A central challenge in microbial ecology is to understand the underlying mechanisms driving community assembly, particularly in the continuum of species sorting and dispersal limitation. However, little is known about the relative importance of species sorting and dispersal limitation in shaping marine microbial communities; especially, how they are related to organism types/traits and water depth. Here, we used variation partitioning and null model analysis to compare mechanisms driving bacterial and protist metacommunity dynamics at the basin scale in the East China Sea, based on MiSeq paired-end sequencing of 16S ribosomal DNA (rDNA) and 18S rDNA, respectively, in surface, deep chlorophyll maximum and bottom layers. Our analyses indicated that protist communities were governed more strongly by species sorting relative to dispersal limitation than were bacterial communities; this pattern was consistent across the three-depth layers, albeit to different degrees. Furthermore, we detected that bacteria exhibited wider habitat niche breadths than protists, whereas, passive dispersal abilities were not appreciably different between them. Our findings support the 'size-plasticity' hypothesis: smaller organisms (bacteria) are less environment filtered than larger organisms (protists), as smaller organisms are more likely to be plastic in metabolic abilities and have greater environmental tolerance.

Summary Currently defined ecotypes in marine cyanobacteria Prochlorococcus and Synechococcus likely contain subpopulations that themselves are ecologically distinct. We developed and applied high‐throughput sequencing for the 16S‐23S rRNA internally transcribed spacer (ITS) to examine ecotype and fine‐scale genotypic community dynamics for monthly surface water samples spanning 5 years at the San Pedro Ocean Time‐series site. Ecotype‐level structure displayed regular seasonal patterns including succession, consistent with strong forcing by seasonally varying abiotic parameters (e.g. temperature, nutrients, light). We identified tens to thousands of amplicon sequence variants (ASVs) within ecotypes, many of which exhibited distinct patterns over time, suggesting ecologically distinct populations within ecotypes. Community structure within some ecotypes exhibited regular, seasonal patterns, but not for others, indicating other more irregular processes such as phage interactions are important. Network analysis including T4‐like phage genotypic data revealed distinct viral variants correlated with different groups of cyanobacterial ASVs including time‐lagged predator–prey relationships. Variation partitioning analysis indicated that phage community structure more strongly explains cyanobacterial community structure at the ASV level than the abiotic environmental factors. These results support a hierarchical model whereby abiotic environmental factors more strongly shape niche partitioning at the broader ecotype level while phage interactions are more important in shaping community structure of fine‐scale variants within ecotypes.

Spatial variation of communities composition (metacommunities) results from multiple assembly mechanisms, including environmental filtering and dispersal; however, whether and why the relative importance of the assembly mechanisms in shaping bacterial metacommunity changes through time in marine pelagic systems remains poorly studied. Here, we applied the elements of metacommunity structure framework and the variation partitioning framework to examine whether temporal variation of hydrographic conditions influences bacterioplankton metacommunity dynamics in the southern East China Sea (ECS). The spatiotemporal variation of bacterial communities composition was revealed using 454 pyrosequencing of 16S rDNA. In addition to the whole bacterial community, we analyzed four dominant taxonomic groups (Cyanobacteria, Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria) separately. Our analyses indicate that, considering the whole community level, the determinism of metacommunity structure varied among seasons. When the degree of connectivity was low (December), the metacommunity exhibited random distribution and was explained mainly by the environmental component. However, Clementsian metacommunity was found at intermediate connectivity (May), during which the environmental and spatial predictors were both significant. When connectivity was high (August), a random distribution pattern was found and no significant effect of environmental filtering or dispersal limitation was detected. Nevertheless, when considering different taxonomic groups, the differences in metacommunity dynamics among groups were found. Our results suggest that the driving forces of metacommunity dynamics varied depending on hydrography, as the degrees of environmental heterogeneity and connectivity among habitat patches were determined by circulation pattern. Moreover, mechanisms varied among different taxonomic groups, suggesting that differential dispersal capacity among taxonomic groups should be integrated into community assembly studies.

Despite the routine use of standards and blanks in virtually all chemical or physical assays and most biological studies (a kind of “control”), microbiome analysis has traditionally lacked such standards. Here we show that unexpected problems of unknown origin can occur in such sequencing runs and yield completely incorrect results that would not necessarily be detected without the use of standards. Assuming that the microbiome sequencing analysis works properly every time risks serious errors that can be detected by the use of mock communities.

Prior cross-sectional analyses have demonstrated an association between subclinical hypothyroidism and metabolic syndrome and selected components. However, the temporal relation between metabolic syndrome and declining thyroid function remains unclear. In a prospective study, an unselected cohort of 66,822 participants with and without metabolic syndrome were followed. A proportional hazards regression model was used to estimate hazard ratios (HRs) and 95% CIs for hypothyroidism. Exploratory analyses for the relation between components of metabolic syndrome and declining thyroid function were also undertaken. During an average follow-up of 4.2 years, the incident rates for subclinical hypothyroidism were substantially higher in participants who began the study with metabolic syndrome compared with metabolically normal controls. After controlling for risk factors, patients with metabolic syndrome were at a 21% excess risk of developing subclinical hypothyroidism (adjusted HR 1.21; 95% CI 1.03–1.42). When individual components were analyzed, an increased risk of subclinical hypothyroidism was associated with high blood pressure (1.24; 1.04–1.48) and high serum triglycerides (1.18; 1.00–1.39), with a trend of increasing risk as participants had additional more components. Individuals with metabolic syndrome are at a greater risk for developing subclinical hypothyroidism, while its mechanisms and temporal consequences of this observation remain to be determined.