Influences of Seasonal Monsoons on the Taxonomic Composition and Diversity of Bacterial Community in the Eastern Tropical Indian Ocean

Abstract

The Indian Ocean is characterized by its complex physical systems and strong seasonal monsoons. To better understand effects of seasonal monsoon-driven circulation on the bacterioplanktonic community structure in surface waters and the bacterial distribution response to vertical stratification, patterns of seasonal, and vertical distribution of bacterial communities in the Eastern Tropical Indian Ocean were investigated using 16S rRNA gene profiling. Water samples were collected during the Southwest monsoon (from June to August), the fall inter-monsoon (from October and November) and the Northeast monsoon (from December to January), respectively, onboard during three cruises from July 2016 to January 2018. Surface bacterioplankton communities in these three seasons and in the upper water (3–300 m with six depths) during the Northeast monsoon contained a diverse group of taxa, mainly Proteobacteria, Cyanobacteria, Actinobacteria, Bacteroidetes, and Chloroflexi. Redundancy discriminant analysis (RDA) uncovered that temperature, salinity, and dissolved oxygen (DO) were crucial environmental parameters that affected the structure of bacterial community in overall surface samples. However, significant differences in the composition of the bacterial community are likely due to changes in concentrations of salinity during the fall inter-monsoon, while phosphate for both the Southwest monsoon and the Northeast monsoon. Pearson's analysis revealed that the seasonal variation rather than the vertical variation of environmental factors had a more significant impact on the composition of bacterial community. In addition, a clear seasonal pattern of bacterial co-occurrence showed that inter-taxa associations during the fall inter-monsoon were closer than during the Northeast monsoon and the Southwest monsoon. Overall, our results implied clear differences in the composition of bacterial community, with more pronounced seasonal variation compared to the vertical variation in response to environmental changes.

Changes of In Situ Prokaryotic and Eukaryotic Communities in the Upper Sanya River to the Sea over a Nine-Hour Period

“…SAR11 clade of the Proteobacteria (Alpha) is one of the most abundant planktonic bacteria in the marine ecosystem [ 69 ]. Meanwhile, AEGEAN-169 marine group and Actinomarinaceae are also common microbial taxa in the ocean [ 70 , 71 ]. At the taxonomic level of eukaryotes, the Chlorellales X , Chlamydomonadales X , Sphaeropleales X , and Trebouxiophyceae XX , all of which are affiliated with the Chlorophyta, and the Annelida XX and Heteroconchia , which are affiliated with the Metazoa, together constituted the main taxa of the eukaryotic microbial community in the samples.…”

Operational taxon pairs in microbial co-occurrence networks imply complicated bacterial associations

“…With the information of abundance data, the microbial co-occurrence networks can be inferred using similarity-based (e.g., Pearson or Spearman correlations) 6-10 or modelbased methods (e.g., regression-and rule-based) [11][12][13] . For example, microbial co-occurrence networks of activated sludge from wastewater treatment plants 14 , human gut 15, and marine environment 10,16 were inferred from data of high-resolution and extended longitudinal and cross-sectional scales of samples.…”

“…With the information of abundance data, the microbial co-occurrence networks can be inferred using similarity-based (e.g., Pearson or Spearman correlations) 6-10 or modelbased methods (e.g., regression-and rule-based) [11][12][13] . For example, microbial co-occurrence networks of activated sludge from wastewater treatment plants 14 , human gut 15, and marine environment 10,16 were inferred from data of high-resolution and extended longitudinal and cross-sectional scales of samples.The inferred microbial co-occurrence networks were widely applied for predicting microbial interactions.But such prediction needs to be carefully examined and cautiously interpreted since most of the predictions were not experimentally demonstrated 5 . A positive relation within a co-occurrence network could be due to cross-feeding, co-colonization, niche overlap, or other random reasons [17][18][19] and a negative relation could be due to competition, predation, and antagonism 20,21 .…”

Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks

“…Microbial co-occurrence networks were usually reconstructed from metagenomic data or high-throughput sequencing of 16S rRNA gene amplicons from environmental and host microbiome DNA molecules 5 , 6 . With the information of abundance data, the microbial co-occurrence networks can be inferred using similarity-based (e.g., Pearson or Spearman correlations) 7 – 11 or model-based methods (e.g., regression- and rule-based) 12 – 14 . For example, microbial co-occurrence networks of activated sludge from wastewater treatment plants 15 , human gut 16 , and marine environment 11 , 17 were inferred from data of high-resolution and extended longitudinal and cross-sectional scales of samples.…”

“…With the information of abundance data, the microbial co-occurrence networks can be inferred using similarity-based (e.g., Pearson or Spearman correlations) 7 – 11 or model-based methods (e.g., regression- and rule-based) 12 – 14 . For example, microbial co-occurrence networks of activated sludge from wastewater treatment plants 15 , human gut 16 , and marine environment 11 , 17 were inferred from data of high-resolution and extended longitudinal and cross-sectional scales of samples. The inferred microbial co-occurrence networks were widely applied for predicting microbial interactions.…”