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<p><i>Smilax excelsa</i> L. is a medicinal plant species with a restricted distribution from the Black Sea to the Caspian Sea region. Despite its ecological importance, little is known about its genetic structure and the environmental factors shaping its variability. In this study, we analyzed twelve geographical populations of <i>S. excelsa</i> in Iran to assess spatial patterns of genetic diversity and morphological divergence. Start codon targeted (SCoT) analysis generated 38 loci, indicating low genetic variability across populations (0.00–0.447%), while the analysis of molecular variance (AMOVA) revealed that 28% of the total genetic variance occurred among populations (<i>p</i> = 0.01). Nei’s genetic distance ranged from 0.03 to 0.60, and discrimination analysis of principal components (DAPC) grouped the populations into four distinct genetic clusters, in agreement with admixture patterns (Gst = 0.59, Nm = 0.33). RDA and CCA (<i>p</i> = 0.01) identified several SCoT loci associated with longitude, latitude, and altitude, with the first two CCA axes explaining 98% of the variance. sPCA revealed significant global and local spatial structuring (<i>p</i> = 0.01) and indicated the formation of genetic clines influenced by geographical variables. Morphological analyses showed significant population-level differentiation (Adonis and analysis of variance (ANOVA), <i>p</i> = 0.01) and a strong correlation between genetic and morphological distances (Mantel test, <i>p</i> = 0.01). Species distribution modeling produced high predictive accuracy (ROC &gt; 0.96) and indicated climate stability in the species’ suitable habitats. The partial least squares (PLS)-structural equation modeling (SEM) model showed that environmental factors strongly explained variation in the system (R<sup>2</sup> = 0.92) and significantly influenced resistance (β = −&#xa0;0.98) and morphology (<i>p</i> = 0.001). The genetic latent factor (R<sup>2</sup> = 0.57) displayed significant paths to environment (β = −&#xa0;1.36), resistance (β = −&#xa0;1.02), bioclimate (β = 0.40), and morphology (β = 0.65). Collectively, our findings demonstrate that spatial, environmental, and climatic factors jointly shape the genetic and morphological divergence of <i>S. excelsa</i> populations and provide a robust foundation for conservation planning.</p>

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Structural equation modeling (SEM) and data mining of the factors shaping the genetic diversity and morphological divergence in Smilax excelsa L. (Genus Smilax)

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Ali Sarvi,
  • Masoud Sheidai,
  • Fahimeh Koohdar

摘要

Smilax excelsa L. is a medicinal plant species with a restricted distribution from the Black Sea to the Caspian Sea region. Despite its ecological importance, little is known about its genetic structure and the environmental factors shaping its variability. In this study, we analyzed twelve geographical populations of S. excelsa in Iran to assess spatial patterns of genetic diversity and morphological divergence. Start codon targeted (SCoT) analysis generated 38 loci, indicating low genetic variability across populations (0.00–0.447%), while the analysis of molecular variance (AMOVA) revealed that 28% of the total genetic variance occurred among populations (p = 0.01). Nei’s genetic distance ranged from 0.03 to 0.60, and discrimination analysis of principal components (DAPC) grouped the populations into four distinct genetic clusters, in agreement with admixture patterns (Gst = 0.59, Nm = 0.33). RDA and CCA (p = 0.01) identified several SCoT loci associated with longitude, latitude, and altitude, with the first two CCA axes explaining 98% of the variance. sPCA revealed significant global and local spatial structuring (p = 0.01) and indicated the formation of genetic clines influenced by geographical variables. Morphological analyses showed significant population-level differentiation (Adonis and analysis of variance (ANOVA), p = 0.01) and a strong correlation between genetic and morphological distances (Mantel test, p = 0.01). Species distribution modeling produced high predictive accuracy (ROC > 0.96) and indicated climate stability in the species’ suitable habitats. The partial least squares (PLS)-structural equation modeling (SEM) model showed that environmental factors strongly explained variation in the system (R2 = 0.92) and significantly influenced resistance (β = − 0.98) and morphology (p = 0.001). The genetic latent factor (R2 = 0.57) displayed significant paths to environment (β = − 1.36), resistance (β = − 1.02), bioclimate (β = 0.40), and morphology (β = 0.65). Collectively, our findings demonstrate that spatial, environmental, and climatic factors jointly shape the genetic and morphological divergence of S. excelsa populations and provide a robust foundation for conservation planning.