Soil Respiration in Tibetan Alpine Grasslands: Belowground Biomass and Soil Moisture, but Not Soil Temperature, Best Explain the Large-Scale Patterns

• Published in: PLoS ONE
• Main Authors: Geng, Yan, Wang, Yonghui, Yang, Kuo, Wang, Shaopeng, Zeng, Hui, Baumann, Frank, Kuehn, Peter, Scholten, Thomas, He, Jin-Sheng
• Format: Text
• Language: English
• Published: Public Library of Science 2012
• Subjects: Research Article; permafrost
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3324551; http://www.ncbi.nlm.nih.gov/pubmed/22509373; https://doi.org/10.1371/journal.pone.0034968

The Tibetan Plateau is an essential area to study the potential feedback effects of soils to climate change due to the rapid rise in its air temperature in the past several decades and the large amounts of soil organic carbon (SOC) stocks, particularly in the permafrost. Yet it is one of the most under-investigated regions in soil respiration (Rs) studies. Here, Rs rates were measured at 42 sites in alpine grasslands (including alpine steppes and meadows) along a transect across the Tibetan Plateau during the peak growing season of 2006 and 2007 in order to test whether: (1) belowground biomass (BGB) is most closely related to spatial variation in Rs due to high root biomass density, and (2) soil temperature significantly influences spatial pattern of Rs owing to metabolic limitation from the low temperature in cold, high-altitude ecosystems. The average daily mean Rs of the alpine grasslands at peak growing season was 3.92 µmol CO2 m−2 s−1, ranging from 0.39 to 12.88 µmol CO2 m−2 s−1, with average daily mean Rs of 2.01 and 5.49 µmol CO2 m−2 s−1 for steppes and meadows, respectively. By regression tree analysis, BGB, aboveground biomass (AGB), SOC, soil moisture (SM), and vegetation type were selected out of 15 variables examined, as the factors influencing large-scale variation in Rs. With a structural equation modelling approach, we found only BGB and SM had direct effects on Rs, while other factors indirectly affecting Rs through BGB or SM. Most (80%) of the variation in Rs could be attributed to the difference in BGB among sites. BGB and SM together accounted for the majority (82%) of spatial patterns of Rs. Our results only support the first hypothesis, suggesting that models incorporating BGB and SM can improve Rs estimation at regional scale.