Assessing Amazon forest vulnerability and resilience to dry periods across soil moisture and microenvironmental gradients

亚马逊雨林中与气候变化相关的干旱可能导致大范围的树木死亡，导致大量的碳排放，并可能对区域和全球气候产生反馈（菲利普斯等人，2009年; Brienen等人，2015年）。然而，我们对热带森林干旱反应的大部分知识来自深地下水位深度（WT）森林。浅水WT森林的研究严重不足，尽管占据了亚马逊盆地的50%，预计对干旱的反应不同（Costa，Schietti，Stark，Smith 2022）。最近的结果表明，浅水WT地区受益于适度干燥，这减少了缺氧并延长了生长季节（Esteban等人，2021年，Sousa等人，2022年）。因此，浅WT森林可能对干旱更具适应力，可能能够抵消预计从深WT森林中产生的大量碳损失。为了准确预测这一全球重要生态系统的未来并设定保护优先事项，我们需要了解浅水WT森林对亚马逊森林碳平衡的贡献，以及土壤水分可用性如何影响热带森林对干旱的脆弱性或恢复力。我们将调查土壤水分可用性（使用WT作为代理）如何影响亚马逊森林对季节性干旱期（如果观察到干旱）的反应。具体来说，我们将量化森林碳垂直叶面积分布（来自地面激光雷达）和树木木质生长（来自树木生长计）的关键组成部分的季节模式，并测试潜在的驱动因素，包括土壤水分，微环境条件和树木水力策略。

Climate-change-related droughts in the Amazon rainforest can cause widespread tree mortality, resulting in large carbon emissions, with potential feedbacks to regional and global climate (Phillips et al. 2009; Brienen et al. 2015). However, much of our knowledge of tropical forest drought responses comes from deep water table depth (WT) forests. Shallow WT forests are severely under-researched, despite occupying ~50% of the Amazon basin, and being expected to respond differently to drought (Costa, Schietti, Stark, Smith 2022). Recent results indicate that shallow WT areas benefit from moderate drying, which reduces anoxia and extends the growing season (Esteban et al. 2021, Sousa et al. 2022). As such, shallow WT forests may be more resilient to drought, potentially capable of offsetting large carbon losses anticipated from deep WT forests. To accurately project the future of this globally important ecosystem and set conservation priorities, we need to understand the contribution of shallow WT forests to Amazon forest carbon balance and how soil water availability influences tropical forest vulnerability or resilience to drought.We will investigate how soil water availability (using WT as a proxy) influences Amazon forest responses to seasonal dry periods (and if observed, droughts). Specifically, we will quantify seasonal patterns of key components of forest carbon-vertical leaf area distributions (from ground-based lidar) and tree woody growth (from dendrometers) -and test potential drivers, including soil moisture, microenvironmental conditions, and tree hydraulic strategy.