Agronomic research has long prioritized efficiency – optimizing resource use to maximize yield under stable conditions. However, as climate variability intensifies, efficiency alone might be insufficient to sustain agricultural production in the future. Instead, robustness – the ability to maintain function across diverse and unpredictable environments – emerges as a critical trait. Robustness is not a simple metric but an emergent property, arising from the interplay of redundancy, heterogeneity and plasticity across biological scales. We examine how the components of robustness (redundance, heterogeneity and plasticity) express themselves at the anatomical, architectural and genomic scale. A major challenge is the lack of a unified framework to measure robustness. We propose integrating empirical metrics – such as vessel grouping indices, root trait heterogeneity and gene expression plasticity – with computational models to quantify redundancy, heterogeneity and plasticity. By synthesizing insights from physiology, genomics and modelling, we outline a path towards designing crops that thrive in ideal settings and under environmental uncertainty.

Magnesium (Mg2+) is essential for plant growth and metabolism, acting as a cofactor in numerous enzymatic and structural processes. This review outlines the main physiological and biochemical functions of Mg2+ and summarizes current knowledge on its transport and homeostatic regulation. We examine how Mg2+ homeostasis intersects with broader signalling networks and metabolic pathways, including its crosstalk with other mineral nutrients, where antagonistic and synergistic interactions influence nutrient acquisition, allocation and stress responses. Emerging evidence further suggests that, beyond its classical roles, Mg2+ may function as a regulatory ion with signalling properties reminiscent of secondary messengers in animal systems. Finally, we highlight recent findings linking Mg2+ dynamics to circadian regulation, suggesting reciprocal interactions between temporal control mechanisms and nutrient fluxes. These insights underscore the central importance of Mg2+ in plant biology and identify key gaps in understanding its regulatory and integrative roles.

Disentangling how forests respond to aridification in terms of carbon storage and use, including bimodal growth, is critical to forecast their mitigation potential. Bimodality, characteristic of Mediterranean trees, refers to the potential to produce a second growth peak after the dry summer, often accompanied by intra-annual wood density fluctuations (IADF). To induce IADF formation, we performed a girdling experiment on Spanish juniper (Juniperus thurifera) branches in a semi-arid site, and monitored changes in branch diameter, and measured non-structural carbohydrate (NSC) concentrations in sapwood and leaves. IADFs were formed in response to wet conditions in late summer in girdled and non-girdled branches. After girdling, the extraordinarily dry 2022 growing season hampered branch radial increment and IADF production. Girdled branches swelled more than control branches after rain pulses. This suggests girdled branches were osmotically more active. Girdled branches presented higher starch leaf concentrations, suggesting that osmolytes could proceed from starch hydrolysis upstream. Girdling did neither trigger bimodal growth nor IADF formation during a very dry year.

Understanding wood formation is critical for interpreting tree growth and carbon allocation under changing environmental conditions. While major progress has been made for gymnosperms, harmonized approaches for studying xylogenesis in angiosperms remain limited. Here, we present practical recommendations for observing and analysing xylogenesis in angiosperm trees, illustrated from examples from temperate and sub-Mediterranean forests. The perspective includes guidance on identifying xylem cell types in histological sections, defining developmental phenophases and establishing a workflow for data collection (and analysis). Annotated images are provided to support reproducibility and inter-observer consistency. We also discuss key challenges unique to angiosperms, including cell-type-specificities and wood type differences. Future research priorities include conserving histological images, extending xylogenesis to branches and coarse roots, enabling cross-biome comparisons and advancing kinetic analysis. This framework supports the coordinated expansion of angiosperm xylogenesis studies, enabling deeper insights into tree functioning in a changing world.

Wood density (WD) is a crucial anatomical trait influencing forest carbon storage. However, dynamic global vegetation models (DGVMs) typically assume a fixed species-level WD, neglecting environment-driven variability. In this proof-of-concept study, we explore the potential impact of dynamic WD on tree- and forest-level carbon storage by integrating a simple temperature-response function of WD into the DGVM LPJ-GUESS from Smith et al., 2014.

