Trajectories of soil microbial recovery in response to restoration strategies in one of the largest and oldest open-pit phosphate mine in Asia

Kai Yan; Ya-Huang Luo; Yun-Ju Li; Ling-Pan Du; Heng Gui; Si-Chong Chen

doi:10.1016/j.ecoenv.2023.115215

Trajectories of soil microbial recovery in response to restoration strategies in one of the largest and oldest open-pit phosphate mine in Asia

Ecotoxicol Environ Saf. 2023 Jul 6:262:115215. doi: 10.1016/j.ecoenv.2023.115215. Online ahead of print.

Authors

Kai Yan¹, Ya-Huang Luo², Yun-Ju Li³, Ling-Pan Du³, Heng Gui⁴, Si-Chong Chen⁵

Affiliations

¹ College of Resources and Environment, Yunnan Agricultural University, Kunming 650201 Yunnan, China.
² CAS Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.
³ The State Phosphorus Resource Development and Utilization Engineering Technology Research Centre, Yunnan Phosphate Chemical Group Co. Ltd, Kunming 650607, China.
⁴ Department of Economic Plants and Biotechnology, Yunnan Key Laboratory for Wild Plant Resources, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China; Centre for Mountain Futures (CMF), Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China. Electronic address: guiheng@mail.kib.ac.cn.
⁵ Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 Hubei, China; Millennium Seed Bank, Royal Botanic Gardens Kew, Wakehurst, West Sussex RH17 6TN, UK. Electronic address: chensichong0528@gmail.com.

PMID: 37421785
DOI: 10.1016/j.ecoenv.2023.115215

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.

Keywords: Dianchi basin; Hierarchical bayesian model; Microbial function; Open-pit phosphate mine reclamation; Restoration strategies; Scenario analysis.