Morphology of organisms is an essential source of evidence for taxonomic decisions and understanding of ecology and evolutionary history. The geometric structure (i.e., numeric description of shape) provides richer and mathematically different information about an organism’s morphology than linear measurements. A little is known on how these two sources of morphological information (shape vs. size) contribute to the identification of organisms when implied simultaneously. This study hypothesized that combining geometric information on the outline with linear measurements results in better species identification than either evidence alone can provide. As a test system for our research, we used the microscopic spores of fungi from the genus Subulicystidium (Agaricomycetes, Basidiomycota). We analyzed 2D spore shape data via elliptic Fourier and principal component analyses. Using flexible discriminant analysis, we achieved the highest species identification success rate for a combination of shape and size descriptors (64.7%). The shape descriptors alone predicted species slightly better than size descriptors (61.5% vs. 59.1%). We conclude that adding geometric information on the outline to linear measurements improves the identification of the organisms. Despite the high relevance of spore traits for the taxonomy of fungi, they were previously rarely analyzed with the tools of geometric morphometrics. Therefore, we supplement our study with an open access protocol for digitizing and summarizing fungal spores’ shape and size information. We propagate a broader use of geometric morphometric analysis for microscopic propagules of fungi and other organisms.
Morphology of organisms is an important source of evidence for biodiversity assessment, taxonomic decisions, and understanding of evolution. Shape information about zoological and botanical objects is often treated quantitatively and in this form improves species identification. In studies of fungi, quantitative shape analysis was almost ignored. The disseminated propagules of fungi, the spores, are crucial for their taxonomy – currently in the form of linear measurements or subjectively defined shape categories. It remains unclear how much quantifying spore shape information can improve species identification. In this study, we tested the hypothesis that shape, as a richer source of information, overperforms size when performing automated identification of fungal species. We used the fungi of the genus Subulicystidium (Agaricomycetes, Basidiomycota) as a study object. We analysed 2D spore shape data via elliptic Fourier and Principal Component analyses. With flexible discriminant analysis, we achieved a slightly higher species identification success rate for shape predictors (61.5%) than for size predictors (59.1%). However, we achieved the highest rate for a combination of both (64.7%). We conclude that quantifying fungal spore shapes is worth the effort. We provide an open access protocol which, we hope, will stimulate a broader use of quantitative shape analysis in fungal taxonomy. We also discuss the challenges of such analyses that are specific to fungal spores.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.