Integrating pH into the metabolic theory of ecology to predict bacterial diversity in soil

Lu Luan, Yuji Jiang, Francisco Dini-Andreote, Thomas W. Crowther, Pengfa Li, Mohammad Bahram, Jie Zheng, Qinsong Xu, Xue Xian Zhang, Bo Sun

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Microorganisms play essential roles in soil ecosystem functioning and maintenance, but methods are currently lacking for quantitative assessments of the mechanisms underlying microbial diversity patterns observed across disparate systems and scales. Here we established a quantitative model to incorporate pH into metabolic theory to capture and explain some of the unexplained variation in the relationship between temperature and soil bacterial diversity. We then tested and validated our newly developed models across multiple scales of ecological organization. At the species level, we modeled the diversification rate of the model bacterium Pseudomonas fluorescens evolving under laboratory media gradients varying in temperature and pH. At the community level, we modeled patterns of bacterial communities in paddy soils across a continental scale, which included natural gradients of pH and temperature. Last, we further extended our model at a global scale by integrating a meta-analysis comprising 870 soils collected worldwide from a wide range of ecosystems. Our results were robust in consistently predicting the distributional patterns of bacterial diversity across soil temperature and pH gradients—with model variation explaining from 7 to 66% of the variation in bacterial diversity, depending on the scale and system complexity. Together, our study represents a nexus point for the integration of soil bacterial diversity and quantitative models with the potential to be used at distinct spatiotemporal scales. By mechanistically representing pH into metabolic theory, our study enhances our capacity to explain and predict the patterns of bacterial diversity and functioning under current or future climate change scenarios.

Original languageEnglish (US)
Article numbere2207832120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number3
DOIs
StatePublished - Jan 17 2023

All Science Journal Classification (ASJC) codes

  • General

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