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Botanical Sciences
versão On-line ISSN 2007-4476versão impressa ISSN 2007-4298
Resumo
HERNANDEZ-DE LA ROSA, Abraham; RUIZ-VALENCIA, Jesús Alejandro e VAZQUEZ-SANCHEZ, Monserrat. Análisis morfométrico de Allium glandulosum Link & Otto (Amaryllidaceae: Allioideae) en México. Bot. sci [online]. 2024, vol.102, n.1, pp.307-317. Epub 13-Fev-2024. ISSN 2007-4476. https://doi.org/10.17129/botsci.3403.
Background:
Allium glandulosum Link & Otto is widely distributed in America, extending from the southern United States to Mexico and Centroamerica. In Mexico, its distribution encompasses 27 states, with contrasting habitats. However, whether this distribution pattern corresponds to a wide morphological variation has not been studied.
Research question and/or Hypothesis:
Is it possible to distinguish populations of Allium glandulosum along its Mexican distribution based on the morphological variations observed in its flowers and vegetative components?
Species under study:
Allium glandulosum Link & Otto.
Study site and dates:
Populations of Allium glandulosum distributed across the Volcanic Axis and the Mexican Plateau, during 2020.
Methods:
Morphological traits, both qualitative and quantitative, were identified and measured from herbarium specimens and field-collected samples. Univariate and multivariate statistical methods were employed to characterize and summarize the variability observed in the field-collected specimens and herbarium materials.
Results:
The principal component analysis revealed that 14 traits accounted for 51.29 % of the total accumulated variation cross the first three components. The linear discriminant analysis resulted in the formation of four groups, with a classification rate of 71.53 % for individuals. The cluster analysis identified four distinct groupings characterized by both quantitative and qualitative traits.
Conclusions:
Variations in vegetative and floral morphology in Allium glandulosum allow for population differentiation across its Mexican distribution.
Palavras-chave : GLM; LDA; morphological variation; multivariate analysis; PCA.