γ蛋白杆菌
Β-变形菌
α蛋白细菌
浮游细菌
溶解有机碳
生物
细胞吞噬
玫瑰杆菌
相对物种丰度
丰度(生态学)
生态学
环境化学
浮游植物
克莱德
细菌
化学
系统发育学
黄杆菌
假单胞菌
放线菌门
16S核糖体RNA
生物化学
营养物
基因
遗传学
作者
Valentina Amaral,Daniel Graeber,Danilo Calliari,Cecilia Alonso
摘要
Abstract Despite considerable research on the linkages between dissolved organic matter (DOM) and bacteria, it is not yet clear how the abundance of the main aquatic clades relates to DOM composition in natural aquatic systems. We evaluated this relation using PARAFAC modeling of excitation–emission fluorescence spectroscopy and spectroscopic indexes to characterize DOM composition, and fluorescence in situ hybridization, to quantify the major bacterial groups in a subtropical lagoon. The DOM exhibited marked temporal variations in concentration, molecular weight, aromaticity, color, degree of humification, and freshness, and proportion of the three different fluorescent components identified. All major bacterial clades ( Alphaproteobacteria , Betaproteobacteria , Gammaproteobacteria , and Cytophaga‐Flavobacteria ) were significantly linked to DOM concentration and/or composition, being those crucial factors for modeling their abundance in situ. The combination and significance of the factors was specific for each bacterial group, strongly indicating that they behave as coherent and distinctive units. Cytophaga‐Flavobacteria and Betaproteobacteria were the groups which correlated with more DOM properties. Alphaproteobacteria and Gammaproteobacteria abundances were significantly explained by low or high dissolved organic carbon concentrations, respectively. The significant relationships between DOM properties and the main bacterial groups delineated a profile of each group regarding DOM preferences/dislikes, in agreement with evidence derived from genome analysis to single‐cell substrate uptake. These results highlight the specificities of the main bacterial clades, providing support for a functional classification of the bacterioplankton regarding DOM processing at the level of bacterial classes.
科研通智能强力驱动
Strongly Powered by AbleSci AI