Mountain pine beetle (Dendroctonus ponderosae) attacks a large range of North American pine species, leading to economic and ecological damage. Part of this damage is related to the fungal partners carried by the beetle, which infect trees and help the beetle overcome the trees’ physical and chemical defenses. Detecting these fungal infections in trees is important for determining which trees are resistant (and therefore more economically viable for forestry), and which trees have been attacked by the beetle. Typically, these infections are detected by observing lesions in the vascular system of trees. However, such lesions are symptoms of an infection but not necessarily related to the extent of the infection in a host tree.
In a recent article in Tree Physiology, Chandra McAllister and colleagues took a genetic approach for detecting fungal infections. They used a modified form of a technique called quantitative polymerase chain reaction (qPCR), which amplifies DNA segments of interest (in this case related to the fungal pathogen) so that they can be detected and quantified. McAllister and colleagues found that, using this technique, they could detect the extent of fungal infection in a tree, and that the size of lesions was not correlated with the extent of infection. This means that trees may appear resistant, with only small lesions, when they may in fact be heavily infected.
The findings of McAllister and colleagues have important implications for forest management. Since the extent of fungal infection is not related to the visible lesions on trees, infected trees may be missed or overlooked, increasing the risk that fungal pathogens spread through a forest. Using the modified qPCR technique, it is now possible to better assess the extent and spread of fungal pathogens through forests. This should help foresters better manage and control fungal outbreaks in forests, in particular by allowing them to selectively plant and breed trees with increased resistance to infection.
McAllister, C. H., Fortier, C. E., St Onge, K. R., Sacchi, B. M., Nawrot, M. J., Locke, T., & Cooke, J. E. K. (2018). A novel application of RNase H2-dependent quantitative PCR for detection and quantification of Grosmannia clavigera, a mountain pine beetle fungal symbiont, in environmental samples. Tree Physiology. https://doi.org/10.1093/treephys/tpx147