Environmental adaptive genetic variation and genetic vulnerability of relict plant Pterocarya hupehensis

doi:10.17521/cjpe.2024.0445

Abstract

Abstract:

Aims The rapid fluctuations of climate are increasingly altering the fate of species, exacerbating their vulnerability, leading to the loss of genetic diversity in many species, and even pushing some to the brink of extinction. Relict plants, having survived extreme climate changes since the Cenozoic era, carry a wealth of genetic information related to environmental adaptation. Investigating the genetic basis of their population-level environmental adaptation and their potential to cope with future climate change can provide valuable insights for biodiversity conservation.
Methods In this study, restriction site-associated DNA sequencing (RAD-seq) was performed on 122 individuals from 18 populations of Pterocarya hupehensis, which is a Cenozoic relict plant distributed around the Sichuan Basin in China. Then, the ecological adaptation and genetic vulnerability of P. hupehensis were studied by landscape genomics. First, we use a latent factor mixed model (LFMM) and Pcadapt to detect selected sites. Second, a Mantel test based on linear models, redundancy analysis (RDA), gradient forest (GF), and generalized dissimilarity modelling (GDM) were used to investigate the response patterns of genetic variation to environmental gradients. Finally, based on the risk of non-adaptedness analysis (RONA), the vulnerability of the P. hupehensis was predicted for the SSP245 and SSP585 scenarios in 2090.
Important findings A total of 398 single nucleotide polymorphism loci (SNPs) were significantly associated with the six climatic factors (isothermality, minimum temperature of coldest month, temperature annual range, mean temperature of wettest quarter, precipitation of wettest month, and precipitation seasonality). In addition, 177 of them were detected as selected SNPs. We found that precipitation seasonality was an important climatic factor affecting the genetic variation of P. hupehensis. A significant signal of isolation by environment (IBE) was detected, indicating that environmental factors account for more genetic variation than geographical factors. Under the SSP585 scenario in 2090, the genetic vulnerability of P. hupehensis was higher than that under SSP126 scenario. The precipitation seasonality has an important effect on the adaptative ability of the population in the northwest range of P. hupehensis. This study not only provides a theoretical foundation for the management and conservation strategies of vulnerable species in the face of future climate change, but also offers a new case study on how relict plants around Sichuan Basin may respond to future climate change.

Key words: relict plants, landscape genomics, ecological adaptation, genetic vulnerability, restriction site-associated DNA sequencing

LU Zi-Jia, WANG Tian-Rui, ZHENG Si-Si, MENG Hong-Hu, CAO Jian-Guo, Gregor KOZLOWSKI, SONG Yi-Gang. Environmental adaptive genetic variation and genetic vulnerability of relict plant Pterocarya hupehensis[J]. Chin J Plant Ecol, 2025, 49(10): 1626-1642.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2024.0445

https://www.plant-ecology.com/EN/Y2025/V49/I10/1626

Figures/Tables 9

Fig. 1 Habitat of Pterocarya hupehensis (A), plant morphology (B-E), and collection sites of P. hupehensis (F). The information of collection sites is shown in Table 1.

Table 1 Sample coding, sampling locations, and individual numbers of 18 Pterocarya hupehensis populations based on restriction-site associated DNA-sequnencing (RAD-seq)

种群编号 Code	采样点 Site	经度 Longitude (° E)	纬度 Latitude (° N)	个体数 n
HH	陕西西安 Xi’an, Shaanxi	107.93	33.87	6
HXC	陕西安康 Ankang, Shaanxi	109.49	32.72	3
FLC	陕西安康 Ankang, Shaanxi	109.40	31.95	5
DSP	陕西宝鸡 Baoji, Shaanxi	107.49	33.84	4
QDZ	河南南阳 Nanyang, Henan	111.97	33.52	6
HKC	河南南阳 Nanyang, Henan	112.02	33.57	6
TSG	河南南阳 Nanyang, Henan	111.72	33.63	6
LJL	河南南阳 Nanyang, Henan	111.70	33.63	4
LYG	甘肃天水 Tianshui, Gansu	106.10	34.23	5
YPC	甘肃陇南 Longnan, Gansu	106.23	33.67	5
HJG	甘肃康县 Kang Xian, Gansu	105.51	33.39	5
MYG	甘肃阳坝镇 Yangba, Gansu	105.74	33.03	6
SNJ	湖北神农架 Shennongjia, Hubei	110.92	31.65	7
XJZ	湖北神农架 Shennongjia, Hubei	110.58	31.59	11
SHJZ	湖北恩施 Enshi, Hubei	109.80	30.16	11
JSZ	重庆金山镇 Jinshan, Chongqing	107.14	29.02	7
DFX	贵州毕节 Bijie, Guizhou	105.88	27.33	12
NYX	贵州毕节 Bijie, Guizhou	105.47	26.70	13

Fig. 2 Abnormal single nucleotide polymorphism (SNP) detected in Pterocarya hupehensis based on “Pcadapt” (A), Principal Component Analysis (PCA) (B), and latent factor mixed models analysis based on six environmental factors (C). The red dotted line represents the threshold of p = 0.01, and the sites above the red dotted line are significantly associated with the environmental factor. MAF, minor allele frequency.

