Model‐based prediction of the potential geographical distribution of the invasive coconut mite, Aceria guerreronis Keifer (Acari: Eriophyidae) based on MaxEnt

Abstract

Abstract The coconut mite Aceria guerreronis Keifer (Acari: Eriophyidae), is a destructive mite pest of coconut, causing significant economic losses. However, an effective pest management strategy requires a clear understanding of the geographical areas at risk of the target pest. Therefore, we predicted the potential global distribution A. guerreronis using a machine learning algorithm based on maximum entropy. The potential future distribution for A. guerreronis covered the 2040 and 2060 periods under two climate change emission scenarios (SSP1‐2.6 and SSP5‐8.5) in the context of the sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change. The MaxEnt model predicts the habitat suitability for A. guerreronis outside its present distribution, with suitable habitats in Oceania, Asia, Africa, and the Americas. The habitat suitability for the pest will decrease from 2040 to 2060. The areas with the highest risk of A. guerreronis are those with an annual average temperature of around 25°C, mean annual precipitation of about 1459 mm, mean precipitation seasonality close to 64%, an average variation of daytime temperature of about 8.6°C, and mean seasonality of temperature of about 149.7°C. Our findings provide information for quarantine measures and policymaking, especially where A. guerreronis is presently still absent.