Bacterial Communities Present Distinct Co-occurrence Networks in Sediment and Water of the Thermokarst Lakes in the Yellow River Source Area

Abstract

Thermokarst lakes are a ubiquitous and important landscape feature in cold regions and are changing tremendously due to the accelerated climate change. In thermokarst lakes, sediment and water are two distinct but highly interconnected habitats, harboring different bacterial communities in terms of taxonomic composition. However, the co-occurrence networks of these bacterial communities remain unclear. Here, we investigate the co-occurrence ecological networks of sediment and water bacterial communities for thermokarst lakes in the Yellow River Source Area on the Qinghai-Tibet Plateau. The results show that the bacterial communities construct distinct co-occurrence networks in sediment and water. The metacommunity network was parsed into four major modules formed by the operational taxonomic units (OTUs) enriched in sediment or water independently, and water-enriched OTUs exhibited much closer interconnections than sediment-enriched OTUs. When considering the sediment and water bacterial networks separately, different topological properties and modular patterns present: the sediment bacterial network was more clustered while the modules less responded to the environmental variables. On the contrary, the water bacterial network was more complex with the OTUs more interconnected and its modules more responded to the environmental variables. Moreover, the results of the structural equation model suggest that, by the influence of environmental variations on individual modules, the water bacterial communities would be more vulnerable under the fact of accelerating climate change. This study provides insights beyond a conventional taxonomic perspective, adding our knowledge of the potential mechanisms structuring bacterial community assembly and improving our prediction of the responses of this fast-changing ecosystem to future climate change.

High-proportions of tailwater discharge alter microbial community composition and assembly in receiving sediments

“…7 ). The majority of the two clusters belonged to peripheral nodes, indicating that most of them were connected within the module, with fewer connections to nodes in other modules 37 . No module or network center was identified in either the low- or high-proportion cluster.…”

“…In general, the discharge of tailwater affects typical environmental factors, particularly the TP and ammonia nitrogen contents, in the receiving water body, which further impacts the succession of microbial communities in the receiving water body and the physicochemical and biological processes in the ecosystem 37 , 52 . Future in-depth work will use metagenomics to illustrate changes in the abundance of functional genes involved in geochemical cycles, such as carbon, nitrogen, phosphorus, and sulfur.…”

Cross-domain interactions confer stability to benthic biofilms in proglacial streams

“…Similarly, Crump et al (Crump et al, 2009) identified microbial keystone taxa, albeit neither rare nor abundant, that are central to ecosystem-level metabolic activity. Additionally, as Ren et al (Ren et al, 2021) highlighted, there may be several causes including but not limited to environmental variables and/or the metacommunity structure of tributaries in Otemma. It is plausible that both metacommunity structure such as connectivity, directionality of dispersal, and also if these sites are rather discrete communities or part of a gradient/ continuum may be contributing to these differences (Leibold and Mikkelson, 2002).…”

Differences in bacterial diversity, composition, and community networks in lake water across three distinct regions on the Qinghai-Tibet Plateau

“…For bacterial networks, only OTUs with average of relative abundance larger than 0.01% that exist in at least 50% of all samples in each region were chosen to construct the bacterial network . Furthermore, the relationships between OTUs with Spearman's correlation coefficient r > 0.8 or r < −0.8 and Benjamini-Hochberg p < 0.01 were selected for network construction (Zhao et al, 2016;Ji et al, 2019;Ren et al, 2021b). Networks were analyzed and visualized using the "igraph" package and Gephi platform (https://gephi.org/) (Zhou et al, 2019).…”