Revisiting Cyanobacteria-Temperature Dynamics: Intraspecific Competition and Trait Diversity as Keys to Predicting Harmful Algal Blooms under Climate Change

Cyanobacterial harmful algal blooms are expanding spatiotemporally, with an increasing occurrence of cold-water cyanobacterial blooms (CWCBs), intensifying ecological and water quality challenges. While abiotic drivers have been identified as contributors to CWCBs, the role of biotic factors─particularly the adaptation induced by the shifts in intraspecific trait distributions─in this process remains largely unexplored. Here, we tested the hypothesis that the thermal history of cyanobacteria affects their thermal adaptations by reshaping the distribution of optimum growth temperature (Topt). Using a trait-based phytoplankton model coupled with a one-dimensional lake model, we simulated cyanobacteria dynamics over 364 days in a large, eutrophic, shallow lake recently experiencing CWCBs. The model demonstrated that Topt diversification promotes cold-adapted strains, leading to CWCBs while mitigating summer blooms. This occurs because the thermal response of Topt-diverse populations depends on their Topt distribution, which is determined by past temperature sequence, allowing Topt-diverse populations to retain a 'memory' of temperatures preceding summer. Consequently, increased summer temperatures inhibit these cold-adapted populations, challenging the prevailing cyanobacteria-temperature paradigm, which suggests that high temperatures universally favor cyanobacteria. These findings reveal that models assuming fixed traits may misrepresent cyanobacterial dynamics under climate change, highlighting the necessity of incorporating trait diversity into predictive frameworks for improved forecasting and to support adaptive lake management strategies.