An integrative modeling approach investigating environmental influences on the catch of Sepia elliptica (Hoyle, 1885) along the Northeastern Arabian Sea

Akter, Sahina and Bhendekar, S N and Nakhawa, A D and Angmo, Sonam and Hoque, Muzammal and Kanubhai, Bharda Sheetal and Abidi, Zeba Jaffer and Nama, Suman and Ramteke, Karankumar (2026) An integrative modeling approach investigating environmental influences on the catch of Sepia elliptica (Hoyle, 1885) along the Northeastern Arabian Sea. Continental Shelf Research, 301. pp. 1-11. ISSN 1873-6955

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Abstract

An integrative modeling approach was employed to investigate the environmental drivers influencing catch variability of Sepia elliptica (Hoyle, 1885) in the Northeastern Arabian Sea during 2017–2019. This study combined machine learning and statistical modeling techniques, such as Random Forest (RF) and Generalized Additive Models (GAMs) to evaluate the predictive capacity of key environmental variables on species catch. Environmental predictors included sea surface temperature (SST), sea surface salinity (SSS), chlorophyll-a concentration (Chl-a), sea bottom temperature (SBT), sea surface height (SSH), mixed layer depth (MLD), and ocean currents (OC), using datasets obtained from the Copernicus Marine Environment Monitoring Service (CMEMS). Catch Per Hour (CPH) of S. elliptica was highest during the post-monsoon season, particularly in November. A pair panel plot was used to visualize the relationships between environmental variables and CPH. The RF model identified SST, chlorophyll-a, ocean currents, SSS, SSH and MLD as the primary predictors of S. elliptica catch along the Northeastern Arabian Sea. GAM analysis further revealed significant associations between catch and SST (p < 0.001), Chl-a (p < 0.001), MLD (p < 0.01), SSH (p < 0.05), SBT (p < 0.05), whereas the effect of OC was not statistically significant(p = 0.25). Based on GAM predictions, an Essential Fish Habitat (EFH) map was generated, delineating potential fishing grounds with an overall predictive accuracy of 91.1% and an r2 value of 0.85. This integrative framework advances marine resource assessment by modeling nonlinear relationships and improving spatial predictions, aiding in identifying productive fishing areas, guiding ecosystem-based management, and supporting adaptive strategies for sustainable and resilient fisheries.

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