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Abstract Closely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine andArabidopsis(~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the ‘cost of complexity’ theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study. 

Direct observation is central to our understanding of adaptation, but evolution is rarely documented in a large, multicellular organism for more than a few generations. In this study, we observed evolution across a century-scale competition experiment, barley composite cross II (CCII). CCII was founded in 1929 in Davis, California, with thousands of genotypes, but we found that natural selection has massively reduced genetic diversity, leading to a single lineage constituting most of the population by generation 50. Selection favored alleles originating from climates similar to that of Davis and targeted loci contributing to reproductive development, including the barley diversification lociVrs1,HvCEN,Ppd-H1, andVrn-H2. Our findings point to selection as the predominant force shaping genomic variation in one of the world’s oldest biological experiments. 

Direct observation is central to our understanding of the process of adaptation, but evolution is rarely documented in a large, multicellular organism for more than a few generations. Here, we observe genetic and phenotypic evolution across a century-scale competition experiment, barley composite cross II (CCII). CCII was founded in 1929 with tens of thousands of unique genotypes and has been adapted to local conditions in Davis, CA, USA for 58 generations. We find that natural selection has massively reduced genetic diversity leading to a single clonal lineage constituting most of the population by generation F50. Selection favored alleles originating from similar climates to that of Davis, and targeted genes regulating reproductive development, including some of the most well-characterized barley diversification loci, Vrs1, HvCEN, and Ppd-H1. We chronicle the dynamic evolution of reproductive timing in the population and uncover how parallel molecular pathways are targeted by stabilizing selection to optimize this trait. Our findings point to selection as the predominant force shaping genomic variation in one of the world’s oldest ongoing biological experiments. 

Abstract Karrikins (KARs) are chemicals in smoke that can enhance germination of many plants. Lettuce (Lactuca sativa) cv. Grand Rapids germinates in response to nanomolar karrikinolide (KAR1). Lettuce is much less responsive to KAR2 or a mixture of synthetic strigolactone analogs, rac-GR24. We investigated the molecular basis of selective and sensitive KAR1 perception in lettuce. The lettuce genome contains two copies of KARRIKIN INSENSITIVE2 (KAI2), which in Arabidopsis (Arabidopsis thaliana) encodes a receptor that is required for KAR responses. LsKAI2b is more highly expressed than LsKAI2a in dry achenes and during early stages of imbibition. Through cross-species complementation assays in Arabidopsis, we found that an LsKAI2b transgene confers robust responses to KAR1, but LsKAI2a does not. Therefore, LsKAI2b likely mediates KAR1 responses in lettuce. We compared homology models of KAI2 proteins from lettuce and a fire-follower, whispering bells (Emmenanthe penduliflora). This identified pocket residues 96, 124, 139, and 161 as candidates that influence the ligand specificity of KAI2. Further support for the importance of these residues was found through a broader comparison of pocket residues among 281 KAI2 proteins from 184 asterid species. Almost all KAI2 proteins had either Tyr or Phe identity at position 124. Genes encoding Y124-type KAI2 are more broadly distributed in asterids than in F124-type KAI2. Substitutions at residues 96, 124, 139, and 161 in Arabidopsis KAI2 produced a broad array of responses to KAR1, KAR2, and rac-GR24. This suggests that the diverse ligand preferences observed among KAI2 proteins in plants could have evolved through relatively few mutations. 

Abstract Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal–Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of ‘hidden’ shape nuances which are otherwise not detected.