Journal
JOURNAL OF PLANT ECOLOGY
Volume 13, Issue 3, Pages 266-272Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jpe/rtaa010
Keywords
annual litterfall production; China; forest; interannual variation; coefficient of variation; stand age
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Funding
- National Key Research and Development Program of China [2017YFC0503906]
- China Special Fund for Meteorological Research in the Public Interest [GYHY201406034]
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Aims Litterfall is a key parameter in forest biogeochemical cycle and fire risk prediction. However, considerable uncertainty remains regarding the litterfall variations with forest ages. Quantifying the interannual variation of forest litterfall is crucial for reducing uncertainties in large-scale litterfall prediction. Methods Based on the available dataset (N = 318) with continuous multi-year (>= 2 years) measurements of litterfall in Chinese planted and secondary forests, coefficient of variation (CV), variation percent (V-P), and the ratio of next-year litterfall to current-year litterfall were used as the indexes to quantify the interannual variability in litterfall. Important Findings The interannual variations of litterfall showed a declining trend with increasing age from 1 to 90 years. The litterfall variations were the largest in 1-10 years (mean CV = 23.51% and mean V-P = -28.59% to 20.89%), which were mainly from tree growth (mean ratio of next-year to current-year= 1.20). In 11-40 years, the interannual variations of litterfall gradually decreased but still varied widely, mean CV was similar to 18% and mean V-P ranged from -17.69% to 21.19%. In 41-90 years, the interannual variations minimized to 8.98% in mean CV and similar to 8% in mean V-P. As a result, forest litterfall remained relatively low and constant when stand age was larger than 40 years. This result was different from the previous assumptions that forest litterfall reached relatively stable when stand age was larger than 30, 20 or even 15 years. Our findings can improve the knowledge about forest litter ecology and provide the groundwork for carbon budget and biogeochemical qcle models at a large scale.
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