Generalized Additive Model (GAM) Approach as an Indicator of Decapterus spp Fishing Areas in the Waters of FMA-RI 573 - Najla, Nareswari; Fitri, Aristi; Setyawan, Hendrik (International Journal of Innovative Research in Advanced Engineering, 2025)
This study applied a Generalized Additive Model (GAM) to identify potential fishing grounds for layang Scad (Decapterus spp) in Fisheries Management Area 573 (FMA-NRI 573). Environmental variables, including chlorophyll-a concentration, sea surface temperature (SST), salinity, and sea surface height (SSH), were analyzed to assess their spatiotemporal dynamics and influence on fish distribution. Chlorophyll-a levels peaked in May and August (>2 mg/m³), concentrated predominantly in coastal zones, while other months showed relatively uniform values (~0.6 mg/m³). SST exhibited seasonal variability, ranging between 27°C and 32°C, with salinity fluctuating from 32 to 35 PSU. SSH demonstrated distinct seasonal patterns, averaging 0.10–0.90 meters, with higher values observed offshore compared to coastal regions. GAM analysis revealed significant relationships between environmental variables and fishing potential. Chlorophyll-a exhibited a linear association, contributing 10.82% to the model, while SST displayed a non-linear relationship with the highest explanatory power (32.1%). Salinity also showed a non-linear influence, accounting for 10.46% of model variability, whereas SSH contributed substantially (29.94%) through a linear relationship. These findings highlight SST and SSH as dominant predictors of layang Scad distribution, likely due to their roles in shaping thermal gradients and oceanographic conditions favourable for prey aggregation. Spatially, potential fishing grounds were identified between latitudes 6°S–11°S and longitudes 108°E–122°E. The most expansive areas occurred from January to May, coinciding with optimal oceanographic conditions and higher catch per Unit Effort (CPUE), marking this period as the peak fishing season. The study underscores the utility of GAM in integrating environmental variables to delineate fishing hotspots, offering actionable insights for sustainable fisheries management.