This study investigated the spatiotemporal dynamics of four major tuna species in the Indian Ocean, utilizing machine learning models to analyze historical trends (1980–2022) and project future habitat shifts (2023–2100) under varied climate scenarios. Historical results revealed distinct annual fluctuations and spatial partitioning, with Yellowfin tuna (YFT) exhibiting a decreasing pattern, Bigeye tuna (BET) peaking in the 1990s, and Albacore (ALB) showing pronounced decadal variability. Skipjack tuna (SKJ) demonstrated fishery-specific divergence, with longline CPUE characterized by interannual oscillations while purse seine CPUE maintained an increasing trend. Spatial analysis identified YFT and BET primarily in northern and equatorial regions, whereas high-CPUE areas for ALB and SKJ longline fisheries were concentrated in the southern Indian Ocean around 20°S. Future environmental projections from the NOAA GFDL-ESM4 model indicate significant transformations, including basin-wide warming exceeding 24°C and a substantial loss of net primary productivity (NPP) alongside the shoaling of the mixed layer depth (MLD) in equatorial regions, particularly under the high-emission SSP5-8.5 scenario. These environmental shifts are projected to drive divergent catchability trajectories and a clear poleward redistribution of tuna stocks. While BET and SKJ are expected to maintain positive CPUE growth toward the end of the century, ALB is projected to undergo a consistent and significant decline. Notably, ALB exhibits the most distinct southward range expansion beyond 20°S, while YFT shows a significant decrease in its northern and equatorial distribution.
 

暂无