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Taxonomic revision of the Malagasy Nesomyrmex madecassus species-group using a quantitative morphometric approach.


ABSTRACT: Here we reveal the diversity of the next fragment of the Malagasy elements of the ant genus Nesomyrmex using a combination of advanced exploratory analyses on quantitative morphological data. The diversity of the Nesomyrmex madecassus species-group was assessed via hypothesis-free nest centroid clustering combined with recursive partitioning to estimate the number of clusters and determine the most probable boundaries between them. This combination of methods provides a highly automated species delineation protocol based on continuous morphometric data, and thereby it obviates the need of subjective interpretation of morphological patterns. Delimitations of clusters recognized by these exploratory analyses were tested via confirmatory Linear Discriminant Analysis (LDA). Our results suggest the existence of four morphologically distinct species, Nesomyrmex flavus sp. n., Nesomyrmex gibber, Nesomyrmex madecassus and Nesomyrmex nitidus sp. n.; all are described here and an identification key for their worker castes using morphometric data is given. Two members of the newly outlined madecasus species-group, Nesomyrmex flavus sp. n. and Nesomyrmex nitidus sp. n., represent true cryptic species. Geographic maps depicting species distributions and elevational information for the sites where populations of particular species were collected are also provided.

SUBMITTER: Csosz S 

PROVIDER: S-EPMC4978003 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Taxonomic revision of the Malagasy Nesomyrmex madecassus species-group using a quantitative morphometric approach.

Csősz Sándor S   Fisher Brian L BL  

ZooKeys 20160706 603


Here we reveal the diversity of the next fragment of the Malagasy elements of the ant genus Nesomyrmex using a combination of advanced exploratory analyses on quantitative morphological data. The diversity of the Nesomyrmex madecassus species-group was assessed via hypothesis-free nest centroid clustering combined with recursive partitioning to estimate the number of clusters and determine the most probable boundaries between them. This combination of methods provides a highly automated species  ...[more]

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