Defining seasonal marine microbial community dynamics

Abstract

Here we describe, the longest microbial time-series analyzed to date using high-resolution 16S rRNA tag pyrosequencing of samples taken monthly over 6 years at a temperate marine coastal site off Plymouth, UK. Data treatment effected the estimation of community richness over a 6-year period, whereby 8794 operational taxonomic units (OTUs) were identified using single-linkage preclustering and 21 130 OTUs were identified by denoising the data. The Alphaproteobacteria were the most abundant Class, and the most frequently recorded OTUs were members of the Rickettsiales (SAR 11) and Rhodobacteriales. This near-surface ocean bacterial community showed strong repeatable seasonal patterns, which were defined by winter peaks in diversity across all years. Environmental variables explained far more variation in seasonally predictable bacteria than did data on protists or metazoan biomass. Change in day length alone explains >65% of the variance in community diversity. The results suggested that seasonal changes in environmental variables are more important than trophic interactions. Interestingly, microbial association network analysis showed that correlations in abundance were stronger within bacterial taxa rather than between bacteria and eukaryotes, or between bacteria and environmental variables.

Evaluating community–environment relationships along fine to broad taxonomic resolutions reveals evolutionary forces underlying community assembly

“…Sequence-based prokaryotic community data sets We used eight data sets: (1) Lauber 'America-Soils' study (Lauber et al, 2009) (referred to as case #1 hereafter), (2) Chu 'Arctic-Soils' study (Chu et al, 2010) (referred to as case #2 hereafter), (3) Ramirez 'NYpark-Soils' study (Ramirez et al, 2014) (referred to as case #3 hereafter), (4) Zarraonaindia 'NYfarmSoils' study (Zarraonaindia et al, 2015) (referred to as case #4 hereafter), (5) Sunagawa 'TaraSur-Seawaters' study (Sunagawa et al, 2015) (referred to as case #5 hereafter), (6) Sunagawa 'TaraChl-Seawaters' study (Sunagawa et al, 2015) (referred to as case #6 hereafter), (7) Gilbert 'WEC-Seawaters' study (Gilbert et al, 2012) (referred to as case #7 hereafter) and (8) Yeh 'SECS-Seawaters' study (Yeh et al, 2015) (referred to as case #8 hereafter) to test our theoretical framework regarding how the strength of communityenvironment relationships varies with changes in taxonomic resolution (Figure 1). We summarize the characteristics of these sequence-based prokaryotic community data sets in Table 1.…”

Marine Oil-Degrading Microorganisms and Biodegradation Process of Petroleum Hydrocarbon in Marine Environments: A Review

“…And, some models were established to describe the biodegradation process [27,76,79,81], such as the MichaelisMenten equation, shown in Eq. 1:…”

Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes

“…Microbial communities throughout the water column show long-term, seasonal and short-term dynamics that relate to various biological, chemical and physical environmental parameters (Gilbert et al, 2012;Giovannoni and Vergin, 2012;Chow et al, 2013;Hatosy et al, 2013;Needham et al, 2013;Cram et al, 2014a;Fuhrman et al, 2015). Microbial interactions have been observed experimentally (Jurgens et al, 1999;Miller and Bassler, 2001;Jürgens and Matz, 2002;Bonilla-Findji et al, 2009) through physical attachment ) and inferred through genomic and physiological information (for example, Bothe et al, 2010).…”

“…In marine surface waters, studies using pairwise association analysis over time have suggested that the abundance of particular bacteria tend to be best predicted by the abundance of other microorganisms, rather than variability of measured parameters (Fuhrman and Steele, 2008;Steele et al, 2011;Chow et al, 2013Chow et al, , 2014. Network analysis has identified many associations between marine microorganisms that are driven by seasonal variability, especially at locations where seasonality is strong (Gilbert et al, 2012), and this seasonal pattern has been de-convoluted in lake environments to show different inter-organismal associations independent of seasonality (Kara et al, 2012). Seasonality has also been seen just above the sea floor and suggests surface influences on deeper depths by way of sinking particle flux and/or migrating zooplankton (see Cram et al, 2014a).…”