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Morphometric analysis of Passiflora leaves: the relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade

Overview of attention for article published in Giga Science, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

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18 X users
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1 YouTube creator

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Title
Morphometric analysis of Passiflora leaves: the relationship between landmarks of the vasculature and elliptical Fourier descriptors of the blade
Published in
Giga Science, January 2017
DOI 10.1093/gigascience/giw008
Pubmed ID
Authors

Daniel H. Chitwood, Wagner C. Otoni

Abstract

Leaf shape among Passiflora species is spectacularly diverse. Underlying this diversity in leaf shape are profound changes in the patterning of the primary vasculature and laminar outgrowth. Each of these aspects of leaf morphology-vasculature and blade-provides different insights into leaf patterning. Here, we morphometrically analyze >3300 leaves from 40 different Passiflora species collected sequentially across the vine. Each leaf is measured in two different ways: using 1) 15 homologous Procrustes-adjusted landmarks of the vasculature, sinuses, and lobes; and 2) Elliptical Fourier Descriptors (EFDs), which quantify the outline of the leaf. The ability of landmarks, EFDs, and both datasets together are compared to determine their relative ability to predict species and node position within the vine. Pairwise correlation of x and y landmark coordinates and EFD harmonic coefficients reveals close associations between traits and insights into the relationship between vasculature and blade patterning. Landmarks, more reflective of the vasculature, and EFDs, more reflective of the blade contour, describe both similar and distinct features of leaf morphology. Landmarks and EFDs vary in ability to predict species identity and node position in the vine and exhibit a correlational structure (both within landmark or EFD traits and between the two data types) revealing constraints between vascular and blade patterning underlying natural variation in leaf morphology among Passiflora species.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 16%
Student > Ph. D. Student 13 14%
Researcher 12 13%
Student > Bachelor 10 11%
Student > Doctoral Student 9 10%
Other 16 17%
Unknown 19 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 57%
Biochemistry, Genetics and Molecular Biology 8 9%
Engineering 4 4%
Computer Science 4 4%
Environmental Science 3 3%
Other 3 3%
Unknown 18 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 12 October 2021.
All research outputs
#3,076,788
of 25,494,370 outputs
Outputs from Giga Science
#607
of 1,171 outputs
Outputs of similar age
#58,113
of 422,932 outputs
Outputs of similar age from Giga Science
#11
of 20 outputs
Altmetric has tracked 25,494,370 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,171 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 422,932 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 86% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.