AI Article Synopsis

  • Understanding how climate change impacts the spatial distribution of species is crucial for conserving biodiversity.
  • This study focused on giant pandas and their competing species in the Qinling Mountains, examining how climate change might alter their spatial relationships through a modeling approach.
  • Results indicate that climate change will significantly reduce the distribution of giant pandas and competitive species while intensifying interspecific competition in various future climate scenarios.

Article Abstract

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 () and three sympatric competitive species (i.e., black bears (), golden takins (), and wild boars ()) 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614526PMC
http://dx.doi.org/10.3390/ani11113332DOI Listing

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