Soil respiration in Tibetan alpine grasslands: belowground biomass and soil moisture, but not soil temperature, best explain the large-scale patterns.
Soil respiration in Tibetan alpine grasslands: belowground biomass and soil moisture, but not soil temperature, best explain the large-scale patterns.
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DOI：
10.1371/journal.pone.0034968
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
He JS
中科院分区：
文献类型：
--
作者：
Geng Y;Wang Y;Yang K;Wang S;Zeng H;Baumann F;Kuehn P;Scholten T;He JS
青藏高原是研究土壤对气候变化潜在反馈效应的重要地区，其气温在过去几十年中迅速上升，土壤有机碳（SOC）储量巨大，特别是在多年冻土中。然而，它是土壤呼吸（Rs）研究中最缺乏调查的地区之一。在这里，在高寒草地的42个地点测定了Rs率在2006年和2007年的生长高峰期，沿着青藏高原的一个样带（包括高山草原和草甸），以测试是否：（1）地下生物量（BGB）与Rs的空间变异关系最密切，因为根系生物量密度高，（2）在寒冷的高海拔生态系统中，由于低温的代谢限制，土壤温度显著影响Rs的空间格局。高峰生长期高寒草地的日平均Rs值为3.92 μ mol CO2 m − 2 s − 1，变化范围为0.39 - 12.88 μ mol CO2 m − 2 s − 1，其中草原和草甸的日平均Rs值分别为2.01和5.49 μ mol CO2 m − 2 s − 1。通过回归树分析，BGB，地上生物量（AGB），SOC，土壤水分（SM），植被类型的15个变量中被选中，作为影响大尺度变化的Rs的因素。通过结构方程模型的方法，我们发现只有BGB和SM对Rs有直接影响，而其他因素通过BGB或SM间接影响Rs。大部分（80%）的变化，在Rs可以归因于BGB网站之间的差异。BGB和SM一起占大多数（82%）的空间格局的Rs。我们的研究结果只支持第一个假设，这表明模型结合BGB和SM可以提高Rs估计在区域尺度上。
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.
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