Department of Earth System Science, University of California, Irvine, CA 92697, USA.
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA.
Philos Trans R Soc Lond B Biol Sci. 2020 May 11;375(1798):20190254. doi: 10.1098/rstb.2019.0254. Epub 2020 Mar 23.
Linking 'omics measurements with biogeochemical cycles is a widespread challenge in microbial community ecology. Here, we propose applying genomic adaptation as 'biosensors' for microbial investments to overcome nutrient stress. We then integrate this genomic information with a trait-based model to predict regional shifts in the elemental composition of marine plankton communities. We evaluated this approach using metagenomic and particulate organic matter samples from the Atlantic, Indian and Pacific Oceans. We find that our genome-based trait model significantly improves our prediction of particulate C : P (carbon : phosphorus) across ocean regions. Furthermore, we detect previously unrecognized ocean areas of iron, nitrogen and phosphorus stress. In many ecosystems, it can be very challenging to quantify microbial stress. Thus, a carefully calibrated genomic approach could become a widespread tool for understanding microbial responses to environmental changes and the biogeochemical outcomes. This article is part of the theme issue 'Conceptual challenges in microbial community ecology'.
将“组学”测量与生物地球化学循环联系起来是微生物群落生态学中的一个普遍挑战。在这里,我们提出应用基因组适应作为微生物投资的“生物传感器”来克服营养压力。然后,我们将这些基因组信息与基于特征的模型相结合,以预测海洋浮游生物群落元素组成的区域变化。我们使用来自大西洋、印度洋和太平洋的宏基因组和颗粒有机物质样本评估了这种方法。我们发现,基于基因组的特征模型显著提高了我们对跨海洋区域颗粒 C:P(碳:磷)的预测。此外,我们检测到以前未被识别的铁、氮和磷胁迫的海洋区域。在许多生态系统中,量化微生物胁迫是非常具有挑战性的。因此,经过精心校准的基因组方法可能成为一种广泛的工具,用于理解微生物对环境变化和生物地球化学结果的反应。本文是主题为“微生物群落生态学中的概念挑战”的特刊的一部分。