If a given phenotypic change is adaptive, it was likely driven by selection – and so one inferential approach is to measure selection in nature.
A number of studies have measured selection in nature to understand how populations adapt to their environment; however, the temporal dynamics of selection are rarely investigated.
A number of studies have measured selection in nature in an effort to understand how populations adapt to their environment over time [ 1, 2].
First, it is difficult to accurately measure selection in nature (Kingsolver et al. 2001, 2012; Kruuk et al. 2003; Hersch and Phillips 2004; Morrissey and Hadfield 2012).
A useful tool to detect and measure selection in viral gene sequences is the ratio between synonymous (dS) and non-synonymous (dN) substitutions.
Briefly, nonsynonymous to synonymous rate ratios, denoted ω, are used to measure selection in protein-coding genes, with ω < 1 indicating negative selection, ω = 1 neutral evolution and ω > 1 positive selection [ 20].
Schluter [ 13] measured selection in sticklebacks and demonstrated that growth rates and survival were depressed in the presence of competitors, and that selection was frequency dependent [ 14]; and Bolnick [ 15] showed that competition could generate disruptive selection regimes in natural populations of sticklebacks.
However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing.
In the nursing home post-acute setting, where patients must achieve a minimum length of stay to be included in quality measures, selection may take the form of discharge from the nursing home using rehospitalization, a particularly costly and undesirable outcome.
Laboratory based measures of selection in socially competitive environments are mixed.
