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Updated: Jan 13, 2026

Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter
Published on: March 12, 2013
Nutrient dynamics of decomposing litter and their effects on subsequent mass loss and nutrient release in a
Shiqi Yi1,2, Boyu Ma1,2, Björn Berg3
1Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, No.20 Nanxincun, Xiangshan, Beijing, 100093, China.
Abstract:
Leaf litter decomposition is a key ecological process that strongly regulates carbon sequestration and nutrient cycling in forests. While the effects of climate and initial litter nutrient chemistry on mass loss are well established, the dynamics of multiple nutrients in decomposing leaf litter, particularly those beyond nitrogen (N) and phosphorus (P), and their downstream effects on subsequent mass loss and nutrient release remain insufficiently understood. We conducted a two-year litterbag decomposition experiment using litter from seven tree species in a mixed evergreen and deciduous broad-leaved forest along an altitudinal gradient (800-1800 m a.s.l.) in subtropical China. Alongside concurrent temporal high-resolution microclimatic data, we followed changes in concentrations of multiple litter nutrients, including potassium (K), calcium (Ca), and magnesium (Mg). Despite initial variation in litter nutrient concentrations and climatic conditions, nutrient concentrations in decomposing litter generally converged over time, except for the dissimilar P and Ca at later decomposition stages. Initial nutrients, particularly P, Ca, and Mg concentrations, displayed modest but consistent effects on nutrient concentrations in decomposing litter. Most importantly, air temperature and nutrient concentrations in the decomposing litter influenced subsequent mass loss and nutrient release in distinct ways. By incorporating multiple nutrients to link nutrient dynamics in decomposing litter to the subsequent decomposition process, our study provides an integrative framework to enhance process-based predictions of nutrient and carbon cycling in forests.
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