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The values of the parameters are expected to differ depending on the dataset, sampling method and the classification algorithm.
Finally we ran the analysis on an empty dataset, sampling from the prior distribution to evaluate the influence of the priors on the posterior distribution estimates [ 103, 104].
We set the length of the Bayesian Markov chain Monte Carlo (MCMC) run to 20 million for NA and 40 million for the larger HA dataset; sampling every 1,000 steps.
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It looks for the most possible distribution pattern of SFS under the constraints of given demographic parameter set that fits the real dataset sampled from the population.
Two separate MCMC chains were run for 108 generations for each dataset, sampled every 10,000th generation.
The second dataset, sampled seven months later (herein t7), was obtained from phage as previously described [20].
The analyses were run for one million generations for the bacterial dataset and 0.5 million generations for the eukaryotic dataset, sampled every 100 generations.
For studies with repeated-sampling design for plantations compared with natural forests, the datasets sampled in the last time were collected.
We then simulated a FST distribution between two datasets sampled 27 generations apart considering only the drift effect associated with the whole population estimated effective size (Ne).
DOI: http://dx.doi.org/10.7554/eLife.03125.013 10.7554/eLiFigure25.014 Figure 3 figure supplement 1. Recruitment and coverage plot of SUP05 viral genome fragments by Saanich Inlet datasets sampled in 2009, 2010, and 2011.
Figure 8 CLEAR [22]evaluation dataset sample.
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