Integrating satellite tagging and remote sensing data to model the habitat selection behaviour of dolphinfish (Coryphaena hippurus) in the northwestern Pacific

Abstract

The dolphinfish (Coryphaena hippurus) is a highly migratory species whose distribution is strongly influenced by oceanographic variability, yet traditional catch-per-unit-effort (CPUE)-based assessments often neglect such dynamics. This study aims to quantify the nonlinear environmental preferences of dolphinfish to improve the accuracy of fisheries forecasts and support sustainable management strategies. To quantify habitat selection mechanisms, we integrated pop-up satellite archival tag (PSAT) tracks from 10 individuals with remote sensing data and oceanographic model outputs (e.g. hybrid coordinate ocean model, HYCOM; and Argo float data) and applied generalized additive models (GAMs) to evaluate the nonlinear relationships among residence times and environmental factors in the northwestern Pacific. Three environmental variables, namely, sea surface temperature (SST), sea surface salinity (SSS), and mixed layer depth (MLD), were identified as the dominant factors driving the residence times of dolphinfish. Our analysis revealed clear ecological thresholds: dolphinfish exhibited the longest residence times at SSTs of 25-27 degrees C, departed rapidly when SSS exceeded similar to 34.25 PSU, and consistently avoided mixed layers (MLD) between 23 and 53 m. These findings indicate that water mass properties, rather than current dynamics, are the dominant drivers of short-term residency decisions. By defining species-specific ecological windows, this study highlights the value of integrating biologging with remote sensing data to capture fine-scale habitat responses. The results provide a scientific basis for dynamic fishing ground forecasting and adaptive management of dolphinfish fisheries under climate-driven ocean change.