Time series analysis to describe potato moth population dynamics

The potato tuber moth P. operculella Zeller is a pest of potential danger to tobacco. Dynamics of population size presents the development of the pest population in space and time, and are described by quantitative indicators. In order to characterize dynamics of potato tuber moth population on tobacco crops was applied the time series analysis. The index of seasonality was used to estimate impacts of seasonal factors on imago population dynamics. A field trapping experiment was carried out on tobacco crops at the Institute of Tobacco and Tobacco Products, Plovdiv, Bulgaria, in 2008-2012. Pheromone traps set on Oriental, Virginia and Burley tobacco crops were used to observe seasonal dynamics of the pest population and quantity of moths in the field. A monthly moth catch was presented as a time series. It has been found that the dynamic statistical analysis, a part of which refers to seasonal fluctuations, can be used to characterize and analyze dynamics of pest populations including potato moth. A strictly manifested seasonality exists in the annual dynamics of the potato moth. A graphical view of the seasonal indices known as a seasonal wave is similar to those in all three tobacco crops. Differences were observed in the values of seasonal indices. These differences are bound up with biological and technological features of different variety types of tobacco. The potency of seasonal factors on the number of adults during a calendar year has different directions and amplitudes. A strong positive impact exists in the second half of the year. In August and October, the number of caught moths was 100 to 246% of the annual average. Seasonal factors negatively affect the pest population size in the first half of the year that is in January, February and March, when the number of caught moths is close to zero.

картофельная моль, динамики популяций, анализ временных рядов, индекс сезонности, сезонность, potato tuber moth, population dynamics, time series analysis, seasonal index, seasonality

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