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

Document Type : Research Article

Authors

• Masoud Latifian ¹
• Hamid Abdollahi ²

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

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

10.22067/jpp.2024.86109.1171

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

Main Subjects

• pests

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©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

 

References

Andow, D.A. (1990). Population dynamics of an insect herbivore in simple and diverse habitats. Ecology, 71(3), 1006-1017. https://doi.org/10.2307/1937369

Behdad, E. (1991). Iran Pests of Fruit Trees (2^nd Ed.), Bahman Nashr Press, Esfahan, Iran, 826 pp. (in Persian)

Blanchet, F.G., Cazelles, K., & Gravel, D. (2020). Co‐occurrence is not evidence of ecological interactions. Ecology Letters, 23(7), 1050-1063. https://doi.org/10.1111/ele.13525

Blomquist,C.L., & Kirkpatrick, B.C. (2002). Frequency and seasonal distribution of pear psylla infected with the pear decline phytoplasma in California pear orchards. Phytopathology, 92(11), 1218-1226. https://doi.org/10.1094/PHYTO.2002.92.11.1218

Boopathi, T., Singh, S. B., Manju, T., Ramakrishna, Y., Akoijam, R. S., Chowdhury, S., Ngachan, S. V., Waisel, Y., & Bose, A. (2015). Development of temporal modeling for forecasting and prediction of the incidence of lychee, Tessaratoma papillosa (Hemiptera: Tessaratomidae), using time-series (ARIMA) analysis. Journal of Insect Science, 15(1), 55. https://doi.org/10.1093/jisesa/iev034

Butt, B. A., Stuart, L. C., & Bell, R. L. (1989). Feeding, longevity, and development of pear psylla (Homoptera: Psyllidae) nymphs on resistant and susceptible pear genotypes. Journal of Economic Entomology, 82(2), 458-461. https://doi.org/10.1093/jee/82.2.458 

Clark, T. J., & Luis, A. D. (2020). Nonlinear population dynamics are ubiquitous in animals. Nature Ecology and Evolution, 4(1), 75-81. https://doi.org/10.5281/zenodo.3470260 

Damos, P. (2016). Using multivariate cross correlations, Granger causality and graphical models to quantify spatiotemporal synchronization and causality between pest populations. BMC ecology, 16, 1-17. https://doi.org/10.1186/s12898-016-0087-7 

Damos, P. T., & Savopoulou-Soultani, M. (2010). Development and statistical evaluation of models in forecasting moth phenology of major lepidopterous peach pest complex for Integrated pest management programs. Crop Protection, 29(10), 1190-1199. https://doi.org/10.1016/j.cropro.2010.06.022

Daniel Kissling, W., Pattemore, D. E., & Hagen, M. (2014). Challenges and prospects in the telemetry of insects. Biological Reviews, 89(3), 511-530. https://doi.org/10.1111/brv.12065

Del Monte‐Luna, P., Brook, B. W., Zetina‐Rejón, M. J., & Cruz‐Escalona, V. H. (2004). The carrying capacity of ecosystems. Global Ecology and Biogeography, 13(6), 485-495. https://doi.org/10.1111/j.1466-822X.2004.00131.x

Dennis, E. B., Kéry, M., Morgan, B. J., Coray, A., Schaub, M., & Baur, B. (2021). Integrated modelling of insect population dynamics at two temporal scales. Ecological Modelling, 441, 109408. https://doi.org/10.1016/j.ecolmodel.2020.109408

Didham, R. K., Basset, Y., Collins, C. M., Leather, S. R., Littlewood, N. A., Menz, M. H. M., Müller, J., Packer, L., Saunders, M. E., Schönrogge, K., Stewart, A. J. A., Stork, N. E., Samways, M. J., & Hassall, C. (2020). Interpreting insect declines: Seven challenges and a way forward. Insect Conservation and Diversity, 13(2), 103-114. https://doi.org/10.1111/icad.12408