Simulations along a temperature gradient show that incorporating environmentally responsive WD can substantially alter simulated stand structure and carbon stocks. Overall, our model experiments illustrated that sites with higher WD had more, but smaller trees, which stored less carbon compared to the standard model. The strongest effects were predicted to appear before canopy closure, where per-tree carbon deviated by up to 32%. This exploratory study suggests the need to represent a mechanism for dynamic WD to better assess ecological feedbacks to forest carbon storage predictions, particularly in young and regenerating forests.

Advances in LED lighting technologies enable increasingly complex light regimes, providing greater insight into plants’ responses to dynamic light – such as seasonality and fluctuating conditions – rather than the traditional discrete (i.e., on/off) lighting. However, current methods of programming such regimes are time-consuming and/or limited to 1–2 wavebands. Robust methods are therefore needed to accurately programme multichannel/waveband LED lighting systems. We present a multistep, multidimensional algorithm to accurately programme multi-waveband LED lights. This algorithm accounts for non-linearity between intensity settings and measured light quantity output, as well as optical crosstalk between channels of different wavebands. It outperforms methods that treat waveband channels as independent variables, allowing users to more accurately programme multichannel light regimes. This will allow the community to probe plant responses to dynamically changing light spectra. We have made this algorithm available as an R package, LightFitR (installable from CRAN with ‘install.packages(“LightFitR”)’.

Nitrogen (N) is a major plant nutrient, and its supply is very often limiting growth. The main forms of inorganic N in soil supplying plants are ammonium and nitrate ions. Although the soil availability of N can vary greatly, the cytoplasmic nutrient ion activities in a typical plant cell are maintained at set points that are independent of changes in supply. By contrast, the storage of N as protein and vacuolar nitrate depends on the external supply. Measurements of cellular homeostasis of ammonium and nitrate are limited by methodology. The upper limits for cytoplasmic set points are likely to depend on toxicity, and for ammonium this is well known but less clear for nitrate. An intracellular set point for N must be maintained by membrane transport systems and assimilation processes. Crop N use efficiency has uptake and assimilation components, and understanding homeostasis is fundamentally important for improving this important trait.

Pavement cells in the Arabidopsis thaliana epidermis span a wide range of sizes and ploidy levels, but rules that generate this heterogeneity across an organ remain unclear. Clark et al. identify a shared genetic pathway that promotes large, polyploid pavement cells in both sepals and leaves, then ask whether the familiar “scattered” distribution of giant cells is truly random. By combining whole-tissue imaging with two independent computational randomization approaches that regenerate tissues from segmented images while preserving cell size distributions and key boundary constraints, together with a stochastic cell-autonomous model, the authors show how an initially random pattern can later appear clustered relative to a changing random baseline as tissues grow and subdivide. The study provides a quantitative framework for testing spatial organization in cellular mosaics where point-based methods fail, and it shows how proliferation history can convert early stochastic fate decisions into a statistically non-random mature pattern.

Plant development relies not only on intracellular biochemical signals but also on physical information that is transmitted across cells and tissues. In growing plant organs, surface geometry and mechanics can act together to channel stress and strain signals beyond the single cell, effectively creating trans-cellular communication pathways that are robust, accurate and instantaneous. It follows that meristematic surfaces act as stress-mechanical waveguides to constrain and redirect internal stress fields. The orientation and patterning of these stress fields correlates with the placement of new cell walls during cell division, thereby linking surface geometry to tissue histogenesis. Here, I consider how meristem surfaces may contribute to developmental signaling via mechanical force transmission. I argue that surface curvature and tissue biomechanics can form a self-sustaining feedback loop: together, they shape force transmission trajectories, which in turn guide the fundamental decision-making processes that determine cell plate orientation during cytokinesis, thus altering organ shape.

