Environmental controlling mechanisms on bacterial abundance in the South China Sea inferred from generalized additive models (GAMs)

被引:18
作者
Chen, Bingzhang [1 ]
Liu, Hongbin [2 ]
Huang, Bangqin [1 ]
机构
[1] Xiamen Univ, State Key Lab Marine Environm Sci, Xiamen 361005, Fujian, Peoples R China
[2] Hong Kong Univ Sci & Technol, Div Life Sci, Kowloon, Hong Kong, Peoples R China
基金
美国国家科学基金会;
关键词
Heterotrophic bacteria; Generalized additive model; South China Sea; TEMPERATURE REGULATION; SURFACE WATERS; GROWTH; BACTERIOPLANKTON; LIMITATION; PHYTOPLANKTON; DEPENDENCE; PLANKTON; BASIN; RATES;
D O I
10.1016/j.seares.2012.05.012
中图分类号
Q17 [水生生物学];
学科分类号
071004 ;
摘要
We modeled the abundance distribution of heterotrophic bacteria collected from 4 cruises in the northern South China Sea using generalized additive models to infer the underlying mechanisms controlling bacterial abundance and to predict bacterial abundance using environmental parameters that can be easily obtained. We incorporated spatial coordinates, depth, month, chlorophyll (Chl) a concentration, temperature, salinity, nutricline and mixed layer depth in the model, which captures the main features of the observations and explains 88% of the total variation of bacterial abundance. The most important factor affecting bacterial abundance is chlorophyll, followed by salinity and nutricline depth, reflecting the importance of carbon and nutrient sources to bacteria. Bacterial abundance shows a unimodal relationship with temperature and decreases with depth. All the functions are nonlinear. After controlling environmental parameters, bacterial abundances are higher in fall and winter than in spring and summer and usually show an onshore-offshore decreasing gradient, which probably signify transportation pathways of terrestrial organic matter to the sea via atmospheric deposition. Comparisons of variograms between raw data and residuals of the model show that positive autocorrelation at small scales is induced by both environmental similarity and geographic proximity, while the negative autocorrelation at large scales is mostly contributed by environmental similarity in remote water masses. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:69 / 76
页数:8
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