ArticleBio-protocol2024
Quantitative Analysis of Fish Morphology Through Landmark and Outline-based Geometric Morphometrics with Free Software.
Article in Bio-protocol, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Size-, sex- and site-dependent morphological variation in the stone flounder (Kareius bicoloratus) from Korean coastal waters.Journal of fish biology · 2026Article
- Research Trends, Hotspots and Future Perspectives of Geometric Morphometrics in Entomology: A Scientometric Review.Insects · 2026Review
- Image-Based Analysis of Morphometric Differences Between Sea-Caught and Farmed Large Yellow Croaker (Animals : an open access journal from MDPI · 2026Article
- Assessing the population structure of Plagioscion squamosissimus (Teleostei, Perciformes, Sciaenidae) from the São Francisco River, Bahia, Brazil, using body morphology and otolith shape signatures.Journal of fish biology · 2026Article
- Revealing Phenotypic Differentiation inAnimals : an open access journal from MDPI · 2025Article
- Article
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1 author.
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Abstract
Morphology underpins key biological and evolutionary processes that remain elusive. This is in part due to the limitations in robustly and quantitatively analyzing shapes within and between groups in an unbiased and high-throughput manner. Geometric morphometrics (GM) has emerged as a widely employed technique for studying shape variation in biology and evolution. This study presents a comprehensive workflow for conducting geometric morphometric analysis of fish morphology. The step-by-step manual provides detailed instructions for using popular free software, such as the TPS series, MorphoJ, ImageJ, and R, to carry out generalized Procrustes analysis (GPA), principal component analysis (PCA), discriminant function analysis (DFA), canonical variate analysis (CVA), mean shape analysis, and thin plate spline analysis (TPS). The Momocs package in R is specifically utilized for in-depth analysis of fish outlines. In addition, selected functions from the dplyr package are used to assist in the analysis. The full process of fish outline analysis is covered, including extracting outline coordinates, converting and scaling data, defining landmarks, creating data objects, analyzing outline differences, and visualizing results. In conclusion, the current protocol compiles a detailed method for evaluating fish shape variation based on landmarks and outlines. As the field of GM continues to evolve and related software develops rapidly, the limitations associated with morphological analysis of fish are expected to decrease. Interoperable data formats and analytical methods may facilitate the sharing of morphological data and help resolve related scientific problems. The convenience of this protocol allows for fast and effective morphological analysis. Furthermore, this detailed protocol could be adapted to assess image-based differences across a broader range of species or to analyze morphological data of the same species from different origins. Key features • This protocol provides a comprehensive set of commonly used GM-analyzing methods and visualizing skills plus supporting information to help assess the appropriate analysis method • By incorporating both landmarks and outlines, this protocol facilitates a thorough analysis of two-dimensional shape variation in fish, covering a wide range of morphological features • The simplified workflow and detailed procedures make it accessible for non-experienced users to successfully complete the analysis while also providing valuable insights for experienced users Graphical overview
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