Taxonomic assessment and a dichotomous key for the identification of five Sri Lankan wild rice species (Oryza spp.) with morphological and ecological insights

Abstract

Sri Lanka harbors five wild Oryza species: O. nivara, O. rufipogon, O. eichingeri, O. granulata, and the endemic O. rhizomatis. Accurate taxonomic identification of these species is essential for biodiversity conservation and rice genetic improvement. However, it remains challenging due to overlapping morphological characters and habitat-driven variations. This study develops a dichotomous identification key for Sri Lankan wild Oryza species based on detailed morphological traits and ecological preferences. Field surveys were conducted across the dry, intermediate, and wet zones in diverse habitats. Results reveal clear diagnostic characters distinguishing the five species, supported by significant segregation in morphological traits. Kruskal-Wallis test was performed to evaluate the interspecific differences. The embryo: seed length ratio of O. granulata and O. eichingeri was found to be significantly higher compared to the other species, whereas O. rufipogon exhibited a significantly lower ratio (H = 13.4, P = 0.009). The seed shape index of O. nivara and O. rufipogon was significantly higher compared to the other species, while O. rhizomatis exhibited a significantly lower index (H = 61.44, P = 1.292 × 10⁻¹²). The seed coat ratio (SCR) of the rice species differed significantly (H = 18.29, P = 0.00109), with O. nivara and O. rhizomatis exhibiting SCR values greater than 0.5. Morphological traits such as awn, rhizome, leaf length, panicle structure, stigma colour and presence of pubescence on lemma or palea shows vast variation among species. Principal component Analysis (PCA) shows the deviation in the qualitative parameters among the species. Also the wild rice species shows variation in their micro habitat conditions: O. nivara and O. rhizomatis dominate dry zones, O. rufipogon prefers intermediate/wet regions, O. eichingeri occurs at forest edges, and O. granulata thrives in shaded, moist forests. We proposed a dichotomous key integrating morphological and ecological data, providing a robust framework for species identification, biodiversity assessments, and conservation planning.