Exploring Litter Decomposition, Nutrient Retention, and Sensitivity to Nitrogen Deposition Among Ancient and Recently Evolved Tree Species.

Investigating the differences among plant functional types (PFTs) and their responses to N deposition is crucial for predicting carbon and nutrient cycles and improving forest management strategies. Our research aimed to examine the decomposition rates and nutrient loss rates of leaf litter and fine roots from ancient and recently evolved species and their response to N deposition. We hypothesized that (1) leaves and fine roots of recently evolved tree species decomposes slower than those of ancient tree species due to their higher C:N ratios and structural compound content; (2) the effect of N addition on decomposition rates differs across different decomposition stages and is influenced by the associated PFT; and (3) litter morphology and substrate quality are key predictors of litter decomposition rates for both ancient and recently evolved species. Field decomposition experiments were conducted with leaf litter and fine roots under both control and N-addition treatment (10 g·m·a), focusing on three ancient tree species and three recently evolved tree species. The decomposition rate constants ( values) of leaves from recently evolved species were lower than those from ancient species, with values of 1.01 and 1.68 under control conditions, and 1.07 and 1.08 under N addition. For fine roots, recently evolved species had lower k values only under N addition (1.05 and 1.40), whereas no significant differences were observed between ancient and recently evolved species under control conditions. Furthermore, the N residual rate in fine roots of recently evolved species was higher under N addition compared to controls, while no such differences were observed in ancient species. The distinct patterns observed in this study provide valuable insights into the complexity of litter decomposition under N deposition, highlighting the importance of considering both PFTs and organ types for predicting ecosystem responses.