While bulk leaf nitrogen (N) content is known to regulate litter decomposition, few studies have explored the importance of N biochemical fractions in determining decomposition rate. The primary objective of this study was to determine if N biochemical fractions explain variation in decomposition that is not explained by bulk N traits, physiological traits, and carbon (C) quality traits. Our secondary objective was to determine if green leaf traits, as opposed to senesced litter traits, can accurately predict rates of litter decomposition. In this litter bag study, we chose nine species with a wide range of traits associated with leaf physiology, N biochemical, and C quality. Litter bags were deployed in two southern California ecosystems that differed in temperature and water availability, with replicates retrieved every three months for 1.5 years. We used principal component analyses (PCA) to analyze axes of variation in both leaf traits and litter traits. We then used multiple regression to determine which traits contributed most to species variation in litter decomposition.
Results/Conclusions
We found leaf N biochemical traits correlated strongly with each other, and generally aligned on a single axis of variation resembling that of the ‘leaf economic spectrum’ (LES). However, the LES signal was not discernable among litter traits. We also found leaf traits to be generally more powerful predictors of decomposition rates than litter traits in our multiple regression analysis. Leaf traits that had primary associations with PC1, including Nmass, leaf mass area, lignin content, total protein, and amino acid content were found to be the most consistent predictors of decomposition. It is important to note that large databases of leaf traits (e.g., TRY) exist within the literature, where litter trait databases are significantly less represented. Thus, we conclude that leaf trait databases may be powerful tools in predicting litter decomposition in future models. We end our discussion by providing suggestions for future decomposition experimental designs.