Genome-wide polymorphism data are increasingly used in conservation biology, and new developments in theoretical population genomics generate refined statistical inference methods. Most theories and methods remain based on human life-history traits and genome characteristics, namely, that the ratio of the population rates of recombination over mutation is approximately one. However, most fungal, invertebrate or plant species exhibit violations of the classic population genetics models due to their peculiar life cycles, such as long-life span and generation overlap, dormancy, clonality, selfing and large variance in offspring production (sweepstakes reproduction). We first present applicable inference methods accounting for these life-history traits. Second, we highlight new inference methods to estimate the timing and magnitude of changes in these traits over evolutionary times. We suggest that methodological and theoretical novelties pave the way to dissect the causes and consequences of changes in ecological and evolutionary (life-history) traits in plant species and in multi-species assemblages (communities) in response to changing environments.

Floral traits determine the reproductive success and the fitness of a plant. We investigated the effect of ambient temperature on three floral and four fitness traits and their plasticities in 34 Arabidopsis thaliana accessions grown at 17 °C, 20 °C, 24 °C and 27 °C. Based on reaction norms of the mean trait values across temperatures, we found that these traits exhibited different degrees of temperature-mediated plasticity. Flower number, measured as number of siliques and number of seeds per silique, showed significant positive correlations with total seed number at each tested temperature, indicating that seed number in siliques is an indicator for reproductive output. The correlation of flower size with the latitudinal origin of the accessions indicates that in the north, larger flowers may confer an adaptive advantage. Altogether, this study provides information on the impact of increased temperature on fitness in selfing A. thaliana.

Nitrogen (N) is an essential macronutrient and its bioavailability plays a major role in tuning plant development to the environmental nutrient status. To find novel factors in early root system architecture responses to N conditions, we performed Arabidopsis thaliana root transcriptome profiling of a short-term time course in limiting and sufficient N conditions. Using this data, we inferred transcriptional regulatory networks in each condition, which revealed the N-condition specific jasmonate responses. We found that the transcription factor (TF) ERF107 plays a generalized role in lateral root development while LBD13 is specific to N-limiting conditions. Further, we used a lateral root single-cell-identity specific transcriptome dataset to model the roles of TFs in early stages of lateral root development. We linked the N time course transcriptomics, lateral root mutant phenotypes and cell type-specific profiling approaches to find and test the roles of TFs that are involved in early root patterning responses to N conditions.

Under saline conditions, plants consistently maintain cytosolic Na+ concentrations between 10 and 30 mM, sequestering excess Na+ to the vacuole. We demonstrate that this cytosolic Na+ homeostasis is regulated by inward Na+-permeable channels and outward Na+:H+ antiporters at both the plasma membrane and tonoplast. Sodium’s interplay with K+ transport adds complexity and selective transport is crucial to avoid conflicting ion fluxes. Our models predict that Na+:H+ antiport regulation at the plasma membrane significantly impacts cytosolic Na+ levels, while channel and antiport regulation are equally important at the tonoplast. The energetic implications of these transport mechanisms are discussed. In contrast to the cytosol, chloroplast Na+ concentrations vary significantly between species and increase with soil salinity, raising questions as to how C4 and CAM plants acquire pyruvate under saline conditions. However, modelling transport activity at the chloroplast membrane requires far more knowledge of the associated transport systems and the chloroplastic Na+ content.

The emergence of complex cropping systems involving dual land use raises new questions regarding plant growth under intermittent shade conditions. Recent meta-analyses have investigated the dose–response relationships of several plant traits to light availability, highlighting the strong interest in understanding plant responses to light temporal variation. However, these studies also reveal significant gaps in our knowledge, particularly concerning plant responses to low-light conditions and the determinants of crop yield under shade. In this context, physiological mechanisms related to photosynthesis, growth, reproduction and, more broadly, carbon allocation appear to play a central role. This article emphasizes the major implications of carbon allocation, storage and use under prolonged and fluctuating shaded conditions for crop production.

Ion homeostasis is the process by which plants maintain the right balance of mineral and non-mineral ions inside their cells. This regulation helps plants survive challenges such as drought, salty soils and changing nutrient levels while supporting healthy growth and development. The enormous advances in our understanding of plant membrane transport at a molecular level now provide a mechanistic basis for understanding plant nutrition at cellular and whole plant levels. And in combination with quantitative mathematical analysis, fundamentally new insights into homeostatic processes are obtained.

