Unravelling drivers of local adaptation through evolutionary functional-structural plant modelling.

Local adaptation to contrasting environmental conditions along environmental gradients is a widespread phenomenon in plant populations, yet we lack a mechanistic understanding of how individual agents of selection contribute to this evolutionary process. Here, we developed a novel evolutionary functional-structural plant (E-FSP) model that recreates local adaptation of virtual plants along an environmental gradient. First, we validate the model by testing if it can reproduce two elevational ecotypes of Dianthus carthusianorum occurring in the Swiss Alps. Second, we use the E-FSP model to disentangle the relative contribution of abiotic (temperature) and biotic (competition and pollination) selection pressures to elevational adaptation in D. carthusianorum. Our results suggest that elevational adaptation in D. carthusianorum is predominantly driven by the abiotic environment. The model reproduced the qualitative differences between the elevational ecotypes in two phenological (germination and flowering time) and one morphological trait (stalk height), as well as qualitative differences in four performance variables that emerge from G × E interactions (flowering time, number of stalks, rosette area and seed production). Our approach shows how E-FSP models incorporating physiological, ecological and evolutionary mechanisms can be used in combination with experiments to examine hypotheses about patterns of adaptation observed in the field.

Integrated root phenotypes for improved rice performance under low nitrogen availability.

Greater nitrogen efficiency would substantially reduce the economic, energy and environmental costs of rice production. We hypothesized that synergistic balancing of the costs and benefits for soil exploration among root architectural phenes is beneficial under suboptimal nitrogen availability. An enhanced implementation of the functional-structural model OpenSimRoot for rice integrated with the ORYZA_v3 crop model was used to evaluate the utility of combinations of root architectural phenes, namely nodal root angle, the proportion of smaller diameter nodal roots, nodal root number; and L-type and S-type lateral branching densities, for plant growth under low nitrogen. Multiple integrated root phenotypes were identified with greater shoot biomass under low nitrogen than the reference cultivar IR64. The superiority of these phenotypes was due to synergism among root phenes rather than the expected additive effects of phene states. Representative optimal phenotypes were predicted to have up to 80% greater grain yield with low N supply in the rainfed dry direct-seeded agroecosystem over future weather conditions, compared to IR64. These phenotypes merit consideration as root ideotypes for breeding rice cultivars with improved yield under rainfed dry direct-seeded conditions with limited nitrogen availability. The importance of phene synergism for the performance of integrated phenotypes has implications for crop breeding.

Mycorrhizal associations change root functionality: a 3D modelling study on competitive interactions between plants for light and nutrients.

Recent studies show that the variation in root functional traits can be explained by a two-dimensional trait framework, containing a 'collaboration' axis in addition to the classical fast-slow 'conservation' axis. This collaboration axis spans from thin and highly branched roots that employ a 'do-it-yourself' strategy to thick and sparsely branched roots that 'outsource' nutrient uptake to symbiotic arbuscular mycorrhizal fungi (AMF). Here, we explore the functionality of this collaboration axis by quantifying how interactions with AMF change the impact of root traits on plant performance. To this end, we developed a novel functional-structural plant (FSP) modelling approach that simulates plants competing for light and nutrients in the presence or absence of AMF. Our simulation results support the notion that in the absence of AMF, plants rely on thin, highly branched roots for their nutrient uptake. The presence of AMF, however, promotes thick, unbranched roots as an alternative strategy for uptake of immobile phosphorus, but not for mobile nitrogen. This provides further support for a root trait framework that accommodates for the interactive effect of roots and AMF. Our modelling study offers unique opportunities to incorporate soil microbial interactions into root functionality as it integrates consequences of belowground trait expression.

Estimating the contribution of plant traits to light partitioning in simultaneous maize/soybean intercropping.

Spatial configuration and plant phenotypic plasticity contribute to increased light capture in relay intercropping, but there is little information on whether these factors also increase light capture in simultaneous intercropping. We developed and validated a three-dimensional functional-structural plant model to simulate light capture in maize and soybean sole crops and intercrop scenarios, using species traits observed in sole crops and intercrops. The intercrop maize phenotype had 2% greater light capture than the sole crop phenotype in a pure stand. The soybean intercrop phenotype had 5-10% lower light capture than the sole crop phenotype in a pure stand. The intercrop configuration increased the light capture of maize by 29% and reduced the light capture of soybean by 42%, compared with the light capture expected from sole crops. However, intercrop configuration only marginally affected total light capture by the intercrop system (+1%). Testing of individual soybean plant traits revealed that plasticity in leaf dimensions was the main reason for differences in light capture by soybean in simulated sole crops and intercrops. The results of this study illustrate a major shift of light capture from shorter species (soybean) to the taller component (maize) in a simultaneous strip intercrop. Plastic plant traits modulate this overall effect, but only marginally.

A functional structural model of grass development based on metabolic regulation and coordination rules.

