AoB PLANTS

Using a simple plant growth model based on the logistic equation I re-evaluate how biomass allocation between roots and shoots articulates dynamically with the rate of whole-plant biomass production. Defined by parameters reflecting lumped physiological properties, the model constrains roots and shoots to grow sigmoidally over time. From those temporal patterns detailed trajectories of allocation and growth rate are reconstructed. Sigmoid growth trajectories of roots and shoots are incompatible with the dominant ‘functional equilibrium’ model of adaptive allocation and growth often used to explain plants’ responses to nutrient shortage and defoliation. Anything that changes the differential rates of growth between roots and shoots will automatically change allocation and, unavoidably, change whole-plant growth rate. Biomass allocation and whole-plant growth rate are not independent traits. Allocation and growth rate have no unique relationship to one another but can vary across a wide spectrum of possible relationships. When root-shoot allocation seems to respond to the environment it is likely to be a secondary illusory consequence of other primary responses such as localised root proliferation in soil or leaf expansion within canopy gaps. Changes in root-shoot allocation cannot themselves compensate directly for an impairment of growth rate caused by an external factor such as nutrient shortage or defoliation; therefore, such changes cannot be ‘adaptive’.‘The reasons are so simple they often escape notice.’ (James 2012, p. 6).Competing Interest StatementThe authors have declared no competing interest.View Full Text

Phenotyping host-pathogen interactions is crucial for understanding infectious diseases in plants. Traditionally, this process has relied on visual assessments or manual measurements, which can be subjective and labor-intensive. Recent advances in image processing and mathematical modeling enable precise and high-throughput phenotyping. In this study, we propose an innovative approach in plant pathology by combining image processing techniques with the level set method. This integrated approach leverages the strengths of both methodologies to provide accurate, robust, and detailed analysis of leaf and lesion evolution. By employing this combination, we achieve precise delineation of lesion boundaries and track their progression over time, offering clear visual feedback. This enhances the ability of the method to monitor plant health status comprehensively. The results, which track the growth of Peyronellaea pinodes on the stipules of two pea cultivars and the associated leaf deformation, provide an accurate visual representation of disease progression. This model represents a significant advancement in plant disease phenotyping, offering precise and detailed insights that can enhance our understanding of host-pathogen interactions.

Growth kinematics and soil mechanics are key to explain how roots overcome the mechanical resistance of soil, yet few studies are linking these two factors. Formulas for cone penetration tests are typically used to infer the friction experienced by roots, but these fail to consider how growth affects the external forces applied on the root. This study formalised how expansive growth in the root apical meristem can reduce soil friction, and applied the framework to analyse the growth strategy of 6 plant species. The results of the analysis revealed trade-offs between reducing frictions, maintaining a desired growth trajectory and elongation rate. A shorter elongation zone can reduce the fraction of the mechanical energy lost to friction, but this is done at the expense of the elongation rate. A sharper tip or increased radius can help roots maintain the elongation rate at no energetic cost, but these strategies come with the cost of growth instability (tortuous roots) and decrease in specific root length respectively. During establishment, root strategies may therefore occupy a 2-dimensional trait space in which the mechanical efficiency of growth is balanced against the explorative-exploitative trade-off. HighlightsGrowth and form of root tips explain how plants overcome mechanical resistance from the soil Trade-offs link the energy lost by friction, growth stability and elongation rate of roots Larger roots allow faster growth independently of these trade-offs New framework formalises plants strategies to acquire soil resources

Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plants life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models to help understand experimental findings about how local phloem anatomical features influence the root system architecture. In particular, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico study highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open the door to a better representation of growth processes in plant computational models.

Grasses grow a series of phytomers during development. The distance between successive leaves is determined by internode lengths. Grasses exhibit genetic, developmental, and environmental variability in phytomer number, but how this affects internode length, biomass, and height is unknown. We hypothesized that a generalized mathematical model of phytomer development wherein between-phytomer competition influences internode length distributions would be sufficient to explain internode length patterns in two Setaria genotypes: weedy A10 and domesticated B100. Our model takes a novel approach that includes the vegetative growth of leaf blade, sheath, and internode at the individual phytomer level, and the shift to reproductive growth. To validate and test our mathematical model, we carried out a greenhouse experiment. We found that the rate of leaf emergence is consistent for both genotypes across development, and that the length of time spent elongating for the leaf and internode can be described as the ratio between the time of phytomer emergence and the elongation completion time. The validated model was simulated across all possible parameter values to predict the influence of phytomer number on internode length. This analysis predicts that different internode length distributions across different numbers of total phytomers are an emergent property, rather than a genotype-specific property requiring genotype-specific models. We applied the model to internode length only field data of S. italica accession B100, grown under both well-watered and drought conditions. The model predicts that droughted plants reduce leaf elongation time, reduce resource allocation to the internodes, and overall experience slower growth. Together, model and data suggest that allometric patterns are driven by competition for resources among phytomer and the shift to reproductive growth in Setaria. The resulting model enables us to predict growth dynamics and final allometries at the phytomer level.

