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Dispersal and aggregation patterns of Triozoida limbata (Enderlein, 1918) adults in guava orchards with multiple
Isaias de Oliveira1, Manoel A Uchoa2, Marcos Gino Fernandes3
1Agency for Agricultural Development and Rural Extension (AGRAER), Dourados, Brazil.
Background:
Triozoida limbata is a key pest affecting guava (Psidium guajava L.), causing significant economic losses. Understanding its dispersal and aggregation patterns in guava orchards with multiple cultivars, a common tactic aimed at production stability, is crucial for developing effective monitoring and management strategies. This study aimed to analyze the population distribution and aggregation patterns of T. limbata adults across different guava cultivars using dispersion indices (variance-to-mean ratio, Morisita's index, negative binomial parameter k, frequency distributions), the probability models according to theoretical frequency distributions (Poisson, negative binomial and positive binomial distributions) and Taylor's power law. Sampling was conducted biweekly in four experimental orchards in the Brazilian Cerrado region, for 15 consecutive months, using both direct (visual inspections) and indirect (yellow sticky traps) sampling methods.
Results:
Our analysis revealed that T. limbata exhibited a highly aggregated distribution across all cultivars and sampling methods. Both dispersion indices and probability models consistently supported this pattern, with more than 70% of the samples best fitting the negative binomial distribution. Regression models based on Taylor's power law confirmed aggregation trends, with R2 > 0.95.
Conclusion:
These findings have direct implications for integrated pest management (IPM) strategies. The observed aggregation pattern indicates that localized pest control measures, such as precision-targeted biological control and optimized sampling strategies, could improve management efficiency while reducing pesticide use and preserving natural enemies. Moreover, the effectiveness of yellow sticky traps for monitoring suggests that they can be incorporated into routine IPM programs to detect population fluctuations. © 2026 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