Table 2 Mantel and partial Mantel test results for the entire population of Pterocarya hupehensis

Mantel检验 Mantel test	Mantel’s r	p	Mantel检验(每个气候因子) Mantel test (Each climatic factor)	Mantel’s r	p
地理隔离 Isolation by distance	0.23	0.062	等温性 Isothermality	-0.17	0.861
环境隔离 Isolation by environment	0.31	0.002	最冷月份最低气温 Minimum temperature of the coldest month	-0.05	0.668
偏Mantel检验 Partial mantel test			气温年较差 Temperature annual range	-0.07	0.675
地理隔离(控制环境距离) Isolation by distance (Conditioned with environmental distance)	0.08	0.298	最湿季度平均气温 Mean temperature of the wettest quarter	0.09	0.212
环境隔离(控制地理距离) Isolation by environment (Conditioned with geographical distance)	0.23	0.029	最湿月份降水量 Precipitation of the wettest month	0.29	0.003
			降水量季节性变化 Precipitation seasonality	0.34	0.007

Fig. 3 Mantel test results showing the relationship between geographic distance and genetic distance, and between environmental distance and genetic distance across all populations (A), as well as the results of the genetic distance versus environmental distance for each individual climate factor (B). Red dashed lines represent significant correlations between variables. FST/(1 - FST), measuring genetic differentiation between paired populations (genetic distance).

Fig. 4 Redundancy analysis (RDA) of P. hupehensis between genetic variation and six climatic factors (A), and between genetic variation and geographical factors (B). As well as partial redundancy analysis (pRDA) between genetic variation and six climatic factors (C), and between genetic variation and geographical factors (D). The length of the vector represents the contribution of the environment variable to the explained variance, and the angle between the arrows represents the correlation between the variables. MEM, Moran’s eigenvector. bio3, isothermality; bio6, minimum temperature of the coldest month; bio7, temperature annual range; bio8, mean temperature of the wettest quarter; bio13, precipitation of the wettest month; bio15, precipitation seasonality.

Table 3 Redundancy analysis (RDA) and partial RDA analysis results of all populations of Pterocarya hupehensis

环境因子 Environmental factor	冗余分析 RDA			偏冗余分析 Partial RDA
环境因子 Environmental factor	PVE	特征值 Eigenvalue	p	PVE	特征值 Eigenvalue	p
地理 Geography	0.13	3.54	0.001	0.09	2.54	0.001
气候 Climate	0.18	4.06	0.001	0.13	3.17	0.001
等温性 Isothermality	0.03	3.63	0.001	0.03	4.05	0.001
最冷月份最低气温 Minimum temperature of the coldest month	0.03	3.70	0.001	0.02	2.60	0.001
气温年较差 Temperature annual range	0.04	5.62	0.001	0.02	2.92	0.001
最湿季度平均气温 Mean temperature of the wettest quarter	0.02	2.82	0.001	0.03	4.24	0.001
最湿月份降水量 Precipitation of the wettest month	0.05	6.71	0.001	0.01	1.81	0.001
降水量季节性变化 Precipitation seasonality	0.01	1.86	0.003	0.02	3.44	0.001

Fig. 5 Environmental variable importance rankings for genetic variation based on Gradient Forest (GF) (A) and Generalized Dissimilarity Model (GDM) (C) analyses, with I-spline curves illustrating genetic composition shifts along environmental gradients (B) and cumulative importance curves (D) in GDM analysis. MEM, Moran’s eigenvector. bio3, isothermality; bio6, minimum temperature of the coldest month; bio7, temperature annual range; bio8, mean temperature of the wettest quarter; bio13, precipitation of the wettest month; bio15, precipitation seasonality.

Fig. 6 Potential range projections (A, B) and risk of non-adaptedness analysis (RONA) (C, D) for future climate scenarios. Only the three climate factors that have the greatest impact on genomic vulnerability are shown in the picture. Data of two global climate models (MIROC and CMCC) were downloaded from WorldClim v2.1 (https://www.worldclim.org/data/cmip6/cmip6_clim2.5m. html). Detailed information of abbreviations for each population is shown in Table 1.

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