3D modelling of branching in plants

Shoot branching is a key determinant of overall aboveground plant form. During plant development, the number of branches formed strongly influences the amount of light absorbed by the plant, and thus the plant’s competitive strength in terms of light capture in relation to neighbouring plants. Branching is regulated by multiple internal factors which are modulated by different environmental signals. A key environmental signal in the context of a plant population is a low red / far-red intensity ratio (R:FR) of the light reflected by neighbouring plants. For instance, low R:FR results in suppression of branching in favour of elongation growth, which is a key aspect of shade avoidance. Shade avoidance enables plants to anticipate future competition by preventing being shaded, rather than to react to prevailing shade conditions. Internally, branching is regulated by a finely tuned plant hormone network. The interactions within this network are modified by environmental cues such as R:FR which is perceived by specific photoreceptors. Combined, internal and external signals enable regulation of branch formation under the influence of environmental conditions. The different aspects of branching control act at different levels of biological organization (organ, whole plant, plant community). These aspects can be integrated in one modelling approach, called functional-structural plant modelling (FSPM), explicitly considering spatial 3D plant development. An FSP model typically contains detailed information at any moment in development of the plant on the number, size, location and orientation of all organs that make up the plant. In FSP models, physiological and physical processes occur within the plant (e.g. photosynthesis and transport of assimilates), and interaction with the environment occurs at the interface of organ and environment (e.g. light absorption by a leaf). Explicit simulation of absorption and scattering of light at the level of the plant organ is an important aspect of FSPM. In combination with dedicated experiments, this modelling tool can be used to analyse the response of plants to (imminent) competition, simulate the competitive advantage of shade avoidance for plants of different architecture, and predict plant form in various light environments. To assess the effect of plant population density through R:FR signalling on tillering (branching) in spring wheat (Triticum aestivum L.), an FSPM study was conducted (Figure 1). A simple descriptive relationship was used to link R:FR as perceived by the plant to extension growth of tiller buds and probability of a bud to form a tiller. A further study included a complete sub-model of branching regulation, aiming at simulating branching as an emergent property in Arabidopsis (Arabidopsis thaliana) under the influence of R:FR. These and other studies show that FSPM is a promising tool to simulate aspects of plant development, such as branching, under the influence of environmental factors. In close combination with dedicated experiments, FSPM can shape our ideas of the mechanisms controlling plant development, can integrate existing knowledge on plant development, and can predict plant development in untested conditions

Quantitative Genetics and Functional-Structural Plant Growth Models: Simulation of Quantitative Trait Loci Detection for Model Parameters and Application to Potential Yield Optimization

Background and Aims: Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. Methods: The GreenLab model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. Key Results and Conclusions: By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits - such as cob weight - and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GreenLab maize model. The paper discusses the potentials of GreenLab to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand

Boom and Bust Carbon-Nitrogen Dynamics during Reforestation

Legacies of historical land use strongly shape contemporary ecosystem dynamics. In old-field secondary forests, tree growth embodies a legacy of soil changes affected by previous cultivation. Three patterns of biomass accumulation during reforestation have been hypothesized previously, including monotonic to steady state, non-monotonic with a single peak then decay to steady state, and multiple oscillations around the steady state. In this paper, the conditions leading to the emergence of these patterns is analyzed. Using observations and models, we demonstrate that divergent reforestation patterns can be explained by contrasting time-scales in ecosystem carbon-nitrogen cycles that are influenced by land use legacies. Model analyses characterize non-monotonic plant-soil trajectories as either single peaks or multiple oscillations during an initial transient phase controlled by soil carbon-nitrogen conditions at the time of planting. Oscillations in plant and soil pools appear in modeled systems with rapid tree growth and low initial soil nitrogen, which stimulate nitrogen competition between trees and decomposers and lead the forest into a state of acute nitrogen deficiency. High initial soil nitrogen dampens oscillations, but enhances the magnitude of the tree biomass peak. These model results are supported by data derived from the long-running Calhoun Long-Term Soil-Ecosystem Experiment from 1957 to 2007. Observed carbon and nitrogen pools reveal distinct tree growth and decay phases, coincident with soil nitrogen depletion and partial re-accumulation. Further, contemporary tree biomass loss decreases with the legacy soil C:N ratio. These results support the idea that non-monotonic reforestation trajectories may result from initial transients in the plant-soil system affected by initial conditions derived from soil changes associated with land-use history

Tree defence and bark beetles in a drying world: carbon partitioning, functioning and modelling.

