Climate change may make pine wilt disease more prevalent

Pine wilt disease is one of the most severe and devastating diseases
affecting pine forests worldwide, resulting in huge economic losses in
many countries. The pinewood nematode, Bursaphelenchus
xylophilus, is the causal agent of pine wilt disease and is obligately
vectored by pine sawyer beetles, of the genus Monochamus. For the
disease to be present, the habitat must be suitable for the pinewood
nematode, and include at least one vector species, and at least one host
species. To predict its potential distribution, a model must consider all
three components. However, no comprehensive study has examined the
influence of climatic suitability on the distribution of this
'biological complex'. This study addresses this gap by
incorporating biotic interactions, specifically involving 13 vectors and
61 host plants, into projections based on the pinewood nematode model. We
predicted the global potential distribution of pine wilt disease and
compared it with the pinewood nematode model to highlight the importance
of including biotic interactions in species distribution models under
climate change. We found that the model revealed an overall trend
of increasing suitability scores for both the pinewood nematode and pine
wilt disease models under future climate scenarios. Furthermore, compared
to the pinewood nematode model, the biotic model results in an apparent
increase in suitability worldwide in the future as the climate will be
more suitable to vector and host complexes, suggesting that pine wilt
disease could potentially spread to other places via available hosts and
vectors. Synthesis and applications: By incorporating biotic interactions,
we projected a more accurate suitable area for pine wilt disease, offering
valuable insights into regions at high risk for future invasions by the
disease and its vectors. This information supports the development of
management and early detection strategies in areas of high suitability,
helping to mitigate potential economic and ecological losses.
Additionally, this study introduces a novel approach for integrating
biotic factors into species distribution models.