Stomatal pores control both leaf gas exchange and are one route for infection of internal plant tissues by many foliar pathogens, setting up the potential for tradeoffs between photosynthesis and defense. Anatomical shifts to lower stomatal density and/or size may also limit pathogen colonization, but such developmental changes could permanently reduce the gas exchange capacity for the life of the leaf. I developed and analyzed a spatially explicit model of pathogen colonization on the leaf as a function of stomatal size and density, anatomical traits which partially determine maximum rates of gas exchange. The model predicts greater stomatal size or density increases the probability of colonization, but the effect is most pronounced when the fraction of leaf surface covered by stomata is low. I also derived scaling relationships between stomatal size and density that preserves a given probability of colonization. These scaling relationships set up a potential anatomical conflict between limiting pathogen colonization and minimizing the fraction of leaf surface covered by stomata. Although a connection between gas exchange and pathogen defense has been suggested empirically, this is the first mathematical model connecting gas exchange and pathogen defense via stomatal anatomy. A limitation of the model is that it does not include variation in innate immunity and stomatal closure in response to pathogens. Nevertheless, the model makes predictions that can be tested with experiments and may explain variation in stomatal anatomy among plants. The model is generalizable to many types of pathogens, but lacks significant biological realism that may be needed for precise predictions.

Leaf shape is a key plant trait that varies enormously. The diversity of leaf shape, and the range of applications for data on this trait, requires frequent methodological developments so that researchers have an up-to-date toolkit with which to quantify leaf shape. We generated a dataset of 468 leaves produced by Ginkgo biloba, and 24 fossil leaves produced by evolutionary relatives of extant Ginkgo. We quantified the shape of each leaf by developing a geometric method based on elastic curves and a topological method based on persistent homology. Our geometric method indicates that shape variation in our modern sample is dominated by leaf size, furrow depth, and the angle of the two lobes at the base of the leaf that is also related to leaf width. Our topological method indicates that shape variation in our modern sample is dominated by leaf size and furrow depth. We have applied both methods to modern and fossil material: the methods are complementary, identifying similar primary patterns of variation, but also revealing some different aspects of morphological variation. Our topological approach distinguishes long-shoot leaves from short-shoot leaves and both methods indicate that leaf shape influences or is at least related to leaf area.

Plant ecophysiology is founded on a rich body of physical and chemical theory, but it is challenging to connect theory with data in unambiguous, analytically rigorous, and reproducible ways. Custom scripts written in computer programming languages (coding) enable plant ecophysiologists to model plant processes and fit models to data reproducibly using advanced statistical techniques. Since many ecophysiologists lack formal programming education, we have yet to adopt a unified set of coding principles and standards that could make coding easier to learn, use, and modify. We identify eight principles to help in plant ecophysiologists without much programming experience to write resilient code: 1) standardized nomenclature, 2) consistency in style, 3) increased modularity/extensibility for easier editing and understanding, 4) code scalability for application to large datasets, 5) documented contingencies for code maintenance, 6) documentation to facilitate user understanding; 7) extensive tutorials, and 8) unit testing. We illustrate these principles using a new R package, {photosynthesis}, which provides a set of analytical and simulation tools for plant ecophysiology. Our goal with these principles is to advance scientific discovery in plant ecophysiology by making it easier to use code for simulation and data analysis, reproduce results, and rapidly incorporate new biological understanding and analytical tools.

