OPUS: CRS: Trade-offs among fungal traits that influence responses to the environment and effects on ecosystems

为了帮助美国应对干旱、野火、热浪和其他环境变化，该项目将重点关注土壤生态系统中看不见的参与者：真菌。虽然它们很小，但土壤真菌很重要，因为它们强烈影响土壤肥力和碳(C)储存。目前，科学家们使用被称为地球系统模型的计算机模拟来预测未来的环境变化。这些模型可以通过纳入土壤真菌在不同环境条件下如何发挥作用的更好信息来改进。在这个项目中，研究人员从阿拉斯加、加利福尼亚和哥斯达黎加的十年真菌实验中收集信息。他们的工作将分析数千种真菌物种如何对环境做出反应，并将这些物种与它们对生态系统功能的影响联系起来。这些信息将用于提高地球系统模型的预测作用。此外，该项目将测试真菌生态学的基本观点，比如真菌是否会在适应更有压力的环境条件下失去在有利条件下良好生长的能力。该项目还包括对一名博士生的培训，以及使实地研究实践更安全的工作，特别是对科学团队中的女性而言。生态系统科学的一个核心挑战是改进对未来环境条件下生态系统中碳(C)循环的预测。为这个真菌生态项目提出的合成是这一过程中的关键一步，并将利用十年来实验现场操作的真菌DNA序列数据。根据这些数据，他们将描述数千种真菌分类群如何对不同的环境变化做出反应。利用新的群落生成的真菌功能性状数据库，PI和一名研究生将把单个分类群的反应与其功能（即形态、生理和遗传）性状联系起来。然后，他们可以预测干旱等环境条件的变化如何选择在生态系统中发挥特定功能的真菌，比如产生顽固性碳。分析还将测试Grime的竞争-压力耐受性-真菌总体方案的适用性，以确定进化或生理上的权衡是否会阻止特定个体在一系列条件下（包括环境压力和干扰）广泛繁殖。PI假设，在真菌分类群中，与适度、稳定条件相关的功能性状套件与与环境压力或干扰相关的功能性状套件将相互负相关，因为这些性状之间存在权衡。通过上述综合活动，结果旨在推动土壤真菌生态学过去的发现阶段，并朝着基本概念框架的讯问。增加的价值将是记录真菌分类群对环境的反应与其对生态系统的潜在影响之间的联系。发现的联系可用于参数化基于微生物特性的模型，以改进生态系统中碳循环的预测。此外，这些分析将扩大真菌功能性状数据库，包括对环境条件的反应。为了方便更广泛的社区使用，所有产品都将是开放获取和开源的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

To help the U.S. prepare for droughts, wildfires, heat waves, and other environmental changes, this project will focus on invisible players in soil ecosystems: fungi. Although they are small, soil fungi are important because they strongly impact soil fertility and carbon (C) storage. Currently, scientists use computer simulations called Earth System Models to predict future environmental change. These models can be improved by including better information about how soil fungi function under different environmental conditions. In this project, researchers are gathering information from ten years of experiments on fungi in Alaska, California, and Costa Rica. Their work will analyze how thousands of fungal species respond to the environment, and link those species to their impact on ecosystem function. This information will be used to improve the predictive role of Earth System Models. In addition, the project will test foundational ideas about fungal ecology, like whether fungi that adapt to more stressful environmental conditions lose their ability to grow well under favorable conditions. The project also includes training for a PhD student and work to make field research practices safer, especially for women on scientific teams.A central challenge in ecosystem science is to improve predictions of carbon (C) cycling in ecosystems under future environmental conditions. The synthesis proposed for this fungal ecology project is a critical step in this process, and will leverage ten years of fungal DNA sequence data from experimental field manipulations. From these data, they will characterize how thousands of fungal taxa respond to contrasting environmental changes. Using the new community-generated Fungal Functional Traits Database, the PI and a graduate student will link the responses of individual taxa to their functional (i.e., morphological, physiological, and genetic) traits. They can then predict how changes in environmental conditions like drought can select for fungi that perform particular functions within ecosystems, like recalcitrant C production. Analyses will also test the applicability of Grime's Competitive-Stress Tolerance-Ruderal scheme for fungi, to determine whether evolutionary or physiological trade-offs preclude a given individual from thriving broadly under an array of conditions, including environmental stress and disturbance. The PI posits that across fungal taxa, the suites of functional traits associated with moderate, stable conditions versus those associated with environmental stress or disturbance will be negatively related to one another, owing to trade-offs among these traits. Via the synthesis activities described above, results are intended to move soil fungal ecology past the discovery phase and toward the interrogation of foundational conceptual frameworks. The value added will be a documentation of links between the responses of fungal taxa to the environment with their potential effects on ecosystems. The linkages discovered can be used to parameterize microbial trait-based models to improve predictions of C cycling in ecosystems. In addition, these analyses will expand the Fungal Functional Traits Database to include responses to environmental conditions. To facilitate use by the broader community, all products will be open-access and open-source.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.