A Temperature-Dependent Model for Tritrophic Interactions Involving Tea Plants, Tea Green Leafhoppers and Natural Enemies.

Abstract

Simple SummaryTritrophic interactions have achieved much attention in research on ecology. The tea plant Camellia sinensis (L.) O. Kuntze is a major economic crop in Asian countries, especially in China. Tea plants suffer infestations from herbivory attack during their lifetime. The tea green leafhopper (Empoasca onukii Matsuda) is a major pest for tea plants. The parasitic or predatory natural enemies of tea green leafhoppers (TGLs) can feed on their eggs, nymphs, or adults. However, a detailed mathematical model for tea plants–TGLs–natural enemies is still lacking. In the current work, we established a novel model based on laboratory measurements or field observations with temperature-dependent effects on tritrophic interactions for tea ecosystems. As expected, cyclic behaviors are identified. Stochastic simulations further showed two TGL outbreaks, the timing of which is consistent with field observations. Effective accumulated temperature (EAT) is possibly an important predictor of TGL outbreak. Applying slow-releasing semiochemicals as either repellents or attractants may be highly efficacious for pest biocontrol. An optimal treatment time of semiochemicals can also be determined. Our detailed model identifies key features of tritrophic interactions involving tea plants and can be extended to other ecosystems.The tea green leaf hopper, Empoasca onukii Matsuda, is a severe pest of tea plants. Volatile emissions from tea shoots infested by the tea green leafhopper may directly repel insect feeding or attract natural enemies. Many studies have been conducted on various aspects of the tritrophic relationship involving tea plants, tea green leafhoppers and natural enemies. However, mathematic models which could explain the dynamic mechanisms of this tritrophic interaction are still lacking. In the current work, we constructed a realistic and stochastic model with temperature-dependent features to characterize the tritrophic interactions in the tea agroecosystem. Model outputs showed that two leafhopper outbreaks occur in a year, with their features being consistent with field observations. Simulations showed that daily average effective accumulated temperature (EAT) might be an important metric for outbreak prediction. We also showed that application of slow-releasing semiochemicals, as either repellents or attractants, may be highly efficacious for pest biocontrol and can significantly increase tea yields. Furthermore, the start date of applying semiochemicals can be optimized to effectively increase tea yields. The current model qualitatively characterizes key features of the tritrophic interactions and provides critical insight into pest control in tea ecosystems.