Time Series Analysis of Pear Psylla Population Dynamics, Cacopsylla pyri (Hemiptera: Psyllidae), using ARIMA Model on Nine Different Pear Pyrus Communis L. Cultivars

پژوهش های حفاظت گیاهان ایران

Article 1, Volume 38, Issue 4 - Serial Number 66, December 2025, Pages 309-325   

Document Type: Research Article

DOI: 10.22067/jpp.2024.86109.1171

Authors

Masoud Latifian^{* 1}; Hamid Abdollahi²

¹Agricultural Research, Education and Extension Organization, Horticultural Sciences Research Institute, Temperate Fruits Research Center, Karaj

²Agricultural Research, Education and Extension Organization, Horticultural Sciences Research Institute, Temperate Fruits Research Center, Karaj, Iran

Abstract

Introduction
Psylla is a key pest in important pear Pyrus Communis -growing areas in Iran. Detection of population fluctuations in ecological models can be traced with time series data. Population dynamics is the result of changes in the population size that ascend from the random and independent contribution of births, deaths, and migrations of population individual members. Population dynamics can be simulated by considering the role of each individual in a time unit as an independent and separate random variable. Mathematical models of resource-consumer relationships can understand miscellaneous patterns of population dynamics, leading to a set of population behavioral patterns under the influence of food resources. This research was conducted on the population dynamics of eggs, nymphs, and adults of pear psylla Cacopsylla pyri L. under feeding conditions from nine pear cultivars in field conditions. Psylla is a key pest of pears and is distributed worldwide. In the climatic conditions of regions like Iran, where spring and summer are dry and rainfall is less, the indirect damage of honeydew secretions is more than the damage from sucking plant sap.
 
Material and Methods
The time series of the population of each growth stage of pear Psylla is a group of observations obtained by regular variable sampling of the eggs, nymphs, and adult Psylla population in a period of four years (2020-2023). The integrated autocorrelated moving average (ARIMA) model was used to forecast the future pear psylla population on nine pear cultivars. ARIMA model suitability criteria and information criterion Bayesian normalized (BIC) were calculated. The plots of residual autocorrelations and partial autocorrelations that show the degree of correlation of the residuals of the model in the actual forecast were premeditated. A cross-correlation model was used to replicate the population model of each developmental stage against the population route of another developmental stage.
 
Results and Discussion
The fitted models were significant at the 0.01% level for the populations of pear Psylla on nine cultivars. The ARIMA time series model showed good capacity in forecasting the population of egg, nymph and Adult. Differences among the fit indices of the model were observed in all three developmental stages. The mobility of the growth stage has been one of the most important reasons for these differences. Because with the increase in mobility, the accuracy of sampling usually decreases and population sampling becomes more difficult. The type of host cultivat has also been effective in determining the predictive power of the model. The values of residual autocorrelations and partial autocorrelations in most growth stages and different pear cultivars were smaller than 0.5 in absolute terms, and this indicates that the remaining effective data was not included in the fitting of the time series model in the minimum amount. A sinusoidal trend occurs in the curve of pears Psylla population density fluctuations. The trend of changes in observations was the same as predicted by the model. In some stages such as adult and in cultivars such as Shah Miveh where the population densities were higher than normal, the sinusoidal trend was not observed in the population forecast by the model. The relationship between the population of alternating developmental stages has been direct and increasing and inverse and decreasing at different periods of seasons. These changes were caused by the difference in the pear cultivars quality for pear psylla growth stages. The average absolute value of the cross-correlation varied between 0.3 and 0.4, which indicates the average detection power of each developmental stage compared to the subsequent developmental stage. The maximum detection power in different cultivars was related to the transition of the population from the nymph stage to the adult. In pear cultivars with higher population density, which most of the times had a population above the normal limit, such as Shah Miveh and Dargezi cultivars, the model recognized ability was less, and instead, in cultivars with subnormal population density, such as Boheme and Harvest Queen, the model recognize ability was lower. In cultivars that have less population carrying capacity, births, and mortality reach the equilibrium point earlier than other cultivars. In natural environments, the measurement of living spaces has different dimensions, including nutrition quality.
 
Conclusion
The flexibility of the models in describing the different annual phenological behaviors of the various growth stages of the pear psylla and the possibility of self-calibrating the models with the help of autocorrelation and cross-correlation models make this approach a valuable tool for monitoring the population of this insect.

Keywords

Autocorrelation; Cross-correlation; Forecast; Population analysis; Time series

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