Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Key Laboratory of Alpine Ecology, Center for Excellence in Tibetan Earth Science, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.
Glob Chang Biol. 2020 Jul;26(7):4104-4118. doi: 10.1111/gcb.15132. Epub 2020 May 16.
Autumnal leaf senescence signals the end of photosynthetic activities in temperate deciduous trees and consequently exerts a strong control on various ecological processes. Predicting leaf senescence dates (LSD) with high accuracy is thus a prerequisite for better understanding the climate-ecosystem interactions. However, modeling LSD at large spatial and temporal scales is challenging. In this study, first, we used 19972 site-year records (848 sites and four deciduous tree species) from the PAN European Phenology network to calibrate and evaluate six leaf senescence models during the period 1980-2013. Second, we extended the spatial analysis by repeating the procedure across Europe using satellite-derived end of growing season and a forest map. Overall, we found that models that considered photoperiod and temperature interactions outperformed models using simple temperature or photoperiod thresholds for Betula pendula, Fagus sylvatica and Quercus robur. On the contrary, no model displayed reasonable predictions for Aesculus hippocastanum. This inter-model comparison indicates that, contrary to expectation, photoperiod does not significantly modulate the accumulation of cooling degree days (CDD). On the other hand, considering the carryover effect of leaf unfolding date could promote the models' predictability. The CDD models generally matched the observed LSD at species level and its interannual variation, but were limited in explaining the inter-site variations, indicating that other environmental cues need to be considered in future model development. The discrepancies remaining between model simulations and observations highlight the need of manipulation studies to elucidate the mechanisms behind the leaf senescence process and to make current models more realistic.
秋季叶片衰老标志着温带落叶树木光合作用的结束，因此对各种生态过程产生强烈的控制。因此，准确预测叶片衰老日期 (LSD) 是更好地理解气候-生态系统相互作用的前提。然而，在大的时空尺度上模拟 LSD 具有挑战性。在这项研究中，首先，我们使用了来自泛欧物候网络的 19972 个站点-年份记录（848 个站点和四种落叶树种），在 1980-2013 年期间对六个叶片衰老模型进行了校准和评估。其次，我们通过使用卫星衍生的生长季结束和森林图在整个欧洲重复该过程来扩展空间分析。总的来说，我们发现考虑光周期和温度相互作用的模型比使用简单的温度或光周期阈值的模型更适合预测 Betula pendula、Fagus sylvatica 和 Quercus robur 的叶片衰老。相反，没有模型可以对 Aesculus hippocastanum 进行合理的预测。这种模型间比较表明，与预期相反，光周期不会显著调节冷却度日 (CDD) 的积累。另一方面，考虑到叶片展开日期的滞后效应可以提高模型的可预测性。CDD 模型通常可以匹配观察到的 LSD 在物种水平及其年际变化，但在解释站点间变化方面存在局限性，这表明在未来的模型开发中需要考虑其他环境线索。模型模拟与观测结果之间的差异表明，需要进行操作研究来阐明叶片衰老过程背后的机制，并使当前模型更加现实。
