《环境昆虫学报》

作者	单位
郭艳兰，张学金，焦旭东，牟德生，金娜，刘伟，李栋，董存元	1. 武威市林业科学研究院，甘肃武威 733000；2. 武威市林业综合服务中心，甘肃武威 733000

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中文摘要:

为明确甘肃武威地区酿酒葡萄园叶蝉种群发生动态及其与气象因子之间的关系。连续2 年应用黄板诱集法对酿酒葡萄园叶蝉种群动态进行定时定点监测；采用相关分析、回归分析、通径分析、主成分分析、灰色关联度等方法分析其与气象因子的关系。结果表明，酿酒葡萄园叶蝉一年有4个发生高峰，分别为5月底6月初、7月中旬、8月上中旬和9月中旬，5月底6月初种群数量达最高，为集中为害关键期。相关性分析结果表明，叶蝉种群动态与平均最低温度、平均最高相对湿度呈显著负相关（ P=0.0129； P =0.0465），与平均相对湿度、平均最低相对湿度呈极显著负相关（ P =0.0031； P =0.0041）。回归分析表明，平均最低温度、平均相对湿度、平均最高相对湿度和平均最高相对湿度综合影响叶蝉种群变化，其中平均相对湿度和平均最低相对湿度是主要因素，对叶蝉种群动态的反向直接作用最大，平均最低温度和平均最高相对湿度对叶蝉种群动态的影响主要通过平均相对湿度和平均最低相对湿度间接发生。主成分分析表明，平均相对湿度和平均最低相对湿度是主要成分，累积方差贡献率达85%。灰色关联度分析结果表明，平均相对湿度和平均最低相对湿度与叶蝉种群数量变化的关联度最大，是影响种群动态的关键影响因子。平均相对湿度和平均最低相对湿度是影响酿酒葡萄园叶蝉种群数量变化的主要气象因子。

英文摘要:

In order to clarify the relationships between the quantitative dynamics of leafhopper population and climatic factors in wine vineyard orchard of Gansu, the population dynamics of leafhopper were regularly monitored using yellow sticky traps in 2017 to 2018, then their relationship with meteorological factors were analyzed by correlation analysis, regression analysis, path analysis, principal components analysis and grey relational analysis. The rusults showed that the leafhopper had four quantitative peaks per year in wine vineyard, there were end of May to early June, mid-July, first and second ten days of August and the middle of September. The population number of leafhopper was the highest in the end of May to early June, which as the damage key stage.The correlativity results indicated that the population dynamics of leafhopper were significantly negatively corrlelated to mean minimum temperature( P =0.0129), mean highest relative humidity( P =0.0465), and high significantly negatively corrleated to mean relative humidity( P =0.0031), mean lowest relative humidity( P =0.0041). The regression analysis results described that the population dynamics of leafhopper were affected comprehensively by mean minimum temperature, mean relative humidity, mean highest relative humidity and mean lowest relative humidity. The average relative humidity and average lowest relative humidity were the key factors that lead to the highest inverse and direct effect on the leafhopper numbers, and the influence of mean minimum temperature and average highest relative humidity both occurred indirectly through average relative humidity and average highest relative humidity.The principal components analysis made it clear that the average relative humidity and mean lowest relative humidity were the main factors which affected 85% population dynamics of leafhopper. The grey relational analysis showed that the key factors were the average relative humidity and the average lowest relative humidity which the degreeofrelation were maximum. The quantitative dynamics of leafhopper mainly influenced by mean relative humidity and mean lowest relative humidity.

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