Predicting the potential distribution of the invasive species, Ophelimus maskelli (Ashmead) (Hymenoptera: Eulophidae), and its natural enemy Closterocerus chamaeleon (Hymenoptera: Eulophidae), under current and future climate conditions

J Econ Entomol. 2024 Nov 21:toae262. doi: 10.1093/jee/toae262. Online ahead of print.

Abstract

Invasive species pose a threat to ecosystems and humans worldwide, which is exacerbated by climate change, causing the expansion of species distributions. Ophelimus maskelli (Ashmead) (Hymenoptera: Eulophidae) causes leaf drying and shedding in eucalyptus trees, forming blister-like galls that negatively impact the growth of the trees. Closterocerus chamaeleon (Hymenoptera: Eulophidae) is a recognized parasitoid of O. maskelli. This study used the MaxEnt and CLIMEX models to predict the potential distribution under current and future climate scenarios for O. maskelli and its natural enemy, C. chamaeleon. The MaxEnt model result indicated that isothermality was the most critical factor predicting the distribution of O. maskelli, while the mean temperature of the driest quarter was the most critical factor predicting the distribution of C. chamaeleon. Under current climate conditions, the CLIMEX model predicted a wider potential distribution for O. maskelli and a smaller distribution for C. chamaeleon than the MaxEnt model. MaxEnt and CLIMEX prediction results indicated that South America and Africa were suitable for O. maskelli and C. chamaeleon. The MaxEnt model indicated that under SSP245 climate conditions, the potentially suitable regions for these species expanded, while under the SSP126 climate scenario, the region contracted significantly. The CLIMEX model indicated that under the A1B and A2 climate scenarios, the marginally suitable areas increased, while the moderately and highly suitable areas decreased. This study provides a theoretical basis for creating early monitoring, quarantine, and control methods for invasive pests.

Keywords: CLIMEX; ecological niche models; ensemble modeling; model optimization.