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.

河流生态系统储存、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方温带。河流中最稳定的全年有机物来源是溶解性有机物或DOM，它们在这些河流中运输和转化。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，河流是二氧化碳排放到大气中的重要来源，对全球碳循环产生影响。将DOM的供应和特征与微生物组合的动态联系起来，对于将流机械地整合到全球碳循环模型中是必要的。然而，DOM是具有不同结构和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。该项目汇集了一个团队，将生态系统科学，微生物和分子生物学，环境化学和数据科学结合在一起，进行受控实验室实验，以研究：复杂的微生物群落如何共同处理环境中DOM分子的复杂混合物，以及微生物群落和DOM特征之间的关系能否解释二氧化碳排放率？更广泛的影响活动通过研究实习和指导研讨会的结合，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表征DOM组成和降解水生环境中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，并且可以生成大量的多维数据，这些数据很难独立解释，更不用说一起或随着时间的推移了。该项目使用模型DOM和富集的微生物组合来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。微生物组合的特点是16 S测序，宏基因组学，和元转录组学，而DOM混合物的特点是使用高分辨率质谱，沿着荧光光谱。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，并通过编织生态系统生态学、计算生物学和环境化学的工具和学科理解，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，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。