Abstract

External inorganic and organic nitrogen (N) inputs can contrastingly affect the transformation and availability of N in forest soils. Studies have mainly focused on the effects of inorganic N enrichment, whereas little is known about the effects of organic N input on soil gross N transformation and the underlying microbial mechanisms. Here we conducted a laboratory 15N tracing study in a temperate needle-broadleaved mixed forest with a fertilization rate of 0, 20, 60, and 120 kg urea-N ha−1 yr−1 over three years. We investigated the key drivers of soil N transformation processes using a 15N tracing model in the context of selected soil chemical properties and microbial characteristics. Urea addition did not change soil gross N mineralization rates, while stimulating mineralization of labile organic N (MNlab). Urea addition at a rate of 120 kg N ha−1 yr−1 significantly increased autotrophic nitrification and gross nitrification rates by 88% and 96%, respectively. In contrast, all the three levels of urea addition significantly reduced gross microbial N immobilization by 28% to 52%, leading to an increase in the accumulation of soil NO3−-N in the top 10 cm soil layer by 38% to 88%. The changes in autotrophic nitrification were primarily driven by acid-tolerant ammonia-oxidizing archaea (AOA). Fungi were responsible for the change in heterotrophic nitrification under organic N enrichment. Gross N transformation rates were predominately regulated by AOA and fungal abundances as well as soil NO3−-N content under high level of organic N addition. The response of soil N transformation to exogenous organic N input depended on N addition level with the threshold rate being estimated to be 60–120 kg N ha−1 yr−1. All lines of evidence showed that the temperate needle-broadleaved mixed forest is moving towards an opener microbial N cycle under elevated organic N deposition. Our finding suggests that the effect of organic N input on soil gross N transformation is different from that of inorganic N input, which should be considered in ecosystem process models.

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