Journal of Sustainable Agriculture and Environment (Dec 2023)

Forest restoration decouple soil C:N:P stoichiometry but has little effects on microbial biodiversity globally

  • Ximei Han,
  • Kaiyan Zhai,
  • Shengen Liu,
  • Hongyang Chen,
  • Yanghui He,
  • Zhenggang Du,
  • Ruiqiang Liu,
  • Dingqin Liu,
  • Lingyan Zhou,
  • Xuhui Zhou,
  • Guiyao Zhou

DOI
https://doi.org/10.1002/sae2.12084
Journal volume & issue
Vol. 2, no. 4
pp. 468 – 478

Abstract

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Abstract Introduction Forest restoration is an effective way to promote ecosystem functions and mitigate climate change. However, how forest restoration affect soil C:N:P stoichiometry and microbial biodiversity, as well as their linkage across contrasting forest types globally remains largely illusive. Materials and Methods Here we conducted a global meta‐analysis by synthesizing 121 published papers with 1649 observations to explore how forest restoration affect soil C:N:P stoichiometry and microbial biodiversity globally. Results Forest restoration significantly increased soil total carbon (C), nitrogen (N) and phosphorus (P) content, whereas having no significant impact on most microbial diversity indicator, except for an enhancement in bacterial operational taxonomic unit and fungal Simpson. Meanwhile, forest restoration effects on soil C:N:P stoichiometry varied with different forest types, with promoting more soil C and P in ectomycorrhizal than those in arbuscular mycorrhizal forests. Meanwhile, forest restoration induced changes in soil N and P were positively correlated with microbial Shannon index. More importantly, forest restoration effects on soil C:N:P stoichiometry and microbial biodiversity were regulated by climate factors such as mean annual temperature and mean annual precipitation. Conclusion Our results highlight the crucial role of forest restoration in decoupling the biogeochemical cycles of C, N and P through changes in microbial biodiversity. Therefore, incorporating the decouple effects of forest restoration on soil C:N:P stoichiometry into Earth system models may improve predictions of climate–forest feedbacks in the Anthropocene.

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