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.

溪流生态系统商店，工艺和运输有机物在相邻的陆地生态系统中产生，特别是在北部温带地区，以落叶林为主。溪流的最稳定，全年有机物的来源是溶解的有机物或DOM，这既可以通过这些溪流运输和转化。大部分有机物被细菌和其他微生物分解，作为过程的一部分释放二氧化碳。结果，流是二氧化碳外排对大气的重要来源，对全球碳循环产生了影响。将DOM的供应和特征与微生物组合动力学联系起来，对于将流将流集成到碳循环的全局模型中是必要的。但是，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桥接生态系统科学，微生物和分子生物学，环境化学和数据科学，以进行受控的实验室实验来询问：复杂的微生物群落如何共同工作以处理环境中DOM分子的复杂混合物，以及微生物群落和圆顶特征之间的关系可以解释碳二氧化碳二氧化碳二氧化物的速率？通过研究实习和指导研讨会的结合，更广泛的影响活动扩大了研究生和本科生的数据科学培训机会。分析和计算技术的预测加速了表征DOM组成以及在水上环境中降级DOM的组合的研究。表征这些混合物的方法在分辨率方面有所不同，并且可以生成大量的多维数据，这些数据是独立解释的挑战，更不用说在一起或通过时间。该项目使用模型DOM和丰富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。通过16S测序，宏基因组学和元文字组学表征微生物组合，而DOM混合物则使用高分辨率质谱以及荧光光谱表征。该项目在对DOM组成和微生物组合上的平等分析重量方面具有独特性，并克服了通过编织工具和生态系统生态生物学，计算生物学和环境化学的纪律理解所带来的挑战。该奖项反映了NSF的法定任务，反映了通过评估的支持者的支持者，其知识范围是众所周知的。