Revealing the mechanisms governing the complex microbial community assembly is a central issue in microbial ecology. Null models are commonly used to quantitatively disentangle the relative importance of deterministic vs. stochastic processes in structuring the compositional variations. However, microbial profiling is influenced by random sampling issues, which lead to overestimated -diversity of microbial communities and may further affect stochasticity inference. By implementing simulated datasets, we investigated whether and how microbial stochasticity inference is affected by random sampling issues. Our results demonstrated solid evidences that random sampling dramatically overestimated the -diversity of microbial communities, which further led to overestimated community stochasticity inference. The effects of random sampling issues on stochasticity inference for the whole community and the abundant subcommunities were different using different null models. The stochasticity of rare subcommunities, however, was persistently overestimated no matter which null model was used. Such effects of random sampling issues on community stochasticity inference were constantly observed for communities with different -diversity. As more studies begin to focus on the different mechanisms governing abundant and rare subcommunities, we urge cautions be taken for microbial stochasticity inference based on -diversity (e.g. null models), especially for rare subcommunities with stochastic ratio slightly higher than 0.5. When necessary, the cutoff used for judging the relative importance of deterministic vs. stochastic processes shall be redefined.

Revealing the ecological mechanisms driving the diversity patterns followed by microbial communities across space and through time is an essential issue in microbial community ecology. In this study, two typical spatial scaling patterns, including diversity-area and distance-decay relationships, were investigated for microbial communities in an ocean sediment ecosystem. Strong spatial scaling patterns were observed at the whole community level and for the rare subcommunities, but hardly for the abundant subcommunities. Rare subcommunities were mainly responsible for the observed spatial scaling patterns, as also confirmed by extending spatial scaling diversity metrics to Hill numbers. Distinct ecological mechanisms underlay the differed spatial scaling patterns followed by abundant and rare subcommunities. Both environmental heterogeneity and local community assembly mechanisms drove the microbial spatial scaling patterns. Environmental heterogeneity was significantly associated with the spatial scaling metrics of rare but not abundant subcommunities. Strong ecological drift and dispersal limitation underlay the spatial scaling patterns of rare subcommunities, whereas high homogeneous selection weakened the spatial scaling patterns of abundant subcommunities. Such differed mechanisms driving the spatial scaling patterns of abundant and rare subcommunities were also experimentally confirmed by deep sequencing experiments. This study links microbial spatial scaling patterns with ecological mechanisms, providing novel mechanistic insights into the diversity patterns followed by different types of microbes.