Dimri, T., Ahmad, S., & Sharif, M. (2020). Time series analysis of climate variables using seasonal ARIMA approach. Journal of Earth System Science, 129, 1-16. https://doi.org/10.1007/s12040-020-01408-x

Dixon, P. A., Milicich, M. J., & Sugihara, G. (1999). Episodic fluctuations in larval supply. Science, 283(5407), 1528-1530. https://www.science.org/doi/abs/10.1126/science.283.5407.1528

Erfani, J., Ebadi, A., Abdollahi, H., & Moghaddam, M. F. (2014). Evaluation of genetic diversity of some pear (Pyrus spp.) genotypes and species based on morphological characteristics. Iranian Journal of Horticultural Science, 45(1), 11-21. (in Persian)

Flores, J. H. F., Engel, P. M., & Pinto, R. C. (2012). Autocorrelation and partial autocorrelation functions to improve neural networks models on univariate time series forecasting. In The 2012 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN.2012.6252470

Forbanka, D. N., Stemele, M. A., Heshula, L. U. N. P., Dzemo, W. D., Kotey, D. A., Taruvinga, A., & Tshivhandekano, P. G. (2023). Distribution and fixed-precision sampling plans for diamondback moth (Lepidoptera: Plutellidae), on winter–spring cabbage. Journal of Economic Entomology, 116(5), 1551-1559. https://doi.org/10.1093/jee/toad156

Heath, R. A. (2014). Nonlinear Dynamics: Techniques and Applications in Psychology. Psychology Press.  United Kingdom. 356 pp.

Henson, S. M., Costantino, R. F., Cushing, J. M., Dennis, B., & Desharnais, R. A. (1999). Multiple attractors, saddles, and population dynamics in periodic habitats. Bulletin of Mathematical Biology, 61, 1121-1149. https://doi.org/10.1006/bulm.1999.0136

Hines, J., van der Putten, W. H., De Deyn, G. B., Wagg, C., Voigt, W., Mulder, C., Minden, V., Engelhardt, K., Scherber, C., & Eisenhauer, N. (2015). Towards an integration of biodiversity-ecosystem functioning and food web theory to evaluate relationships between multiple ecosystem services. In Advances in Ecological Research Academic Press, 53, 161-199 pp. https://doi.org/10.1016/bs.aecr.2015.09.001

Hirano, H., & Takemoto, K. (2019). Difficulty in inferring microbial community structure based on co-occurrence network approaches. BMC bioinformatics, 20(1), 1-14. https://doi.org/10.1186/s12859-019-2915-1

Kawatsu, K., Ushio, M., van Veen, F. F., & Kondoh, M. (2021). Are networks of trophic interactions sufficient for understanding the dynamics of multi‐trophic communities? Analysis of a tri‐trophic insect food‐web time‐series. Ecology Letters, 24(3), 543-552. https://doi.org/10.1111/ele.13672

Kéfi, S., Miele, V., Wieters, E. A., Navarrete, S. A., & Berlow, E. L. (2016). How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience. PLoS Biology, 14(8), e1002527. https://doi.org/10.1371/journal.pbio.1002527

Kim, D., Cho, M., Jeon, H., Yiem, M., & Lee, J. (2000). Population trends and temperature-dependent development of pear psylla, Cacopsylla pyricola (Foerster)(Homoptera: Psyllidae). Korean Journal of Applied Entomology, 39(2), 73-82.

Liu, D., Yu, J., Macchiarella, N. D., & Vincenzi, D. A. (2008). Simulation fidelity. In Human factors in simulation and training. CRC Press, United State, pp. 91-108.

Liu, S. S., Chen, F. Z., & Zalucki, M. P. (2002). Development and survival of the diamondback moth (Lepidoptera: Plutellidae) at constant and alternating temperatures. Environmental Entomology, 31(2), 221-231. https://doi.org/10.1603/0046-225X-31.2.221

Machlitt, D. (1998). Persea mite on avocados: Quick field counting method. Subtropcal Fruit News, 6, 1-4.

