Abstract
Southwestern China has the largest geological phosphorus-rich mountain in the world, which is seriously degraded by mining activities. Understanding the trajectory of soil microbial recovery and identifying the driving factors behind such restoration, as well as conducting corresponding predictive simulations, can be instrumental in facilitating ecological rehabilitation. Here, high-throughput sequencing and machine learning-based approaches were employed to investigate restoration chronosequences under four restoration strategies (spontaneous re-vegetation with or without topsoil; artificial re-vegetation with or without the addition of topsoil) in one of the largest and oldest open-pit phosphate mines worldwide. Although soil phosphorus (P) is extremely high here (max = 68.3 mg/g), some phosphate solubilizing bacteria and mycorrhiza fungi remain as the predominant functional types. Soil stoichiometry ratios (C:P and N:P) closely relate to the bacterial variation, but soil P content contributes less to microbial dynamics. Meanwhile, as restoration age increases, denitrifying bacteria and mycorrhizal fungi significantly increased. Significantly, based on partial least squares path analysis, it was found that the restoration strategy is the primary factor that drives soil bacterial and fungal composition as well as functional types through both direct and indirect effects. These indirect effects arise from factors such as soil thickness, moisture, nutrient stoichiometry, pH, and plant composition. Moreover, its indirect effects constitute the main driving force towards microbial diversity and functional variation. Using a hierarchical Bayesian model, scenario analysis reveals that the recovery trajectories of soil microbes are contingent upon changes in restoration stage and treatment strategy; inappropriate plant allocation may impede the recovery of the soil microbial community. This study is helpful for understanding the dynamics of the restoration process in degraded phosphorus-rich ecosystems, and subsequently selecting more reasonable recovery strategies.
Original language | English |
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Article number | 115215 |
Journal | Ecotoxicology and Environmental Safety |
Volume | 262 |
DOIs | |
State | Published - 1 Sep 2023 |
Externally published | Yes |
Keywords
- Dianchi basin
- Hierarchical bayesian model
- Microbial function
- Open-pit phosphate mine reclamation
- Restoration strategies
- Scenario analysis
ASJC Scopus subject areas
- Pollution
- Public Health, Environmental and Occupational Health
- Health, Toxicology and Mutagenesis