Asymmetric introgression and thermal advantage jointly drive climate-mediated lineage turnover in a mixed-ploidy reed
- School of Life Sciences, Shandong University, China
- National Glycoengineering Research Center, Shandong University, China
- Ecology and Nature Conservation Institute, Chinese Academy of Forestry, No.1 Dongxiaofu, Xiangshan Road, Haidian district, China
- Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Finland
- Natural History Museum of China, China
- School of Renewable Natural Resources, Louisiana State University Agricultural Center, United States
Figures
Figure 1 with 4 supplements
Evolutionary lineages of P. australis in China.
(A) Bayesian clustering analysis based on 42 nuclear microsatellites at the optimal genetic cluster number K=2. (B) Principal coordinate analysis (PCoA) based on Bruvo’s genetic distance. Colors indicate chloroplast haplotypes, where P_r denotes P-related haplotypes and ‘Unknown’ refers to samples without haplotype data. Shapes denote geographical origins: circles (northern China, CN_N), squares (southern China, CN_S), and triangles (northwestern China, CN_W). (C) Geographical distribution of sampling locations for each genetic lineage by PCoA or/and chloroplast haplotypes. (D) Bioclimatic variable differences among lineages. Bio1, annual mean temperature; bio2, mean diurnal range; bio3, isothermality; bio4, temperature seasonality; bio8, mean temperature of wettest quarter; bio14, precipitation of driest month; bio15, precipitation seasonality; bio18, precipitation of warmest quarter. Significance levels are indicated as: * p<0.05, ** p<0.01, *** p<0.001.
Figure 1—figure supplement 1
k - delta k plot in STRUCTURE analysis.
The optimal number of clusters is determined by identifying the k value corresponding to the first peak in the delta k value.
Figure 1—figure supplement 2
Population genetic structure of P. australis in China (n=458) as inferred from (A) Bayesian clustering analysis of 42 nuclear microsatellites at k=3.
Figure 1—figure supplement 3
Principal Components Analysis (PCA) of environmental variables for the CN and FEAU lineages.
(A) Scatter plot of occurrences in the PCA space defined by the first two principal components (PC1 and PC2). Points represent individual occurrences, colored by lineage. (B) Loading plot showing the contribution and direction of original bioclimatic variables to PC1 and PC2. Arrows indicate variable loadings, with longer arrows representing stronger contributions to the components.
Figure 1—figure supplement 4
Niche characterization and overlap between CN and FEAU lineages.
(A) and (B) show the gridded density of occurrences in the environmental space, derived from the first two PCA axes, for each lineage. Grey colors indicate higher density of occurrences. (C) Niche overlap in the gridded environmental space, with areas of shared niche space highlighted. (D) Results of niche similarity tests, presenting statistical metrics (D statistics) and significance values indicating the degree of niche conservatism or divergence.
Figure 2
Admixture and introgression between CN and FEAU lineages revealed by STRUCTURE.
(A) Scatter plot of individual ancestry coefficients for the two ancestral components (CN and FEAU). Dashed lines indicate the 0.2 and 0.8 thresholds used to define pure and admixed individuals. Points are colored by admixture group. STRUCTURE is used here to detect hybridization, not to define lineages (see Figure 1). (B) Histograms of CN ancestry values across three geographic regions: Northern China, Southern China, and Northwestern China. (C) Geographic distribution of admixture groups across Chinese provinces. Pie charts show the relative proportions of pure CN (blue), pure FEAU (orange), and mixed (red) individuals in each province. (D) Relationship between latitude and log-transformed admixture level. Points are colored by admixture group. The regression line (solid if p<0.05, dashed if p>0.05) and p-value (from linear mixed-effects model with Province as random effect) are shown. The admixture level for each individual was calculated as 1–2 × |CN−0.5|, where CN is the ancestry coefficient of the CN lineage (range 0–1). This value approaches 1 when CN = 0.5 (high admixture) and approaches 0 when CN is near 0 or 1 (near-pure ancestry). To improve visualization, we plotted log (admixture level +1), which ranges from 0 (pure) to log (2) ≈ 0.69 (maximally admixed). (E) Relationship between longitude and log-transformed admixture level. Visualization follows the same conventions as panel D. Pure individuals are defined as having >80% ancestry from one component; individuals with 20–80% ancestry from each component are classified as mixed.
Figure 3 with 1 supplement
Comparative growth performance and heat tolerance of the CN and FEAU lineages of P. australis across common garden environments.
