Integrating Demography, Predation Rate, and Computer Simulation for Evaluation of Orius Strigicollis as Biological Control Agent Against Frankliniella Intonsa

Abstract

Erankliniella intonsa (Trybom) (Thysanoptera: Thripidae) is an important pest of numerous horticultural and agricultural crops in Taiwan. Orius strigicollis (Poppius) (Hemiptera: Anthocoridae) is a predator with high predation capacity against many pests of legumes and flowers. We used the age-stage, two-sex life table method to integrate the life table data with the predation rate of O. strigicollis fed on F. intonsa. The preadult duration, adult longevity, net reproductive rate, intrinsic rate of increase, and finite rate of increase for O. strigicollis were 13.6 d, 12.5 d, 18.8 offspring/individual, 0.1437 d(-1), and 1.1546 d(-1), respectively. The total predation of O. strigicollis during their preadult and adult stages was 60.4 and 107.3 thrips, respectively. The net predation rate was 101 prey/individual. To demonstrate the effect of releasing predators of different stages on the population growth and predation capacity, we used population projection to evaluate the predation potential of O. strigicollis, and the uncertainty of predation potential was determined by using the life tables from the 0.025th, and 0.975th, bootstrap percentiles of the finite rate of increase. Releasing third instars or adults of O. strigicollis can effectively control the pest sooner than releasing O. strigicollis eggs. In biological control, both predator and prey populations are age-stage-structured and (in most instances) individuals of both sexes are present. It is imperative that the age-stage, two-sex life table be used to precisely incorporate the variability that occurs in the developmental rate, stage differentiation, survival rates, and predation rates among individuals and between sexes. Our results demonstrate that integrating life table and predation rate data generated by using the age-stage, two-sex life table is an important technique for improving biological control programs by refining the timing and estimating the release of natural enemies. Moreover, by using the multinomial theorem, we demonstrated that a large resampling (B =100,000) is necessary to obtain more precise estimates of population parameters in applications of the bootstrap technique.