Collaborative Research: RoL: Impacts of plants and communities on soil microbial composition and function across phylogenetic scales

合作研究：RoL：植物和群落对跨系统发育尺度的土壤微生物组成和功能的影响

• 批准号: 1933925
• 负责人: Joshua Phillips
• 金额: $ 20.3万
• 依托单位: Middle Tennessee 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: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1933925&HistoricalAwards=false
• 关键词: Collaborative; Research; RoL; Impacts; plants

Soils provide critical ecosystem services that support agriculture, determine nutrient and carbon cycles, maintain clean air and water, and sustain biodiversity. These services are controlled by microorganisms, including bacteria and fungi. Plants can manipulate these microbial communities through the chemicals they release—root exudates—with subsequent consequences for soil functions. Characterizing root exudates and their relationships with other plant traits will improve understanding of plant effects on microbial communities and the ecosystem services that they provide. This project will use laboratory, greenhouse, field, and computational approaches to evaluate the effects of plant root exudates on soil microbial composition and function. The work will be conducted in the context of restored tallgrass American prairies, an endangered ecosystem, and deliver our results directly to land owners and natural resource managers responsible for the creation and maintenance of this landscape. Additionally, the project provides educational opportunities for members of the public ranging from high school students to retired adults and will train early-career scientists in cutting-edge techniques. This project will develop a predictive, mechanistic framework to understand how phylogenetic and functional characteristics of plants and plant communities influence soil microbial communities and the ecosystem functions via root exudates. It will combine exometabolomic plant measurements, sampling in a biodiversity experiment, functional microbial characterizations, and machine learning approaches to forecast microbial community structure and function in a field setting. Exudate characterizations from a taxonomically and functionally diverse pool of plants will greatly advance knowledge of plant biochemistry. The use of exudates in mesocosm incubation and metabolic characterization experiments will demonstrate their direct impacts on microbial community structure and function. The project will also sample soil microbes in a biodiversity experiment and use machine learning computational approaches to build predictive models of plant community impacts on soil microbes. These models will be tested in a field setting using large-scale tallgrass prairie restorations to produce generalizable conclusions that span molecular- to landscape-scales.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.

土壤提供关键的生态系统服务，支持农业，确定养分和碳循环，维持清洁的空气和水，并维持生物多样性。这些服务由微生物控制，包括细菌和真菌。植物可以通过它们释放的化学物质--根分泌物--来操纵这些微生物群落，从而对土壤功能产生影响。表征根系分泌物及其与其他植物性状的关系将有助于更好地了解植物对微生物群落及其提供的生态系统服务的影响。该项目将使用实验室、温室、田间和计算方法来评估植物根分泌物对土壤微生物组成和功能的影响。这项工作将在恢复的Tallgras美国草原这一濒危生态系统的背景下进行，并将我们的成果直接传递给负责创造和维护这一景观的土地所有者和自然资源管理者。此外，该项目为从高中生到退休成年人的公众提供教育机会，并将培训职业生涯早期的科学家尖端技术。该项目将开发一个可预测的机械框架，以了解植物和植物群落的系统发育和功能特征如何通过根系分泌物影响土壤微生物群落和生态系统功能。它将结合外代谢植物测量、生物多样性实验中的采样、功能微生物特征和机器学习方法来预测田间环境中的微生物群落结构和功能。从分类和功能上不同的植物池中提取分泌物的特征将极大地促进对植物生物化学的了解。渗出物在中微体培养和代谢特性实验中的使用将证明它们对微生物群落结构和功能的直接影响。该项目还将在生物多样性实验中对土壤微生物进行采样，并使用机器学习计算方法来建立植物群落对土壤微生物影响的预测模型。这些模型将在使用大规模Tallgras草原恢复的现场环境中进行测试，以产生从分子到景观尺度的概括性结论。该奖项反映了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。

项目成果

STrXL: Approximating Permutation Invariance/Equivariance to Model Arbitrary Cardinality Sets

• DOI: 10.32473/flairs.37.1.135568
• 发表时间: 2024-05
• 期刊: The International FLAIRS Conference Proceedings
• 作者: Kendra Givens;David Ludwig;Joshua L. Phillips