College of Water Sciences, Beijing Normal University, Beijing, China.
Plants and Ecosystems, Department of Biology, University of Antwerp, Antwerp, Belgium.
Glob Chang Biol. 2022 Aug;28(16):4935-4946. doi: 10.1111/gcb.16227. Epub 2022 Jun 1.
Autumn phenology plays a key role in regulating the terrestrial carbon and water balance and their feedbacks to the climate. However, the mechanisms underlying autumn phenology are still poorly understood, especially in subtropical forests. In this study, we extracted the autumn photosynthetic transition dates (APTD) in subtropical China over the period 2003-2017 based on a global, fine-resolution solar-induced chlorophyll fluorescence (SIF) dataset (GOSIF) using four fitting methods, and then explored the temporal-spatial variations of APTD and its underlying mechanisms using partial correlation analysis and machine learning methods. We further predicted the APTD shifts under future climate warming conditions by applying process-based and machine learning-based models. We found that the APTD was significantly delayed, with an average rate of 7.7 days per decade, in subtropical China during 2003-2017. Both partial correlation analysis and machine learning methods revealed that soil moisture was the primary driver responsible for the APTD changes in southern subtropical monsoon evergreen forest (SEF) and middle subtropical evergreen forest (MEF), whereas solar radiation controlled the APTD variations in the northern evergreen-broadleaf deciduous mixed forest (NMF). Combining the effects of temperature, soil moisture and radiation, we found a significantly delayed trend in APTD during the 2030-2100 period, but the trend amplitude (0.8 days per decade) was much weaker than that over 2003-2017. In addition, we found that machine learning methods outperformed process-based models in projecting APTD. Our findings generate from different methods highlight that soil moisture is one of the key players in determining autumn photosynthetic phenological processes in subtropical forests. To comprehensively understand autumn phenological processes, in-situ manipulative experiments are urgently needed to quantify the contributions of different environmental and physiological factors in regulating plants' response to ongoing climate change.
秋季物候在调节陆地碳和水分平衡及其对气候的反馈方面起着关键作用。然而，秋季物候的机制仍知之甚少，特别是在亚热带森林中。在本研究中，我们基于一个全球、高分辨率的太阳诱导叶绿素荧光（SIF）数据集（GOSIF），利用四种拟合方法，提取了 2003-2017 年中国亚热带地区的秋季光合转换日期（APTD），然后利用偏相关分析和机器学习方法探讨了 APTD 的时空变化及其潜在机制。我们进一步通过应用基于过程和基于机器学习的模型，预测了未来气候变暖条件下 APTD 的变化。我们发现，2003-2017 年间，中国亚热带地区的 APTD 明显延迟，平均每十年延迟 7.7 天。偏相关分析和机器学习方法均表明，土壤湿度是导致南部亚热带季风常绿林（SEF）和中部亚热带常绿林（MEF）APTD 变化的主要驱动因素，而太阳辐射则控制了北部常绿-阔叶落叶混交林（NMF）APTD 的变化。综合考虑温度、土壤湿度和辐射的影响，我们发现 2030-2100 年期间 APTD 呈显著延迟趋势，但趋势幅度（每十年 0.8 天）明显小于 2003-2017 年。此外，我们发现机器学习方法在预测 APTD 方面优于基于过程的模型。我们从不同方法中得出的发现强调了土壤湿度是决定亚热带森林秋季光合物候过程的关键因素之一。为了全面了解秋季物候过程，迫切需要进行现场控制实验，以量化不同环境和生理因素在调节植物对持续气候变化的响应方面的贡献。