Root system architecture (RSA) underpins plant access to water and nutrients, making its characterization critical for improving crop performance in environments with limited soil fertility. However, current methods for quantifying root features face several challenges. They may rely on 2D images that suffer from occlusion, use expensive sensing technologies like X-ray computed tomography, or depend on 3D modeling approaches with assumptions about branching that make them difficult to generalize. To address these challenges, we introduce an open-source Python framework for quantifying RSA samples from 3D point clouds generated from low-cost photogrammetry. Critically, this method incorporates no assumptions about taxon-specific branching orientation, making it both well-suited for modeling naturally grown annual dicots such as soybean and generalizable across species. Using field-grown soybean as a test case, we demonstrate the utility of this framework to extract biologically meaningful 3D features of divergent root systems sampled across developmental stages and soil environments, and enable new analyses not possible with 2D approaches, such as modeling metabolic scaling relationships. Results indicate that, in our soybean samples, while certain individual features like taproot tortuosity are potentially influenced by the soil environment, and while roots in sandy loam exhibited greater feature plasticity, fundamental scaling properties remain consistent. By combining low-cost photogrammetry with 3D reconstruction of root systems from point clouds, this approach provides the plant science community with new opportunities for more comprehensive root studies.

Drought is a significant factor in agricultural losses, making it imperative to understand how root system architecture (RSA) adapts to environmental condition like water deficit. HydroRoot is a functional-structural plant model (FSPM) aimed at analyzing and simulating hydraulic and solute transport of RSA. The model integrates a static hydraulic solver, a coupled water-solute transport solver, a statistical generator of RSA based on Markov model, and a dynamic hydraulic model accounting for root growth. This paper presents the model, the mathematical description of the formalism of solvers, and use cases with their associated tutorials. Five use cases illustrate capabilities of HydroRoot, which has been successfully used for phenotyping root hydraulics across various species, including Arabidopsis, maize, and millet. The model-driven phenotyping method "cut and flow" is presented to characterize axial and radial conductivities on a given root genotype. Finally, three step-by-step tutorials provide a structured way to learn how to use HydroRoot 1) to simulate hydraulic on a given architecture, 2) to simulate water and solute transport on a maize root, and 3) to simulate hydraulic on two pearl millet genotypes with varying soil conditions. Hydroroot is an open-source package of the OpenAlea platform, with the code publicly available on Github. A comprehensive documentation is available with a reproducible gallery of examples.

High throughput phenotyping is important to bridge the gap between genotype and phenotype. The methods used to describe the phenotype therefore should be robust to measurement errors, relatively stable over time, and most importantly, provide a reliable estimate of elementary phenotypic components. In this study, we use functional-structural modeling to evaluate quantitative phenotypic metrics used to describe root architecture to determine how they fit these criteria. Our results show that phenes such as root number, root diameter, lateral root branching density are stable, reliable measures and are not affected by imaging method or plane. Metrics aggregating multiple phenes such as total length, total volume, convexhull volume, bushiness index etc. estimate different subsets of the constituent phenes, they however do not provide any information regarding the underlying phene states. Estimates of phene aggregates are not unique representations of underlying constituent phenes: multiple phenotypes having phenes in different states could have similar aggregate metrics. Root growth angle is an important phene which is susceptible to measurement errors when 2D projection methods are used. Metrics that aggregate phenes which are complex functions of root growth angle and other phenes are also subject to measurement errors when 2D projection methods are used. These results support the hypothesis that estimates of phenes are more useful than metrics aggregating multiple phenes for phenotyping root architecture. We propose that these concepts are broadly applicable in phenotyping and phenomics.

Efforts to cope with hunger by breeding highly productive annual crops for rain-fed agriculture in stochastic-rainfall environments have had only minor success, which we attribute to biological constraints that limit the crops yields. We use optimization modelling to interpret experimentally measured transpiration trajectories of wild barley plants following a rain event: the plants first maximized biomass accumulation by employing their maximal transpiration rate, then switched to their minimal transpiration rate to ensure survival until maturity. Thus, breeding plants with lower minimal transpiration rates combined with higher water-use efficiency and maximal transpiration rates could increase expected yields. However, our experimental results indicate that biological constraints impose tradeoffs among maximal and minimal transpiration rates and water-use efficiency. A proposed breeding methodology identifies less biologically constrained cultivar candidates.

