δ18O
δ13C
树木年代学
马尾松
环境科学
大气科学
湿度
旱季
树木气候学
生长季节
气候学
相对湿度
稳定同位素比值
农学
生态学
生物
植物
气象学
地质学
地理
古生物学
物理
量子力学
作者
Guobao Xu,Xiaohong Liu,Jia Hu,Isabel Dorado‐Liñán,Mary Gagen,Paul Szejner,Tuo Chen,Valérie Trouet
标识
DOI:10.1093/treephys/tpac076
摘要
Abstract Tree-ring intra-annual stable isotopes (δ13C and δ18O) are powerful tools for revealing plant ecophysiological responses to climatic extremes. We analyzed interannual and fine-scale intra-annual variability of tree-ring δ13C and δ18O in Chinese red pine (Pinus massoniana) from southeastern China to explore environmental drivers and potential trade-offs between the main physiological controls. We show that wet season relative humidity (May–October RH) drove interannual variability of δ18O and intra-annual variability of tree-ring δ18O. It also drove intra-annual variability of tree-ring δ13C, whereas interannual variability was mainly controlled by February–May temperature and September–October RH. Furthermore, intra-annual tree-ring δ18O variability was larger during wet years compared with dry years, whereas δ13C variability was lower during wet years compared with dry years. As a result of these differences in intra-annual variability amplitude, process-based models (we used the Roden model for δ18O and the Farquhar model for δ13C) captured the intra-annual δ18O pattern better in wet years compared with dry years, whereas intra-annual δ13C pattern was better simulated in dry years compared with wet years. This result suggests a potential asymmetric bias in process-based models in capturing the interplay of the different mechanistic processes (i.e., isotopic source and leaf-level enrichment) operating in dry versus wet years. We therefore propose an intra-annual conceptual model considering a dynamic trade-off between the isotopic source and leaf-level enrichment in different tree-ring parts to understand how climate and ecophysiological processes drive intra-annual tree-ring stable isotopic variability under humid climate conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI