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A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes

Overview of attention for article published in Evolutionary Applications, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
10 tweeters
facebook
1 Facebook page

Citations

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Readers on

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46 Mendeley
Title
A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes
Published in
Evolutionary Applications, July 2017
DOI 10.1111/eva.12496
Pubmed ID
Authors

Matthew Carl Yates, Thais A. Bernos, Dylan J. Fraser

Abstract

Technological and methodological advances have facilitated the use of genetic data to infer census population size (Nc) in natural populations, particularly where traditional mark-and-recapture is challenging. The effective number of breeders (Nb) describes how many adults effectively contribute to a cohort and is often correlated with Nc. Predicting Nc from Nb or vice versa in species with overlapping generations has important implications for conservation by permitting (i) estimation of the more difficult to quantify variable and (ii) inferences of Nb/Nc relationships in related species lacking data. We quantitatively synthesized Nb/Nc relationships in three salmonid fishes where sufficient data have recently accumulated. Mixed-effects models were analysed in which each variable was included as a dependent variable or predictor term (Nb from Nc and vice versa). Species-dependent Nb/Nc slope estimates were significantly positive in two of three species. Variation in species slopes was likely due to varying life histories and reinforce caution when inferring Nb/Nc from taxonomically related species. Models provided maximum probable estimates for Nb and Nc for two species. However, study, population and year effects explained substantial amounts of variation (39%-57%). Consequently, prediction intervals were wide and included or were close to zero for all population sizes and species; model predictive utility was limited. Cost-benefit trade-offs when estimating Nb and/or Nc were also discussed using a real-world system example. Our findings based on salmonids suggest that no short cuts currently exist when estimating population size and researchers should focus on quantifying the variable of interest or be aware of caveats when inferring the desired variable because of cost or logistics. We caution that the salmonid species examined share life-history traits that may obscure relationships between Nb and Nc. Sufficient data on other taxa were unavailable; additional research examining Nb/Nc relationships in species with potentially relevant life-history trait differences (e.g., differing survival curves) is needed.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Student > Master 10 22%
Researcher 8 17%
Other 6 13%
Student > Bachelor 2 4%
Other 4 9%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 54%
Environmental Science 8 17%
Biochemistry, Genetics and Molecular Biology 3 7%
Computer Science 1 2%
Decision Sciences 1 2%
Other 2 4%
Unknown 6 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 15 February 2018.
All research outputs
#1,787,627
of 17,897,867 outputs
Outputs from Evolutionary Applications
#308
of 1,203 outputs
Outputs of similar age
#40,479
of 280,004 outputs
Outputs of similar age from Evolutionary Applications
#4
of 17 outputs
Altmetric has tracked 17,897,867 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,203 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.4. This one has gotten more attention than average, scoring higher than 74% 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 280,004 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 85% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.