Integrating Remote Sensing and Satellite Tracking to Unveil Whale Shark Spatial Ecology in the Dynamic Indo-Pacific Oceans Mochamad Iqbal Herwata Putra (a*),(b), Anindya Wiasatriya (c),(d), Abraham Sianipar (e), Ismail Syakurachman (b), Abdi Hasan (b), Edy Setyawan (e), Mark V. Erdmann (f), Jatna Supriatna (g), Masita Dwi Mandini Manessa(a)
(a) Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
(b) Focal Species Conservation Program, Ocean and Science Department, Konservasi Indonesia, Jakarta, Jakarta, Indonesia
(c) Department of Oceanography, Faculty of Fisheries and Marine Science, Universitas Diponegoro, Semarang, Indonesia
(d) Center for coastal Rehabilitation and Disaster Mitigation Studies, Universitas Diponegoro, Semarang, Indonesia
(e) Elasmobranch Institute Indonesia, Denpasar, Bali, Indonesia
(f) Re:wild, Austin, Texas, USA
(g) Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia
Abstract
Understanding the spatial ecology of the endangered whale shark (Rhincodon typus) is essential for informing conservation strategies, particularly in the Indo-Pacific, where populations have declined by nearly 60%. This study investigates how remote sensing data on sea level anomalies (SLA) and eddy kinetic energy (EKE) can reveal the environmental drivers of whale shark movements. From 2015 to 2025, 70 individuals (65 males and 5 females, 91% of which were large juveniles) were equipped with SPLASH tags across four aggregation sites in Indonesia: Saleh Bay (n = 24), Cenderawasih Bay (n = 34), Kaimana (n = 8), and Tomini Bay (n = 3). Whale shark tracks were reconstructed using a continuous-time correlated random walk state-space model (crw-SSM), while behavioral states were classified with a move persistence model (MPM) based on gamma (g) thresholds distinguishing foraging (g < 0.5538) from migratory (g >0.5538) behaviors. The results indicate that SLA gradients and EKE hotspots strongly influence movement pathways, with sharks frequently associating with eddy peripheries where planktonic prey is concentrated. Juvenile males showed strong responses to seasonal EKE variations, whereas juvenile females were more closely linked to SLA anomalies and deep-sea geomorphological features such as canyons. Adult males, though rarely detected, tended to follow seamount and escarpment corridors, likely utilizing localized upwelling zones as foraging stepping stones. These findings highlight the critical role of dynamic oceanographic variables particularly SLA and mesoscale eddies in shaping whale shark spatial ecology and emphasize the need to integrate these factors into habitat modeling and spatial management to safeguard key habitats and migratory routes in Indonesian waters, which remain underrepresented in current marine protected area networks.
Keywords: whale shark- altimetry- satellite telemetry- movement ecology- Indo-Pacific Oceans
