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Digital Morphometrics of Two North American Grapevines (Vitis: Vitaceae) Quantifies Leaf Variation between Species, within Species, and among Individuals

Overview of attention for article published in Frontiers in Plant Science, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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Title
Digital Morphometrics of Two North American Grapevines (Vitis: Vitaceae) Quantifies Leaf Variation between Species, within Species, and among Individuals
Published in
Frontiers in Plant Science, March 2017
DOI 10.3389/fpls.2017.00373
Pubmed ID
Authors

Laura L. Klein, Madeleine Caito, Chad Chapnick, Cassandra Kitchen, Regan O’Hanlon, Dan H. Chitwood, Allison J. Miller

Abstract

Recent studies have demonstrated that grapevine (Vitis spp.) leaf shape can be quantified using digital approaches which indicate phylogenetic signal in leaf shape, discernible patterns of developmental context within single leaves, and signatures of local environmental conditions. Here, we extend this work by quantifying intra-individual, intraspecific, and interspecific variation in leaf morphology in accessions of North American Vitis riparia and V. rupestris in a common environment. For each species at least four clonal replicates of multiple genotypes were grown in the Missouri Botanical Garden Kemper Center for Home Gardening. All leaves from a single shoot were harvested and scanned leaf images were used to conduct generalized Procrustes analysis, linear discriminant analysis, and elliptical Fourier analysis. Leaf shapes displayed genotype-specific signatures and species distinctions consistent with taxonomic classifications. Leaf shape variation within genotypes and among clones was the result of pest and pathogen-induced leaf damage that alters leaf morphology. Significant trends in leaf damage caused by disease and infestation were non-random with respect to leaf position on the shoot. Digital morphometrics is a powerful tool for assessing leaf shape variation among species, genotypes, and clones under common conditions and suggests biotic factors such as pests and pathogens as important drivers influencing leaf shape.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 18%
Student > Master 18 18%
Researcher 13 13%
Student > Doctoral Student 10 10%
Student > Bachelor 6 6%
Other 17 17%
Unknown 17 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 59%
Biochemistry, Genetics and Molecular Biology 13 13%
Engineering 2 2%
Computer Science 2 2%
Pharmacology, Toxicology and Pharmaceutical Science 1 1%
Other 5 5%
Unknown 18 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2022.
All research outputs
#4,408,134
of 24,770,025 outputs
Outputs from Frontiers in Plant Science
#2,251
of 23,619 outputs
Outputs of similar age
#78,298
of 339,266 outputs
Outputs of similar age from Frontiers in Plant Science
#63
of 544 outputs
Altmetric has tracked 24,770,025 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 23,619 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 90% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 339,266 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 544 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.