Current global models of vegetation dynamics are largely carbon (C) source-driven, with behaviour primarily determined by the environmental responses of photosynthesis. However, real plants operate as integrated wholes, with feedbacks between sources, such as photosynthesis, and sinks, such as growth, resulting in homeostatic concentrations of metabolites such as sugars. A parsimonious approach to implementing this homeostatic coupling of C sources and sinks in a tree growth model is presented, and its implications for the responses of net photosynthesis and growth to environmental factors and tree size assessed. Hill functions describe inhibition of C sources (net photosynthesis) and activation of sinks (structural growth) as sucrose concentration increases. The model is parameterised for a typical tree growing at a site in the Amazonian rainforest and its qualitative behaviour is found to be consistent with observations. A key outcome is that sinks and sources strongly regulate each other. Hence environmental factors that affect potential net photosynthesis, such as atmospheric CO2, have greatly reduced effects on growth when homeostatic feedbacks from sucrose concentrations are considered. For example, compared with a C-source-only-driven approach (as in most current global models), the response of tree biomass for a tree currently 300 yr old, to increasing atmospheric CO2 projected to the end of this century under a high scenario, is reduced by ca.77%, from +122% to +29%, with net photosynthesis and growth rate responses reduced by a similar amount. Furthermore, in this coupled approach, any direct controls on growth (either environmental or through phenological controls on xylogensis) will influence source activity through the sucrose feedback. For example, a reduction in potential growth through temperature constraints on cell-wall construction increases sucrose concentrations, resulting in a compensating reduction in net photosynthesis. While net photosynthesis controls growth, growth controls net photosynthesis. In addition, we find a strong effect of changing tree allometry on C source-sink relations as the tree grows. Larger trees are less source-limited due to a higher ratio of sapwood area (and hence potential C assimilation rate) to potential growth rate, consistent with the observed decline in growth response to atmospheric CO2 as trees age. We suggest that the implications of including C source-sink coupling in models of vegetation dynamics, such as dynamic global vegetation models, are likely to be profound.

Walnuts are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022-23 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever-growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy-to-open shells. Past and current efforts select for these traits using hand-held calipers and eye-based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel weight. Through allometric relationships --relative growth of one tissue to another--, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits, and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume and thickness-based traits, especially inner air content, we can successfully encode several of the commercial traits. Core IdeasO_LIX-ray Computed Tomography (CT) imaging is used to compute a broad array of morpho-logical phenotypes in walnuts. C_LIO_LIThese morphological traits suggest biophysical constraints at play during walnut development. C_LIO_LIRelative inner air, shell, and packing tissue volumes are significantly correlated to the rest of shape phenotypes. C_LIO_LIThese volumes produce the best prediction models for traits of commercial interest such as shell strength. C_LIO_LIInexpensive phenotyping platforms that focus solely on volume measurement would enable better walnut breeding. C_LI

Trait heterogeneity in a population increases the likelihood that some individuals will survive an unpredictable environmental stress. Individual plants mature at different rates due to genetics, environmental factors, and random chance. All of this contributes to trait heterogeneity. EARLY FLOWERING3 (ELF3), a core circadian clock component, determines developmental timing, and here we tested the hypothesis that it would shape trait heterogeneity. We developed a model predicting that faster-developing populations exhibit greater heterogeneity during development but reduced heterogeneity at maturity, while slower-growing populations show the opposite pattern. Experiments in Arabidopsis elf3 and barley Hvelf3 mutants supported this prediction. In Arabidopsis, ELF3 controlled hypocotyl elongation variability via maturation rate rather than direct regulation. Bolting time heterogeneity also decreased in faster-growing plants. In barley, Hvelf3 altered growth heterogeneity, but this was initially masked by germination timing differences. Finally, smaller barley plants were found to be more resilient to osmotic stress, suggesting that ELF3-driven trait heterogeneity may contribute to bet hedging. These findings highlight how modifying developmental rates influences population-level trait distributions and stress resilience.

Plant morphologies exhibit a wide array of outcomes that have evolved as a consequence adapting to a wide array of ecological pressures. These disparate morphologies have provided a rich field for comparative morphologists, developmental biologists, and geneticists to explore. Ultimately the array of variation observed in nature across different plant species is built on the same functional unit, the phytomer, which is composed of a leaf, a node, and an internode. Sequentially produced phytomers exhibit heteroblasty, that is, a gradual or abrupt change in shape, either due to size changes or changes due to reproductive phase. The progression of shape change over time is often indirectly measured by sampling several stages of plant growth and comparing allometric relationships between shape variables. However, a more precise method is to use an absolute time scale and measure shape change of sequential organs directly. In this study we use such time-dependent measurements to build a general model of organ growth for several Setaria genotypes, for both leaves and internodes. We term this the second-order function-value trait (2FVT) model, because it generalizes individual function-value trait models generated for each organ. This model reduces phenotypic noise by averaging the general trend of ontogeny and provides a quantitative tool to describe where and when phenotypic shifts occur during the ontogenies of different genotypes. The ability to recognize how ontogenetic variation is distributed within equivalent positions of the body plan at the interspecific level can be used as a tool to explore various questions related to growth and form in plants both for comparative morphology and developmental genetics.

