Data from: Estimating the variation, autocorrelation, and environmental sensitivity of phenotypic selection

Despite considerable interest in temporal and spatial variation of
phenotypic selection, very few methods allow quantifying this variation
while correctly accounting for the error variance of each individual
estimate. Furthermore, the available methods do not estimate the
autocorrelation of phenotypic selection, which is a major determinant of
eco-evolutionary dynamics in changing environments. We introduce a new
method for measuring variable phenotypic selection using random
regression. We rely on model selection to assess the support for
stabilizing selection, and for a moving optimum that may include a trend
plus (possibly autocorrelated) fluctuations. The environmental sensitivity
of selection also can be estimated by including an environmental
covariate. After testing our method on extensive simulations, we apply it
to breeding time in a great tit population in the Netherlands. Our
analysis finds support for an optimum that is well predicted by spring
temperature, and occurs about 33 days before a peak in food biomass,
consistent with what is known from the biology of this species. We also
detect autocorrelated fluctuations in the optimum, beyond those caused by
temperature and the food peak. Because our approach directly estimates
parameters that appear in theoretical models, it should be particularly
useful for predicting eco-evolutionary responses to environmental change.