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

河流生态系统储存、加工和运输邻近陆地生态系统产生的有机物质，特别是在以落叶森林为主的北部温带地区。河流中最稳定、全年最稳定的有机物质来源是溶解有机物或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，在这个过程中释放出二氧化碳。因此，溪流是二氧化碳排入大气的重要来源，对全球碳循环产生影响。将DOM的供应和特征与微生物组合的动态联系起来，对于将河流机械地整合到全球碳循环模型中是必要的。然而，DOM是具有不同结构和可降解性的分子的复杂混合物，微生物组合是复杂的物种混合物。该项目汇集了一个连接生态系统科学、微生物和分子生物学、环境化学和数据科学的团队，以进行受控的实验室实验，以提出以下问题：复杂的微生物群落如何协同工作，以处理环境中复杂的DOM分子混合物，以及微生物群落和DOM特征之间的关系能否解释二氧化碳的排放速度？更广泛的影响活动通过研究实习和有指导的工作坊相结合，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快了表征DOM的组成和在水环境中降解DOM的微生物组合的研究。描述这些混合物的方法在分辨率上各不相同，可能会产生大量的多维数据，这对单独解释是一个挑战，更不用说一起或随着时间的推移了。该项目使用模型DOM和丰富的微生物组合建立简化的实验系统，以建立预测二氧化碳排放的机器学习模型。微生物组合正在用16S测序、元基因组学和元转录组学来表征，而DOM混合物则用高分辨率质谱学和荧光光谱来表征。该项目在对DOM组成和微生物组合给予同等分析权重方面是独一无二的，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来克服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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.