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Methods of lipid‐normalization for multi‐tissue stable isotope analyses in tropical tuna

Overview of attention for article published in Rapid Communications in Mass Spectrometry, May 2015
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Article details
Title
Methods of lipid‐normalization for multi‐tissue stable isotope analyses in tropical tuna
Published in
Rapid Communications in Mass Spectrometry, May 2015
DOI 10.1002/rcm.7215
Pubmed ID
Authors
Abstract

The bias associated with lipid contents in fish tissues is a recalcitrant topic for trophic studies using stable isotopes. Lipids are depleted in the heavy carbon isotope ((13) C) and the lipid content varies considerably among species, tissues and in both time and space. We have applied and assessed different correction methods for tropical tuna tissues. We tested two types of normalization methods to deal with variable lipid content in liver, gonads, and white and red muscles of yellowfin, bigeye and skipjack tuna: a chemical extraction using dichloromethane and a mathematical correction based on three modeling approaches (linear, non-linear and mass balance models). We measured isotopic ratios of bulk and lipid-free tissues and assessed the predictive ability of the correction models with the lipid-free measurements. The parameters of the models were estimated from our dataset and from results from published studies on other species. Comparison between bulk, lipid-free and lipid-corrected isotopic ratios demonstrated that (1) chemical extraction using dichloromethane did not affect δ(15) N values; (2) the change in δ(13) C values after extraction was tissue-specific; (3) lipid-normalization models using published parameter estimates failed to predict lipid-corrected δ(13) C values; and (4) linear and non-linear models using parameters estimated for each tissue from our dataset provided accurate δ(13) C predictions for all tissues, and mass balance model for white muscle only. Models using published estimates for parameters from other species cannot be used. Based on a range of lipid content that do not exceed 45%, we recommend the linear model to correct the bulk δ(13) C values in the investigated tissues but the parameters have to be estimated from a proportion of the original data for which chemical extraction is required and the isotopic values of bulk and lipid-free tissues are measured. Copyright © 2015 John Wiley & Sons, Ltd.

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The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 11 19%
Student > Ph. D. Student 9 16%
Researcher 8 14%
Student > Doctoral Student 5 9%
Other 3 5%
Other 7 12%
Unknown 14 25%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 28 49%
Environmental Science 10 18%
Veterinary Science and Veterinary Medicine 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Immunology and Microbiology 1 2%
Other 1 2%
Unknown 15 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 October 2020.
All research outputs
#20,657,128
of 25,374,917 outputs
Outputs from Rapid Communications in Mass Spectrometry
#3,909
of 4,966 outputs
Outputs of similar age
#205,734
of 280,043 outputs
Outputs of similar age from Rapid Communications in Mass Spectrometry
#20
of 51 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,966 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 14th percentile – i.e., 14% 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 280,043 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 51 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.