Biotic synergy and trophic uncoupling underpin the "Cold-Winner" strategy of recurrent winter Peridinium gatunense blooms.

Authors

Pengcheng Feng, Jie Zhang, Liyin Chen, Xin Quan, Jun Jin, Xin Wang, Yanzhao Li, Qing He, Mingcan Wu

Published in

Harmful algae. Volume 158. Pages 103163. Epub Jun 14, 2026.

Abstract

The global expansion of freshwater dinoflagellate blooms poses a growing threat to water security, yet the mechanisms governing their recurrent outbreaks during the cold winter months-traditionally considered a "non-growing" season for most phytoplankton remain poorly understood. Conventional linear statistical approaches often fail to disentangle the complex, non-linear feedbacks driving these events. Here, we investigated the recurrent winter proliferation of Peridinium gatunense in Lake Erhai, a mesotrophic plateau lake, by integrating high-frequency spatiotemporal monitoring with an Explainable AI (XAI) framework (Gradient Boosting coupled with SHAP). While traditional spatial analysis suggested a misleading positive correlation with temperature, XAI correctly decoded a non-linear "Cold-Winner" strategy, identifying low water temperature (< 15°C) as the critical environmental filter and temporal trigger. Notably, biotic predictors significantly outweighed traditional abiotic factors in the model. We found that localized anthropogenic nutrient loading ("Resource Traps") facilitated the formation of a synergistic "Winter Consortium," where Peridinium spatially co-occurred with Ceratium and Microcystis in littoral hotspots. This co-domination reflects functional redundancy and shared tolerance to severe abiotic stress, rather than defaulting to competitive exclusion. Crucially, spatial network analysis revealed a mechanism of trophic uncoupling: while the bloom supported small rotifers, it was spatially segregated from large grazers (Copepoda), effectively neutralizing top-down control. We conclude that these winter blooms are orchestrated by the interplay of thermal triggers, localized nutrient enrichment, and biotic synergies. Our findings underscore the utility of Explainable AI in resolving complex ecological dynamics and suggest that monitoring co-occurring taxa like Ceratium is essential for early bloom warning systems.

PMID:
42595411
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.

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