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Potential Geographic Range of the Endangered Reed Parrotbill Paradoxornis heudei under Climate Change.

Wan ChenKeer MiaoKun GuoWeiya QianWan SunHao WangQing ChangChaochao Hu
Published in: Biology (2023)
The phenomenon of global climate change can impact the geographic range and biodiversity, thereby heightening the vulnerability of rare species to extinction. The reed parrotbill ( Paradoxornis heudei David, 1872) is endemic to central and eastern China, it is mainly distributed in the middle and lower reaches of the Yangtze River Plain and the Northeast Plain. In this study, eight of ten algorithms of the species distribution model (SDM) were used to evaluate the impact of climate change on the potential distribution of P. heudei under current and future climate scenarios and to analyze the possible related climate factors. After checking the collected data, 97 occurrence records of P. heudei were used. The relative contribution rate shows that among the selected climatic variables, temperature annual range (bio7), annual precipitation (bio12), and isothermality (bio3) were the principal climatic factors to limit the habitat suitability of P. heudei . The suitable habitat for P. heudei is primarily concentrated in the central-eastern and northeast plains of China, particularly in the eastern coastal region, spanning a mere area of 57,841 km 2 . The habitat suitability of P. heudei under different representative concentration pathway (RCP) scenarios was predicted to be different under future climatic conditions, but all of them had a larger range than the current one. The species distribution range could expand by more than 100% on average compared with the current range under the four scenarios in 2050, while it could contract by approximately 30% on average relative to the 2050 range in 2070 under different climate change scenarios. In the future, northeastern China may serve as a potential suitable habitat for P. heudei . The changes in the spatial and temporal distributions of P. heudei 's range are of utmost importance in identifying high-priority conservation regions and devising effective management strategies for its preservation.
Keyphrases
  • climate change
  • human health
  • machine learning
  • current status
  • deep learning
  • heavy metals
  • genetic diversity
  • neural network