Shoot architecture is a key component of the interactions between plants and their environment. We present a novel model of grass, which fully integrates shoot morphogenesis and the metabolism of carbon (C) and nitrogen (N) at organ scale, within a three-dimensional representation of plant architecture. Plant morphogenesis is seen as a self-regulated system driven by two main mechanisms. First, the rate of organ extension and the establishment of architectural traits are regulated by concentrations of C and N metabolites in the growth zones and the temperature. Second, the timing of extension is regulated by rules coordinating successive phytomers instead of a thermal time schedule. Local concentrations are calculated from a model of C and N metabolism at organ scale. The three-dimensional representation allows the accurate calculation of light and temperature distribution within the architecture. The model was calibrated for wheat (Triticum aestivum) and evaluated for early vegetative stages. This approach allowed the simulation of realistic patterns of leaf dimensions, extension dynamics, and organ mass and composition. The model simulated, as emergent properties, plant and agronomic traits. Metabolic activities of growing leaves were investigated in relation to whole-plant functioning and environmental conditions. The current model is an important step towards a better understanding of the plasticity of plant phenotype in different environments.

MuSCA: a multi-scale source-sink carbon allocation model to explore carbon allocation in plants. An application to static apple tree structures.

BACKGROUND AND AIMS: Carbon allocation in plants is usually represented at a topological scale, specific to each model. This makes the results obtained with different models, and the impact of their scales of representation, difficult to compare. In this study, we developed a multi-scale carbon allocation model (MuSCA) that allows the use of different, user-defined, topological scales of a plant, and assessment of the impact of each spatial scale on simulated results and computation time. METHODS: Model multi-scale consistency and behaviour were tested on three realistic apple tree structures. Carbon allocation was computed at five scales, spanning from the metamer (the finest scale, used as a reference) up to first-order branches, and for different values of a sap friction coefficient. Fruit dry mass increments were compared across spatial scales and with field data. KEY RESULTS: The model was able to represent effects of competition for carbon assimilates on fruit growth. Intermediate friction parameter values provided results that best fitted field data. Fruit growth simulated at the metamer scale differed of ~1 % in respect to results obtained at growth unit scale and up to 60 % in respect to first order branch and fruiting unit scales. Generally, the coarser the spatial scale the more predicted fruit growth diverged from the reference. Coherence in fruit growth across scales was also differentially impacted, depending on the tree structure considered. Decreasing the topological resolution reduced computation time by up to four orders of magnitude. CONCLUSIONS: MuSCA revealed that the topological scale has a major influence on the simulation of carbon allocation. This suggests that the scale should be a factor that is carefully evaluated when using a carbon allocation model, or when comparing results produced by different models. Finally, with MuSCA, trade-off between computation time and prediction accuracy can be evaluated by changing topological scales.

Parameter estimation for functional-structural plant models when data are scarce: using multiple patterns for rejecting unsuitable parameter sets.

BACKGROUND AND AIMS: Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. METHODS: We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. KEY RESULTS: POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. CONCLUSIONS: Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

Ecological significance of light quality in optimizing plant defence.

Plants balance the allocation of resources between growth and defence to optimize fitness in a competitive environment. Perception of neighbour-detection cues, such as a low ratio of red to far-red (R:FR) radiation, activates a suite of shade-avoidance responses that include stem elongation and upward leaf movement, whilst simultaneously downregulating defence. This downregulation is hypothesized to benefit the plant either by mediating the growth-defence balance in favour of growth in high plant densities or, alternatively, by mediating defence of individual leaves such that those most photosynthetically productive are best protected. To test these hypotheses, we used a 3D functional-structural plant model of Brassica nigra that mechanistically simulates the interactions between plant architecture, herbivory, and the light environment. Our results show that plant-level defence expression is a strong determinant of plant fitness and that leaf-level defence mediation by R:FR can provide a fitness benefit in high densities. However, optimal plant-level defence expression does not decrease monotonically with plant density, indicating that R:FR mediation of defence alone is not enough to optimize defence between densities. Therefore, assessing the ecological significance of R:FR-mediated defence is paramount to better understand the evolution of this physiological linkage and its implications for crop breeding.

Using virtual 3-D plant architecture to assess fungal pathogen splash dispersal in heterogeneous canopies: a case study with cultivar mixtures and a non-specialized disease causal agent.

