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The neglected role of micronutrients in predicting soil microbial structure.

Ziheng PengChunling LiangMin GaoYu QiuYanjing PanHang GaoYu LiuXiaomeng LiGehong WeiShuo Jiao
Published in: NPJ biofilms and microbiomes (2022)
Predicting the distribution patterns of soil microbial communities requires consideration of more environmental drivers. The effects of soil micronutrients on composition of microbial communities are largely unknown despite micronutrients closely relating to soil fertility and plant communities. Here we used data from 228 agricultural fields to identify the importance of micronutrients (iron, zinc, copper and manganese) in shaping structure of soil microbial communities (bacteria, fungi and protist) along latitudinal gradient over 3400 km, across diverse edaphic conditions and climatic gradients. We found that micronutrients explained more variations in the structure of microbial communities than macronutrients in maize soils. Moreover, micronutrients, particularly iron and copper, explained a unique percentage of the variation in structure of microbial communities in maize soils even after controlling for climate, soil physicochemical properties and macronutrients, but these effects were stronger for fungi and protist than for bacteria. The ability of micronutrients to predict the structure of soil microbial communities declined greatly in paddy soils. Machine learning approach showed that the addition of micronutrients substantially increased the predictive power by 9-17% in predicting the structure of soil microbial communities with up to 69-78% accuracy. These results highlighted the considerable contributions of soil micronutrients to microbial community structure, and advocated that soil micronutrients should be considered when predicting the structure of microbial communities in a changing world.
Keyphrases
  • heavy metals
  • machine learning
  • plant growth
  • climate change
  • human health
  • artificial intelligence