Abstract

The warming trend of the Arctic is punctuated by several record‐breaking warm years with very low sea ice concentrations. The nature and reversibility of marine ecosystem responses to these multiple extreme climatic events (ECEs) are poorly understood. Here, we investigate the ecological signatures of three successive bottom temperature maxima concomitant with surface ECEs between 2004 and 2017 in the Barents Sea across spatial and organizational scales. We observed community‐level redistributions of fish concurrent with ECEs at the scale of the whole Barents Sea. Three groups, characterized by different sets of traits describing their capacity to cope with short‐term perturbations, reacted with different timing and intensity to each ECE. Arctic species co‐occurred more frequently with large predators and incoming boreal taxa during ECEs, potentially affecting food web structures and functional diversity, accelerating the impacts of long‐term climate change. On the species level, responses were highly diversified, with different ECEs impacting different species, and species responses (expansion, geographical shift) varying from one ECE to another, despite the environmental perturbations being similar. Past ECEs impacts, with potential legacy effects, lagged responses, thresholds, and interactions with the underlying warming pressure, could constantly set up new initial conditions that drive the unique ecological signature of each ECE. These results highlight the complexity of ecological reactions to multiple ECEs and give prominence to several sources of process uncertainty in the predictions of climate change impact and risk for ecosystem management. Long‐term monitoring and studies to characterize the vertical extent of each ECE are necessary to statistically link demersal species and environmental spatial–temporal patterns. In the future, regular monitoring will be crucial to detect early signals of change and understand the determinism of ECEs, but we need to adapt our models and management to better integrate risk and stochasticity from the complex impacts of global change.

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