Optimizing sampling design for landscape genomics

Landscape genomic approaches for detecting genotype-environment
associations (GEA), isolation by distance (IBD), and isolation by
environment (IBE) have seen a dramatic increase in use, but there have
been few thorough analyses of the influence of sampling strategy on their
performance under realistic genomic and environmental conditions. We
simulated 24,000 datasets across a range of scenarios with complex
population dynamics and realistic landscape structure to evaluate the
effects of the spatial distribution and number of samples on common
landscape genomics methods. Our results show that common analyses are
relatively robust to sampling scheme as long as sampling covers enough
environmental and geographic space. We found that for detecting adaptive
loci and estimating IBE, sampling schemes that were explicitly designed to
increase coverage of available environmental space matched or outperformed
sampling schemes that only considered geographic space. When sampling does
not cover adequate geographic and environmental space, such as with
transect-based sampling, we detected fewer adaptive loci and had higher
error when estimating IBD and IBE. We found that IBD could be detected
with as few as nine sampling sites, while large sample sizes (e.g.,
greater than 100 individuals) were crucial for detecting adaptive loci and
IBE. We also demonstrate that, even with optimal sampling strategies,
landscape genomic analyses are highly sensitive to landscape structure and
migration ⁠— when spatial autocorrelation and migration are weak, common
GEA methods fail to detect adaptive loci.