five

Consistent predictors of microbial community composition across spatial scales in grasslands reveal low context-dependency

收藏
DataCite Commons2025-10-15 更新2026-05-07 收录
下载链接:
https://unisa.figshare.com/articles/dataset/Consistent_predictors_of_microbial_community_composition_across_spatial_scales_in_grasslands_reveal_low_context-dependency/30364027
下载链接
链接失效反馈
官方服务:
资源简介:
Environmental circumstances shaping soil microbial communities have been studied extensively. However, due to disparate study designs, it has been difficult to resolve whether a globally consistent set of predictors exists, or context-dependency prevails. Here, we used a network of 18 grassland sites (11 of those containing regional plant productivity gradients) to examine i) if similar abiotic or biotic factors predict both large-scale (across sites) and regional-scale (within sites) patterns in bacterial and fungal community composition, and ii) if microbial community composition differs consistently at two levels of regional plant productivity (low vs high). We found that bacteria were consistently associated with certain soil properties and both bacteria and fungi were consistently associated with plant community composition within different sites. Moreover, there was a microbial community signal that clearly distinguished high and low-productivity soils that was shared across different grasslands independent of their location in the world. In this study, we show that there is high congruence between predictors of bacterial and fungal community composition at different spatial scales and that regional productivity differences are typified by characteristic soil microbial communities across the grassland biome. These results suggest that it might be feasible to predict the overall effects of global changes on soil microbial community composition in different grasslands, as well as to discriminate fertile from infertile systems using generally applicable microbial indicators.
提供机构:
University of South Africa
创建时间:
2025-10-15
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作