重庆缙云山针阔混交林地土壤呼吸速率及温度敏感性特征分析

Variations of Soil Respiration Characteristics and Temperature Sensitivity in the Mixed Forest at Jinyun Mountain, Chongqing

  • 摘要: 以重庆缙云山亚热带针阔混交林为研究区,研究了土壤呼吸及其Q10(温度敏感性系数,指温度增加10℃所造成的呼吸速率改变的商)的时间变异特征,并深入分析二者受土壤温度、湿度变化的影响. 2011年4—12月采用LI-8100二氧化碳通量测量系统观测选取样地的RS (土壤呼吸速率)、土壤5cm深处的T5(土壤温度)和W5(土壤湿度),分析RS与Q10的变化规律;同时利用单一和二元混合模型探讨T5和W5对RS、Q10的影响. 结果表明:①在观测期内RS和T5月均值均呈单峰曲线变化;RS的变化范围在(1.38±0.15)~(3.94±0.21)μmol/(m2·s)之间,T5的变化范围在(9.28±0.65)~(22.99±1.14)℃之间;由于受到自然降水影响,W5的月际变化不规律. ②Q10季节差异明显,最大值(3.31)出现在春季,观测期内的平均值为2.01. ③RS与T5之间呈显著正相关(P<0.05),与W5的关系不明显(P>0.05);RS与T5、W5的关系模型拟合度分别为87%和26%;T5与W5的复合模型对RS的变化解释能力为89%,高于单一模型. ④影响Q10的主要因素是T5,其次为W5.

     

    Abstract: The subtropical coniferous and broad-leaved mixed forests at Jinyun Mountain, Chongqing were selected to investigate temporal variation characteristics of soil respiration temperature sensitivity (Q10), and the variation affected by soil temperature and soil moisture was further analyzed. The soil respiration rate was measured by LI-8100soil respiration carbon dioxide flux measurement system. The soil temperature (T5) and soil moisture (W5) at 5cm depth were observed by DDTWS-Ⅱtype of soil temperature and moisture logger from April to December in 2011. Subsequetly, variation rule of RS and Q10 were analyzed, and the effects of T5 and W5 on RS and Q10 were discussed by using simple models and binary mixed models. The results showed that:(1) Monthly average value of RS and T5 showed the curves with a single peak. RS ranged from (1.38±0.15) to (3.94±0.21)μmol/(m2·s), while T5 ranged from (9.28±0.65) to (22.99±1.14) ℃. The monthly variation of W5 was not regular due to the precipitation. (2) Seasonal differences of Q10 were apparent. The values of Q10 showed a maximum at 3.31in spring and their average values during observation process was 2.01. (3) RS showed a significant positive correlation (P<0.05) with T5 but showed no significant correlation (P>0.05) with W5.The goodness of fit of simple fitted model of RS and T5 was 87%, while that of simple fitted model of RS and W5 was 26%. The goodness of fit of the binary mixed model showed that the combined effects of T5 and W5 on RS was 89%. The binary mixed model can better interpret the variation of RS better then simple models. (4) T5 was the main factor affecting Q10 following by and the secondary factor, W5. Compared to the simple model, the binary mixed model can more clearly explain the variation of Q10.

     

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