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The confounding effect of population structure on Bayesian skyline plot inferences of demographic history.

Heller R, Chikhi L, Siegismund HR - PLoS ONE (2013)

Bottom Line: We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale.Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations.We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

View Article: PubMed Central - PubMed

Affiliation: Instituto Gulbenkian de Ciência, Oeiras, Portugal. rheller@bio.ku.dk

ABSTRACT
Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences have so far not been addressed and quantified. We simulated DNA sequence data under a variety of scenarios involving structured populations with variable levels of gene flow and analysed them using BSPs as implemented in the software package BEAST. Results revealed that BSPs can show false signals of population decline under biologically plausible combinations of population structure and sampling strategy, suggesting that the interpretation of several previous studies may need to be re-evaluated. We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

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Related in: MedlinePlus

Three different sampling strategies for real data from the buffalo.Local, pooled and scattered sampling of real D-loop data from 34 African buffalo populations. The replication of each sampling strategy involved random drawing of the appropriate number of samples from demes as explained in the main text.
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pone-0062992-g004: Three different sampling strategies for real data from the buffalo.Local, pooled and scattered sampling of real D-loop data from 34 African buffalo populations. The replication of each sampling strategy involved random drawing of the appropriate number of samples from demes as explained in the main text.

Mentions: The African buffalo data yielded different results depending on the sampling strategy (Fig. 4). Under the pooled sampling we observed a conspicuous boom-bust signal that resembled the signal in the data-informed simulated data closely (Fig. 3F) and was very distinct from any of the false signals observed in Fig. 1. Under the scattered sampling the EBSP approached Fig. 2F and Fig. 3D, where the decline phase towards the present was almost absent. The qualitative differences between Fig. 4B and 4C were confirmed by the corresponding PSC values, with lower PSC values for the scattered sampling strategy (Fig. 4 insert panels). Finally, the locally sampled buffalo data showed variable skyline plots including boom-bust-like dynamics, pure expansions, pure declines and nearly constant population sizes (Fig. 4A). Note that low sample sizes for some of the demes (the three smallest samples consisted of three, four and ten individuals respectively) could lead to unreliable EBSPs.


The confounding effect of population structure on Bayesian skyline plot inferences of demographic history.

Heller R, Chikhi L, Siegismund HR - PLoS ONE (2013)

Three different sampling strategies for real data from the buffalo.Local, pooled and scattered sampling of real D-loop data from 34 African buffalo populations. The replication of each sampling strategy involved random drawing of the appropriate number of samples from demes as explained in the main text.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3646956&req=5

pone-0062992-g004: Three different sampling strategies for real data from the buffalo.Local, pooled and scattered sampling of real D-loop data from 34 African buffalo populations. The replication of each sampling strategy involved random drawing of the appropriate number of samples from demes as explained in the main text.
Mentions: The African buffalo data yielded different results depending on the sampling strategy (Fig. 4). Under the pooled sampling we observed a conspicuous boom-bust signal that resembled the signal in the data-informed simulated data closely (Fig. 3F) and was very distinct from any of the false signals observed in Fig. 1. Under the scattered sampling the EBSP approached Fig. 2F and Fig. 3D, where the decline phase towards the present was almost absent. The qualitative differences between Fig. 4B and 4C were confirmed by the corresponding PSC values, with lower PSC values for the scattered sampling strategy (Fig. 4 insert panels). Finally, the locally sampled buffalo data showed variable skyline plots including boom-bust-like dynamics, pure expansions, pure declines and nearly constant population sizes (Fig. 4A). Note that low sample sizes for some of the demes (the three smallest samples consisted of three, four and ten individuals respectively) could lead to unreliable EBSPs.

Bottom Line: We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale.Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations.We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

View Article: PubMed Central - PubMed

Affiliation: Instituto Gulbenkian de Ciência, Oeiras, Portugal. rheller@bio.ku.dk

ABSTRACT
Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences have so far not been addressed and quantified. We simulated DNA sequence data under a variety of scenarios involving structured populations with variable levels of gene flow and analysed them using BSPs as implemented in the software package BEAST. Results revealed that BSPs can show false signals of population decline under biologically plausible combinations of population structure and sampling strategy, suggesting that the interpretation of several previous studies may need to be re-evaluated. We found that a balanced sampling strategy whereby samples are distributed on several populations provides the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure previous to BSP analyses, and that alternative scenarios should be evaluated when interpreting signals of population size change.

Show MeSH
Related in: MedlinePlus