BACKGROUND AND AIMS: Recent developments in plant disease management have led to a growing interest in alternative strategies, such as increasing host diversity and decreasing the use of pesticides. Use of cultivar mixtures is one option, allowing the spread of plant epidemics to be slowed down. As dispersal of fungal foliar pathogens over short distances by rain-splash droplets is a major contibutor to the spread of disease, this study focused on modelling the physical mechanisms involved in dispersal of a non-specialized pathogen within heterogeneous canopies of cultivar mixtures, with the aim of optimizing host diversification at the intra-field level. METHODS: Virtual 3-D wheat-like plants (Triticum aestivum) were used to consider interactions between plant architecture and disease progression in heterogeneous canopies. A combined mechanistic and stochastic model, taking into account splash droplet dispersal and host quantitative resistance within a 3-D heterogeneous canopy, was developed. It consists of four sub-models that describe the spatial patterns of two cultivars within a complex canopy, the pathway of rain-splash droplets within this canopy, the proportion of leaf surface area impacted by dispersal via the droplets and the progression of disease severity after each dispersal event. KEY RESULTS: Different spatial organization, proportions and resistance levels of the cultivars of two-component mixtures were investigated. For the eight spatial patterns tested, the protective effect against disease was found to vary by almost 2-fold, with the greatest effect being obtained with the smallest genotype unit area, i.e. the ground area occupied by an independent unit of the host population that is genetically homogeneous. Increasing both the difference between resistance levels and the proportion of the most resistant cultivar often resulted in a greater protective effect; however, this was not observed for situations in which the most resistant of the two cultivars in the mixture had a relatively low level of resistance. CONCLUSIONS: The results show agreement with previous data obtained using experimental approaches. They demonstrate that in order to maximize the potential mixture efficiency against a splash-dispersed pathogen, optimal susceptible/resistant cultivar proportions (ranging from 1/9 to 5/5) have to be established based on host resistance levels. The results also show that taking into account dispersal processes in explicit 3-D plant canopies can be a key tool for investigating disease progression in heterogeneous canopies such as cultivar mixtures.

Modelling of Soybean (Glycine max (L.) Merr.) Response to Blue Light Intensity in Controlled Environments.

Low photosynthetic photon flux density (PPFD) under shade is associated with low blue photon flux density (BPFD), which independent from PPFD can induce shade responses, e.g., elongation growth. In this study, the response of soybean to six levels of BPFD under constant PPFD from LED lighting was investigated with regard to morphology, biomass and photosynthesis to increase the knowledge for optimizing the intensity of BPFD for a speed breeding system. The results showed that low BPFD increased plant height, leaf area and biomass and decreased leaf mass ratio. Photosynthetic rate and internode diameter were not influenced. A functional structural plant model of soybean was calibrated with the experimental data. A response function for internode length to the perceived BPFD by the internodes was derived from simulations and integrated into the model. With the aim to optimize lighting for a speed breeding system, simulations with alternative lighting scenarios indicated that decreasing BPFD during the growth period and using different chamber material with a higher reflectance could reduce energy consumption by 7% compared to the experimental setup, while inducing short soybean plants.

Ecological interactions shape the adaptive value of plant defence: Herbivore attack versus competition for light.

Plants defend themselves against diverse communities of herbivorous insects. This requires an investment of limited resources, for which plants also compete with neighbours. The consequences of an investment in defence are determined by the metabolic costs of defence as well as indirect or ecological costs through interactions with other organisms. These ecological costs have a potentially strong impact on the evolution of defensive traits, but have proven to be difficult to quantify.We aimed to quantify the relative impact of the direct and indirect or ecological costs and benefits of an investment in plant defence in relation to herbivory and intergenotypic competition for light. Additionally, we evaluated how the benefits of plant defence balance its costs in the context of herbivory and intergenotypic competition.To this end, we utilised a functional-structural plant (FSP) model of Brassica nigra that simulates plant growth and development, morphogenesis, herbivory and plant defence. In the model, a simulated investment in defences affected plant growth by competing with other plant organs for resources and affected the level and distribution of herbivore damage.Our results show that the ecological costs of intergenotypic competition for light are highly detrimental to the fitness of defended plants, as it amplifies the size difference between defended and undefended plants. This leads to herbivore damage counteracting the effects of intergenotypic competition under the assumption that herbivore damage scales with plant size. Additionally, we show that plant defence relies on reducing herbivore damage rather than the dispersion of herbivore damage, which is only beneficial under high levels of herbivore damage.We conclude that the adaptive value of plant defence is highly dependent on ecological interactions and is predominantly determined by the outcome of competition for light. plain language summary is available for this article.

Dynamic Plant-Plant-Herbivore Interactions Govern Plant Growth-Defence Integration.

Plants downregulate their defences against insect herbivores upon impending competition for light. This has long been considered a resource trade-off, but recent advances in plant physiology and ecology suggest this mechanism is more complex. Here we propose that to understand why plants regulate and balance growth and defence, the complex dynamics in plant-plant competition and plant-herbivore interactions needs to be considered. Induced growth-defence responses affect plant competition and herbivore colonisation in space and time, which has consequences for the adaptive value of these responses. Assessing these complex interactions strongly benefits from advanced modelling tools that can model multitrophic interactions in space and time. Such an exercise will allow a critical re-evaluation why and how plants integrate defence and competition for light.