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

为了帮助美国为干旱、野火、热浪和其他环境变化做准备，该项目将专注于土壤生态系统中的无形角色：真菌。虽然它们很小，但土壤真菌很重要，因为它们强烈地影响土壤肥力和碳(C)储存。目前，科学家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。通过包含关于土壤真菌在不同环境条件下如何发挥作用的更好信息，这些模型可以得到改进。在这个项目中，研究人员正在从阿拉斯加、加利福尼亚州和哥斯达黎加长达十年的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。这些信息将用于改善地球系统模型的预测作用。此外，该项目还将测试关于真菌生态学的基本观点，比如适应更紧张环境条件的真菌是否会在有利的条件下失去良好生长的能力。该项目还包括对博士生的培训，以及努力使实地研究实践更安全，特别是对科学团队中的女性来说。生态系统科学的一个核心挑战是提高对未来环境条件下生态系统中碳(C)循环的预测。为这个真菌生态学项目提出的合成是这一过程中的关键一步，将利用来自试验田操作的十年真菌DNA序列数据。从这些数据中，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。利用新的社区产生的真菌功能特征数据库，PI和一名研究生将把个体分类群的反应与它们的功能(即形态、生理和遗传)特征联系起来。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，如顽固的C生产。分析还将测试Grime的竞争压力耐受-普通真菌方案的适用性，以确定是否进化或生理权衡排除了给定个体在包括环境压力和干扰在内的一系列条件下广泛茁壮成长。PI假设，在整个真菌分类群中，由于这些特征之间的权衡，与适度、稳定的条件相关联的一套功能性状与那些与环境胁迫或干扰有关的功能性状将彼此负相关。通过上述综合活动，结果旨在推动土壤真菌生态学越过发现阶段，走向对基本概念框架的询问。增加的价值将是真菌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的文件。所发现的联系可用于将基于微生物特征的模型参数化，以改进对生态系统中碳循环的预测。此外，这些分析将扩展真菌功能特性数据库，以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。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.