(A) Geographic locations of the four common garden sites (Panjin, Jinan, Qingdao, Shanghai) overlaid on the mean annual temperature (Bio1) raster map of eastern China. Color gradient represents annual mean temperature (℃). (B–E) Comparison of four growth traits between lineages across four common garden locations: (B) Total Biomass, (C) Shoot Height, (D) Density, and (E) Specific Leaf Area (SLA). (F) Comparison of key heat tolerance parameters (Tcrit, T50, and T95) between the CN and FEAU lineages. Bar heights represent mean values, and error bars indicate the standard error of the mean. Sample sizes: for growth traits (B–E), n = 11 (CN) and 9 (FEAU) populations in Jinan and Panjin, and n = 9 (CN) and 8 (FEAU) populations in Qingdao and Shanghai; for heat tolerance (F), n = 6 genotypes per lineage, with five leaf replicates per genotype per temperature treatment. Significance levels from pairwise t tests within each garden (B–E) and unpaired t tests for heat tolerance parameters (F) are denoted by asterisks: * p<0.05, ** p<0.01, *** p<0.001; ‘ns’ indicates not significant.
Figure 3—figure supplement 1
Representative fitted curve of photosystem II (PSII) heat tolerance for 11 samples, showing the decline in Fv/Fm with increasing temperature.
The points represent observed data, the solid line indicates the predicted logistic curve, and the dashed lines show the 95% confidence interval of the fit based on bootstrap resampling. The vertical dashed lines mark the estimated mean critical temperature (Tcrit, light gray), the temperature of 50% Fv/Fm reduction (T50, gray), and the temperature of 95% inhibition (T95, black), each derived from the model.
Figure 4 with 2 supplements
Predicted potential distribution of three P. australis lineages (CN, FEAU, and SW) in China under current and future climate scenarios.
The first row (A, B, C) shows the results for the CN lineage, the second row (D, E, F) for the FEAU lineage, and the third row (G, H, I) for the SW lineage. Columns represent different time periods: current distribution (A, D, G), and projected distributions for 2061–2080 under the low-emission scenario SSP1-2.6 (B, E, H) and the high-emission scenario SSP5-8.5 (C, F, I). Predictions were generated using the Maximum Entropy (MaxEnt) model. The depth of color (color intensity) corresponds to the level of habitat suitability, ranging from dark blue (unsuitable) to bright yellow (highly suitable).
Figure 4—figure supplement 1
Predicted potential distribution of three P. australis lineages (CN, FEAU, and SW) in China under current and future climate scenarios.
The first row (A, B, C) shows the results for the CN lineage, the second row (D, E, F) for the FEAU lineage, and the third row (G, H, I) for the SW lineage. Columns represent different time periods: current distribution (A, D, G), and projected distributions for 2061–2080 under the low-emission scenario SSP1-2.6 (B, E, H) and the high-emission scenario SSP5-8.5 (C, F, I). Predictions were generated using the Maximum Entropy (MaxEnt) model. Suitable habitats are classified into three levels: red indicates high suitability, followed by orange (moderate), and yellow (low).
Figure 4—figure supplement 2
A correlation heatmap of 19 bioclimatic variables derived from global climate data at 2.5 arc-minute resolution.
Values represent Pearson correlation coefficients between variable pairs. Climate data were extracted based on spatially thinned species occurrence records to reduce sampling bias. Correlations were calculated from worldwide bioclimatic layers available from the WorldClim database.
Additional files
-
Supplementary file 1
Supplementary tables.
(A) Heat tolerance metrics of different Phragmites australis genotypes, derived from chlorophyll fluorescence curve analysis. (B) Best-performing parameter combinations for the species distribution models of different Phragmites australis lineages. (C) Projected changes in suitable habitat distribution (%) for three Phragmites australis lineages in China under current and future climate scenarios (2061–2080). (D) Collection details of Phragmites australis samples used for microsatellite analysis. (E) Characteristics of the microsatellite markers used in this study, including marker name, chromosomal location, quality metrics, sequence, and primer information. (F) Phragmites australis samples used in the common garden experiment, detailing their designated name, haplotype, and geographical origin.
- https://cdn.elifesciences.org/articles/112160/elife-112160-supp1-v1.docx
-
MDAR checklist
- https://cdn.elifesciences.org/articles/112160/elife-112160-mdarchecklist1-v1.docx
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Asymmetric introgression and thermal advantage jointly drive climate-mediated lineage turnover in a mixed-ploidy reed
eLife 15:RP112160.
https://doi.org/10.7554/eLife.112160.3
