Differential vulnerability of mineral-associated and particulate soil organic carbon to nitrogen addition in a subtropical forest

Abstract. Nitrogen (N) deposition rates of terrestrial ecosystems have gradually declined but are still high in some areas, and previous studies have reported that N addition elicits diverse impacts on soil organic carbon (SOC) pools. SOC can be divided into different functional fractions, namely particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). The responses of these fractions to N addition should be elucidated to better understand the changes in SOC pools. We investigated the effects of N addition treatments (+0, +40, and +80 kg N ha⁻¹ yr⁻¹) in a subtropical Castanopsis fabri forest to simulate N deposition. The surface (0–10 cm) SOC content in different fractions, aboveground litter biomass, fine root (diameter < 2 mm) biomass, soil exchangeable cation content, and soil enzyme activity under different N addition treatments were measured. The results showed that 1) N addition exerted a positive effect on POC content but did not significantly affect MAOC content. 2) POC content was negatively correlated with pH and soil enzyme activity and positively correlated with aboveground litter biomass, suggesting that POC accumulation was influenced by aboveground litter input and microbial decomposition. 3) Root biomass was unaffected significantly by the addition of N, which could be responsible for the limited response of MAOC to N addition. Furthermore, a close negative relationship was observed between exchangeable Al³⁺ and Ca²⁺ or K⁺ contents, indicating the presence of a trade-off between negative effects of exchangeable cations on SOC bridging and their positive effects on SOC adsorption, thus resulting in an insignificant reaction of MAOC to N addition. Overall, N addition reduces the persistence and increases the nutrient density of SOC, and MAOC with more protection is less vulnerable to N addition than POC with less protection. By incorporating these nuances into ecosystem models, it is possible to more accurately predict SOC dynamics in response to global change.