Investigating the Effect of Invasion Characteristics on Onion Thrips (Thysanoptera: Thripidae) Populations in Onions With a Temperature-Driven Process Model

Abstract

A temperature-driven process model was developed to describe the seasonal patterns of populations of onion thrips, Thrips tabaci Lindeman, in onions. The model used daily cohorts (individuals of the same developmental stage and daily age) as the population unit. Stage transitions were modeled as a logistic function of accumulated degree-days to account for variability in development rate among individuals. Daily survival was modeled as a logistic function of daily mean temperature. Parameters for development, survival, and fecundity were estimated from published data. A single invasion event was used to initiate the population process, starting at 1-100 d after onion emergence (DAE) for 10-100 d at the daily rate of 0.001-0.9 adults/plant/d. The model was validated against five observed seasonal patterns of onion thrips populations from two unsprayed sites in the Riverina, New South Wales, Australia, during 2003-2006. Performance of the model was measured by a fit index based on the proportion of variations in observed data explained by the model (R (2)) and the differences in total thrips-days between observed and predicted populations. Satisfactory matching between simulated and observed seasonal patterns was obtained within the ranges of invasion parameters tested. Model best-fit was obtained at invasion starting dates of 6-98 DAE with a daily invasion rate of 0.002-0.2 adults/plant/d and an invasion duration of 30-100 d. Under the best-fit invasion scenarios, the model closely reproduced the observed seasonal patterns, explaining 73-95% of variability in adult and larval densities during population increase periods. The results showed that small invasions of adult thrips followed by a gradual population build-up of thrips within onion crops were sufficient to bring about the observed seasonal patterns of onion thrips populations in onion. Implications of the model on timing of chemical controls are discussed.