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A Fatty Acid Based Bayesian Approach for Inferring Diet in Aquatic Consumers

Overview of attention for article published in PLOS ONE, June 2015
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Article details
Title
A Fatty Acid Based Bayesian Approach for Inferring Diet in Aquatic Consumers
Published in
PLOS ONE, June 2015
DOI 10.1371/journal.pone.0129723
Pubmed ID
Authors
Abstract

We modified the stable isotope mixing model MixSIR to infer primary producer contributions to consumer diets based on their fatty acid composition. To parameterize the algorithm, we generated a 'consumer-resource library' of FA signatures of Daphnia fed different algal diets, using 34 feeding trials representing diverse phytoplankton lineages. This library corresponds to the resource or producer file in classic Bayesian mixing models such as MixSIR or SIAR. Because this library is based on the FA profiles of zooplankton consuming known diets, and not the FA profiles of algae directly, trophic modification of consumer lipids is directly accounted for. To test the model, we simulated hypothetical Daphnia comprised of 80% diatoms, 10% green algae, and 10% cryptophytes and compared the FA signatures of these known pseudo-mixtures to outputs generated by the mixing model. The algorithm inferred these simulated consumers were comprised of 82% (63-92%) [median (2.5th to 97.5th percentile credible interval)] diatoms, 11% (4-22%) green algae, and 6% (0-25%) cryptophytes. We used the same model with published phytoplankton stable isotope (SI) data for δ13C and δ15N to examine how a SI based approach resolved a similar scenario. With SI, the algorithm inferred that the simulated consumer assimilated 52% (4-91%) diatoms, 23% (1-78%) green algae, and 18% (1-73%) cyanobacteria. The accuracy and precision of SI based estimates was extremely sensitive to both resource and consumer uncertainty, as well as the trophic fractionation assumption. These results indicate that when using only two tracers with substantial uncertainty for the putative resources, as is often the case in this class of analyses, the underdetermined constraint in consumer-resource SI analyses may be intractable. The FA based approach alleviated the underdetermined constraint because many more FA biomarkers were utilized (n < 20), different primary producers (e.g., diatoms, green algae, and cryptophytes) have very characteristic FA compositions, and the FA profiles of many aquatic primary consumers are strongly influenced by their diets.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 148 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 %
South Africa 1 <1%
Uruguay 1 <1%
United States 1 <1%
Italy 1 <1%
Brazil 1 <1%
Unknown 143 97%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Master 34 23%
Student > Ph. D. Student 33 22%
Researcher 29 20%
Student > Doctoral Student 6 4%
Professor > Associate Professor 6 4%
Other 17 11%
Unknown 23 16%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 63 43%
Environmental Science 39 26%
Biochemistry, Genetics and Molecular Biology 5 3%
Earth and Planetary Sciences 5 3%
Chemical Engineering 1 <1%
Other 2 1%
Unknown 33 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 July 2015.
All research outputs
#16,309,921
of 27,712,151 outputs
Outputs from PLOS ONE
#147,929
of 240,603 outputs
Outputs of similar age
#135,211
of 280,752 outputs
Outputs of similar age from PLOS ONE
#3,210
of 6,618 outputs
Altmetric has tracked 27,712,151 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 240,603 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one is in the 37th percentile – i.e., 37% 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 280,752 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 6,618 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.