The influence of forest-to-cropland conversion on temperature sensitivity of soil microbial respiration across tropical to temperate zones

问题10 温带气候 土壤呼吸 亚热带 环境科学 热带 碳循环 土壤碳 气候变化 呼吸 生物量(生态学) 纬度 全球变化 温带雨林 生态学 大气科学 土壤水分 土壤科学 生物 生态系统 地理 地质学 植物 大地测量学
作者
Jun Pan,Yuan Liu,Nianpeng He,Chao Li,Mingxu Li,Li Xu,Osbert Jianxin Sun
出处
期刊:Soil Biology & Biochemistry [Elsevier]
卷期号:191: 109322-109322 被引量:5
标识
DOI:10.1016/j.soilbio.2024.109322
摘要

As one of the most important drivers of global climate change, land use change (LUC) has markedly altered the regional and global carbon (C) cycles. However, the geographic variations and the key drivers in the effects of LUC on temperature sensitivity (Q10) of soil microbial respiration (Rs) are still not fully elucidated, hence impeding the spatially explicit predictions of soil C cycling under climate change. Here, we used a paired-plot approach with data for 19 locations distributed from the tropical to temperate zones in eastern China, and compared the temperature responses of Rs between forest and cropland soil. Results showed that the latitudinal patterns of Q10 in forest soils were better explained by climatic variables; whereas in cropland, soil Q10 trended higher with increasing latitude, with climatic factors, pH, clay, and soil organic C (SOC) jointly modulating the spatial variations in Q10. Overall, the values of Q10 tended to converge with latitude between forests and croplands, with change in Q10 from forest to cropland, ΔQ10, significantly decreasing from the tropical region (9.23 ± 3.58 %) to the subtropical (0.58 ± 1.93 %) and temperate (−0.97 ± 1.11 %) regions. Moreover, the spatial variations of ΔQ10 were significantly affected by climatic factors, ΔpH, Δmicrobial biomass C (ΔMBC), and their interactions. Our findings highlight the potential impacts of LUC-related biogeographic variations in the temperature response of Rs, and emphasize the importance of incorporating the land-use effects on the temperature sensitivity of soil microbial respiration into terrestrial C cycle models to improve predictions of carbon-climate feedbacks in the future.

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