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. 2015 Jul;115(1):93-5.
doi: 10.1038/hdy.2015.45. Epub 2015 May 20.

Resource specialisation and the divergence of killer whale populations

A R Hoelzel  1 , A E Moura  2
Affiliations

Resource specialisation and the divergence of killer whale populations

A R Hoelzel et al. Heredity (Edinb). 2015 Jul.
No abstract available

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Figures

Figure 1
Figure 1
Scenarios simulated using the DIY-ABC software, version 2.0.4 (Cornuet et al., 2014) and relative fit to the observed data. a–d illustrate the simulated scenarios along the top (M= Marion Island, R= residents, O= offshores, T= transients), while the relative proportions of each scenario found in the selected closest data sets are plotted below. The logistic regression is shown, but the direct estimate (not shown) gave similar results with a and b higher than c and d. At 40 000 data sets, scenario A is 0.4196 (95% confidence interval: 0.4053–0.4340), B is 0.5705 (0.5562–0.5848), C is 0.0097 (0.0000–0.0199) and D is 0.0002 (0.0000–0.0106). Branching order was coded by constraining the relative timings between split events, but giving wide priors (10–10 000) for the absolute timings. As estimating absolute splitting times was not the purpose of our simulations, this approach minimises bias resulting from uncertainty in splitting times. We coded different demographic scenarios for each split by including a change of Ne for the derived lineage (e.g., in scenario A, offshore separate from residents, while in scenario B, residents separate from offshores, even though the timing of the split is the same). Wide priors (10–10 000) were used for all Ne values again to minimise bias. Summary statistics associated with genic diversities, FST distances and Nei's genetic distance were included for all possible population combinations. 1 000 000 data sets were simulated for each scenario, and comparative assessment based on the 40 000 most similar simulations. Scenarios A and B reflect inferences obtained from the nuclear phylogenetic tree; scenario C represented inference from the mitogenome tree only; scenario D represents the mitogenomic tree, but allowing for gene flow (again with wide priors: 0.01–0.99) between offshores and transients after secondary contact, as proposed by Foote and Morin (2015). Simulations were generated based on a sample of 1000 SNPs from Moura et al. (2014a) and repeated twice for different sets of SNP's to check for consistency (no difference found and so only one version shown).
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References

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