10.5061/DRYAD.4875K
Wiser, Michael J.
Michigan State University
Lenski, Richard E.
Michigan State University
Data from: A comparison of methods to measure fitness in Escherichia coli
Dryad
dataset
2015
method
Holocene
2015-07-29T17:47:39Z
2015-07-29T17:47:39Z
en
https://doi.org/10.1371/journal.pone.0126210
57132 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
In order to characterize the dynamics of adaptation, it is important to be
able to quantify how a population’s mean fitness changes over time. Such
measurements are especially important in experimental studies of evolution
using microbes. The Long-Term Evolution Experiment (LTEE) with Escherichia
coli provides one such system in which mean fitness has been measured by
competing derived and ancestral populations. The traditional method used
to measure fitness in the LTEE and many similar experiments, though, is
subject to a potential limitation. As the relative fitness of the two
competitors diverges, the measurement error increases because the less-fit
population becomes increasingly small and cannot be enumerated as
precisely. Here, we present and employ two alternatives to the traditional
method. One is based on reducing the fitness differential between the
competitors by using a common reference competitor from an intermediate
generation that has intermediate fitness; the other alternative increases
the initial population size of the less-fit, ancestral competitor. We
performed a total of 480 competitions to compare the statistical
properties of estimates obtained using these alternative methods with
those obtained using the traditional method for samples taken over 50,000
generations from one of the LTEE populations. On balance, neither
alternative method yielded measurements that were more precise than the
traditional method.
Methods.3.FormsThis file contains all the raw data for this
manuscriptMethods.MeansThis file has the precalculated means for each
method at each time point. All of this can be calculated from the raw data
file, but this makes things more convenient.Methods Executed ScriptThis is
the analysis script for this manuscript. Please note that it is saved as a
.txt file, though it is designed for use in R. Anyone wishing to repeat
the analysis will need to either copy and paste this into an R window, or
else save the file as an appropriate R script extension.
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