Collaborative Research: Developing integrated trait-based scaling theory to predict community change and forest function in light of global change

热带森林储存了大量的碳，仅亚马逊就占地球初级生产力的10%。 热带森林生产力对干旱的反应是碳循环的一个重要反馈;然而，我们目前对这些森林的生物量、生产力和物种组成如何对温度和水的可用性变化作出反应的了解非常不完整。 该项目将采取一种新的方法来理解热带森林干旱反应，重点是植物功能性状，代谢缩放理论和气候驱动因素之间的关系。 功能性状是易于测量的指标，使我们能够更好地预测植物生长，繁殖和森林变化。 代谢比例理论描述了生物体的大小、生长速度和温度之间的关系。 该项目将试图大大推进我们对热带生态系统如何应对温度和降水变化的理解。研究人员将利用尺度理论提供一个预测框架，将森林对干旱的反应与使用新的地面和遥感技术测量的已充分了解的植物特征联系起来。 该项目将评估生产力因干旱而发生的变化，以及树木死亡率和森林枯死率。 这将利用实地测量以及来自秘鲁亚马逊海拔梯度森林的现有激光雷达和高光谱遥感数据来实现。 具体来说，研究人员将使用一套植物功能性状来提供森林冠层结构和性状空间分布的详细3D地图。 新的缩放理论与这些数据（性状驱动理论，TDT），然后将被用来预测生态系统功能的性状分布随时间的变化，以应对干旱。 该项目还将涉及一项实地试验，利用穿透雨收集器模拟干旱，以帮助参数化TDT模型功能。 TDT结果还将与生态系统人口模型ED 2的预测进行比较。 模型代码、图像和算法将在公共存储库中提供，任何新的植物功能性状数据都将添加到全球数据库中。 该项目将为一些博士后研究人员、本科生和K-12科学教师提供培训，并将利用GEM网络Geoweb门户网站向公众推广。

Tropical forests store an enormous amount of carbon, with the Amazon alone accounting for 10% of the Earth's primary productivity. Changes in tropical forest productivity in response to drought are an important feedback in the carbon cycle; yet, we currently have a very incomplete understanding of how biomass, productivity, and species composition of these forests respond to changes in temperature and water availability. This project will take a new approach to understanding tropical forest drought responses by focusing on the relationships between plant functional traits, metabolic scaling theory, and climate drivers. Functional traits are easily measureable metrics that allow us to better predict plant growth, reproduction, and forest change. Metabolic scaling theory describes the relationships between the size of an organism, its growth rate, and temperature. This project will attempt to significantly advance our understanding of how tropical ecosystems respond to changes in temperature and precipitation. Researchers will use scaling theory to provide a predictive framework that links forest responses to drought with well understood plant traits measured using novel ground and remote sensing technology. This project will assess changes in productivity in response to drought, as well as tree mortality and forest dieback. This will be accomplished using both field measurements as well as pre-existing LIDAR and hyperspectral remote sensing data from forests across an elevation gradient in the Peruvian Amazon. Specifically, researchers will use a suite of plant functional traits to provide detailed, 3D maps of forest canopy structure and the spatial distribution of traits. The novel scaling theory developed with these data (Trait Driver Theory, TDT) will then be used to predict ecosystem function from changes in trait distributions over time in response to drought. The project will also involve a field experiment to simulate drought with throughfall collectors to help parameterize TDT model functions. The TDT results will also be compared to predictions from the ecosystem demography model ED2. Model code, images, and algorithms will be made available in public repositories, and any new plant functional trait data will be added to global databases. The project will provide training for several post-doctoral researchers, undergraduate students, and K-12 science teachers and will use the GEM Network Geoweb Portal for outreach to the general public.