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Taxonomic Characterization of Honey Bee (Apis mellifera) Pollen Foraging Based on Non-Overlapping Paired-End Sequencing of Nuclear Ribosomal Loci

Overview of attention for article published in PLOS ONE, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
2 blogs
twitter
3 tweeters

Citations

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41 Dimensions

Readers on

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116 Mendeley
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Title
Taxonomic Characterization of Honey Bee (Apis mellifera) Pollen Foraging Based on Non-Overlapping Paired-End Sequencing of Nuclear Ribosomal Loci
Published in
PLOS ONE, December 2015
DOI 10.1371/journal.pone.0145365
Pubmed ID
Authors

R. Scott Cornman, Clint R. V. Otto, Deborah Iwanowicz, Jeffery S. Pettis

Abstract

Identifying plant taxa that honey bees (Apis mellifera) forage upon is of great apicultural interest, but traditional methods are labor intensive and may lack resolution. Here we evaluate a high-throughput genetic barcoding approach to characterize trap-collected pollen from multiple North Dakota apiaries across multiple years. We used the Illumina MiSeq platform to generate sequence scaffolds from non-overlapping 300-bp paired-end sequencing reads of the ribosomal internal transcribed spacers (ITS). Full-length sequence scaffolds represented ~530 bp of ITS sequence after adapter trimming, drawn from the 5' of ITS1 and the 3' of ITS2, while skipping the uninformative 5.8S region. Operational taxonomic units (OTUs) were picked from scaffolds clustered at 97% identity, searched by BLAST against the nt database, and given taxonomic assignments using the paired-read lowest common ancestor approach. Taxonomic assignments and quantitative patterns were consistent with known plant distributions, phenology, and observational reports of pollen foraging, but revealed an unexpected contribution from non-crop graminoids and wetland plants. The mean number of plant species assignments per sample was 23.0 (+/- 5.5) and the mean species diversity (effective number of equally abundant species) was 3.3 (+/- 1.2). Bray-Curtis similarities showed good agreement among samples from the same apiary and sampling date. Rarefaction plots indicated that fewer than 50,000 reads are typically needed to characterize pollen samples of this complexity. Our results show that a pre-compiled, curated reference database is not essential for genus-level assignments, but species-level assignments are hindered by database gaps, reference length variation, and probable errors in the taxonomic assignment, requiring post-hoc evaluation. Although the effective per-sample yield achieved using custom MiSeq amplicon primers was less than the machine maximum, primarily due to lower "read2" quality, further protocol optimization and/or a modest reduction in multiplex scale should offset this difficulty. As small quantities of pollen are sufficient for amplification, our approach might be extendable to other questions or species for which large pollen samples are not available.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Serbia 1 <1%
Sweden 1 <1%
Unknown 111 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 24 21%
Researcher 20 17%
Student > Ph. D. Student 20 17%
Student > Bachelor 12 10%
Professor > Associate Professor 5 4%
Other 20 17%
Unknown 15 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 51%
Environmental Science 13 11%
Biochemistry, Genetics and Molecular Biology 6 5%
Engineering 3 3%
Nursing and Health Professions 2 2%
Other 11 9%
Unknown 22 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 17 November 2016.
All research outputs
#1,757,479
of 20,585,116 outputs
Outputs from PLOS ONE
#23,254
of 177,676 outputs
Outputs of similar age
#37,660
of 396,425 outputs
Outputs of similar age from PLOS ONE
#555
of 4,822 outputs
Altmetric has tracked 20,585,116 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 177,676 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.4. This one has done well, scoring higher than 86% 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 396,425 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 4,822 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.