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Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics
Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics
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Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics
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Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics
Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics

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Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics
Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics
Journal Article

Adaptive genetic differentiation in Pterocarya stenoptera (Juglandaceae) driven by multiple environmental variables were revealed by landscape genomics

2018
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Overview
Background The investigation of the genetic basis of local adaptation in non-model species is an interesting focus of evolutionary biologists and molecular ecologists. Identifying these adaptive genetic variabilities on the genome responsible can provide insight into the genetic mechanism of local adaptation. Results We investigated the spatial distribution of genetic variation in 22 natural populations of Pterocarya stenoptera across its distribution area in China to provide insights into the complex interplay between multiple environmental variables and adaptive genetic differentiation. The Bayesian analysis of population structure showed that the 22 populations of P. stenoptera were subdivided into two groups. Redundancy analysis demonstrated that this genetic differentiation was caused by the divergent selection of environmental difference. A total of 44 outlier loci were mutually identified by Arlequin and BayeScan, 43 of which were environment-associated loci (EAL). The results of latent factor mixed model analysis showed that solar radiation in June (Sr6), minimum temperature of the coldest month (Bio6), temperature seasonality (Bio4), and water vapor pressure in January (Wvp1) were associated with the highest numbers of EAL. Sr6 was associated with the ecological habitat of “prefered light”, and Bio6 and Wvp1 were associated with the ecological habitat of “warm and humid environment”. Conclusions Our results provided empirical evidence that environmental variables related to the ecological habitats of species play key roles in driving adaptive differentiation of species genome.