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To address these questions, we use the proportion of loci showing significantly elevated levels of between-population differentiation (i.e. outlier behaviour in a genome scan; e.g. [ 30, 31]) as a measure of putatively adaptive divergence (see e.g. [ 32]).
Samples presenting outlier behaviour were excluded from the analyses.
The precise mechanism that drives the outlier behaviour of CE is not clear.
Particularly, we looked for replicated outlier behaviour that would provide evidence of independent and parallel divergent selection.
In fact, the outlier behaviour of several markers appears to be driven by one (or a few) populations that differ markedly in average AFLP band frequency from all other populations.
Despite these potential caveats, several genomewide studies have identified genes that may be good candidates for selected loci because of their outlier behaviour with respect to the rest of the genome (Carlson et al. 2005; Voight et al. 2006; Frazer et al. 2007; Sabeti et al. 2007; Tang et al. 2007; Barreiro et al. 2008).
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A key factor is the understanding they wish to obtain, for example, arriving at the best possible model for a given system (in which case model development time might not be too important), or generating alternative plausible models representing competing experimental hypothesis (in which case the availability of rapid prototyping, averages and outlier behaviours are needed).
But it's very important to understand that an outlier's behaviour is different to that of a regular user.
After detecting outlier loci, it is then a challenging prospect to verify whether they are involved in local adaptation and to isolate the different ecological factors responsible for the behaviour of each outlier from a complex natural environment [ 4, 12].
The second outlier refers to the NOSGER subscale "Social Behaviour".
The pilot study can detect "outlier" observers and attempt to modify their behaviour, but this is unlikely to eliminate systematic differences for some types of measurement.
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