Data from: Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach

An increasing number of studies are using landscape genomics to
investigate local adaptation in wild and domestic populations. The
implementation of this approach requires the sampling phase to consider
the complexity of environmental settings and the burden of logistic
constraints. These important aspects are often underestimated in the
literature dedicated to sampling strategies. In this study, we computed
simulated genomic datasets to run against actual environmental data in
order to trial landscape genomics experiments under distinct sampling
strategies. These strategies differed by design approach (to enhance
environmental and/or geographic representativeness at study sites), number
of sampling locations and sample sizes. We then evaluated how these
elements affected statistical performances (power and false discoveries)
under two antithetical demographic scenarios. Our results highlight the
importance of selecting an appropriate sample size, which should be
modified based on the demographic characteristics of the studied
population. For species with limited dispersal, sample sizes above 200
units are generally sufficient to detect most adaptive signals, while in
random mating populations this threshold should be increased to 400 units.
Furthermore, we describe a design approach that maximizes both
environmental and geographical representativeness of sampling sites and
show how it systematically outperforms random or regular sampling schemes.
Finally, we show that although having more sampling locations (between 40
and 50 sites) increase statistical power and reduce false discovery rate,
similar results can be achieved with a moderate number of sites (20
sites). Overall, this study provides valuable guidelines for optimizing
sampling strategies for landscape genomics experiments.