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地数百万英亩，通过储存碳（C），改善水质和调节气候来提供重要的生态系统服务。由于人类活动，这些森林的组成已经发生并正在发生巨大变化。本研究的目标是建立一个预测框架，以研究树种及其相关的土壤微生物如何影响中央阔叶林的碳和养分循环。关于树种在影响碳和养分循环中的作用，人们所知道的大部分是基于对森林冠层中发生的过程的测量，尽管根和其他地下过程在储存碳和释放养分方面起着至关重要的作用。该项目将把观测和实验样地级数据与模型和卫星图像相结合，以研究具有不同共生真菌协会的树木如何影响碳和养分动态。为了确定地下过程对碳和养分循环的重要性，将对树木进行实验性的环绕，这是一个物理上阻止糖流向根部的过程。将通过更新养分吸收模型和根据遥感数据进行土地覆盖分类，建立一个预测框架，将碳养分循环的地块数据按生态系统、区域和全球尺度进行缩放。总的来说，这些方法将使植物-土壤相互作用纳入C循环-气候模型，并导致新的见解如何在森林组成的变化可能会影响生态系统functioning.This多学科项目将提供综合培训的本科生和研究生在生态学，生态系统建模和遥感。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）编写的报告传播。为传播这一信息，还将编写为私人土地所有者编写的报告。关于含有不同树种的森林中碳和养分动态的知识将使私人土地所有者、州推广办公室和非政府组织能够做出管理决策，增强生态系统服务，如碳储存。重要的是，这些知识将使关键的植物-土壤相互作用纳入碳循环的区域尺度模型，并解决决策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.