Marcasan, L. I. S., Hulujan, I. B., Florian, T., Somsai, P. A., Militaru, M., Sestras, A. F., Moldovan, I. C., Crisan, I. A., Roman, I., & Sestras, R. E. (2022). The importance of assessing the population structure and biology of psylla species for pest monitoring and management in pear orchards. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 50(4), 13022. https://doi.org/10.15835/nbha50313022

Moore, J. C., de Ruiter, P. C., McCann, K. S., Wolters, V., Gellner, G., & Serván, C. (Eds.). (2017). Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems. Cambridge University Press, United Kingdom. 326 pp. https://doi.org/10.1017/9781316871867   

Nelson, B. K. (1998). Time series analysis using autoregressive integrated moving average (ARIMA) models. Academic Emergency Medicine, 5(7), 739-744. https://doi.org/10.1111/j.1553-2712.1998.tb02493.x

Palma, G. R., Godoy, W. A., Engel, E., Lau, D., Galvan, E., Mason, O., Souza, L. E., Ribeiro, F. T., Miranda, J. R., & Moral, R. A.  (2023). Pattern-based prediction of population outbreaks. Ecological Informatics, 77, 102220. https://doi.org/10.1016/j.ecoinf.2023.102220

Pasqualini, E., Civolani, S., Musacchi, S., Ancarani, V., Dondini, L., Robert, P., & Baronio, P. (2006). Cacopsylla pyri behaviour on new pear selections for host resistance programs. Bulletin of Insectology, 59(1), 27-37.

Sanchez, J. A., & Ortín-Angulo, M. C. (2012). Abundance and population dynamics of Cacopsylla pyri (Hemiptera: Psyllidae) and its potential natural enemies in pear orchards in southern Spain. Crop Protection, 32, 24-29. https://doi.org/10.1016/j.cropro.2011.11.003

Schaub, L., Graf, B., & Butturini, A. (2005). Phenological model of pear psylla Cacopsylla pyri. Entomologia Experimentalis et Applicata, 117(2), 105-111. https://doi.org/10.1111/j.1570-7458.2005.00339.x

Scutareanu, P., Lingeman, R., Drukker, B., & Sabelis, M. W. (1999). Cross‐correlation analysis of fluctuations in local populations of pear psyllids and anthocorid bugs. Ecological Entomology, 24(3), 354-363. https://doi.org/10.1046/j.1365-2311.1999.00199.x

Shenk, T. M., White, G. C., & Burnham, K. P. (1998). Sampling‐variance effects on detecting density dependence from temporal trends in natural populations. Ecological Monographs, 68(3), 445-463. https://doi.org/10.1890/0012-9615(1998)068[0445:SVEODD]2.0.CO;2

Soukhovolsky, V., Kovalev, A., Ivanova, Y., & Tarasova, O. (2023). Autoregression, first order phase transition, and stochastic resonance: A comparison of three models for forest insect outbreaks. Mathematics, 11(19), 4212. https://doi.org/10.3390/math11194212

Stankevych, S. V., Biletskyj, Y. M., Zabrodina, I. V., Yevtushenko, M. D., Dolya, M. M., Lezhenina, I. P., Tymchyshyn, O. V., Dmytrenko, V. O., Petrova, O. M., & Klymenko, I. V. (2020). Cycle populations dynamics of harmful insects. Ukrainian Journal of Ecology, 10(3), 147-161. https://doi.org/10.15421/2020_148.

Wong, T. T., & Madsen, H. F. (1967). Laboratory and field studies on the seasonal forms of pear psylla in northern California. Journal of Economic Entomology, 60(1), 163-168. https://doi.org/10.1093/jee/60.1.163

Yeh, C. K., Rice, G., & Dubin, J. A. (2023). Functional spherical autocorrelation: A robust estimate of the autocorrelation of a functional time series. Electronic Journal of Statistics, 17(1), 650-687. https://doi.org/10.1214/23-EJS2112