藏东南典型暗针叶林不同土壤剖面微生物群落特征

深层土壤中的微生物群落对陆地生态系统养分和能量循环转化过程不可或缺，研究青藏高原典型暗针叶林带土壤微生物群落在土壤垂直剖面的变化特征，对深入认识高寒区域森林生态系统土壤微生物群落构建特征及全球变化影响预测具有重要意义。运用Illumina Miseq高通量测序技术和分子生态网络分析，研究藏东南色季拉山暗针叶林带表层（0-20 cm）和底层土壤（40-60 cm）微生物群落组成及分子生态网络结构。研究结果表明随着土壤深度增加，真菌和细菌的丰富度和Shannon多样性指数显著降低。主坐标分析（PCoA）显示土壤深度显著影响真菌和细菌的群落结构（P < 0.01）。不同微生物种群对土壤深度的响应有显著差异，座囊菌纲（Dothideomycetes）、银耳纲（Tremellomycetes）和拟杆菌门（Bacteroidetes）、变形菌门（Proteobacteria）的相对丰度随剖面加深而显著降低，而古菌根菌纲（Archaeorhizomycetes）和绿弯菌门（Chloroflexi）则显著增加。分子生态网络分析发现，真菌网络以负相关连接为主（占总连接数65%-98%），而细菌网络以正相关连接为主（69%-75%），真菌和细菌网络中正相关连接的比例均随剖面加深而增加。底层土壤真菌和细菌网络的平均连接度和平均聚类系数均高于表层土壤，说明微生物网络随土壤深度的增加而变得更复杂。真菌网络的平均路径距离和模块性在底层土壤均大于表层土壤，意味着真菌网络应对环境变化的稳定性随剖面加深而增加，而细菌网络则正相反，在表层土壤的稳定性更强。真菌网络中连接节点的个数随剖面加深而增加，锤舌菌纲（Leotiomycetes）是连接网络模块的关键菌种；在细菌网络中模块枢纽和连接节点则随剖面加深而降低，并且放线菌门、变形菌门等关键种群在分子生态网络中的功能在表层和底层土壤有明显差异。综上所述，藏东南色季拉山暗针叶林带深层土壤中微生物群落特征与表层土壤有显著差别，揭示影响深层土壤微生物网络构建和稳定的关键种群，对深入理解和预测青藏高原森林生态系统对全球变化的响应与反馈有重要意义。

Depth-related characteristics of soil microbial community along the soil profile of typical dark coniferous forest in southeast Tibet

Microbial communities in subsurface soil are essential for the cycling and transformation of nutrient and energy in terrestrial ecosystems. Therefore, investigation of the variation in soil microbial community along the soil profile in typical dark coniferous forest in Tibetan Plateau is of great significance to improve the knowledge of soil microbial assembly and predict their responses to global changes in the forest ecosystems of alpine area. In this study, methods of Illumina Miseq high-throughput sequencing and the molecular ecological network analysis were applied to investigate the composition and ecological network of microbial communities in the topsoil (0-20 cm) and subsoil (40-60 cm) of the dark coniferous forest in Mount Segrila, southeast Tibet. The richness and Shannon diversity index of fungi and bacteria decreased significantly with the increase of soil depth. Results of the principal coordinate analysis (PCoA) showed that the community structure of both fungi and bacteria was significantly (P < 0.01) affected by soil depth. Moreover, the relative abundance of Dothideomycetes, Tremellomycetes, Bacteroidetes, and Proteobacteria decreased significantly with increasing soil depth, whereas Archaeorhizomycetes and Chloroflexi increased significantly with soil depth. For both topsoil and subsoil, fungal networks were dominated by negative links (65%-98% of total links), while more positive links (69%-75%) were observed in bacterial networks. In addition, the proportion of positive links in both fungal and bacterial networks increased with soil depth. The average connectivity and the average clustering coefficient of fungal and bacterial networks were both higher in topsoil than those in subsoil, indicating that microbial networks were more complex in the deeper soil layers. The average path distance and modularity of fungal network in topsoil were higher than those in subsoil, implying that the stability of the fungal network to environmental changes increased with soil depth. By contrast, those indices of bacterial network decreased with soil depth, suggesting that bacterial network was likely more stable in the topsoil. There were more connectors in subsoil fungal networks, and Leotiomycetes was the key taxa in connecting different modules in both topsoil and subsoil fungal networks. By contrary, there were less module hubs and connectors in subsoil bacterial networks. Actinobacteria, Proteobacteria and other bacterial taxa that were identified as key network structuring taxa functioned differentially between the topsoil and subsoil. In summary, there were significant differences in the characteristics of microbial communities between the subsoil and topsoil in the dark coniferous forest of Mount Segrila, southeast Tibet. It is of great significance to reveal the key taxa for the formation and stabilization of microbial networks in deeper soil layers, which will help to improve further understanding and prediction of the response and feedback of the forest ecosystem of the Tibet Plateau to global change.