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Data from: Environmental change, if unaccounted, prevents detection of cryptic evolution in a wild population

Citation

Potter, Tomos et al. (2020), Data from: Environmental change, if unaccounted, prevents detection of cryptic evolution in a wild population, Dryad, Dataset, https://doi.org/10.5061/dryad.jm63xsj7k

Abstract

Detecting contemporary evolution requires demonstrating that genetic change has occurred. Mixed-effects models allow estimation of quantitative genetic parameters and are widely used to study evolution in wild populations. However, predictions of evolution based on these parameters frequently fail to match observations. Furthermore, such studies often lack an independent measure of evolutionary change against which to verify predictions. Here, we applied three commonly used quantitative genetic approaches to predict the evolution of size at maturity in a wild population of Trinidadian guppies. Crucially, we tested our predictions against evolutionary change observed in common garden experiments performed on samples from the same population. We show that standard quantitative genetic models underestimated or failed to detect the cryptic evolution of this trait as demonstrated by the common garden experiments. The models failed because: 1) size at maturity and fitness both decreased with increases in population density, 2) offspring experienced higher population densities than their parents, and 3) selection on size was strongest at high densities. When we accounted for environmental change, predictions better matched observations in the common garden experiments, although substantial uncertainty remained. Our results demonstrate that predictions of evolution are unreliable if environmental change is not appropriately captured in models.

Methods

This dataset includes:

guppy_data.csv - individual phenotypic, fitness and environmental data from the study population
guppy_ped.csv - pedigree for the study population
common_garden.csv - phenotypic data from the common garden experiments

In addition, we include code for performing the analyses described in the manuscript as well as model output objects.

Usage Notes

Please read the manuscript and supplementary material, and the READ_ME file included with this dataset.

Funding

Natural Environment Research Council, Award: DTP studentship

National Science Foundation, Award: EF-062362, DEB-1258231, DEB-1556884, and DEB-0846175.

St. John's College, University of Oxford, Award: Lamb and Flag scholarship