Abu Bakar Sambah, Sunardi, Fuad, Mihrobi Khalwatu Rihmi, Vian Dedi Pratama, Sukree Hajisamae, Supat Khongpuang
Dynamic oceanographic processes shape tuna distribution, yet spatially explicit habitat-suitability products derived from historical catch and satellite data remain limited for operational planning. This study models tuna habitat suitability in response to monsoon-driven oceanographic variability by integrating remote-sensing indicators with a habitat-suitability framework and an index-based mapping output. Satellite-derived sea surface temperature (SST) and chlorophyll-a (Chl-a) were combined with catch per trip (used as a CPUE proxy) to fit Generalized Additive Models (GAMs). The GAM-fitted CPUE values (log(CPUE+1)) were then used to derive a spatial pelagic habitat index (PHI). This process involved rescaling fitted values to a 0–1 habitat suitability score, assigning monthly scores to fishing locations, spatially interpolating the suitability field, and classifying PHI into low, medium, and high suitability zones. Tuna occurrence and catch intensity concentrated within distinct environmental windows (SST: 26–30.5℃; Chl-a: 0.1–0.9 mg m-3), indicating coupled effects of thermal habitat and primary productivity. The GAM showed low explanatory power (≈2–4% deviance explained), but the PHI maps still highlighted recurrent high-suitability zones that were consistent across monsoon phases. Rather than claiming real-time forecasting, this indicator-based framework prioritizes interpretability and spatial specificity, providing a replicable workflow for generating habitat suitability maps that can inform fishing ground selection and spatial planning within ecosystem-based fisheries management under monsoon variability. ©2026 The authors.
Department of Fisheries and Marine Resources Utilization, Faculty of Fisheries and Marine Science, Universitas Brawijaya, Malang, 65145, Indonesia; Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Pattani, 94000, Thailand
