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The Relationship between Microbial Community Evenness and Function in Slow Sand Filters

Overview of attention for article published in mBio, October 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 (80th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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11 tweeters
facebook
1 Facebook page

Citations

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21 Dimensions

Readers on

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82 Mendeley
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Title
The Relationship between Microbial Community Evenness and Function in Slow Sand Filters
Published in
mBio, October 2015
DOI 10.1128/mbio.00729-15
Pubmed ID
Authors

Sarah-Jane Haig, Christopher Quince, Robert L. Davies, Caetano C. Dorea, Gavin Collins

Abstract

Two full-scale slow sand filters (SSFs) were sampled periodically from April until November 2011 to study the spatial and temporal structures of the bacterial communities found in the filters. To monitor global changes in the microbial communities, DNA from sand samples taken at different depths and locations within the SSFs and at different filters ages was used for Illumina 16S rRNA gene sequencing. Additionally, 15 water quality parameters were monitored to assess filter performance, with functionally relevant microbial members being identified by using multivariate statistics. The bacterial diversity in the SSFs was found to be much larger than previously documented, with community composition being shaped by the characteristics of the SSFs (filter age and depth) and sampling characteristics (month, side, and distance from the influent and effluent pipes). We found that several key genera (Acidovorax, Halomonas, Sphingobium, and Sphingomonas) were associated with filter performance. In addition, at the whole-community level, a strong positive correlation was found between species evenness and filter performance. This study is the first to comprehensively characterize the microbial community of SSFs and link specific microbes to water quality parameters. In doing so, we reveal key patterns in microbial community structure that relate to overall community function. The supply of sustainable, energy-efficient, and safe drinking water to an increasing world population is a huge challenge faced by the water industry. SSFs have been used for hundreds of years to provide a safe and reliable source of potable drinking water, with minimal energy requirements. However, a lack of knowledge pertaining to the treatment mechanisms, particularly the biological processes, underpinning SSF operation has meant that SSFs are still operated as "black boxes." Understanding these dynamics alongside performance-induced effects associated with operational differences will promote optimized SSF design, maintenance, and operation, creating more efficient and environmentally sustainable filters. Through a spatial-temporal survey of full-scale SSFs at various points of operation, we present the most detailed characterization to date of the functional microbial communities found in SSFs, linking various taxa and community metrics to optimal water quality production.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 1 1%
Canada 1 1%
Unknown 80 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 29%
Researcher 17 21%
Student > Bachelor 10 12%
Student > Master 9 11%
Student > Doctoral Student 4 5%
Other 8 10%
Unknown 10 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 23%
Environmental Science 14 17%
Engineering 11 13%
Biochemistry, Genetics and Molecular Biology 11 13%
Immunology and Microbiology 3 4%
Other 7 9%
Unknown 17 21%

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 20 August 2017.
All research outputs
#3,176,435
of 17,829,815 outputs
Outputs from mBio
#2,428
of 4,506 outputs
Outputs of similar age
#50,603
of 260,956 outputs
Outputs of similar age from mBio
#68
of 133 outputs
Altmetric has tracked 17,829,815 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 4,506 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one is in the 45th percentile – i.e., 45% 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 260,956 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 80% of its contemporaries.
We're also able to compare this research output to 133 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.