石榴园西花蓟马种群动态及其与气象因素的关系

2007-2008年,对云南省建水石榴园西花蓟马种群动态进行了系统调查,并采用回归分析(逐步回归分析、通径分析)、主成分分析及灰色系统分析就气象因子对该虫种群动态的影响进行了系统分析。结果表明,西花蓟马在建水石榴园常年发生,冬季较低,夏季最高,成虫全年种群消长呈单峰型,高峰期为5月份。相关性分析结果表明,西花蓟马种群数量与月相对湿度间呈极显著正相关性(P<0.01),与月均气温和月最低气温间呈显著正相关性(P<0.05),与月最高气温、月均降雨量和月均蒸发量间无相关性(P>0.05)。回归分析结果表明,石榴园西花蓟马种群动态的决定因子中影响最大的气象因素是月最低气温,而月均气温和月相对湿度是影响种群数量变动的主要因素。主成分分析表明,月最低气温是主要成分,其累积方差贡献率达73.03%。灰色系统分析结果表明,影响石榴园6种蓟马种群动态最关键的因子是月相对湿度;年度间影响最大的是年总降雨量;石榴花期各蓟马的种群数量与气象因素间关联度最大的是月最低温;果期各种蓟马的种群数量与气象因素间关联度最大的是月均降雨量。

Population dynamics of Frankliniella occidentalis (Thysanoptera:Thripidae) along with analysis on the meteorological factors influencing the population in pomegranate orchards

The western flower thrips Frankliniella occidentalis (Pergande) has been a major pest of agricultural and hotricultural crops in fields and greenhouses worldwide. Since its found in Beijing Municipality of China in 2003, and now it is the serious pest in pomegranate tree of Jianshui County, Yunnan Province in recent years. The population dynamics of the F. occidentalis in pomegranate orchards was surveyed by using gathering thrips in different positions of pomegranate including flower, leaf, shoot and young fruit and using blue sticky cards from 2007 to 2008 in Jianshui County, Yunnan Province. The relationship between meteorological factors including monthly mean temperature, monthly maximum temperature, monthly minimum temperature, monthly rainfall, monthly relative humidity (RH) and monthly evaporation and population dynamics were analyzed using the regression analysis (stepwise regression analysis and path analysis), the principal component analysis and the grey system analysis. The results indicated that during the study periods, adults of the F. occidentalis occurred throughout the year, the lowestpopulation was in winter season and the highest population was in the summer season in each year. The population dynamics was single-peaked, the most serious injury stage was from March to July, with the peak in May. The correlativity results showed that the population of F. occidentalis was significantly positively corrlelated to monthly RH (P<0.01), and it was positively corrleated to monthly mean temperature, monthly minimum temperature (P<0.05), followed the regression equation as Y= -131.15+0.04 X₁+22.71 X₃-15.76 X₅, (P=0.002), the multiple realtice coefficient was 0.875, the X₁, X₃ and X₅ was monthly mean temperature, monthly minimum temperature and monthly RH. No significant correlation was found between the population of F. occidentalis and monthly maximum temperature, monthly rainfall amounts, monthly evaporation capacity (P>0.05). Use the stepwise regression analysis, path analysis and decision coefficient analysis on the relations between the population of F. occidentalis and monthly mean temperature, monthly minimum temperature and monthly RH. The results described that the monthly minimum temperature was the major decisive factor, whose the decision coefficient was R²₍₃₎= 3.662, and the monthly mean temperature and the monthly RH were the primary limiting factors, whose the decision coefficient respectively were R²₍₁₎= -0.484 and R²₍₅₎= -2.621. The principal component analysis indicated that the monthly minimum temperature of the first principal component was the primary decisive factor, whose cumulative variance proportion was 73.03% and monthly RH of the second principal component was also the primary decisive factor, whose Load was 0.978. The total cumulative variance proportion of previous two principal components was 95.30%. It could be the comprehensive index of meteorological factors to analysis the population dynamics of F. occidentalis. The grey system analysis on the relationship between the population and the meteorological factors showed that the monthly RH was the key factors of seasonal dynamics, whose integral was 10. And the total annual rainfall amounts was the key factors of annual dynamics, whose degreeofrelation was 1.012, and that all the meteorological factors ranked as the Toal Annual Rainfall >Annual Mean RH>Annual Maximum Temperature >Annual Minimum Temperature >Annual Mean Temperature.