Ngaba Mbezele Junior Yannick, Uwiragiye Yves, Hu Bin, Zhou Jianbin, Dannenmann Michael, Calanca Pierluigi, Bol Roland, de Vries Wim, Kuzyakov Yakov, Rennenberg Heinz
Center of Molecular Ecophysiology (CMEP), College of Resources and Environment, Southwest University, Chongqing 400715, China; Higher Technical Teacher' Training College of Ebolowa, University of Ebolowa (HTTTC), 886 Ebolowa, Cameroon.
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China; University of Technology and Arts of Byumba, Rwanda.
Sci Total Environ. 2024 Nov 20;952:175943. doi: 10.1016/j.scitotenv.2024.175943. Epub 2024 Aug 31.
Soil respiration (R) is projected to be substantially affected by climate change, impacting the storage, equilibrium, and movement of terrestrial carbon (C). However, uncertainties surrounding the responses of R to climate change and soil nitrogen (N) enrichment are linked to mechanisms specific to diverse climate zones. A comprehensive meta-analysis was conducted to address this, evaluating the global effects of warming, increased precipitation, and N enrichment on R across various climate zones and ecosystems. Data from 123 studies, encompassing a total of 10,377 worldwide observations, were synthesized for this purpose. Annual R were modeled and their uncertainties were associated with a 1-km resolution global R database spanning from 1961 to 2022. Calibrating R using ensemble machine learning (EML) and employing 10-fold cross-validation, 13 environmental covariates were utilized. The meta-analysis findings revealed an upsurge in R rates in response to warming, with tropical, arid, and temperate climate zones exhibiting increases of 12 %, 13 %, and 16 %, respectively. Furthermore, increased precipitation led to stimulated R rates of 11 % and 9 % in tropical and temperate zones, respectively, while N deposition affected R in cold (+6 %) and tropical (+5 %) climate zones. The machine learning technique estimated the global soil respiration to range from 91 to 171 Pg C yr, with an average R of 700 ± 300 g C m yr. The values ranged between 314 and 2500 g C m yr, with the lowest and highest values observed in cold and tropical zones, respectively. Spatial variation in R was most pronounced in low-latitude areas, particularly in tropical rainforests and monsoon zones. Temperature, precipitation, and N deposition were identified as crucial environmental factors exerting significant influences on R rates worldwide. These factors underscore the interconnectedness between climate and ecosystem processes, therefore requiring explicit considerations of different climate zones when assessing responses of R to global change.
预计土壤呼吸(R)将受到气候变化的显著影响,进而影响陆地碳(C)的储存、平衡和迁移。然而,R对气候变化和土壤氮(N)富集的响应存在不确定性,这与不同气候区的特定机制有关。为此进行了一项全面的荟萃分析,评估了变暖、降水增加和N富集对不同气候区和生态系统中R的全球影响。为此综合了来自123项研究的数据,这些研究总共包含全球10377个观测数据。对年R进行建模,并将其不确定性与一个分辨率为1公里、涵盖1961年至2022年的全球R数据库相关联。使用集成机器学习(EML)校准R并采用10折交叉验证,利用了13个环境协变量。荟萃分析结果显示,变暖导致R速率上升,热带、干旱和温带气候区的增幅分别为12%、13%和16%。此外,降水增加分别导致热带和温带地区的R速率提高11%和9%,而N沉降对寒冷(+6%)和热带(+5%)气候区的R有影响。机器学习技术估计全球土壤呼吸范围为91至171 Pg C yr,平均R为700±300 g C m yr。数值范围在314至2500 g C m yr之间,最低值和最高值分别出现在寒冷和热带地区。R的空间变化在低纬度地区最为明显,特别是在热带雨林和季风区。温度、降水和N沉降被确定为对全球R速率有重大影响的关键环境因素。这些因素强调了气候与生态系统过程之间的相互联系,因此在评估R对全球变化的响应时需要明确考虑不同的气候区。