Abstract
Argentine ant, Linepithema humile (Mayr) (Hymenoptera: Formicidae), is a pest in southern California citrus orchards because it protects honeydew-producing hemipteran pests from natural enemies. A major impediment to controlling L. humile is estimating ant densities in orchards. Ants use irrigation lines to travel across orchard floors to reach trees infested with hemipterans. However, for making ant control decisions, it is the number of ants in trees, not on pipes that is critical. Work completed here demonstrates that the number of ants counted on pipes is highly correlated with the number of ants counted on trunks. Densities of ants counted on trunks are correlated with trunk diameter, citrus variety, and time of year and time of day counts. Six regression models, linear regression, zero-inflated Poisson regression, and zero-inflated negative binomial regression models, and each of their mixed model extensions, indicated a strong positive relationship between ant counts on irrigation pipes and ant counts on tree trunks. Mean squared prediction error and 5-fold cross-validation analyses indicated that the best performing of these 6 models was the zero-inflated Poisson mixed regression model. A binary classification model developed using support vector machine learning for ant infestation severity levels, categorized as low (<100 ants counted in 1 min) or high (≥100 ants counted in minutes), predicted ant densities on trunks with 85% accuracy. These models can be used to estimate the number of ants on the trunks of citrus trees by using counts of ants made on irrigation pipes.