Water availability is perhaps the greatest environmental determinant of plant yield and fitness. However, our understanding of plant-water relations is limited because—like many studies of organism-environment interaction—it is primarily informed by experiments considering performance at two discrete levels—wet and dry—rather than as a continuously varying environmental gradient. Here we used experimental and statistical methods based on function-valued traits to explore genetic variation in responses to a continuous soil moisture gradient in physiological and morphological traits among ten genotypes across two species of the model grass genus Brachypodium. We find that most traits exhibit significant genetic variation and non-linear responses to soil moisture variability. We also observe differences in the shape of these non-linear responses between traits and genotypes. Emergent phenomena arise from this variation including changes in trait correlations and evolutionary constraints as a function of soil moisture. Our results point to the importance of considering diversity in non-linear organism-environment relationships to understand plastic and evolutionary responses to changing climates.

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Despite the tremendous diversity and complexity of life forms, there are certain forms of life that are never observed. Organisms like angels might not emerge because of developmental constraint, or because they have low fitness in any environment yet available on Earth. Given that both developmental constraint and selection may create similar phenotypes, it is difficult to distinguish between the two causes of evolutionary stasis among related taxa. For example, remarkably invariant traits are observed spanning million years, such as wing shape inDrosophilawherein qualitative differences are rare within genera. We thus ask whether the absence of combinations of traits, indicated by genetic correlation, reflects developmental bias limiting the possibility of change. However, much confusion and controversy remain over definitions of developmental bias and quantifying it is challenging. We present an approach aiming to estimate developmental bias by leveraging recombination in genetic mapping populations. We reason that information rendered by such mild perturbations captures inherent interdependencies between traits -- developmental bias. Through empirical analyses, we find that our developmental bias metric is a strong indicator of genetic correlation stability across conditions. Our framework presents a feasible way to quantify developmental bias between traits and opens up the possibility to dissect patterns of genetic correlation.

How do selection and standing genetic variation shape population divergence across landscapes? Henry and Stinchcombe (2023a) estimated selection gradients on traits in the ivy-leaved morning glory (Ipomoea hederacea) in the field and compared them with the G-matrix and population divergence for four populations in North America. The authors show that population divergence and genetic covariances are largely unaligned with the selection gradient at the species’ range edge. These findings raise the question of whether limited evolvability or multivariate genetic variation of populations at range edges prevent species from range expansion, which is important for understanding the role of genetic constraint in population divergence and predicting local adaptation in the face of climate change.

Transcriptional Regulatory Networks (TRNs) orchestrate the timing, magnitude, and rate of organismal response to many environmental perturbations. Regulatory interactions in TRNs are dynamic but exploiting temporal variation to understand gene regulation requires a careful appreciation of both molecular biology and confounders in statistical analysis. Seeking to exploit the abundance of RNASequencing data now available, many past studies have relied upon population-level statistics from cross-sectional studies, estimating gene co-expression interactions to capture transient changes of regulatory activity. We show that population-level co-expression exhibits biases when capturing transient changes of regulatory activity in rice plants responding to elevated temperature. An apparent cause of this bias is regulatory saturation, the observation that detectable co-variance between a regulator and its target may be low as their transcript abundances are induced. This phenomenon appears to be particularly acute for rapid onset environmental stressors. However, exploiting temporal correlations appears to be a reliable means to detect transient regulatory activity following rapid onset environmental perturbations such as temperature stress. Such temporal correlation may lose information along a more gradual-onset stressor (e.g., dehydration). We here show that rice plants exposed to a dehydration stress exhibit temporal structure of coexpression in their response that can not be unveiled by temporal correlation alone. Collectively, our results point to the need to account for the nuances of molecular interactions and the possibly confounding effects that these can introduce into conventional approaches to study transcriptome datasets.