factors like year, latitude, longitude, month significantly influence squid, while surface water temperature, 200 150–200 layer also play role in GBT model. Similar take effects GAM. Comparing models, both GAM align reality predicting resource distribution, but precision indices model outperform those predicted 2021 by has higher overlap actual than GAM, indicating GBT’s superior forecasting Ocean.