Epigenetic architecture of Pseudotaxus chienii: Revealing the synergistic effects of climate and soil variables

Abstract

Whether conifers can withstand environmental changes especially temperature fluctuations has been controversial. Epigenetic analysis may provide new perspectives for solving the issue. Pseudotaxus chienii is an endangered gymnosperm species endemic to China. In this study, we have examined the genetic and epigenetic variations in its natural populations aiming to disentangle the synergistic effects of climate and soil on its population (epi)genetic differentiation by using amplified fragment length polymorphism (AFLP) and methylation-sensitive AFLP (MSAP) techniques. We identified 23 AFLP and 26, 7, and 5 MSAP outliers in P. chienii. Twenty-one of the putative adaptive AFLP loci were found associated with climate and/or soil variables including precipitation, temperature, K, Fe, Zn, and Cu, whereas 21, 7, and 4 MSAP outliers were significantly related to precipitation of wettest month (Bio13), precipitation driest of month (Bio14), percent tree cover (PTC), and soil Fe, Mn, and Cu compositions. Total precipitation and precipitation in the driest seasons were the most influential factors for genetic and epigenetic variation, respectively. In addition, a high full-methylation level and a strong correlation between genetic and epigenetic variation were detected in P. chienii. Climate is found of greater importance than soil in shaping adaptive (epi)genetic differentiation, and the synergistic effects of climate and climate-soil variables were also observed. The identified climate and soil variables should be considered when applying ex situ conservation.

Keywords: MSAP; Pseudotaxus chienii; climate and soil variables; population epigenetic variation; synergistic effects.

FIGURE 1

The 11 sampling sites across species distribution for Pseudotaxus chienii in China. Yellow dot represents the location of each population. ZJ, JX, HN, and GX denote Zhejiang Province, Jiangxi Province, Hunan Province, and the Guangxi Zhuang Autonomous Region, respectively. Population abbreviations, detailed locations, GPS coordinates, and sample sizes are shown in Table 1.

FIGURE 2

The methylation proportions at population (a) and province or region (b) level.

FIGURE 3

The UPGMA cluster analysis (above) and Nei's distance heatmap (below) based on AFLP (a), MSAP‐M (b), MSAP‐H (c), and MSAP‐U (d) datasets.

FIGURE 4

The genetic and epigenetic structure based on STRUCTURE. The optimal number of clusters is two, two, two, and three for AFLP, MSAP‐M, MSAP‐H, and MSAP‐U datasets, respectively.

FIGURE 5

The genetic and epigenetic structures of Pseudotaxus chienii populations based on discriminant analysis of principal components (DAPC). (a) AFLP; (b) MSAP‐M; (c) MSAP‐H; (d) MSAP‐U.

FIGURE 6

Significant associations between environmental variables and adaptive loci in redundancy analysis (RDA). Black arrows represent the environmental variables from forward selection. Colorful dots refer to individuals of different populations. (a, b, c, and d) correspond to AFLP, MSAP‐M, MSAP‐H, and MSAP‐U datasets, respectively.

FIGURE 7

Variance partitioning between environment and space based on outlier (a) and neutral loci (b) derived from AFLP, MSAP‐M, MSAP‐H, and MSAP‐U datasets, respectively. Orange, green, and blue colors indicate the variance explained by environment, both environment and space, and solely space, respectively.