Population Genetic Structure and Demographic History of Primula fasciculata in Southwest China.

Guangpeng Ren,Nicolas Salamin,Elena Conti,Rubén G Mateo

doi:10.3389/fpls.2020.00986

Abstract

Understanding the factors that drive the genetic structure of a species and its responses to past climatic changes is an important first step in modern population management. The response to the last glacial maximum (LGM) has been well studied, however, the effect of previous glaciation periods on plant demographic history is still not well studied. Here we investigated the population structure and demographic history of Primula fasciculata that widely occurs in the Hengduan Mountains and Qinghai-Tibetan Plateau. We obtained genomic data for 234 samples of the species using restriction site-associated DNA (RAD) sequencing and combined approximate Bayesian computation (ABC) and species distribution modeling (SDM) to evaluate the effects of multiple glaciation periods by testing several population divergence models and demographic scenarios. The analyses of population structure showed that P. fasciculata displays a striking population structure with six groups that could be identified genetically. Our ABC modeling suggested that the current groups diverged from ancestral populations located in the eastern Hengduan Mountains after the largest glaciation occurred in the region (~ 0.8–0.5 million years ago), which is consistent with the result of SDMs. Each current group has survived in different glacial refugia during the LGM and experienced expansions and/or bottlenecks since their divergence during or across the following Quaternary glacial cycles. Our study demonstrates the usefulness of population genomics for evaluating the effects of past climatic changes in alpine plant species with shallow population structure.

Highlights

Plant populations are not randomly arranged assemblages of genotypes but are structured in space and time (Loveless and Hamrick, 1984)
We stratified the procedure in three steps (Figure 2): (1) we investigated the most likely tree topologies for the three main lineages that were identified by the principal component analysis (PCA) analyses among 13 scenarios describing all possible topologies (Figure 2; Table S2); (2) we split the three main lineages into six groups based on analysis of molecular variance (AMOVA) analysis on multiple grouping strategies, i.e., L3 was split into G3, G1, and G2; L1 was split into G4 and G5; L2 was not split and renamed as G6
The patterns of genetic differentiation detected by different structure analyses were congruent, and we identified six groups of populations that capture the main characteristics of the population history of this species

Summary

Introduction

Plant populations are not randomly arranged assemblages of genotypes but are structured in space and time (Loveless and Hamrick, 1984). Identifying the factors that drive the genetic structure of a plant species is an important first step to understand speciation, adaptation, and genetic change (Antonovics, 1968), and to help in population management. In the latter case, the spatio-temporal dynamics of population histories can profoundly impact their future evolutionary potential (e.g., Lanier et al, 2015). The spatio-temporal dynamics of population histories can profoundly impact their future evolutionary potential (e.g., Lanier et al, 2015) This is especially true for climate-sensitive species inhabiting highly fragmented environments, such as mountain ranges

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