Comparative study reveals habitat transformation alters microbial diversity in urban parks, suggesting management strategies.
The structure of microbial communities is deeply influenced by the nature of urban green spaces. Our research evaluated the effects of habitat transformation on soil microbial species composition, functional gene diversity, and microbial co-occurrence networks across three urban parks in Guiyang, spanning natural to semi-natural environments. Bacterial evenness, as indicated by Pielou's index, was elevated in artificial green spaces, whereas bacterial and archaeal richness were substantially higher in remnant forests. This disparity underscores a crucial shift in microbial diversity linked with urbanization and landscape management. Common bacterial, fungal, and archaeal species were present across all sites, yet specific taxa distribution varied significantly with habitat type. Remnant forests harbored a greater abundance of functional genes associated with virulence factors and potential pathogenic impacts. In contrast, artificial green spaces contained a higher prevalence of genes involved in metabolic pathways, carbohydrate-activation, and antibiotic resistance, suggesting a shift toward functional adaptations that possibly compensate for the reduced microbial species richness in more managed environments. Moreover, the complexity of microbial co-occurrence networks was notably greater in remnant forests than in artificial green spaces, reflecting an enhanced interconnectivity that supports robust ecosystem resilience and functionality. These findings emphasize that while artificial green spaces can foster communities with greater metabolic flexibility, they do so at the cost of reduced overall microbial richness and abundance. This reduction potentially undermines ecosystem microbial diversity and ecological connectivity. Therefore, our recommendations for park management include focusing on preserving native vegetation in remnant forests and reducing organic soil amendments or chemical inputs.
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Cen et al. (2025) studied this question.
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