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Abstract In a rapidly changing environment, predicting changes in the growth and survival of local populations can inform conservation and management. Plastic responses vary as a result of genetic differentiation within and among species, so accurate rangewide predictions require characterization of genotype-specific reaction norms across the continuum of historic and future climate conditions comprising a species’ range. Natural hybrid zones can give rise to novel recombinant genotypes associated with high phenotypic variability, further increasing the variance of plastic responses within the ranges of the hybridizing species. Experiments that plant replicated genotypes across a range of environments can characterize genotype-specific reaction norms; identify genetic, geographic, and climatic factors affecting variation in climate responses; and make predictions of climate responses across complex genetic and geographic landscapes. The North American hybrid zone ofPopulus trichocarpaandP. balsamiferarepresents a natural system in which reaction norms are likely to vary with underlying genetic variation that has been shaped by climate, geography, and introgression. Here, we leverage a dataset containing 45 clonal genotypes of varying ancestry from this natural hybrid zone, planted across 17 replicated common garden experiments spanning a broad climatic range, including sites warmer than the natural species ranges. Growth and mortality were measured over two years, enabling us to model reaction norms for each genotype across these tested environments. Genomic variation associated with species ancestry and northern/southern regions significantly influenced growth across environments, with genotypic variation in reaction norms reflecting a trade-off between cold tolerance and growth. Using modeled reaction norms for each genotype, we predicted that genotypes with moreP. trichocarpaancestry may gain an advantage under warmer climates. Spatial shifts of the hybrid zone could facilitate the spread of beneficial alleles into novel climates. These results highlight that genotypic variation in responses to temperature will have landscape-level effects. 

Abstract PremiseThe selection ofArabidopsisas a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled research. MethodsHere, we examine the ability of models based onArabidopsisgene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested onArabidopsisdata achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained onArabidopsisdata, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. ResultsThe identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance fromArabidopsis.k‐nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. DiscussionOur data‐driven results highlight that the assertion that knowledge fromArabidopsisis translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis onArabidopsisand prioritize plant diversity.