The mechanisms of community assembly are a central focus in the field of microbial ecology. However, to what extent these mechanisms differ in importance by traits of groups is poorly understood. Here we quantified the importance of neutral and niche processes in community assembly for bacteria, habitat specialists and generalists in 21 plateau lakes of China. Results showed that both neutral and niche processes played a critical role in the assembly of entire bacterial communities, shaping a unique biogeographical pattern. A few habitat generalists and many specialists were identified. Interestingly, habitat specialists were only governed by niche process, with seven significant environmental variables—salinity, dissolved oxygen, water transparency, total phosphorus, ammonium-nitrogen, temperature and total nitrogen—independently explaining 40.3% of the biological variation. By contrast, habitat generalists were strongly driven by neutral process, with 50.9% of the variation of detection frequency explained in neutral community model. Only three environmental variables—salinity, total nitrogen and dissolved oxygen—significantly affected the distribution of habitat generalists, independently explaining 13.6% of the variation. Governed by different assembly mechanisms, habitat specialists and generalists presented disparate biogeographical patterns. Our result emphasizes the importance of investigating the bacterial community assembly at more refined levels than entire communities.

The revolution of molecular techniques in the field of microbial ecology not only allows an in‐depth exploration of abundant bacteria, but also confirms the existence of “rare biosphere.” However, the mechanisms underlying the assembly process of abundant and rare bacteria in plateau lakes are still poorly understood. In the present study, distribution of abundant and rare bacteria in 21 lakes on Yungui Plateau, China and the relative importance of deterministic and neutral factors to their biogeographic patterns were examined. Results surprisingly showed that similar biogeographic patterns of rare and abundant taxa were generally shaped via similar assembly mechanisms. For both bacterial groups, deterministic process strongly influenced the distribution, while neutral process was not at play. In addition, they both exhibited a weak distance–decay relationship. Nevertheless, environmental drivers of rare and abundant taxa and their importance were not exactly the same. Water physicochemical property was the most dominant driver shaping the biogeographic pattern of rare taxa, followed by lake morphology and watershed land‐use, while for abundant taxa, watershed land‐use was the most dominant driver and lake morphology did not play a significant role. This study suggested that rare bacteria in high‐altitude aquatic ecosystems might experience equally low extinction risk and respond to environmental changes in a similar manner to abundant ones, but their ecological niches and functions were not identical. To fully understand the assembly mechanism of bacterial community, it is necessary to differentiate the community by traits of taxa.

Shotgun metagenomics has become a pivotal technology in microbiome research, enabling in‐depth analysis of microbial communities at both the high‐resolution taxonomic and functional levels. This approach provides valuable insights of microbial diversity, interactions, and their roles in health and disease. However, the complexity of data processing and the need for reproducibility pose significant challenges to researchers. To address these challenges, we developed EasyMetagenome, a user‐friendly pipeline that supports multiple analysis methods, including quality control and host removal, read‐based, assembly‐based, and binning, along with advanced genome analysis. The pipeline also features customizable settings, comprehensive data visualizations, and detailed parameter explanations, ensuring its adaptability across a wide range of data scenarios. Looking forward, we aim to refine the pipeline by addressing host contamination issues, optimizing workflows for third‐generation sequencing data, and integrating emerging technologies like deep learning and network analysis, to further enhance microbiome insights and data accuracy. EasyMetageonome is freely available at https://github.com/YongxinLiu/EasyMetagenome.