期刊
FORESTS
卷 10, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/f10110953
关键词
fine root biomass; fine root necromass; forest biome; monthly; precipitation; seasonal dynamics; temperature
类别
资金
- Natural Science Foundation of China [41971052, 31500354]
- National Key R&D Program of China [2019YFA0607300]
- China Postdoctoral Science Foundation [2016M601343, 2018T110232]
Research Highlights: A detailed picture of the seasonality in fine root biomass (FRB), necromass (FRN), and the biomass/necromass ratio (FRBN) throughout the whole year is crucial to uncover profound effects of long-term environmental changes on fine root dynamics. Materials and Methods: We used meta-analysis to characterize the variability of FRB, FRN and FRBN, and determined their relations with climatic (monthly versus annual), edaphic and geomorphic factors for tropical, temperate and boreal forest biomes across the Northern Hemisphere. Results: Boreal forests exhibited the highest FRB and FRN, while tropical forests yielded the lowest FRN, and thus the greatest FRBN. FRB and FRN significantly decreased with sampling depth, but increased with soil organic carbon content and elevation, while an opposite pattern was found for FRBN. Temperature and precipitation at different time scales (monthly versus annual) and latitude had varying influences on fine roots. High FRB and FRN were observed during dry season for tropical forests, but in the late growing season for temperate forests. The three forest biomes exhibited the high root activity (measured as FRBN) in June or July. Conclusions: It is crucial to realize the universal and specific responses of fine roots to multiple environmental factors when attempting to incorporate these parameters into fine root monthly dynamic models in forest ecosystems. The biome-specific fluctuation of fine roots contributes to identify the influence factors on fine root seasonal patterns throughout the whole year. Our analysis is expected to improve the understanding of the key role of fine roots at monthly level in modeling and predicting carbon budget of various forest biomes under future climate change.
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