Morphometrics has been applied in several fields of science including botany. Plant leaves are been one of the most important organs in the identification of plants due to its high variability across different plant groups. The differences between and within plant species reflect variations in genotypes, development, evolution, and environment. While traditional morphometrics has contributed tremendously to reducing the problems that come with the identification of plants and delimitation of species based on morphology, technological advancements have led to the creation of deep learning digital solutions that made it easy to study leaves and detect more characters to complement already existing leaf datasets. In this study, we demonstrate the use of MorphoLeaf in generating morphometric dataset from 140 leaf specimens from seven Cucurbitaceae species via scanning of leaves, extracting landmarks, data extraction, landmarks data quantification, and reparametrization and normalization of leaf contours. PCA analysis revealed that blade area, blade perimeter, tooth area, tooth perimeter, height of (each position of the) tooth from tip, and the height of each (position of the) tooth from base are important and informative landmarks that contribute to the variation within the species studied. Our results demonstrate that MorphoLeaf can quantitatively track diversity in leaf specimens, and it can be applied to functionally integrate morphometrics and shape visualization in the digital identification of plants. The success of digital morphometrics in leaf outline analysis presents researchers with opportunities to apply and carry out more accurate image-based researches in diverse areas including, but not limited to, plant development, evolution, and phenotyping.

O_LIThe competitor, stress-tolerator, ruderal (CSR) theory, first proposed by John Philip Grime, is a useful framework for understanding plant ecological strategies and predicting responses to environmental changes and pressures. However, current tools for assigning CSR strategies are limited to an Excel sheet format and have yet to be integrated into modern computational platforms that enable reproducible research. C_LIO_LIWe present CSRcalculator (https://github.com/TeddyGaskin/CSRcalculator), an open-source R package and shiny application that calculates CSR scores and assigns strategies based on user-uploaded trait data. CSRcalculator supports CSR assignments according to the three most prominent models: The original soft approach, the global StrateFy, and a morpho-physiological model. C_LIO_LIThe R package outputs a table including CSR scores, assigned strategies and intermediate traits for the selected model. The shiny application produces this same table alongside an interactive ternary plot to visualise the strategy distribution and an optional summary table describing the overall CSR strategy distribution, using metrics such as, the modal strategy, axis means, standard deviations, and ranges. Group-level analyses calculate the same statistics across user-defined categories. C_LIO_LIWe provide a worked example using our tool to assign CSR strategies to plants for an example dataset. C_LI

O_LIRationale: Plants in urban environments often experience heat stress and responses to heat stress often include vegetative and reproductive traits like rosette width and fruit morphology. However, our understanding of natural variation in vegetative and reproductive traits in urban environments is severely limited. C_LIO_LIMethods: We grew an urban weed, Capsella bursa-pastoris, in common garden environments that simulate an urban heat gradient to determine how heat affected growth, survival and reproduction. Additionally, we used geometric morphometric techniques alongside deterministic techniques to quantify variation in C. bursa-pastoris fruit shape and investigated the predictive relationship between fruit shape and seed production. C_LIO_LIKey results: We found that temperatures above 30C act as an environmental constraint on both C. bursa-pastoris fruit shape and reproduction, resulting in malformed fruits and no seed production. However, leaf number and plant survival were unaffected by high urban heat. C_LIO_LIMain conclusions: While plants may grow and survive in the high urban heat, heat could still limit population persistence. C_LI

Root water uptake efficiency depends on root system architecture and anatomical features of individual root segments. Beyond cell wall, membrane, and plasmodesmata hydraulic properties, root anatomy critically influences profiles of radial conductivity and axial conductance. While these structural factors have been well-characterized in monocotyledons, their role in dicotyledons--where developmental anatomy, secondary growth, and hydrophobic barrier dynamics differ--remains poorly understood. Here, we integrate structural and functional models to assess how dicotyledon-specific anatomy, hydrophobic depositions (suberin/lignin in exo-/endodermis), and aquaporin contribution influence root hydraulics. Using tomato (Solanum lycopersicum L., cv. Moneymaker) as a dicotyledon model, our simulations show that: - Exodermal suberin has negligible effects on radial conductivity when a lignin cap is present, and exodermal barriers are less effective than endodermal ones. - Secondary growth and dicotyledon-specific anatomy are essential for sustaining high axial conductance, ensuring efficient water uptake across soil profiles and maintaining root system hydraulic conductance.