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Effects of habitat fragmentation and human disturbance on the population dynamics of the Yunnan snub-nosed monkey from 1994 to 2016.

Xumao ZhaoBaoping RenDayong LiZuofu XiangPaul A GarberMing Li
Published in: PeerJ (2019)
In this study, we integrate data from field investigations, spatial analysis, genetic analysis, and Generalized Linear Models (GLMs) to evaluate the effects of habitat fragmentation on the population dynamics, genetic diversity, and range shifts in the endangered Yunnan snub-nosed monkey (Rhinopithecus bieti). The results indicate that from 1994 to 2016, R. bieti population size increased from less than 2,000 to approximately 3,000 individuals. A primary factor promoting population recovery was the establishment of protected nature reserves. We also found that subpopulation growth rates were uneven, with the groups in some areas, and the formation of new groups. Both the fragmentation index, defined as the ratio of the number of forest patches to the total area of forest patches (e.g., increased fragmentation), and increasing human population size had a negative effect on population growth in R. bieti. We recommend that government conservation plans prioritize the protection of particular R. bieti populations, such as the Baimei and Jisichang populations, which have uncommon haplotypes. In addition, effective conservation strategies need to include an expansion of migration corridors to enable individuals from larger populations such as Guyoulong (Guilong) to serve as a source population to increase the genetic diversity of smaller R. bieti subpopulations. We argue that policies designed to protect endangered primates should not focus solely on total population size but also need to determine the amount of genetic diversity present across different subpopulations and use this information as a measure of the effectiveness of current conservation policies and the basis for new conservation policies.
Keyphrases
  • genetic diversity
  • climate change
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  • healthcare
  • machine learning
  • social media
  • big data
  • electronic health record
  • induced pluripotent stem cells