Nitrogen fertilization accelerated early-stage decomposition but suppressed late-stage decomposition: a ten-year experiment in a Dahurian larch plantation

• Ten-year nitrogen fertilization accelerated early-stage but suppressed late-stage decomposition of larch litter. • Nitrogen increased early cellulolytic activity but reduced later ligninolytic activity, causing recalcitrant accumulation. • The asymptotic model best captured long-term, stage-dependent decomposition dynamics. • Magnesium correlated with late-stage decomposition under control conditions, highlighting nutrient interactions. Understanding litter decomposition processes is essential for predicting carbon cycling under global change. Although nitrogen (N) deposition has been widely studied, most research emphasizes short-term effects, leaving long-term dynamics poorly understood. Here, we report a ten-year decomposition experiment of larch foliar litter under different N-fertilization treatments. Decomposition rates were modeled using single-exponential, double-exponential, and asymptotic models to capture temporal dynamics. We measured extracellular enzyme activities (β-1,4-glucosidase, cellobiohydrolase, endocellulase, phenol oxidase and peroxidase) and initial litter traits (nitrogen, phosphorus, calcium, magnesium, manganese, non-structural carbohydrates, hemicellulose, cellulose and lignin) to identify underlying mechanisms. N-fertilization accelerated early-stage decomposition by enhancing cellulolytic enzyme activity but suppressed ligninolytic activity at later stages, resulting in the accumulation of recalcitrant organic matter. Among models, the asymptotic function best described the decomposition trajectory, highlighting the importance of slow-decomposing fractions for long-term carbon retention. Our findings demonstrate that N-fertilization alters decomposition trajectories through stage-dependent microbial mechanisms, with implications for carbon turnover in forest ecosystems. Incorporating both N and nutrient interactions into decomposition models may improve carbon cycle predictions under changing environmental conditions.