Impacts of climate change on pine wilt disease outbreaks and associated carbon stock losses

Abstract

Pine wilt disease (PWD) is a significant threat to forests worldwide. Large-scale PWD outbreaks in China underline the need to understand the current and future distributions of PWD. We constructed a principal components analysis (PCA)-based environmental filter method in a three dimensional (3D) space to correct the sample bias caused by the strong autocorrelation of PWD outbreaks. The MaxEnt model was used to estimate the relationships between PWD occurrence and environmental characteristics and to predict potential PWD risk areas under four shared socioeconomic pathways (SSP) scenarios (SSP126, SSP245, SSP370, and SSP585) over the period 2020–2100. The model showed improved performance by correcting the sampling bias, with high accuracy (area under the curve values of 0.87 and 0.92 for model training and testing, respectively). Isothermality and annual precipitation contributed most strongly to the distribution of PWD. The potential PWD distribution will increase in the near future (2021–2040) and will expand northward in 2061–2100 under the SSP370 and SSP585 scenarios. This study also provides detailed maps of current and future carbon stock losses caused by PWD, which show the current maximum losses (∼24.9% of total forest carbon stocks) and an increase of ∼39,000 km2 in carbon stock losses in the northeast China for the period 2061–2100 under the SSP370 and SSP585 scenarios. Overall, our results show dramatic spatiotemporal shifts in the PWD distribution and forest carbon stock losses under climate change scenarios, and will provide a useful reference for implementing pest control techniques and forest management strategies.