According to cut-off points in either direction of selection, plants with lower values for LIBR were randomly mated to obtain the first cycle for short-length of the internode basal ring (Short_LIBR) and plants with higher values for LIBR were randomly mated to obtain the first cycle for large-length of the internode basal ring (Large_LIBR).
It can be shown that, for model (3)–(4), the mean of the parameter distribution decreases monotonically with time (dE β (t)/ dt ≤ 0 for any t, see Corollary 2 in MA) from the initial value E β (0) to the final value η, which shows the direction of selection: the cells with lower parameter values are selected for.
This enables investigating how the direction of selection on g changes with increasing mean trait values of the population and to analyze how the ESS in g depends on angling intensity.
Studying a well-known and relatively simple gene cascade has enabled us to relate and confront the evolution of a network structure with the direction of selection on the amino acids of the genes participating in that network.
Or, if they do separate out different conditions and investigate differences in selection across environments (Donohue et al. 2000; Stanton et al. 2000, 2004), there is a focus on the strength and direction of selection in the different environments, albeit with little focus on how selection may be constrained by treatment combinations.
An alternative explanation, however, is that evolution can sometimes be constrained by antagonistic genetic correlations (i.e., genetic correlations among traits that are not in accord with the direction of selection [ 26, 27]) arising from linkage disequilibrium between alleles at different loci, and patterns of linkage disequilibrium can vary among populations or seasons [ 28, 29].
The direction of selection depends upon whether immunopathology positively correlates with exploitation in such hosts (Day et al. 2007).
We show that the direction of selection statistic exhibits a strong nonmonotonic relationship with the strength of purifying selection S, making it inappropriate for estimating S. We instead use counts in synonymous versus nonsynonymous frequency classes to infer the distribution of S coefficients acting on nonsynonymous mutations in each subspecies.
In contrast, ROC SEMPPR estimates selection coefficients and other key parameters by assuming a common direction of selection on CUB, but where the strength of selection varies with protein synthesis rate.
