Emergent Linkages Among Dissolved Organic Matter Composition, Microbial Assemblages and Respiration in Streams

河流中溶解有机物成分、微生物组合和呼吸之间的新兴联系

• 批准号: 2141535
• 负责人: Amy Marcarelli
• 金额: $ 30万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141535&HistoricalAwards=false
• 关键词: Emergent; Linkages; Among; Dissolved; Organic

Stream ecosystems store, process, and transport organic matter produced in adjacent terrestrial ecosystems, particularly in northern temperate zones dominated by deciduous forests. The most stable, year-round source of organic matter to streams is dissolved organic matter or DOM, which is both transported by and transformed in these streams. Much of the organic matter is broken down by bacteria and other microbes, releasing carbon dioxide as part of the process. As a result, streams are important sources of carbon dioxide efflux to the atmosphere, with consequences for the global carbon cycle. Linking the supply and characteristics of DOM with dynamics of microbial assemblages is necessary to mechanistically integrate streams into global models of carbon cycling. However, DOM is a complex mixture of molecules with different structures and degradability, and microbial assemblages are complicated mixtures of species. This project brings together a team bridging ecosystem science, microbial and molecular biology, environmental chemistry, and data science to conduct controlled laboratory experiments to ask: How do complex microbial communities work together to process complex mixtures of DOM molecules in the environment, and can relationships between microbial communities and DOM characteristics explain rates of carbon dioxide emission? The broader impact activities expand data science training opportunities for graduate and undergraduate students through a combination of research internships and guided workshops.Advances in analytical and computational techniques have accelerated research characterizing the composition of DOM and assemblages of microbes that degrade DOM in aquatic environments. Methods to characterize these mixtures vary in resolution and can generate vast amounts of multi-dimensional data that are a challenge to interpret independently, let alone together or through time. The project builds simplified experimental systems using model DOM and enriched microbial assemblages to build machine learning models that predict carbon dioxide emissions. Microbial assemblages are being characterized with 16S sequencing, metagenomics, and metatranscriptomics, while DOM mixtures are characterized using high-resolution mass spectrometry, along with fluorescence spectroscopy. This project is unique in placing equal analytical weight on DOM composition and microbial assemblages, and overcomes the challenge posed by multidimensional datasets by weaving tools and disciplinary understanding from ecosystem ecology, computational biology, and environmental chemistry.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.

溪流生态系统储存、处理和运输邻近陆地生态系统产生的有机物质，特别是在以落叶林为主的北温带地区。河流中最稳定的、全年有机物来源是溶解有机物或 DOM，它既由这些河流输送又在这些河流中转化。大部分有机物被细菌和其他微生物分解，并在此过程中释放二氧化碳。因此，溪流是二氧化碳流入大气的重要来源，对全球碳循环产生影响。将 DOM 的供应和特性与微生物组合的动态联系起来对于将流机械地整合到碳循环的全球模型中是必要的。然而，DOM是具有不同结构和可降解性的分子的复杂混合物，微生物组合是物种的复杂混合物。该项目汇集了一个涵盖生态系统科学、微生物和分子生物学、环境化学和数据科学的团队，进行受控实验室实验，以探究：复杂的微生物群落如何协同工作来处理环境中 DOM 分子的复杂混合物，以及它们之间的关系微生物群落和 DOM 特征之间的关系可以解释二氧化碳排放率吗？影响更广泛的活动通过研究实习和指导研讨会相结合，扩大了研究生和本科生的数据科学培训机会。分析和计算技术的进步加速了对 DOM 组成和在水生环境中降解 DOM 的微生物组合的研究。表征这些混合物的方法在分辨率上各不相同，并且会产生大量的多维数据，这些数据对于独立解释来说是一个挑战，更不用说一起解释或随着时间的推移而解释了。该项目使用 DOM 模型和丰富的微生物组合构建简化的实验系统，以构建预测二氧化碳排放的机器学习模型。微生物组合通过 16S 测序、宏基因组学和宏转录组学进行表征，而 DOM 混合物则使用高分辨率质谱和荧光光谱进行表征。该项目的独特之处在于对 DOM 组成和微生物组合给予同等的分析权重，并通过编织工具和对生态系统生态学、计算生物学和环境化学的学科理解克服了多维数据集带来的挑战。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。