Abstract:
The mango mealybug Rastrococcus invadens (Williams) (Homoptera:
Pseudococcidae) is a destructive and important insect pest of fruit trees in Africa
and Asia, especially the mango. Females and nymphs feed on plant leaves and fruits and produce honeydew that causes sooty mold, leading to yield reduction.
Although it is an important pest, the distribution of R. invadens under different
climate change scenarios has not been established. In this study, we predicted the
suitable habitat for R. invadens occurrence under current and future [two Shared
Socioeconomic Pathways (SSPs) scenarios: (SSP2-4.5 and SSP5-8.5) for the years 2050s and 2070s], using environmental variables and four ecological niche models viz., maxent, random forest, boosted regression trees, and support vector
machines. The performance and accuracy of these models were evaluated using
the area under the curve (AUC), the true skill statistic (TSS), correlation (COR), and
deviance. All models had high accuracy (AUC ≥ 0.96, TSS ≥ 0.88, COR ≥ 0.74 and
deviance ≤ 0.3) in predicting the potential distribution of R. invadens. Among the
four models, the random forest algorithm had the highest performance (AUC =
0.99, TSS = 0.95, COR = 0.91 and deviance = 0.14) in predicting the potential
distribution of R. invadens, followed by maxent (AUC = 0.97, TSS = 0.90, COR =
0.81 and deviance = 0.22). However, the maxent model was the best among the
four algorithms in predicting the ecological niche of R. invadens.