The Impacts of Climate Change on the Potential Distribution of Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) in China

Abstract

The Indian meal moth Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) is a notorious stored-grain pest that can be found in most parts of China. The corpses, excretions, and other secretions of P. interpunctella larvae cause serious grain pollution, seriously affecting the nutritional and economic value of stored grain in China. To elucidate the potential distribution of P. interpunctella in China, we used the CLIMEX 4.0 model to project the potential distribution of the pest using historical climate data (1960–1990) and estimated future climate data (2030, 2050, and 2070). Under the historical climate situation, P. interpunctella was distributed in most areas of China, and its highly favorable habitats account for 48.14% of its total potential distribution. Because of temperature change in the future climate, suitable habitats will increase in the eastern part of Qinghai and will decrease in the mid-eastern, northeastern, and southeastern parts of China. Under these scenarios, the area of this pest’s highly favorable habitat will be reduced by 1.24 million km2, and its proportion will decrease to about 28.48%. These predicted outcomes will help to distinguish the impact of climate change on the potential distribution of P. interpunctella, thereby providing important information to design early forecasting and strategies to prevent pest harm to stored grain.

Which SDM Model, CLIMEX vs. MaxEnt, Best Forecasts Aeolesthes sarta Distribution at a Global Scale under Climate Change Scenarios?

“…CLIMEX focuses on the species' ecophysiological response to its environmental niche, while MaxEnt emphasizes the statistical relationship between species occurrence sites and environmental variables [32]. CLIMEX, grounded in physiological parameters and biological traits, aims to elucidate the climate impact on invasive species [33][34][35]. MaxEnt, a machine learning algorithm, predicts the species' probability distribution with maximum entropy while considering environmental constraints [36].…”

Modelling potential distribution of Tuta absoluta in China under climate change using CLIMEX and MaxEnt

“…MaxEnt), CLIMEX mainly centre on the ecophysiological response of the species to the environmental envelope, and the MaxEnt concentrates in the statistical relationship between species occurrence sites and environmental variables (Shabani et al, 2016). In more detail, CLIMEX based on physiological parameters and biological characteristics aims to explore the impact of climate on invasive species (Kriticos et al, 2015; Stoeckli et al, 2020; Zhao et al, 2022). MaxEnt is a machine learning algorithm that estimates the species probability distribution of maximum entropy under the restraints of environmental characteristics (Srivastava et al, 2019).…”

Comprehensive metabolomic variations of hawthorn before and after insect infestation based on the combination analysis of 1H NMR and UPLC-MS

“…The Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae) and Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae) are two primary injurious insects for stored food products, including various grains, dried fruits, pulses, and dried vegetables. After being infested, these food products are contaminated with excreta, dead bodies, and other secretions of insects, resulting in a serious reduction in nutritional value, quality, and safety level ( Gao et al, 2022 ; Zhao et al, 2022 ; Zhou et al, 2022 ). According to our previous investigation on the quality of hawthorn, it was easily infested by P .…”