Collaborative Research: Microbes, memory, and moisture: leveraging DroughtNet to predict how microbial moisture responses will impact carbon cycling

合作研究：微生物、记忆和水分：利用 DroughtNet 预测微生物水分反应将如何影响碳循环

• 批准号: 2016437
• 负责人: Sarah Evans
• 金额: $ 45.85万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016437&HistoricalAwards=false
• 关键词: Collaborative; Research; Microbes; memory; moisture

This project will determine how soils and soil organisms respond to severe drought, a condition that will be more common in the future, and how this response will impact the global carbon cycle. The increasing frequency of drought due to changing rainfall patterns is threatening the world's ecosystems and food security. Rainfall frequency also can affect whether the immense amount of carbon present in soil stays there, or is emitted by soil microbes into the atmosphere as carbon dioxide (CO2). In order to accurately predict the impact of drought on soils, soil microbes and the carbon cycle, this project will use samples from the Drought-Net Research Coordination Network supported by NSF. Drought-Net consists of a series of simple, inexpensive experiments that control rainfall at over 150 plots of land worldwide using the same standard protocols. Soil samples from 39 Drought-Net sites that have been exposed to drought for four years will be manipulated in the laboratory to determine how they respond to different amounts of moisture. Soil microbial community changes and CO2 emission will be measured, and the results will be incorporated into computer models of global carbon cycling. These models can then predict how drought will cause changes and feedbacks in carbon cycling and, in turn, ecosystem function and stability. The studies will also involve training at of postdoctoral researchers, graduate and undergraduate students, and other educational and outreach activities. Successful completion of the goals and objectives of this project will help society understand how it needs to adapt and respond to global environmental change. While the recent inclusion of microbial mechanisms in ecosystem models has improved our ability to predict soil carbon (C) cycling, even the most advanced of these models explains only 50% of the variation in current C pools, leaving little confidence in projections of future soil C stocks. Past efforts have mainly focused on microbial temperature responses, but moisture and drought may be an even more important constraint on microbial activity. Thus, an understanding of microbial moisture responses is required to improve ecosystem C models. This research addresses four key questions: (1) what drives differences in moisture response functions across ecosystems? (2) how does severe drought alter moisture response across ecosystems? (3) what microbial mechanisms influence differences in moisture response functions? (4) what are the implications of different moisture responses for C storage? In order to build a robust predictive framework for soil microbial moisture functions, this project will leverage the Drought-Net Research Coordination Network, which has implemented standardized, coordinated rainfall manipulation experiments across the world. Intact soil cores from ambient and drought treatments at 39 sites will be exposed to a range of soil water potentials to quantify how the moisture response of heterotrophic respiration depends on historical climate and soil factors. Underlying microbial mechanisms will be examined by characterizing physiological traits and tolerances at individual and community levels. Results will be scaled to the ecosystem level by first testing how aggregated community responses influence function in a trait-based model, and then by incorporating response functions into conventional and microbially-explicit ecosystem models of soil C cycling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将确定土壤和土壤生物如何应对严重的干旱，这种情况在将来会更普遍，以及这种反应将如何影响全球碳循环。由于变化的降雨模式而引起的干旱频率不断增加，这威胁到世界的生态系统和粮食安全。降雨频率还会影响土壤中存在的巨大碳量是否留在那里，或者被土壤微生物作为二氧化碳（CO2）发射到大气中。为了准确预测干旱对土壤，土壤微生物和碳循环的影响，该项目将使用NSF支持的干旱网络研究协调网络中的样本。 干旱网络由一系列简单，廉价的实验组成，这些实验使用相同的标准方案控制着全球150多个土地的降雨。在实验室中，将操纵来自39个干旱的干旱地点的土壤样本，以确定它们对不同量的水分的反应。将测量土壤微生物群落的变化和CO2排放，结果将纳入全球碳循环的计算机模型中。然后，这些模型可以预测干旱将如何导致碳循环的变化和反馈，进而导致生态系统功能和稳定性。这些研究还将涉及在博士后研究人员，研究生和本科生以及其他教育和外展活动的培训。成功完成该项目的目标和目标将有助于社会了解其如何适应和应对全球环境变化所需的方式。虽然最近在生态系统模型中纳入微生物机制已经提高了我们预测土壤碳（C）循环的能力，但即使是这些模型中最先进的也只能解释当前C池中50％的变化，几乎没有对未来土壤C种群预测的信心。过去的努力主要集中在微生物温度反应上，但是水分和干旱可能是对微生物活性的更重要的限制。因此，需要了解微生物水分反应以改善生态系统C模型。这项研究解决了四个关键问题：（1）什么推动了生态系统的水分反应功能差异？ （2）严重的干旱如何改变生态系统的水分反应？ （3）哪些微生物机制会影响水分反应功能的差异？ （4）不同水分反应对C存储的影响是什么？为了为土壤微生物水分功能建立强大的预测框架，该项目将利用干旱网络研究协调网络，该网络已在全球实施了标准化的协调降雨操纵实验。来自39个地点的环境和干旱处理的完整土壤核心将暴露于一系列土壤潜力，以量化异养呼吸的水分反应如何取决于历史气候和土壤因素。基本的微生物机制将通过表征个人和社区层面的生理特征和公差来检查。 Results will be scaled to the ecosystem level by first testing how aggregated community responses influence function in a trait-based model, and then by incorporating response functions into conventional and microbially-explicit ecosystem models of soil C cycling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.