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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).
It is very unlikely, however, that the large populations (see estimated effective size) that we generated in the laboratory by mixing several strains of C. remanei (for increasing allelic diversity) reached such an equilibrium.
We then estimated effective size from the simulated genetic data, using the LD method, and assessed bias by comparing the estimates with the true Ne and Nb.
We used the approach of Wang [36] [37] to obtain a likelihood point estimate of Ne the effective size and the distribution of the log-likelihood around this estimated effective size.
The estimated effective size of today's population is ∼380,000, much larger than that reported in [38] (20,000), but smaller than that reported in [14] (900,000).
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> Harmonic means of estimated effective sizes, 2.5% and 97.5% estimate quantiles, coefficients of variation (calculated across 1000 replicate datasets), expected CV(N e ), relative biases, and the mean numbers of independent allelic comparisons for small N e sturgeon and mussel scenarios simulated in this study and all three allele exclusion criteria (Pcrit).
However, these studies have limited value in estimating effective size since disease-modification, rather than symptomatic improvement, is the ultimate goal.
Caution should therefore be used in interpreting results for such samples when estimating effective size in iteroparous species.
In contrast, estimating effective size with any precision in populations that are large (Ne∼ 1000 or larger) is very challenging.
The values of delta F computed for a pre-defined reference subset can be averaged and used to estimate effective size.
It seems clear that previous efforts to estimate effective size in natural populations have not extracted as much information as possible from genetic data.
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