Spatiotemporal dynamics and driving factors of Dioryctria in China

Dioryctria is a genus of forest pests that cause long-term harm to the economy and forest ecosystems. However, due to its complex species diversity, research on the driving factors of its spread and dispersal remains insufficient. This study proposes a research framework to address the macro-level analysis of the driving factors behind the spread of multi-species pest genera. Based on long-term analysis, we delineated the spatiotemporal distribution clusters of Dioryctria in China, analyzed its population dynamics, and explored the key factors influencing its spread across the country.

The research results indicate that from 2004 to 2023, Dioryctria infestations exhibited spatiotemporal clustering characteristics in the north-eastern, north-western, and central regions of China, with the overall trend of occurrence following a pattern of 'stable-decline after outbreak-growth-secondary outbreak.' In addition to natural climate factors, this study identified a significant impact of artificial lighting at night on Dioryctria occurrence, and its interaction with climatic factors significantly increased the risk of Dioryctria infestation. The introduction of spatiotemporal clustering detection improved the accuracy of Dioryctria spread simulation by 13.25%, validating the feasibility of the research framework.

This study provides a new perspective for the macro-level research on pest genera with complex species diversity. Through the analysis of the spatiotemporal patterns of Dioryctria infestations in China and the exploration of its driving factors, this study reveals the development trends of Dioryctria over the past 20 years and into the future, offering new scientific foundations for the control of Dioryctria. © 2025 Society of Chemical Industry.