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Seed morphology: An addition to the taxonomy of Astragaleae and Trifolieae (Leguminosae: Papilionoidae) from Pakistan.

Neelam RashidMuhammad ZafarMushtaq AhmadRabia Asma MemonMuhammad Salim AkhterKhafsa MalikNafeesa Zahid MalikShazia SultanaSyed Nasar Shah
Published in: Microscopy research and technique (2020)
Seed morphology was described in detail for 12 species belonging to 5 genera of tribes Astragaleae and Trifolieae (Leguminosae; Papilionoideae) using scanning electron microscopy (SEM) to evaluate the taxonomic relevance of macromorphological and micromorphological seed characters. The study aims to search for diagnostic seed ultrastructural features that may help to elucidate species identification. For SEM analysis, seed morphological characters including seed form and shape, color and size, ornamentation, epidermal cell shape, and anticlinal wall pattern were investigated. As a result of the study, species-specific characters have been determined. Based on seed exomorphology, three characteristic cell patterns; irregular, round, and flat were observed. In majority of studied taxa, species may be further differentiated based on seed shape, size, and surface ornamentation. The inconsistency in testa cell pattern, shape, and distribution of papillae or protuberances may probably give further insight and significant morphological features at specific and generic level within the tribe. This study illustrated that considerable taxonomic knowledge can be obtained by examining the seed characters of Astragaleae and Trifolieae, particularly at the species level. The results demonstrated that the use of SEM in seed morphology could play a role in the identification of taxa particularly at genus and species level. Twelve species of Astragaleae and Trifolieae were studied in order to describe and investigate the seed morphology and to evaluate the diagnostic value of this character using a SEM. A broader taxon sampling is required for classification at generic and tribal level, besides molecular and phylogenetic studies.
Keyphrases
  • electron microscopy
  • single cell
  • machine learning
  • genetic diversity
  • deep learning