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Evaluating the Effects of Climate Change on Spatial Aggregation of Giant Pandas and Sympatric Species in a Mountainous Landscape.

Naxun ZhaoXiming ZhangGuoyu ShanXinping Ye
Published in: Animals : an open access journal from MDPI (2021)
Understanding how climate change alters the spatial aggregation of sympatric species is important for biodiversity conservation. Previous studies usually focused on spatial shifting of species but paid little attention to changes in interspecific competitions under climate change. In this study, we evaluated the potential effects of climate change on the spatial aggregation of giant pandas (Ailuropoda melanoleuca) and three sympatric competitive species (i.e., black bears (Ursus thibetanus), golden takins (Budorcas taxicolor), and wild boars (Sus scrofa)) in the Qinling Mountains, China. We employed an ensemble species distribution modeling (SDM) approach to map the current spatial distributions of giant pandas and sympatric animals and projected them to future climate scenarios in 2050s and 2070s. We then examined the range overlapping and niche similarities of these species under different climate change scenarios. The results showed that the distribution areas of giant pandas and sympatric species would decrease remarkably under future climate changes. The shifting directions of the overlapping between giant pandas and sympatric species vary under different climate change scenarios. In conclusion, future climate change greatly shapes the spatial overlapping pattern of giant pandas and sympatric species in the Qinling Mountains, while interspecific competition would be intensified under both mild and worst-case climate change scenarios.
Keyphrases
  • climate change
  • human health
  • genetic diversity
  • rare case
  • machine learning
  • working memory
  • deep learning