Data and scripts for: Quantitative assessment of observed vs. predicted responses to selection

Although artificial-selection experiments seem well suited to testing our
ability to predict evolution, the correspondence between predicted and
observed responses is often ambiguous due to the lack of uncertainty
estimates. We present equations for assessing prediction error in direct
and indirect responses to selection that integrate uncertainty in genetic
parameters used for prediction and sampling effects during selection.
Using these, we analyzed a selection experiment on floral traits
replicated in two taxa of the Dalechampia scandens (Euphorbiaceae) species
complex for which G-matrices were obtained from a diallel breeding design.
After four episodes of bidirectional selection, direct and indirect
responses remained within wide prediction intervals, but appeared
different from the predictions. Combined analyses with structural-equation
models confirmed that responses were asymmetrical and lower than predicted
in both species. We show that genetic drift is likely to be a dominant
source of uncertainty in typically-dimensioned selection experiments in
plants and a major obstacle to predict short-term evolutionary
trajectories.