Evolutionary and ecological perspectives on the wheat phenotype

Inter-virus relationships in mixed infections and virus-drought relationships in plants: a quantitative review

Inter-virus relationships in mixed infections and virus-drought relationships are important in agriculture and natural vegetation. In this quantitative review, we sampled published factorial experiments to probe for relationships against the null hypothesis of additivity. Our sample captured antagonistic, additive and synergistic inter-virus relationships in double infections. Virus-drought relationships in our sample were additive or antagonistic, reinforcing the notion that viruses have neutral or positive effects on droughted plants, or that drought enhances plant tolerance to viruses. Both inter-virus and virus-drought relationships vary with virus species, host plant to the level of cultivar or accession, timing of infection, plant age and trait and growing conditions. The trait-dependence of these relationships has implications for resource allocation in plants. Owing to lagging theories, more experimental research in these fields is bound to return phenomenological outcomes. Theoretical work can advance in two complementary directions. First, the effective theory models the behaviour of the system without specifying all the underlying causes that lead to system state change. Second, mechanistic theory based on a nuanced view of the plant phenotype that explicitly considers downward causation; the influence of the plant phenotype on inter-virus relations and vice versa; the impact of timing, intensity and duration of drought interacting with viruses to modulate the plant phenotype; both the soil (moisture) and atmospheric (vapour pressure deficit) aspects of drought. Theories should scale in time, from short term to full growing season, and in levels of organisation up to the relevant traits: crop yield in agriculture and fitness in nature.

Critical period for yield determination across grain crops

Studies across different crops demonstrated that grain or seed number per unit area (GN m-2) is the dominant yield component. Although grains or seeds derive from floret or flower production and survival, the timing of the critical period for GN m-2 determination is known to vary noticeably, from mainly pre-flowering to strongly post-flowering, across major grain crops. Here, we demonstrate that discrepancy between crops in the timing of their critical period is related to the flowering phase duration and the proportion of the whole cycle allocated to pre-flowering development. Changing the perspective, positioning the critical period at the end of the phase when grain abortion occurs instead of flowering, results in the critical period virtually coinciding among contrasting grain crops.

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Introduction

Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.

Methods

Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images.

Results

For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.

Explaining pre-emptive acclimation by linking information to plant phenotype

We review mechanisms for pre-emptive acclimation in plants and propose a conceptual model linking developmental and evolutionary ecology with the acquisition of information through sensing of cues and signals. The idea is that plants acquire much of the information in the environment not from individual cues and signals but instead from their joint multivariate properties such as correlations. If molecular signalling has evolved to extract such information, the joint multivariate properties of the environment must be encoded in the genome, epigenome, and phenome. We contend that multivariate complexity explains why extrapolating from experiments done in artificial contexts into natural or agricultural systems almost never works for characters under complex environmental regulation: biased relationships among the state variables in both time and space create a mismatch between the evolutionary history reflected in the genotype and the artificial growing conditions in which the phenotype is expressed. Our model can generate testable hypotheses bridging levels of organization. We describe the model and its theoretical bases, and discuss its implications. We illustrate the hypotheses that can be derived from the model in two cases of pre-emptive acclimation based on correlations in the environment: the shade avoidance response and acclimation to drought.

The Interaction Between Nitrogen Supply and Light Quality Modulates Plant Growth and Resource Allocation

Nitrogen availability and light quality affect plant resource allocation, but their interaction is poorly understood. Herein, we analyzed the growth and allocation of dry matter and nitrogen using lettuce (Lactuca sativa L.) as a plant model in a factorial experiment combining three light regimes (100% red light, R; 50% red light + 50% blue light, RB; 100% blue light, B) and two nitrogen rates (low, 0.1 mM N; high, 10 mM N). Red light increased shoot dry weight in relation to both B and RB irrespective of nitrogen supply. Blue light favored root growth under low nitrogen. Allometric analysis showed lower allocation to leaf in response to blue light under low nitrogen and similar leaf allocation under high nitrogen. A difference in allometric slopes between low nitrogen and high nitrogen in treatments with blue light reflected a strong interaction effect on root-to-shoot biomass allocation. Shoot nitrate concentration increased with light exposure up to 14 h in both nitrogen treatments, was higher under blue light with high nitrogen, and varied little with light quality under low nitrogen. Shoot nitrogen concentration, nitrogen nutrition index, and shoot NR activity increased in response to blue light. We conclude that the interaction between blue light and nitrogen supply modulates dry mass and nitrogen allocation between the shoot and root.