摘要
基于对三峡库区不同林龄马尾松林下土壤养分和酶活性的测定及典范对应分析,探讨了不同林龄马尾松林土壤养分、酶活性特征及其相互关系.结果表明:马尾松林0~20cm土壤有机质、总氮、铵态氮和有效磷含量均表现出成熟林>中龄林>近熟林;随着林龄的增加,土壤转化酶活性先降低后增加,纤维素酶、多酚氧化酶活性逐渐降低,而脲酶和过氧化物酶活性先增加后降低.经典范对应分析,不同林分中主要土壤养分对酶活性的影响顺序为总氮>有机质>pH>容重>铵态氮>有效磷,转化酶与土壤有机质、总氮、总磷呈显著正相关,过氧化物酶与有机质、总氮、总磷、容重呈显著负相关,土壤主要养分含量高,转化酶活性较高,过氧化物酶活性相对较低.转化酶、纤维素酶和过氧化物酶是评价土壤质量及肥力较好的生物学指标.
Based on the measurements of soil nutrient contents and enzyme activities and the canonical correspondence analysis (CCA),this paper studied the relationships between soil nutrient contents and soil enzyme activities in different age Pinus massoniana stands in Three Gorges Reservoir Area.Among the test stands,mature stand had the highest contents of organic matter,total nitrogen,ammonium nitrogen,and available phosphorus in 0-20 cm soil layer,followed by middle-aged stand,and nearly-mature stand.With the increase of the stand age,soil invertase activity increased after an initial decrease,cellulase and polyphenoloxidase activities decreased gradually,while urease and peroxidase activities decreased after an initial increase.CCA analysis showed that the effects of the main soil parameters on the soil enzyme activities in the stands ranked in the sequence of total nitrogen organic matter pH bulk density ammonium nitrogen available phosphorus.Soil invertase activity had significant positive correlations with soil organic matter,total nitrogen,and total phosphorus,while soil peroxidase activity significantly negatively correlated with soil organic matter,total nitrogen,total phosphorus,and bulk density.The soil was rich in main nutrients,invertase activity was relatively high,while peroxidase activity was relatively low.The activities of soil invertase,cellulase and peroxidase could be used as the good biological indicators in evaluating soil quality and fertility.
出处
《应用生态学报》
CAS
CSCD
北大核心
2012年第2期445-451,共7页
Chinese Journal of Applied Ecology
基金
林业公益性行业科研专项资金(201104008)资助
关键词
三峡库区
土壤养分
土壤酶活性
林龄
典范对应分析
Three Gorges Reservoir area
soil nutrient
soil enzyme activity
stand age
canonical correspondence analysis (CCA).