The evolution of quantitative traits in complex environments

Abstract

Species inhabit complex environments and respond to selection imposed by numerous abiotic and biotic conditions that vary in both space and time. Environmental heterogeneity strongly influences trait evolution and patterns of adaptive population differentiation. For example, heterogeneity can favor local adaptation, or can promote the evolution of plastic genotypes that alter their phenotypes based on the conditions they encounter. Different abiotic and biotic agents of selection can act synergistically to either accelerate or constrain trait evolution. The environmental context has profound effects on quantitative genetic parameters. For instance, heritabilities measured in controlled conditions often exceed those measured in the field; thus, laboratory experiments could overestimate the potential for a population to respond to selection. Nevertheless, most studies of the genetic basis of ecologically relevant traits are conducted in simplified laboratory environments, which do not reflect the complexity of nature. Here, we advocate for manipulative field experiments in the native ranges of plant species that differ in mating system, life-history strategy and growth form. Field studies are vital to evaluate the roles of disparate agents of selection, to elucidate the targets of selection and to develop a nuanced perspective on the evolution of quantitative traits. Quantitative genetics field studies will also shed light on the potential for natural populations to adapt to novel climates in highly fragmented landscapes. Drawing from our experience with the ecological model system Boechera (Brassicaceae), we discuss advancements possible through dedicated field studies, highlight future research directions and examine the challenges associated with field studies.

Figure 1

Genetic correlations of flowering times in the same B. stricta recombinant inbred lines exposed to six laboratory and two field environments. If laboratory conditions reasonably simulated natural environments, we would expect a tight correlation between flowering phenology in lab and field. Instead, our data indicate that family-mean flowering times are significantly correlated in laboratory and Montana field conditions (a), but little of the genotypic variation in flowering time in the field is explained by conditions in the growth chamber, reflected in the low R² value. We found no significant correlation between family-mean flowering times in our Colorado garden and any of our six growth chamber conditions (one representative growth chamber treatment is displayed in (b)). Furthermore, flowering time values are uncorrelated between our disparate field sites (c), highlighting the importance of investigating life-history transitions and ecologically relevant traits under multiple natural environments. The genetic correlation of flowering time is much tighter in comparisons of different growth chamber treatments (d). The data presented in these panels come from Anderson et al. (2011, , and are available in the associated Dryad files.

Figure 2

Distributions of fitness and underlying phenotypes in a mapping population for individuals with two alleles (‘white' and ‘black') at a putatively adaptive QTL controlling the ratio of two types of chemical defenses. (a) The fitness distribution has a large variance and very little differentiation between the groups, indicating that this QTL has a small effect on fitness. (b) The QTL has a more defined effect on herbivory; variance is smaller and individuals with the black allele suffer significantly more herbivore damage than those with the white allele when grown together in a common garden. (c) The phenotype A:B has low variance and strong contrast, making it easy to map to the same QTL that has more diluted effects on herbivory and fitness. Herbivore resistance is controlled by fewer genes than fitness (a) but more genes than chemical defense ratio (c).

Figure 3

Stepwise approach to discovery and verification of adaptive QTL in natural environments. (a) Genetic control of a putatively adaptive phenotypic difference observed between two geographically separated ecotypes must be confirmed by growing them in a common location. (b) Family-based genetic mapping can be used to identify genomic regions associated with the phenotype of interest. (c) To pinpoint the effects of the candidate adaptive QTL, confounding genetic variation can be eliminated via repeated backcrossing of heterozygotes, so that the segregating alleles of the QTL are preserved. (d) The resulting near-isogenic lines, genetically identical except for the QTL of interest, can be grown together in the parental environments. Phenotypic and fitness differences between the genotypes reflect variation at the candidate QTL.