Integrating classical and spatial multivariate analyses for assessing morphological variability in the endemic Iberian viper Vipera seoanei

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

martinez-freiria, f (Autor) Outra; brito, jc (Autor) FCUP

Revista

Título: Journal of Zoological Systematics and Evolutionary Research

Vol. 51

Páginas: 122-131

ISSN: 0947-5745

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da terra e ciências do ambiente

Outras Informações

ID Authenticus: P-005-18X

DOI: 10.1111/jzs.12015

Abstract (EN): Historical and ecological processes have deeply affected biogeographic patterns of animals. Studying morphological variability of species, using classical and spatial analyses, can elucidate these patterns and give insights on both processes. Morphological variability of the endemic Iberian viper Vipera seoanei is examined to identify morphological coherent groups, biogeographic patterns and the putative role of abiotic pressures in the geographic variation of morphological variation. Results from classic and spatial multivariate analyses over 27 morphometric traits for 468 specimens from the global range of the species were integrated. Classic analyses reported large morphological variability and confirmed the differentiation of two coherent groups, which are representatives of current subspecies. Spatial analyses reported a geographic gradient pattern from western Cantabrian Mountains to the rest of the study area. Areas of high morphological variability were found, and two spatial coherent groups with an integration zone were recognized. Significant spatial correlations and trends suggest that some traits could be under selection and may display adaptations to local environments. Although observed patterns can be attributed to Pleistocene climatic cycles, an adaptive diversification of the species is supported. The combination of classical and spatial multivariate analyses is a useful methodology to identify morphological patterns and infer underlying factors.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10