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A Remarkable New Species of Liparis (Orchidaceae) from China and Its Phylogenetic Implications

Overview of attention for article published in PLOS ONE, November 2013
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Title
A Remarkable New Species of Liparis (Orchidaceae) from China and Its Phylogenetic Implications
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0078112
Pubmed ID
Authors

Lin Li, Haifei Yan

Abstract

In the present study, we formally describe Liparis pingxiangensis as a new species from Guangxi, China on the basis of morphological and molecular phylogenetic analyses. It is easily distinguished from closely related species by strongly curved column without column wings, and broadly rhombic-elliptic lip with 2 uncinate calli at the base. In particular, it differs most markedly from its congeners in possessing two pollinia attached by long and prominent caudicles (not stipes), to a distinct sticky disc. This type of pollinarium, as far as we know, is not found in any other species of Liparis, and is also unique among the orchids with waxy pollinia. We then proceeded to a phylogenetic analysis to ascertain the systematic position of this enigmatic species. Molecular study based on nuclear ribosomal ITS and plastid matK DNA sequence data supports L. pingxiangensis as a distinct species, which forms an independent lineage sister to L. nervosa and its allies (93% BS, 1.00 BPP). In the light of previous work, the findings have important implications for a better understanding of the well-supported pattern mainly based on vegetative features in Malaxideae.

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Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 39%
Researcher 3 11%
Student > Bachelor 2 7%
Other 2 7%
Professor 1 4%
Other 3 11%
Unknown 6 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 57%
Environmental Science 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Computer Science 1 4%
Unknown 7 25%