Optimising Sampling Design for Landscape Genomics.

Abstract

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.