Abstract
Forests are considered as one of the most complex terrestrial ecosystems due to their high level of biodiversity, including genetic diversity. Understanding the genetic diversity of keystone species at a population level is vital to forest managers and policymakers for the conservation and sustainable utilization of forest genetic resources. Quercus semecarpifolia, commonly known as brown oak, is a keystone species of climax community thriving in the alpine zone of the Himalayas, which is presently experiencing population decline and range shift under the changing climate. In the present study, a landscape genetic approach was employed for deciphering the population genetic structure of Q. semecarpifolia in the western Himalayas using nuclear simple sequence repeat (SSR) markers. By analysing 718 individuals of 24 populations at 10 SSR loci, a high gene diversity (expected heterozygosity, He = 0.72; Allelic richness, Ar = 8.37) was recorded with a moderate genetic differentiation (FST = 0.16; P < 0.001). Genetic clustering and STRUCTURE analysis have displayed two major gene pools which appear to be primarily differentiated by the landscape and ecological constraints rather than the linear geographical distances. The hierarchical AMOVA analysis further supports the regional genetic divergence with a substantial proportion of genetic variation detected among the regions. Diversity maps generated by spatial interpolation elucidated the distribution pattern of genetic diversity and structure across the range, and aid in the demarcation of the diversity hotspots for conservation implications. To the best of our knowledge, this is the first comprehensive genetic study carried out in any Himalayan oaks, and the information generated herein is novel and of paramount importance in guiding conservation decisions.
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Data availability
This manuscript did not generate any genetic resource in the form of nucleic acid or protein sequences, SNPs, expression data, etc. which could be deposited in the databases. The SSR sequences used here for the genotyping work are already in the public domain and their references have been cited in the manuscript. Further, all the data generated herein are presented either in the manuscript or supplementary material.
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Acknowledgements
This research was funded by the Ministry of Environment Forests and Climate Change, Govt. of India through National CAMPA grant awarded to FRI Dehradun in the form of a research project. We would like to thank the Director, Forest Research Institute, Dehradun for providing all infrastructure and logistic support for conducting this work. Also, we would like to extend our gratitude to state forest departments of Uttarakhand for providing permissions and support during field surveys and sample collection. At last, we duly acknowledge the contribution of anonymous reviewers for imparting their knowledge and wisdom into this manuscript.
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This research was funded by Ministry of Environment Forests and Climate Change, Govt. of India through National CAMPA grant awarded to FRI Dehradun in the form of a research project.
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HSG and RKM, conceptualized the problem, interpreted data and prepared final manuscript. CS, RS, AY, MSB, SB, RK and RKM performed the field sampling. AR and HK performed laboratory work and analyzed the marker data. MSB and RS carried out geo-spatial analysis. Further, all the authors contributed in preparation and editing of the manuscript.
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Fig. S1
: Spatial overlaying of private allelic richness (PAr) over distribution map of Q. semecarpifolia. The areas or populations depicted in dark red shows presence of high proportion of private alleles. Fig. S2: Relationship of pairwise genetic distance with the pairwise geographic distances: a and b) shows the relationship of genetic distance with horizontal and vertical distance when analyzed all the 24 populations combined, c and d) shows relationship of genetic distance with horizontal and vertical distance when analyzed the seven populations of Cluster I separately, e and f) shows relationship of genetic distance with horizontal and vertical distance when analyzed the remaining 17 populations of Cluster II separately. Fig S3: Graphical representation of the estimated Ln probability of data (a) and ΔK (b) in relation to the set K values from 1-10. Fig S4: Principal coordinate analysis (PCoA) showing sub-clustering of Q. semecarpifolia populations of Cluster 1 (a) and Cluster II (b). Fig S5: STRUCTURE analysis of the Q. semecarpifolia populations of Cluster I: a) Graphical representation of the ΔK in relation to the set K values from 1-10; b-c) Bar plot showing pattern of genetic admixture among individual genotypes and populations at K=2 (b) and K=5. Each population separated by a vertical line are labelled with a number below which correspond the code given in table 1. Fig S6: STRUCTURE analysis of the Q. semecarpifolia populations of Cluster II: a) Graphical representation of the ΔK in relation to the set K values from 1-10; b) Bar plot showing pattern of genetic admixture among individual genotypes and populations at K=3. Each population separated by a vertical line are labelled with a number below which correspond the code given in table 1. Table S1. Comparison of diversity indices calculated in this study for Quercus semecarpifolia (Sl. No. 1) and other oaks species studied earlier
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Ginwal, H.S., Rawat, A., Shekhar, C. et al. Population genetic structure of a timberline oak (Quercus semecarpifolia Sm.) of western Himalayas and conservation implications. Conserv Genet 25, 133–147 (2024). https://doi.org/10.1007/s10592-023-01558-7
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DOI: https://doi.org/10.1007/s10592-023-01558-7