Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?
Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?
复制标题
微生物作为生态系统功能的发动机：群落结构何时增强生态系统过程的预测？
DOI：
10.3389/fmicb.2016.00214
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发表时间：
2016
影响因子：
5.2
通讯作者：
Nemergut DR
中科院分区：
文献类型：
--
作者：
Graham EB;Knelman JE;Schindlbacher A;Siciliano S;Breulmann M;Yannarell A;Beman JM;Abell G;Philippot L;Prosser J;Foulquier A;Yuste JC;Glanville HC;Jones DL;Angel R;Salminen J;Newton RJ;Bürgmann H;Ingram LJ;Hamer U;Siljanen HM;Peltoniemi K;Potthast K;Bañeras L;Hartmann M;Banerjee S;Yu RQ;Nogaro G;Richter A;Koranda M;Castle SC;Goberna M;Song B;Chatterjee A;Nunes OC;Lopes AR;Cao Y;Kaisermann A;Hallin S;Strickland MS;Garcia-Pausas J;Barba J;Kang H;Isobe K;Papaspyrou S;Pastorelli R;Lagomarsino A;Lindström ES;Basiliko N;Nemergut DR
微生物在调节地球生物地球化学循环中至关重要；然而，尽管我们探索复杂环境微生物群落的能力迅速增强，但微生物群落结构与生态系统过程之间的关系仍然知之甚少。在这里，我们解决了微生物生态学中一个基本且悬而未决的问题：我们何时需要了解微生物群落结构才能准确预测功能？我们提供了一个统计分析，独立地和结合地调查环境数据和微生物群落结构的价值，以解释82个全球数据集中的碳和氮循环过程的速率。环境变量是过程速率的最强预测因子，但平均而言，44%的变异无法解释，这表明微生物数据有可能提高模型的准确性。虽然只有29%的数据集通过添加关于微生物群落结构的信息而得到显著改善，但我们观察到通过功能基因数据通过狭窄的系统发育行会介导的过程模型的改进，以及通过群落多样性度量的兼性微生物过程模型的改进。我们的结果还表明，微生物多样性可以加强对微生物生物量参数以外的呼吸速率的预测，因为53%的模型通过纳入这两组预测因子而得到改进，而单独使用微生物生物量的模型只有35%。我们的分析是首次对考察微生物群落结构和生态系统功能之间联系的研究进行全面分析。综上所述，我们的结果表明，更好地理解由生态学原理提供信息的微生物群落，可能会增强我们预测生态系统过程速率的能力，而不是基于环境变量和微生物生理学的评估。
Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.