Management measures to control pine wood nematode spread in Europe

Terpene Production Varies in Pinus thunbergii Parl. with Different Levels of Resistance, with Potential Effects on Pinewood Nematode Behavior

Determining the mechanisms of pine wilt disease (PWD) resistance in Pinus is a popular research topic, but information on volatile organic substances (VOS) and their role in PWD is lacking. Whether the difference in VOS among Pinus thunbergii parl. that have different levels of resistance with pine wood nematodes (PWNs) is the reason for the differing resistance needs to be studied. In this study, resistant P. thunbergii introduced from Japan and susceptible P. thunbergii native to China were used to investigate the effects of different lines inoculated with PWN. We determined the expression levels of the terpene synthesis-related genes geranylgeranyl diphosphate synthase (GGPPS), 3-hydroxy-3-methylglutaryl-coenzyme A reductase 1 (HMDH1), two kinds of alpha-farnesene synthase (PT) genes. The types and the relative percentage content of terpenoids in the pine needles were also determined by gas chromatography coupled with mass spectrometry (GC-MS). Results show that the growth, population size and migration of PWNs were significantly inhibited. The expression of terpene synthesis genes in the resistant P. thunbergii was higher than that in the susceptible one. The analysis of terpenoids revealed a total of 41 terpenoids, of which resistant P. thunbergii contained 39 and susceptible P. thunbergii only 28; 14 terpenoids were specific to resistant P. thunbergii, in which 8 of the terpenoids were constitutive terpenes and 6 were inducible terpenes. There were 3 terpenes unique to the susceptible P. thunbergii, and only 1 inducible terpene. Our results showed that the reduction in the expression of disease symptom and suppression of PWNs in resistant P. thunbergii was likely related to differences in the types and content of resistance-related substances in the trees. This study does not specifically connect elevated compounds in resistant P. thunbergii to resistance to PWN and assays should be conducted to establish direct effects of terpenoids on pinewood nematode activity and reproduction.

Long-Term Projections of the Natural Expansion of the Pine Wood Nematode in the Iberian Peninsula

The invasive pine wood nematode (PWN), Bursaphelenchus xylophilus, causal agent of pine wilt disease, was first reported in Europe, near Lisbon, in 1999, and has since then spread to most of Portugal. We here modelled the spatiotemporal patterns of future PNW natural spread in the Iberian Peninsula, as dispersed by the vector beetle Monochamus galloprovincialis, using a process-based and previously validated network model. We improved the accuracy, informative content, forecasted period and spatial drivers considered in previous modelling efforts for the PWN in Southern Europe. We considered the distribution and different susceptibility to the PWN of individual pine tree species and the effect of climate change projections on environmental suitability for PWN spread, as we modelled the PWN expansion dynamics over the long term (>100 years). We found that, in the absence of effective containment measures, the PWN will spread naturally to the entire Iberian Peninsula, including the Pyrenees, where it would find a gateway for spread into France. The PWN spread will be relatively gradual, with an average rate of 0.83% of the total current Iberian pine forest area infected yearly. Climate was not found to be an important limiting factor for long-term PWN spread, because (i) there is ample availability of alternative pathways for PWN dispersal through areas that are already suitable for the PWN in the current climatic conditions; and (ii) future temperatures will make most of the Iberian Peninsula suitable for the PWN before the end of this century. Unlike climate, the susceptibility of different pine tree species to the PWN was a strong determinant of PWN expansion through Spain. This finding highlights the importance of accounting for individual tree species data and of additional research on species-specific susceptibility for more accurate modelling of PWN spread and guidance of related containment efforts.

Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms

Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.