Morphological and Leaf-Epidermal Trait Variation among 21 Poaceae Species from Layyah District, Pakistan
DOI:
https://doi.org/10.53560/PPASB(63-3)1141Keywords:
Poaceae, Functional Traits, Leaf Micromorphology, Stomatal Architecture, Epidermal Traits, Scanning Electron Microscopy, Arid EcosystemsAbstract
Poaceae is an ecologically dominant plant family, yet integrated morpho-anatomical assessments of grasses from the semi-arid plains of Pakistan remain limited. We aimed to (i) quantify morphological functional traits and leaf micromorphology of 21 Poaceae species from diverse habitats in Layyah District, Punjab, Pakistan, and (ii) identify the traits that contribute most to separating species and leaf surfaces. We quantified plant height, culm number, node number, internodal distance, and leaf dimensions. Stomatal and epidermal characteristics were examined using light microscopy and scanning electron microscopy (SEM). Trait variation among species was assessed via principal component analysis (PCA) and hierarchical clustering; anatomical variation was evaluated using stomatal and epidermal trait-based PCA. For the 19 species with complete morphological data, the first two PCA axes explained 78.6% of total variation (PC1 = 62.6%, PC2 = 16.0%) and reflected mainly plant size and leaf dimensions. Hierarchical clustering separated four large grasses from 15 smaller species (mean silhouette width = 0.54). Anatomical PCA explained 90.05% of total variation (PC1 = 70.21%, PC2 = 19.84%), with stomatal traits contributing most to species separation. Adaxial and abaxial leaf surfaces differed significantly (paired tests and PERMANOVA restricted within species; p ≤ 0.001). SEM revealed modified stomata, dense trichomes, thick cuticles, silica bodies, and other epidermal features that are commonly associated with drought resistance in grasses. These results document distinct combinations of morphological and leaf-epidermal traits among grasses of Layyah District and provide a baseline for future studies that test trait–environment relationships with measured habitat variables and physiological data.
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