Aryal Sugam, Grießinger Jussi, Dyola Nita, Gaire Narayan Prasad, Bhattarai Tribikram, Bräuning Achim
Institut für Geographie Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Bayern Germany.
Institute of Tibetan Plateau Research Chinese Academy of Sciences, State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE) Beijing China.
Ecol Evol. 2023 Oct 20;13(10):e10626. doi: 10.1002/ece3.10626. eCollection 2023 Oct.
The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra-annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra-annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra-annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of Sarg. from the subtropical mid-elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra-annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.
气候变化对全球陆地生态系统的影响日益加剧，这就需要对树木的生长模式和生理适应性进行可靠预测，以实现可持续林业和成功的保护工作。以年内分辨率了解这些动态变化，可以更深入地洞察各种未来气候情景下树木的反应。然而，现有的推断形成层或叶片物候变化的方法主要集中在某些气候带（如高纬度地区）或在秋季叶片变色的物种上。在本研究中，我们展示了一种新方法（INTRAGRO），将树木测量仪生成的年内周长记录与气候模型的输出相结合，使用一系列有监督和无监督的机器学习算法，以年内分辨率预测未来树木的生长。使用我们的数据集（即来自尼泊尔亚热带中海拔带的黄杉树木测量仪数据），INTRAGRO表现良好，测试统计数据稳健。我们的生长预测显示，在21世纪中叶和末期，我们研究地点的树木生长增强。这一结果很显著，因为由于季节性降水的变化，INTRAGRO预测的生长季节预计会缩短。INTRAGRO的关键优势在于有机会在全球范围内分析树木年内生长动态的变化，而不论所研究的树种、区域气候和地理条件如何。此类信息对于评估不同气候情景下树种的生长表现和对生长季节变化的生理适应性非常重要。
