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- W4387002193 abstract "Natural restoration (NR, e.g., secondary succession) and artificial restoration (AR, e.g., afforestation) are key approaches for rehabilitating degraded land; however, a comparative assessment of microbial network between these approaches is lacking. We compared bacterial networks under NR and AR in two different watersheds on the Loess Plateau. Our findings revealed significantly heightened network complexity under NR compared to AR, including metrics such as node, edge, modularity, degree, centrality, and keystone nodes. NR's network robustness exceeded AR by 19.45-35.9% and 7.79-17.74% in the two watersheds, aligning with the ecological principle that complexity begets stability. The significantly higher negative/positive cohesion and natural connectivity under NR also support its better network stability than AR. Integrated analysis of paired sequencing data from five Loess Plateau studies conducted on the Loess Plateau further confirmed the higher complexity and stability of bacterial networks under NR. Further analysis unveiled biological interactions as primary drivers of bacterial co-occurrence (on average 84.21% of links), surpassing the influence of environmental filtering (5.17%) or dispersal limitation (4.2%). Importantly, networked communities under NR exhibited generally stronger linkages with various ecosystem function than AR. Overall, our study provides insights into vegetation restoration strategies from the perspective of microbial network, underscoring natural regeneration's potential as a superior remedy for degraded-land restoration." @default.
- W4387002193 created "2023-09-26" @default.
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- W4387002193 date "2023-11-01" @default.
- W4387002193 modified "2023-10-14" @default.
- W4387002193 title "Natural restoration exhibits better soil bacterial network complexity and stability than artificial restoration on the Loess Plateau, China" @default.
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- W4387002193 doi "https://doi.org/10.1016/j.jenvman.2023.119052" @default.
- W4387002193 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37742562" @default.
- W4387002193 hasPublicationYear "2023" @default.
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