Employing hydrogen stable isotope analysis for traceability of invasive pine wood nematode vector insects

Bursaphelenchus xylophilus, the pathogen responsible for pine wilt disease, significantly threatens pine forests globally, with rapid infection leading to tree mortality within 40 days. This disease spreads efficiently through vector beetles, with Monochamus alternatus and Monochamus saltuarius serving as the primary vectors and Monochamus galloprovincialis being considered a potential vector in China. Molecular traceability and stable isotope traceability are both important techniques for insect tracing, each with its own advantages and limitations. Molecular traceability, such as DNA-based methods, offers high specificity and accuracy in identifying insect species, but it may require more complex procedures and higher costs due to the need for advanced equipment and skilled personnel. Stable isotope tracing, however, provides conservative and high-precision signatures, and is label-free, making it suitable for tracing the origins and pathways of insects in complex environments. Using the method of degreasing, the fat of the sample epidermis was removed, only the bone part was left, and the abdominal cavity was emptied to prevent the food from affecting the determination of hydrogen isotope. The present study aimed to trace the geographic origins of these vector insects in China using hydrogen stable isotope ratios, providing a novel tool for disease management.

Data processing and modeling were conducted using R and Origin software, yielding the equation y = 0.9822x + 19.1765. Statistical tests confirmed the model's significance and fit. Results indicate a significant positive correlation of temperature and precipitation with atmospheric hydrogen isotopic composition.

Our findings demonstrate the model's applicability in tracking the spread of pine wilt vectors, offering a promising approach to forest pathogen management. If the sample isotope test value is brought into the model and marked in the partition diagram, it can be proved that the model can effectively identify the source information of the sample. © 2025 Society of Chemical Industry.