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Modeling Individual Specific Fish Length from Capture–Recapture Data Using the von Bertalanffy Growth Curve

Overview of attention for article published in Biometrics, October 2013
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Article details
Title
Modeling Individual Specific Fish Length from Capture–Recapture Data Using the von Bertalanffy Growth Curve
Published in
Biometrics, October 2013
DOI 10.1111/biom.12069
Pubmed ID
Authors
Abstract

We use Bayesian methods to explore fitting the von Bertalanffy length model to tag-recapture data. We consider two popular parameterizations of the von Bertalanffy model. The first models the data relative to age at first capture; the second models in terms of length at first capture. Using data from a rainbow trout Oncorhynchus mykiss study we explore the relationship between the assumptions and resulting inference using posterior predictive checking, cross validation and a simulation study. We find that untestable hierarchical assumptions placed on the nuisance parameters in each model can influence the resulting inference about parameters of interest. Researchers should carefully consider these assumptions when modeling growth from tag-recapture data.

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Mendeley demographics

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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.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 12 26%
Researcher 11 24%
Student > Bachelor 5 11%
Student > Master 4 9%
Professor > Associate Professor 3 7%
Other 6 13%
Unknown 5 11%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 23 50%
Environmental Science 9 20%
Engineering 2 4%
Veterinary Science and Veterinary Medicine 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 1 2%
Unknown 9 20%
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 11 October 2013.
All research outputs
#16,691,248
of 24,549,201 outputs
Outputs from Biometrics
#1,324
of 1,929 outputs
Outputs of similar age
#134,578
of 216,075 outputs
Outputs of similar age from Biometrics
#10
of 25 outputs
Altmetric has tracked 24,549,201 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,929 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.