A comparison of microbial composition under three tree ecosystems using the stochastic process and network complexity approaches

Abstract

Soil microbes act as “players” in regulating biogeochemical cycles, whereas environmental heterogeneity drives microbial community assembly patterns and is influenced by stochastic and deterministic ecological processes. Currently, the limited understanding of soil microbial community assembly patterns and interactions under temperate forest stand differences pose a challenge in studying the soil microbial involvement during the succession from coniferous to broad-leaved forests. This study investigated the changes in soil bacterial and fungal community diversity and community structure at the regional scale and identified the pathways influencing soil microbial assembly patterns and their interactions. The results showed that broad-leaved forest cover in temperate forests significantly increased soil pH, and effectively increased soil water content, total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents. Both soil bacterial and fungal alpha diversity indices were correlated with soil physicochemical properties, especially in broad-leaved forest. The bacterial and fungal community composition of coniferous forest was dominated by deterministic process (bacteria: 69.4%; fungi: 88.9%), while the bacterial community composition of broad-leaved forest was dominated by stochastic process (77.8%) and the fungal community composition was dominated by deterministic process (52.8%). Proteobacteria, Acidobacteriota, Actinobacteriota, and Verrucomicrobiota were the dominant phyla of soil bacterial communities in temperate forests. Whereas Ascomycota, Mortierellomycota, Basidiomycota, and Rozellomycota were the dominant phyla of soil fungal communities in temperate forests. Most members of dominant phylum were regulated by soil physical and chemical properties. In addition, the succession from temperate coniferous forest to broad-leaved forest was conducive to maintaining the complex network of soil bacteria and fungi, and the top 20 degree of the major taxa in the network reflected the positive response of microbial interactions to the changes of soil nutrients during forest succession. This study not only shows the mechanism by which species differences in temperate forests of northern China affect soil microbial community assembly processes, but also further emphasizes the importance of the soil microbiome as a key ecosystem factor through co-occurrence network analysis.

Response of soil bacteria on habitat-specialization and abundance gradient to different afforestation types

“…This was consistent with the fact that rare subgroup owns a large number of species but a low abundance, which resulted in a more even distribution of abundance than the abundant and medium abundance subgroups 64 , 65 . In addition, the most uniform distribution in broad-leaved forests and the worst distribution uniformity in shrub and coniferous forests, was consistent with the conclusion on the increasing complexity of soil bacterial network from temperate coniferous forests succession to broad-leaved forests in Liupanshan Mountain 66 , which promoted co-existence and niche differentiation by reducing intraspecific and interspecific competition of bacteria 67 . Coordinate points of community structure of each habitat-specialization subgroup of some broad-leaved forests were obviously seperated from that of other stands.…”

Fungal diversities and community assembly processes show different biogeographical patterns in forest and grassland soil ecosystems

“…Another parameter that can impact fungal community assembly processes is plant communities through direct host-microbial interactions and rhizosphere effects ( Martínez-García et al, 2015 ) and indirect mediation of soil physicochemical properties ( Zak et al, 2003 ). The results of this study do not explain the impacts of abiotic factors on fungal community assemblies, but some studies showed that fungal community assemblies could be impacted by seasonal and successional variations ( Liu et al, 2021 ; Kang et al, 2022 ; Wang et al, 2022 ). Our study’s shortcomings accommodate the paucity of evidence of the soil edaphic parameters and the consideration of other environmental variables like diversity vegetation canopies.…”

Response of Phyllosphere and Rhizosphere Microbial Communities to Salt Stress of Tamarix chinensis

“…In the calculation of the network, there were eight replicates in each group, and only seven OTUs that were not 0 were selected for correlation calculations out of the eight samples. After obtaining the correlation matrix data, the data were visualized using Cytoscape software (3.7.1) ( Figure 5 ) [ 60 ]. After that, sub-network data such as node, edge, average density, transitivity, diameter, and average path length were calculated for each sample using the “igraph” package [ 61 , 62 ].…”