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Abiotic and biotic drivers of soil microbial diversity in an intensively grazed natural ecosystem.


ABSTRACT: Many ecosystems worldwide are threatened by anthropogenic causes, with high-intensity grazing by large herbivores as a significant risk factor for biodiversity. Although the drivers of α-diversity are well-studied for animal and plant communities, they are often overlooked for soil microbes, particularly in natural systems. We therefore used a novel innovative information-theoretic approach to structural equation model selection and multimodel path coefficient averaging to identify these drivers. Our findings show that abiotic soil characteristics, primarily soil pH, significantly shape the α-diversity of both bacteria and fungi. Biotic factors like vegetation Shannon diversity and aboveground biomass also significantly drive microbial α-diversity, especially for fungi. Our statistical approach adds robustness to our results and conclusions, offering valuable insights into the complex interactions shaping soil microbial communities in intensively grazed natural systems. These insights are crucial for developing more effective and comprehensive future ecosystem management and restoration strategies.

SUBMITTER: Kinsbergen DTP 

PROVIDER: S-EPMC11955547 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature

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Abiotic and biotic drivers of soil microbial diversity in an intensively grazed natural ecosystem.

Kinsbergen Daan T P DTP   Kooijman Annemieke M AM   Morriën Elly E   English Katherine K   Oostermeijer J Gerard B JGB  

npj biodiversity 20250330 1


Many ecosystems worldwide are threatened by anthropogenic causes, with high-intensity grazing by large herbivores as a significant risk factor for biodiversity. Although the drivers of α-diversity are well-studied for animal and plant communities, they are often overlooked for soil microbes, particularly in natural systems. We therefore used a novel innovative information-theoretic approach to structural equation model selection and multimodel path coefficient averaging to identify these drivers  ...[more]

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