A belowground framework for predicting how plant-microbe interactions couple carbon and nutrient economies of forests

温带落叶林在美国东部和中西部占据了数百万英亩的土地，通过储存碳(C)、改善水质和调节气候来发挥重要的生态系统服务。由于人类活动，这些森林已经并正在经历森林构成的戏剧性变化。这项研究的目标是开发一个预测框架，以检验树种及其相关的土壤微生物如何影响中部阔叶林的碳和养分循环。尽管树根和其他地下过程在储存碳和释放养分方面发挥着至关重要的作用，但人们对树种在影响碳和养分循环中的作用所知的很大一部分是基于对林冠中发生的过程的测量。该项目将把观测和实验的地块数据与模型和卫星图像相结合，以研究具有不同共生真菌组合的树木如何影响碳和营养动态。为了确定地下过程对碳和养分循环的重要性，将对树木进行环剥试验，这一过程在物理上阻止了糖分流向根部。将通过更新养分吸收模型和根据遥感数据制定土地覆盖分类，制定一个预测框架，将碳养分循环的地块数据扩展到生态系统、区域和全球尺度。总体而言，这些方法将使植物-土壤相互作用纳入C循环-气候模型，并对森林组成的变化如何影响生态系统功能产生新的见解。这个多学科项目将为本科生和研究生提供生态学、生态系统建模和遥感方面的综合培训。此外，这项研究的结果将通过为公共土地管理者(州和联邦森林)编写的报告来传播。还将制作为私人土地所有者设计的报告，以传播这一信息。有关不同树种森林中碳和养分动态的知识将使私人土地所有者、国家推广办公室和非政府组织能够做出管理决策，以加强生态系统服务，如碳储存。重要的是，这些知识将能够将关键的植物-土壤相互作用纳入区域尺度的碳循环模型，并解决政策制定者预测气候变化影响的能力方面的关键知识差距。

Temperate deciduous forests occupy millions of acres across the Eastern and Midwestern US and perform vital ecosystem services by storing carbon (C), improving water quality and mediating climate. These forests have undergone and are currently undergoing dramatic changes in forest composition owing to human activities. The goal of this research is to develop a predictive framework for examining how tree species and their associated soil microbes influence C and nutrient cycles in central hardwood forests. Much of what is known about the role of tree species in influencing C and nutrient cycling is based on measurements of processes occurring in the forest canopy, despite the fact that roots and other belowground processes play a vital role in storing C and releasing nutrients. This project will integrate observational and experimental plot-level data with models and satellite imagery to examine how trees with different symbiotic fungal associations influence C and nutrient dynamics. To determine the importance of belowground processes to C and nutrient cycling, trees will be experimentally girdled, a process which physically blocks the flow of sugars to roots. A predictive framework for scaling plot-level data of C-nutrient cycling to ecosystem, regional and global scales will be developed by updating a nutrient uptake model and developing a land cover classification from remotely sensed data. Collectively, these approaches will enable the incorporation of plant-soil interactions into C cycle-climate models, and result in novel insights into how shifts in forest composition may influence ecosystem functioning.This multidisciplinary project will provide integrative training of undergraduate and graduate students in ecology, ecosystem modeling, and remote sensing. Moreover, the results of this research will be disseminated through reports developed for public land managers (state and federal forests). Reports designed for private landowners will also be produced to disseminate this information. Knowledge about C and nutrient dynamics in forests containing different tree species will enable private landowners, state extension offices and NGOs to make management decisions that enhance ecosystem services such as C storage. Importantly, this knowledge will enable the incorporation of critical plant-soil interactions into regional scale models of C cycling, and address a critical knowledge gap in the ability of policy makers to predict climate change impacts.