Leaf traits can be used to predict rates of litter decomposition. Issue 10 (20th July 2020)

• Record Type: Journal Article
• Authors: Rosenfield, Marc V.; Keller, Jason K.; Clausen, Catrina; Cyphers, Kimberlee; Funk, Jennifer L.
• Is Part Of: Oikos. Volume 129:Issue 10(2020)
• Page Start: 1589
• Page End: 1596
• Publication Date: 2020-07-20
• Subjects: decomposition -- leaf economics spectrum -- leaf nitrogen -- lignin -- litter decomposition -- plant functional traits; Ecology -- Periodicals; 570
• DOI: 10.1111/oik.06470
• Languages: English
• ISSNs: 0030-1299

Abstract

Abstract : Strong relationships exist between litter chemistry traits and rates of litter decomposition. However, leaf traits are more commonly found in online trait databases than litter traits and fewer studies have examined how well leaf traits predict litter decomposition rates. Furthermore, while bulk leaf nitrogen (N) content is known to regulate litter decomposition, few studies have explored the importance of N biochemistry fractions, such as protein and amino acid concentration. Here, we decomposed green leaves and naturally senesced leaf litter of nine species representing a wide range of leaf functional traits. We evaluated the ability of traits associated with leaf and litter physiology, N biochemistry and carbon quality to predict litter decomposition. The objectives of this study were to determine if 1) N fractions explain variation in decomposition that is not explained by bulk N parameters alone and 2) green leaf traits, as opposed to litter traits, can accurately determine rates of litter decomposition. We found N biochemistry traits to have similar predictive power to that of bulk N. We also found that leaf N biochemistry traits correlated strongly with each other and aligned on a single axis of variation resembling that of the 'leaf economic spectrum.' We noted that green leaf traits associated with this axis, including bulk N, N fractions, leaf mass per area and lignin, were better predictors of decomposition than litter traits and concluded that leaf trait databases may be used to accurately predict litter decomposition. Future decomposition studies should consider fitting the more flexible Weibull distribution model to litter cohorts, as this model is much less rigid than the classic exponential decay model traditionally used in decomposition studies.