从2013年11月至2014年11月,采用尼龙网袋法对华西雨屏区天然常绿阔叶林凋落物进行原位分解试验,模拟N(NH4NO3)沉降水平分别为对照(0g N·m-2 ·a-1)、低氮沉降(5gN·m-2·a-1)、中氮沉降(15 g N·m-2 · a-1)和高氮沉降(30gN· m-2·a-1),研究了N沉降对常绿阔叶林凋落物分解及其木质素和纤维素降解的影响.结果表明:华西雨屏区天然常绿阔叶林凋落物在夏季分解较快,明显快于其他季节.N沉降显著抑制了阔叶林凋落物的分解,抑制作用随N沉降量的增加而加强.N沉降使凋落物质量损失95％的时间与对照(4.81年)相比增加了0.53～1.88年.经过1年的分解,中氮沉降和高氮沉降处理木质素和纤维素残留率显著高于对照,表明N沉降显著抑制了凋落物木质素和纤维素的降解.凋落物质量残留率与木质素和纤维素残留率呈显著正相关.N沉降抑制凋落物分解的原因可能是无机N的添加对木质素和纤维素的降解造成了阻碍.
Effects of simulated nitrogen deposition on lignin and cellulose degradation of foliar litter in natural evergreen broad-leaved forest in Rainy Area of Western China
In order to detect the effects of simulated nitrogen deposition on litter decomposition and degradation of lignin and cellulose,a one-year field experiment of simulated nitrogen deposition has been conducted using litter bag method from November 2013 to November 2014 in an evergreen broad-leaved forest,Rainy Area of West China.Four levels of nitrogen deposition were set,i.e.,control (0 g N· m-2 · a-1),low (5 g N · m-2 · a-1),medium (15 g N · m-2 · a-1) and high (30 g N · m-2 · a-1) nitrogen deposition.The results indicated that foliar litter decomposed faster in summer,obviously faster than in the other seasons.N deposition significantly inhibited the decomposition of foliar litter in this evergreen broad-leaved forest.As N deposition increased,the inhibition effect was enhanced.The time of 95％ mass loss (T95％) of foliar litter due to simulated N deposition was increased by 0.53-1.88 years compared with T95％ of control (4.81 years).N deposition significantly inhibited the degradation of lignin and cellulose.The mass remaining after one year of decomposition of lignin and cellulose in the medium and high nitrogen deposition treatments were significantly higher than that in the control.There was a significant positive linear relationship among mass remaining rate and lignin and cellulose remaining rates.The inhibiting effects of inorganic N on degradation of lignin and cellulose explained the inhibitory effect of N on foliar litter decomposition.
国家科技期刊平台
NSTL
万方数据
维普
