AUTHOR=Azrag Abdelmutalab G. A. , Mohamed Samira A. , Ndlela Shepard , Ekesi Sunday TITLE=Invasion risk by fruit trees mealybug Rastrococcus invadens (Williams) (Homoptera: Pseudococcidae) under climate warming JOURNAL=Frontiers in Ecology and Evolution VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1182370 DOI=10.3389/fevo.2023.1182370 ISSN=2296-701X 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. The precipitation of the wettest month (bio13) was the most crucial environmental variable that contributed to the predictions of the four models. The results revealed that most areas in East, Central, and West Africa were projected with high suitability for R. invadens to occur under current climatic conditions. Similarly, Bangladesh, Laos, Myanmar, India, Thailand, Vietnam and Cambodia in Asia, as well as Brazil, and Venezuela in South America showed high suitability for R. invadens establishment. However, under future climatic conditions (the years 2050s and 2070s), the suitable areas for R. invadens will increase regardless of the SSPs scenario (SSP2-4.5 and SSP5-8.5) indicating an expansion of the geographical range for this pest. This expansion is projected to be higher for the years 2070s than the 2050s. Similarly, the invasion risk of R. invadens is predicted to be higher under SSP2-4.5 scenario compared to SSP5-8.5 scenario, regardless of the year of the projection. Our results serve as an early warning tool that could serve as a guide to prevent further spread and invasion of this pest to new areas as well as help in developing an effective management strategy against R. invadens.