The Potential Global Distribution of Sirex juvencus (Hymenoptera: Siricidae) under Near Current and Future Climatic Conditions as Predicted by the Maximum Entropy Model

Abstract

Wood wasp species in the genus Sirex are known pests of forestry. They cause significant economic losses due to their impacts on plant health and wood quality. S. juvencus (Hymenoptera: Siricidae), widely distributed in Asia, Europe, and North America, is known to negatively impact forestry, infesting Picea, Pinus, Larix, Abies, Cupressus, and Pseudotsuga species. This pest destroys plants by depositing eggs, mucus, and its obligate mutualistic fungus, Amylostereum areolatum. Its obligate mutualistic fungus is to provide nutrition for S. juvencus larva. Despite its extensive distribution range, little is known about which environmental variables significantly impact current and future distribution patterns of S. juvencus for pest control and monitoring. Here we used the maximum entropy model in conjunction with occurrence points of S. juvencus and environmental variables to predict the current and future global potential distribution of S. juvencus. We used the jackknife method and Pearson’s correlation analysis to select the environmental variables that influence the geographic distribution of S. juvencus, which resulted in the inclusion of the monthly average maximum temperature in February, the max temperature of warmest month, monthly average minimum temperature in July, monthly total precipitation in June, precipitation of the driest month, monthly total precipitation in September, and the temperature annual range. Temperature and precipitation are mainly likely to drive the distribution enabled by its obligate mutualistic fungus and the potential to co-infect with other Sirex species. The high temperature and low humidity influence S. juvencus eggs and larvae directly and indirectly via fungus-growth, which enables the larvae to survive. Furthermore, S. juvencus may increase its distribution to moderately suitable areas due to competition or dependency on other Sirex species during the infestation. Under the future climatic conditions, the highly suitable area increased by 32.79%, while the moderately suitable area, low suitable area, and unsuitable area increased by 28.14%, 3.30%, and 2.15%. Under climate changes, S. juvencus may spread in previously unsuitable areas rapidly.

Distributional Response of the Rare and Endangered Tree Species Abies chensiensis to Climate Change in East Asia

“…In this study, the AUC value was selected as the index to evaluate the accuracy of the model ( Figure S1 ). The greater the AUC value in the prediction results, the better the prediction effect, where an AUC value of 0.1–0.6 represents the failure of the prediction, 0.6–0.7 represents a poor prediction effect, 0.7–0.8 represents a moderate prediction effect, 0.8–0.9 represents a good prediction effect, and 0.9–1 represents an excellent prediction effect [ 45 , 46 ]. The simulation results showed that the AUC value of the test set was 0.983 in the current period ( Figure S1 ).…”

How could climate change influence the distribution of the black soldier fly, Hermetia illucens (Linnaeus) (Diptera, Stratiomyidae)?

“…Furthermore, climatic changes displaced other species, such as sandflies ( Psychodidae ) ( Moo-Llanes et al 2013 ) and the invasive species of the tropical fire ant, Solenopsis geminata (Fabricius) ( Hymenoptera , Formicidae ) ( Lee et al 2021 ). Therefore, environmental modifications caused by climate change, particularly changes in rainfall and temperature patterns, would influence the survival capacity of insects by affecting their reproductive performance, the survival of their larvae and symbiotic or mutualistic interactions with other organisms ( Mellanby et al 1967 , Hurlbert and Liang 2012 , Gao and Shi 2021 ).…”

The Potential Distribution of Rice Water Weevil (<i>Lissorhoptrus oryzophilus</i>) in China under Current and Future Climatic Conditions

“…However, the default parameters of the MaxEnt model were predominantly used in previous studies, which could lead to excessive model fitting [50,51]. To solve this problem, adjusted RM and FC parameters in the MaxEnt model would reduce the complexity and model overfitting in the species distribution [52,53]. This study used the ENMeval packet based on R developed by Muscarella et al [36] to optimize the MaxEnt model parameters.…”