Understanding how organisms alter their life-history strategies in response to environmental change is critical for informing effective management. However, for pelagic marine species, limited observability of their life history often constrains our understanding of climate-driven shifts in their migration patterns. Oceanic cephalopods exhibit highly climate-mediated movement, making them ideal models for studying climate-driven migrations. In the study, we investigate the migration dynamics of jumbo squid (Dosidicus gigas), one of the world’s most economically significant cephalopods, using ensemble machine learning to analyze migration patterns and drivers from 1997 to 2024. We discover a distinct annual migratory cycle that is altered by El Niño–Southern Oscillation events. During strong El Niño periods, spawning peaks advance in the year, and migration routes shorten significantly, with surface temperature identified as the primary driver of these shifts. With the duration and occurrence of extreme El Niño events expected to rise due to greenhouse warming, our findings provide mechanistic insights into future migration changes in an economically important species and offer new perspectives for the conservation and management of marine living resources. During strong El Niño–Southern Oscillation events, shifts in surface temperature drive shortened migration routes and earlier spawning in jumbo squid, according to ensemble machine learning analysis of commercial fishing data from 1997-2024.
Jiang et al. (Wed,) studied this question.