Taxonomic Significance of Seed Morphology in Veronica L. (Plantaginaceae) Species from Central Europe

Mazur, Małgorzata; Marcysiak, Katarzyna; Dunajska, Agnieszka; Gawlak, Magdalena; Kałuski, Tomasz

doi:10.3390/plants11010088

Cited by 8 publications

(4 citation statements)

References 32 publications

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“…Clustering methods were efficient in verifying the existence of genetic variability and similarity between genotypes as well as the principal component to concentrate the number of variables to facilitate the interpretation of results. The results obtained through seed image analysis and multivariate analysis showed that seeds provide important information about the adaptation and evolution of Anchusa L. taxa in Sardinia (Farris et al, 2020), and helped to identify seeds from different locations (Pan et al, 2021;Cecco, Musciano, D'Archivio, Frattaroli, & Martino, 2019), and the distribution of species from Central Europe (Mazur, Marcysiak, Dunajska, Gawlak, & Kałuski, 2022). In the present study, seeds from closer populations also presented similar biometric characteristics.…”

Section: Discussionsupporting

confidence: 66%

Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species

Felix,

Chagas,

Araújo

et al. 2023

Acta Sci. Agron.

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Invasive species threaten crops and ecosystems worldwide. Therefore, we sought to understand the relationship between the geographic distribution of species populations and the characteristics of seeds using new techniques such as seed image analysis, multivariate analysis, and machine learning. This study aimed to characterize Leucaena leucocephala (Lam.) de Wit. seeds from spatially dispersed populations using digital images and analyzed their implications for genetic studies. Seed size and shape descriptors were obtained using image analysis of the five populations. Several analyses were performed including descriptive statistics, principal components, Euclidean distance, Mantel correlation test, and supervised machine learning. This image analysis technique proved to be efficient in detecting biometric differences in L. leucocephala seeds from spatially dispersed populations. This method revealed that spatially dispersed L. leucocephala populations had different biometric seed patterns that can be used in studies of population genetic divergence. We observed that it is possible to identify the origin of the seeds from the biometric characters with 80.4% accuracy (Kappa statistic 0.755) when we applied the decision tree algorithm. Digital imaging analysis associated with machine learning is promising for discriminating forest tree populations, supporting management activities, and studying population genetic divergence. This technique contributes to the understanding of genotype-environment interactions and consequently identifies the ability of an invasive species to spread in a new area, making it possible to track and monitor the flow of seeds between populations and other sites.

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Section: Discussionsupporting

confidence: 66%

Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species

Felix,

Chagas,

Araújo

et al. 2023

Acta Sci. Agron.

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“…Descriptive terminology for fruit and seed characters followed Stearn (1983), Barthlott (1984), Verma & al. (2017), Mazur & al. (2021).…”

Section: Fruit and Seed Observation And Measurementsmentioning

confidence: 99%

“…Fruit and seed morphology has been long recognized as important for classification and can be successfully used in identification, taxonomic circumscription and phylogenetic implication (Arambarri 2000;Mazur & al. 2021;among others).…”

Section: Introductionmentioning

confidence: 99%

Systematic implications of fruits and seeds of Plantaginaceae and some related genera of Scrophulariaceae

A. Shehata,

K. Taia,

K. Ibrahim

et al. 2023

PhB

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Fruit and seed macro-and micromorphological analyses were carried out of 18 Plantago species representing two subgenera (Euplantago and Psyllium), and 18 taxa representing nine genera related to Scrophulariaceae sensu lato (s.l.) were investigated by light microscopy (LM) and scanning electron microscopy (SEM). The main aim of this study was by studying the fruit and seed morphological characters to investigate the phenetic relationships among Plantago species, as well as between certain taxa of Plantaginaceae sensu lato (s.l.) formerly assigned to the family Scrophulariaceae. The phenetic analysis of fruit and seed morphological characters of all studied taxa produced a phenogram that showed two main series: the first comprising the Plantago species [Plantaginaceae sensu sricto (s.str.)] and the second including the studied Scrophulariaceae taxa. The resulting phenogram supported the retaining of the studied Scrophulariaceae taxa within the family Scrophulariaceae s.l. and Plantago in a distinct monogeneric family Plantaginaceae s.str., and was incompatible with the new circumscription of Planataginaceae s.l. Moreover, the present findings were in line to a certain extent with Shipunov's infrageneric classification of Plantago.

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“…Veronica L. is the largest genus in the flowering plant family Plantaginaceae, with about 500 species distributed in the world (Mazur et al. 2021 ). However, there were only six plastomes sequenced and submitted to GenBank (retrieved July 2022 from https://www.ncbi.nlm.nih.gov ), which hinder to resolve the phylogeny of Veronica.…”

mentioning

confidence: 99%

Characterization of the complete chloroplast genome ofVeronica arvensisand its phylogenomic inference in plantaginaceae

Liu

Zhang

et al. 2022

Mitochondrial DNA Part B

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Veronica arvensis , which is an annual flowering plant in the plantain family Plantaginaceae, has commonly used as a Chinese herbal medicine to treat malaria in China. Here, the complete plastome of V. arvensis was successfully assembled based on genome skimming sequencing. The plastome of V. arvensis was 149,386 bp in length, comprising a pair of inverted repeats (IR; 24,946 bp) separated by a large single-copy (LSC) region (82,004 bp) and a small single-copy (SSC) region (17,490 bp). The plastid genome encoded 113 unique genes, consisting of 79 protein-coding genes, 30 tRNA genes, and four rRNA genes, with 19 duplicated genes in the IR regions. Six plastid hotspot regions ( trn H- psb A, trn K- rps 16, atp I- rps 2, ndh F- rpl 32, ccs A- ndh D and rps 15- ycf 1) were identified within Veronica . Phylogenetic analysis showed that the representative species from Veronica was monophyletic. V. persica and V. polita formed a maximum clade, followed by sister to V. arvensis .

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Taxonomic Significance of Seed Morphology in Veronica L. (Plantaginaceae) Species from Central Europe

Cited by 8 publications

References 32 publications

Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species

Image analysis of seeds and machine learning as a tool for distinguishing populations: Applied to an invasive tree species

Systematic implications of fruits and seeds of Plantaginaceae and some related genera of Scrophulariaceae

Characterization of the complete chloroplast genome ofVeronica arvensisand its phylogenomic inference in plantaginaceae

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