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Genomic insights into the genotype–environment mismatch and conservation units of a Qinghai–Tibet Plateau endemic cypress under climate change.

Authors :
Yang, Heng
Li, Jialiang
Milne, Richard Ian
Tao, Wenjing
Wang, Yi
Miao, Jibin
Wang, Wentao
Ju, Tsam
Tso, Sonam
Luo, Jian
Mao, Kangshan
Source :
Evolutionary Applications. Jun2022, Vol. 15 Issue 6, p919-933. 15p.
Publication Year :
2022

Abstract

Habitat loss induced by climate warming is a major threat to biodiversity, particularly to threatened species. Understanding the genetic diversity and distributional responses to climate change of threatened species is critical to facilitate their conservation and management. Cupressus gigantea, a rare conifer found in the eastern Qinghai–Tibet Plateau (QTP) at 3000–3600 m.a.s.l., is famous for its largest specimen, the King Cypress, which is >55 m tall. Here, we obtained transcriptome data from 96 samples of 10 populations covering its whole distribution and used these data to characterize genetic diversity, identify conservation units, and elucidate genomic vulnerability to future climate change. After filtering, we identified 145,336, 26,103, and 2833 single nucleotide polymorphisms in the whole, putatively neutral, and putatively adaptive datasets, respectively. Based on the whole and putatively neutral datasets, we found that populations from the Yalu Tsangpo River (YTR) and Nyang River (NR) catchments could be defined as separate management units (MUs), due to distinct genetic clusters and demographic histories. Results of gradient forest models suggest that all populations of C. gigantea may be at risk due to the high expected rate of climate change, and the NR MU had a higher risk than the YTR MU. This study deepens our understanding of the complex evolutionary history and population structure of threatened tree species in extreme environments, such as dry river valleys above 3000 m.a.s.l. in the QTP, and provides insights into their susceptibility to global climate change and potential for adaptive responses. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17524563
Volume :
15
Issue :
6
Database :
Academic Search Index
Journal :
Evolutionary Applications
Publication Type :
Academic Journal
Accession number :
157665218
Full Text :
https://doi.org/10.1111/eva.13377