Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes

Abstract

The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

Recent advances in conservation and population genomics data analysis

“…The reasons for the popularity of GEA analyses are practical: They require no phenotypic data or prior genomic resources, do not require experimental approaches (such as reciprocal transplants) to demonstrate local adaptation, and are often more powerful than differentiation‐based outlier detection methods (De Mita et al., 2013; de Villemereuil, Frichot, Bazin, François, & Gaggiotti, 2014; Forester, Lasky, Wagner, & Urban, 2018; Lotterhos & Whitlock, 2015). In particular, participants considered how and why detection rates differed between univariate and multivariate GEAs, exploring the use of latent factor mixed models (Frichot, Schoville, Bouchard, & Francois, 2013) and redundancy analysis (Forester, Jones, Joost, Landguth, & Lasky, 2016; Lasky et al., 2012), respectively. Recent work has shown that RDA is an effective means of detecting adaptive processes that result in weak, multilocus molecular signatures (Forester et al., 2018), providing a powerful tool for investigating the genetic basis of local adaptation and informing management actions to conserve evolutionary potential (Flanagan et al., 2017; Harrisson et al., 2014; Hoffmann et al., 2015).…”

Integrative Approaches for Studying Mitochondrial and Nuclear Genome Co-evolution in Oxidative Phosphorylation

“…Given that mitochondrial and nuclear genomes can have largely independent evolutionary histories (e.g., different introgression patterns and mutation load), knowledge of the demographic history of the study system is especially useful to interpret patterns of mitonuclear co-evolution (Bar-Yaacov et al, 2015; Morales et al, 2016a,b; Pereira et al, 2016; Sloan et al, 2016). Given that mitonuclear co-evolution is likely to respond to environmental variation (Burton et al, 2013), approaches to detecting selection that rely on gene-environment associations could be particularly useful to identify candidate loci under selection (Rellstab et al, 2015; Forester et al, 2016). …”

Phenotypes in phylogeography: Species’ traits, environmental variation, and vertebrate diversification

“…If environmental conditions favor one phenotype, then populations may diverge phenotypically and genetically through local adaptation (43). The spatial arrangement of suitable habitat in heterogeneous landscapes, such as mosaics or clines, can also promote geographically clustered phenotypic variation (44). For example, strawberry poison dart frogs are highly polymorphic, and genetic distances among populations are more strongly associated with phenotypic differences than with geographic distances, suggesting a role for local adaptation related to predation and aposematism (25).…”