Temperature legacies predict microbial metabolic quotient across forest biomes. Issue 1 (15th November 2022) - Liu, Shengen; Sun, Zhaolin; Tian, Peng; Zhao, Xuechao; Zhou, Guiyao; Dietrich, Peter; Wang, Qingkui; Delgado‐Baquerizo, Manuel
Abstract: Aim: Palaeoclimate legacies have been reported to influence microbial communities and carbon (C) stocks even after thousands of years. However, the direct and indirect influences of climate legacies on microbial C processes remain poorly understood and thus limit our capacity to predict how climate legacies regulate C cycling. Here, we conducted microbial, soil and vegetation surveys along a continental latitudinal transect of 4200 km covering a wide range of forest biomes. With these data, we evaluated the potential capacity of climate legacies to predict direct and indirect variations in microbial metabolic quotient (MMQ) across and within three main forest biomes: tropical, subtropical and temperate forests. Location: North–south transect (4200 km), China. Time period: 2019. Major taxa studied: Soil microbes. Methods: We used molecular ecology technology to determine microbial biomass and diversity, in addition to a soil incubation experiment to measure MMQ. Results: Palaeoclimate explained a unique portion of the variation in the continental distribution of MMQ, which showed a hump‐shaped pattern with latitude. Locations with increased isothermality (an index of temperature) over the last 20, 000 years also showed the highest MMQ in the present day. Moreover, we found multiple indirect effects of climate legacies on MMQ caused either by changes in key soil properties, such as soil organic carbon and ammonium (NH4 + ), in lower latitudinal regions or by plant traits in higher latitudinal regions. Furthermore, MMQ was positively related to bacterial richness but negatively to fungal richness across forest biomes. Main conclusions: Climate legacies associated with continuous changes in temperature over the last 20, 000 years influenced MMQ across forest biomes. Our findings demonstrate that including climate legacies in climate carbon models is essential for better prediction of the microbe‐driven ecosystem processes under global environmental change.