Metabolite‐Mediated Trophic Interactions: Long‐Term Responses of Thalassiosira weissflogii to High CO2 and Warming Have Cascading Effects on Consumer Metabolism

代谢组学 营养水平 海链藻 生态系统 食物网 代谢物 全球变暖 初级生产者 生物量(生态学) 生态学 气候变化 生物 浮游植物 营养物 生物化学 生物信息学
作者
Fenghuang Wu,Hao Zhang,John Beardall,C‐Elisa Schaum,Xuejia He,Xiao Liang,Mengcheng Ye,Jiamin Lin,Jingyao Li,Baoyi Peng,Leyao Xu,Jia Yuan,Bin Huang,Fangzhou Liu,Peixuan Liu,Jianrong Xia,Peng Jin
出处
期刊:Global Change Biology [Wiley]
卷期号:31 (7): e70343-e70343
标识
DOI:10.1111/gcb.70343
摘要

ABSTRACT Rising CO 2 and warming are key components of climate change that are reshaping ecosystems by altering trophic interactions. Although a large body of literature describes changes in biomass in response to these drivers, the biochemical mechanisms driving these shifts remain unclear. This study investigated the long‐term response of the marine diatom Thalassiosira weissflogii to high CO 2 , warming, and their combinations (3.5 years, ~2000 generations) and examined how these changes affect its metabolomic profile and influence a primary consumer (a clam), using metabolomics and biochemical analysis. Our results reveal significant metabolomic shifts in diatoms after 2000 generations, including increased glutaric acid and gluconolactone, reflecting enhanced carbon metabolism and biosynthesis. Warming induces higher pyruvic acid levels and decreased propionic acid, indicating metabolic reprogramming to maintain energy production and redox balance. These biochemical alterations cascade to consumers, creating alterations in metabolite profiles and affecting their energy metabolism, with implications for similar effects on other consumers. These findings highlight the critical role of metabolite‐mediated interactions in shaping food web dynamics under global change. By integrating molecular and ecological perspectives, our study advances the understanding of how environmental drivers affect ecosystem processes such as nutrient cycling, energy flow, and trophic efficiency. We emphasized the need to incorporate metabolomic data into ecological models to predict ecosystem responses to global change.
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