Plants exhibit highly plastic stomatal distributions on the leaf epidermis driven by environmental factors. However, methods to model this complex spatial organization as a global topological system remain limited, as most existing tools focus on segmentation and counting. This study proposes a graph-based classification framework to characterize these distributions. We use stomatal centroids to construct networks and employ Life-Like Network Automata (LLNA) to extract dynamical signatures from them. This approach simulates network emergent behaviours to derive feature vectors based on optimized transition rules. Compared against traditional network metrics and some graph-based algorithms, our results indicate that our proposed method can effectively classify environmental stressors across the three studied species. In these restricted data scenarios, the evaluated models achieved accuracies from 90.00 ($\pm $5.65)% to 95.47 ($\pm $4.61)%, outperforming the chosen traditional and graph-embedding techniques. We present a pattern recognition framework capable of distinguishing environmental conditions based on the stomatal topological distribution.

Plants and their pathogens coevolve over long time periods, and the history of coevolution is recorded in plant genes that confer pathogen resistance (many coding for nucleotide-binding leucine-rich repeat proteins, NLRs). Advances in the ability to sequence genomes from many different species as well as many genomes from the same species reveal that 1) the number of NLR genes differs widely between species and 2) NLR genes may exhibit extensive nucleotide variation as well as presence/absence polymorphism within species. In the light of the latest insights into the genomic features associated with NLR gene diversity, we aim here to evaluate the contribution of forces involved in NLR gene family evolution: mutation, recombination, gene duplication and deletion and natural selection. We highlight novel combinations of population genomics methods and statistics that can provide an improved framework for describing NLR gene family evolution in the genome space.

Plant morphogenesis relies on dynamic growth deformations at the cell and tissue scales driven by osmotic fluxes. A mechanistic understanding of this phenomenon demands a physical framework that integrates cell imbibition, tissue mechanics and water fluxes, as well as their biophysical and molecular regulations, within a theory of plant active matter capturing the open-system and out-of-equilibrium properties of tissues. Building on historical insights into growth geometry, physics and mechanics, combined with recent experimental results, we outline the key challenges in modelling plant growth and propose steps towards a unified physical theory of plant morphogenesis, in which biological regulation, mechanical forces and water fluxes interact to shape biological form through the fundamental principles of living matter.

Maintaining epidermal integrity is essential for plant growth and survival, yet quantitative methods to assess defects in cell adhesion, cell integrity and cuticle function remain limited. Here, we present RRQuant, a standardised, high-throughput workflow for quantifying epidermal integrity defects in seedlings using Ruthenium Red staining. RRQuant integrates deep learning–based segmentation with automated image analysis, staining and morphological quantification, and statistical evaluation. The workflow is accompanied by a detailed user guide, and an interactive R Shiny application for data visualisation and quality control. This pipeline converts qualitative staining patterns into reproducible quantitative measurements, enabling large-scale phenotyping and comparative studies across genotypes and treatments. We demonstrate the usefulness of RRQuant by quantitatively comparing adhesion defective phenotypes and discuss its potential for adaptation to other organs, staining methods, and pigment-based assays. By providing an open and extensible platform, RRQuant bridges the gap between traditional qualitative assessments and modern quantitative phenotyping in plant biology.

A definition of biological complexity is proposed grounded in the topology of interconnectivity among a system’s parts. When new parts and their connections become integrated within a network, the cost of their loss increases with the depth of their interdependency resulting in an asymmetry in which gains require toleration, whereas losses increasingly risk disrupting multiple interdependencies. This ‘ratcheting’ is reversible, i.e., apparent reductions in complexity can occur (e.g., parasitism). The limited published data for cell-type diversity, proteome and interactome size, and bulk-flow mechanisms are consistent with the framework’s predictions, motivating additional detailed empirical analyses. The ratchet is distinguished from, yet complementary to, the Zero Force Evolutionary Law, constructive neutral evolution, generative entrenchment, and the phenotype-first tradition, extending their insights to all levels of biological organization. The quantification of complexity thereby becomes an empirical program that asks not whether complexity must increase, but under what conditions history makes interconnectivities difficult to reduce.