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As companies standardize selection processes, new challenges are cropping up.
In order to standardize selection of differentially expressed transcripts the threshold of significance was set to an experiment wide false discovery rate (FDR) [ 65] lower than 0.05 for all tests.
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Standardized selection differentials and standard errors were generated by dividing selection differentials by trait standard deviations, and standardized selection gradients and standard errors were generated by multiplying selection gradients and standard errors by trait standard deviations (Janzen and Stern 1998; Matsumura et al. 2012).
In the present study, we compute and report the mean-and-variance-standardized selection differential (hereafter termed standardized selection differential, Sstd).
We calculated the covariance between standard length and relative fitness to measure the absolute selection differential, s (reported in cm), and standardized selection differential, s' (reported in units of phenotypic standard deviations; Lande and Arnold 1983).
Standardized selection differentials were within 0 to ± 0.5 phenotypic standard deviations as reported in Hard et al. (2008) with the exception of 2006, a year characterized by extremely heavy exploitation rates due to an uncharacteristically large run.
As path coefficients, we present standardized selection gradients to make comparisons between wealth classes meaningful [51].
Selection differentials (S) and selection intensities (i), i.e. the standardized selection differentials, were calculated for all cohorts of wolves containing both breeders and non-breeders (Table 1).
Thus standardized selection gradients for SBA are equal to the selection differential.
Standardized selection differentials (SSDs) were also calculated to facilitate comparison across years.
All standardized selection differentials were negative, with an average of −1.0 ± 0.1 across all traits.
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