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From top to bottom: Do Lake Trout diversify along a depth gradient in Great Bear Lake, NT, Canada?

Overview of attention for article published in PLoS ONE, March 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

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4 tweeters

Citations

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3 Dimensions

Readers on

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23 Mendeley
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Title
From top to bottom: Do Lake Trout diversify along a depth gradient in Great Bear Lake, NT, Canada?
Published in
PLoS ONE, March 2018
DOI 10.1371/journal.pone.0193925
Pubmed ID
Authors

Louise Chavarie, Kimberly L. Howland, Les N. Harris, Michael J. Hansen, William J. Harford, Colin P. Gallagher, Shauna M. Baillie, Brendan Malley, William M. Tonn, Andrew M. Muir, Charles C. Krueger

Abstract

Depth is usually considered the main driver of Lake Trout intraspecific diversity across lakes in North America. Given that Great Bear Lake is one of the largest and deepest freshwater systems in North America, we predicted that Lake Trout intraspecific diversity to be organized along a depth axis within this system. Thus, we investigated whether a deep-water morph of Lake Trout co-existed with four shallow-water morphs previously described in Great Bear Lake. Morphology, neutral genetic variation, isotopic niches, and life-history traits of Lake Trout across depths (0-150 m) were compared among morphs. Due to the propensity of Lake Trout with high levels of morphological diversity to occupy multiple habitat niches, a novel multivariate grouping method using a suite of composite variables was applied in addition to two other commonly used grouping methods to classify individuals. Depth alone did not explain Lake Trout diversity in Great Bear Lake; a distinct fifth deep-water morph was not found. Rather, Lake Trout diversity followed an ecological continuum, with some evidence for adaptation to local conditions in deep-water habitat. Overall, trout caught from deep-water showed low levels of genetic and phenotypic differentiation from shallow-water trout, and displayed higher lipid content (C:N ratio) and occupied a higher trophic level that suggested an potential increase of piscivory (including cannibalism) than the previously described four morphs. Why phenotypic divergence between shallow- and deep-water Lake Trout was low is unknown, especially when the potential for phenotypic variation should be high in deep and large Great Bear Lake. Given that variation in complexity of freshwater environments has dramatic consequences for divergence, variation in the complexity in Great Bear Lake (i.e., shallow being more complex than deep), may explain the observed dichotomy in the expression of intraspecific phenotypic diversity between shallow- vs. deep-water habitats. The ambiguity surrounding mechanisms driving divergence of Lake Trout in Great Bear Lake should be seen as reflective of the highly variable nature of ecological opportunity and divergent natural selection itself.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 26%
Student > Master 5 22%
Student > Bachelor 4 17%
Student > Ph. D. Student 4 17%
Professor 2 9%
Other 2 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 57%
Environmental Science 7 30%
Unspecified 2 9%
Biochemistry, Genetics and Molecular Biology 1 4%

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 23 March 2018.
All research outputs
#7,078,525
of 13,791,430 outputs
Outputs from PLoS ONE
#60,902
of 145,506 outputs
Outputs of similar age
#114,225
of 274,840 outputs
Outputs of similar age from PLoS ONE
#1,314
of 2,727 outputs
Altmetric has tracked 13,791,430 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 145,506 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.3. This one has gotten more attention than average, scoring higher than 57% 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 274,840 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 57% of its contemporaries.
We're also able to compare this research output to 2,727 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 51% of its contemporaries.