Acleris fimbirana Thunberg risk assessment and predicting its potential geographical distribution in China
[Aim]Based on the initial temperature and effective accumulated temperature for the development of Acleris fimbirana Thunberg the CLIMEX system was used to predict the potential habitat of A.fimbirana.Additionally,the risk diffusion of A.fimbirana was analyzed to provide a quantitative reference for the formulation of comprehensive management strategies for this species.[Methods]The effects of five constant temperatures(18,21,24,27 and 30 ℃)on the development duration of A.fimbirana were examined.Based on this,the developmental starting temperature and effective accumulated temperature of A.fimbirana were obtained and the CLIMEX system was used to predict the optimal geographical regions for A.fimbirana within China.Furthermore,a comprehensive pest risk analysis method was used to assess the potential risks associated with A.fimbirana.[Results]The growth rate of A.fimbirana increased with increasing temperature.The initial temperature and effective accumulated temperature for A.fimbirana were(10.59±0.35)℃ and(560.60±33.19)degree days,respectively.Additionally,a total of 3.51 generations per year were completed in Xinjiang.Based on the CLIMEX and ArcGIS analyses,potential suitable habitat for A.fimbirana was found to span 5 797 500 km2.Within this,the area of highest adaptability spans up to 943 900 km2,including Henan,Hebei,Shannxi and Shanxi,which had the highest fitness range for A.fimbirana.Xinjiang was classified as a low-to-moderate occurrence area,although there is a potential threat of A.fimbirana spreading to most parts of this region.[Conclusion]Based on this findings of this study,A.fimbirana has the potential to inhabit up to 60％of China's regions.Hence,it poses a significant threat in China,particularly within the forestry and fruit industry.Thus,proactive measures such as early warning systems and effective control measures are crucial in mitigating the potential impact and reducing the associated risks in China.
NETL
NSTL
万方数据
