Anthropogenic climate change has already altered the timing of major life-history transitions, such as the initiation of reproduction. Both phenotypic plasticity and adaptive evolution can underlie rapid phenological shifts in response to climate change, but their relative contributions are poorly understood. Here, we combine a continuous 38 year field survey with quantitative genetic field experiments to assess adaptation in the context of climate change. We focused on Boechera stricta (Brassicaeae), a mustard native to the US Rocky Mountains. Flowering phenology advanced significantly from 1973 to 2011, and was strongly associated with warmer temperatures and earlier snowmelt dates. Strong directional selection favoured earlier flowering in contemporary environments (2010 -2011). Climate change could drive this directional selection, and promote even earlier flowering as temperatures continue to increase. Our quantitative genetic analyses predict a response to selection of 0.2 to 0.5 days acceleration in flowering per generation, which could account for more than 20 per cent of the phenological change observed in the long-term dataset. However, the strength of directional selection and the predicted evolutionary response are likely much greater now than even 30 years ago because of rapidly changing climatic conditions. We predict that adaptation will likely be necessary for long-term in situ persistence in the context of climate change.

Plants provide unique opportunities to study the mechanistic basis and evolutionary processes of adaptation to diverse environmental conditions. Complementary laboratory and field experiments are important for testing hypothesis reflecting long term ecological and evolutionary history. For example, these approaches can infer whether local adaptation results from genetic tradeoffs (antagonistic pleiotropy), where native alleles are best adapted to local conditions, or if local adaptation is caused by conditional neutrality at many loci, where alleles show fitness differences in one environment, but not in the contrasting environment. Ecological genetics in natural populations of perennial or outcrossing plants also may differ substantially from model systems. In this review of the evolutionary genetics of plant adaptation, we emphasize the importance of field studies for understanding the evolutionary dynamics of model and non-model systems, highlight a key life history trait (flowering time), and discuss emerging conservation issues.

Divergent natural selection promotes local adaptation and can lead to reproductive isolation of populations in contrasting environments; however, the genetic basis of local adaptation remains largely unresolved in natural populations. Local adaptation might result from antagonistic pleiotropy, where alternate alleles are favored in distinct habitats, and polymorphism is maintained by selection. Alternatively, under conditional neutrality some alleles may be favored in one environment but neutral at other locations. Antagonistic pleiotropy maintains genetic variation across the landscape; however, there is a systematic bias against discovery of antagonistic pleiotropy since the fitness benefits of local alleles need to be significant in at least two environments. Here, we develop a generally applicable method to investigate polygenic local adaptation and identify loci that are the targets of selection. This approach evaluates allele frequency changes after selection at loci across the genome to distinguish antagonistic pleiotropy from conditional neutrality and deleterious variation. We investigate local adaptation at the QTL-level in field experiments, in which we expose 177 F6 recombinant inbred lines (RILs) and parental lines of Boechera stricta (Brassicaceae) to their parental environments over two seasons. We demonstrate polygenic selection for native alleles in both environments, with 2.8% of the genome exhibiting antagonistic pleiotropy, and 8% displaying conditional neutrality. Our study strongly supports antagonistic pleiotropy at one large-effect flowering phenology QTL (nFT): native homozygotes had significantly greater probabilities of flowering than foreign homozygotes in both parental environments. Such large-scale field studies are essential to elucidate the genetic basis of adaptation in natural populations.

Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala × Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r 2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.Oryza sativa | QTL | three-dimensional | live root imaging | multivariate analysis R oot systems are high-value targets for crop improvement because of their potential to boost or stabilize yields in saline, dry, and acid soils, improve disease resistance, and reduce the need for unsustainable fertilizers (1-7). Root system architecture (RSA) describes the spatial organization of root systems, which is critical for root function in challenging environments (1-10). Modern genomics could allow us to leverage both natural and engineered variation to breed more efficient crops, but the lack of parallel advances in plant phenomics is widely considered to be a primary hindrance to developing "next-generation" agriculture (3,11,12). Root imaging and analysis have been particularly intractable: Decades of phenotyping efforts have failed to identify genes controlling quantitative RSA traits in crop species. Several factors confound RSA gene identification, including polygenic inheritance of root traits, soil opacity, and a complex 3D morphology that can be influenced heavily by the environment. Most phenotyping efforts have relied on small numbers of basic measurements to extrapolate system-wide traits. For example, given the length and mass of a few sample roots and the excavated root system mass, one can estimate the total root length, volume, and average root width of the entire root system (13,14). Other common measurements involve measuring the root surface exposed on a soil core or pressed against a transparent surface to estimate root coverage at a certain soil horizon. In these cases, the choices of sample roots and phenotyping standards, the size and shape of the container, and the limitations of 2D descriptions of 3D struct...

Identifying the causal genes that control complex trait variation remains challenging, limiting our appreciation of the evolutionary processes that influence polymorphisms in nature. We cloned a QTL that controls plant defensive chemistry, damage by insect herbivores, survival, and reproduction in the natural environments where this polymorphism evolved. These ecological effects are driven by duplications in the BCMA loci controlling this QTL and by two selectively favored amino acid changes in the glucosinolate-biosynthetic P450s that they encode. These changes cause a gain of novel enzyme function, modulated by allelic differences in catalytic rate and gene copy number. Ecological interactions in diverse environments likely contribute to the widespread polymorphism of this biochemical function.