ArticleApplications in plant sciences2020
Species complex delimitations in the genus
Article in Applications in plant sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Article
- Mitochondrial Perspective on Species Complexes and Evolutionary Dynamics Within Genus Channa.Biochemical genetics · 2026Article
- Cutting-edge computational approaches to plant phenotyping.Plant molecular biology · 2025Review
- Characterization of SSR markers from draft genome assembly and genotypic data inData in brief · 2024Article
- Proteomic Changes of Osteoclast Differentiation in Rheumatoid and Psoriatic Arthritis Reveal Functional Differences.Frontiers in immunology · 2022Article
- Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots.Applications in plant sciences · 2020Article
- Species complex delimitations in the genusApplications in plant sciences · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
premiseStatistical methods used by most morphologists to validate species boundaries (such as principal component analysis [PCA] and non-metric multidimensional scaling [nMDS]) are limiting because these methods are mostly used as visualization methods, and because the groups are identified by taxonomists (i.e., supervised), adding human bias. Here, we use a spectral clustering algorithm for the unsupervised discovery of species boundaries followed by the analysis of the cluster-defining characters.
methodsWe used spectral clustering, nMDS, and PCA on 16 morphological characters within the genus
resultsSpectral clustering revealed five, nine, and 12 clusters of taxa in the species complexes examined here. Character selection identified at least four characters that defined these clusters. DISCUSSION: Together with our proposed character analysis methods, spectral clustering enabled the unsupervised discovery of species boundaries along with an explanation of their biological significance. Our results suggest that spectral clustering combined with a character selection analysis can enhance morphometric analyses and is superior to current clustering methods for species delimitation.
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