A study on ecological characteristics and occurrence prediction of Casmara patrona larvae
[Objective]Casmara patrona is an important branch pest on Camellia oleifera.The occurrence and damage of C.patrona are closely related to ecological factors.Grasping the ecological characteristics and prediction model of the pest's borer damage can provide the theoretical basis for the pest monitoring,early warning and ecological control.[Method]Through a 6-year systematic investigation into the harm of C.patrona larvae in the major C.oleifera planting bases in Jiangxi,the stepwise regression analysis was used to analyze the impacts of 17 ecological factors covering 3 aspects of forest stand,meteorology,and food on the occurrence degree(quantity)of C.patrona larvae.[Result]The results showed that six factors including forest canopy density(x9),slope aspect(x6),density(x10),vegetation coverage(x11),forest edge(x8)and tree age(x1)were the main stand factors affecting the percentage of worm-bearing plants in C.oleifera forests,while the canopy density and slope aspect of the main forest layer were two key stand factors.The occurrence degree of the damage of C.patrona larvae could be predicted by using the regression model(y=58.468-14.223x9-49.637x6-0.024x10+0.124x11-4.340x8+0.066x1).Under the conditions of identical other factors,the average temperature in April and May were two key meteorological factors.The best multiple linear regression model is y=3.262x41+0.524x51-50.137.After testing,the prediction accuracy of this model reached 97.05%,indicating that the model was reliable.In addition,biological factors(food)were also the main factors affecting the occurrence of C.patrona larvae.There were significant differences in the degree of damage of C.patrona larvae from different varieties of C.oleifera.Among 11 main varieties of C.oleifera in Jiangxi,the variety of Changlin 180 was the most resistant to the insects,and Changlin 166 and Changlin 27 were the most severely affected.[Conclusion]The canopy density of the main forest layer,slope aspect,average temperatures in April and May are key factors affecting the insect infestation rate of C.patrona larvae.The constructed prediction model can accurately predict the degree of harm caused by C.patrona larvae.
Casmara patronastand factorbiological factormeteorological factorsprediction model
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