Climate Change Promotes the Large-Scale Population Growth of Grapholita molesta (Busck) (Lepidoptera: Tortricidae) within Peach Orchards in China

• Published in: Agronomy
• Main Authors: Hongchen Li, Qiulian Peng, Su Wang, Fan Zhang, Xiaojun Guo, Quan Jiang, Ningxing Huang, Hu Li
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2022
• Subjects: Grapholita molesta; population dynamics; climate change; Antarc*; Antarctica
• Online Access: https://doi.org/10.3390/agronomy12122954

Cosmopolitan agricultural herbivorous pests are provided with a wide range of potential hosts. Therefore, they have high carrying capacity, and can cause extremely severe damage in agroecosystems. Understanding the ecological mechanisms of their population dynamics, especially as they relate to large-scale meteorological variations and geographical landscape influences, can help us to reveal how they became such important pests. The oriental fruit moth, Grapholita molesta, is a typical example of a significant pest distributed on a large scale, which is capable of damaging fruit trees of economic value such as peach, apple, pear, etc. This pest not only occurs in China, but exists on all continents except Antarctica. In order to prevent major pests and diseases, a system of plant protection has been established gradually in peach orchards within the Modern Agro-industry Technology Research System in China (CARS) since 2009. In the system, we collected the monitoring data of G. molesta by using pheromone traps at 17 experimental stations, and then used the corresponding climate data (temperature and precipitation) to explore the link between climate factors using mixed models. The results show that both monthly mean temperature and precipitation had a significant positive correlation with the occurrence of G. molesta. Therefore, global warming with higher levels of precipitation may favor G. molesta, allowing it to outperform other potential pests at the population level in peach orchards, on a large scale.