Drought has promoted large-scale, insect-induced tree mortality in recent years, with severe consequences for ecosystem function, atmospheric processes, sustainable resources and global biogeochemical cycles. However, the physiological linkages among drought, tree defences, and insect outbreaks are still uncertain, hindering our ability to accurately predict tree mortality under on-going climate change. Here we propose an interdisciplinary research agenda for addressing these crucial knowledge gaps. Our framework includes field manipulations, laboratory experiments, and modelling of insect and vegetation dynamics, and focuses on how drought affects interactions between conifer trees and bark beetles. We build upon existing theory and examine several key assumptions: (1) there is a trade-off in tree carbon investment between primary and secondary metabolites (e.g. growth vs defence); (2) secondary metabolites are one of the main component of tree defence against bark beetles and associated microbes; and (3) implementing conifer-bark beetle interactions in current models improves predictions of forest disturbance in a changing climate. Our framework provides guidance for addressing a major shortcoming in current implementations of large-scale vegetation models, the under-representation of insect-induced tree mortality

Integrating trait-based empirical and modeling research to improve ecological restoration

A global ecological restoration agenda has led to ambitious programs in environmental policy to mitigate declines in biodiversity and ecosystem services. Current restoration programs can incompletely return desired ecosystem service levels, while resilience of restored ecosystems to future threats is unknown. It is therefore essential to advance understanding and better utilize knowledge from ecological literature in restoration approaches. We identified an incomplete linkage between global change ecology, ecosystem function research, and restoration ecology. This gap impedes a full understanding of the interactive effects of changing environmental factors on the long-term provision of ecosystem functions and a quantification of trade-offs and synergies among multiple services. Approaches that account for the effects of multiple changing factors on the composition of plant traits and their direct and indirect impact on the provision of ecosystem functions and services can close this gap. However, studies on this multilayered relationship are currently missing. We therefore propose an integrated restoration agenda complementing trait-based empirical studies with simulation modeling. We introduce an ongoing case study to demonstrate how this framework could allow systematic assessment of the impacts of interacting environmental factors on long-term service provisioning. Our proposed agenda will benefit restoration programs by suggesting plant species compositions with specific traits that maximize the supply of multiple ecosystem services in the long term. Once the suggested compositions have been implemented in actual restoration projects, these assemblages should be monitored to assess whether they are resilient as well as to improve model parameterization. Additionally, the integration of empirical and simulation modeling research can improve global outcomes by raising the awareness of which restoration goals can be achieved, due to the quantification of trade-offs and synergies among ecosystem services under a wide range of environmental conditions

Spring water stress in Scots pine

Water use and net carbon assimilation during spring was examined on Scots pine trees exposed to different soil warming dynamics in the field. Sap flow, needle water potential and net carbon assimilation were measured on trees that were exposed to a wide range of soil temperature regimes caused by manipulating the snow cover on tree-scale soil plots. This made it possible to quantify the sensitivity of water uptake and recovery of gas exchange by Scots pine in the critical transition from winter dormancy to the growing season, which can be influenced by silvicultural practices. A part of the study was to find a tool for estimating the coupled effect of belowground and aboveground climate on transpiration, as well as to adapt this tool to the harsh climate of the boreal forest. Combining the results of field experiments on tree susceptibility to water stress with a physically based SVAT model as well as a model for estimating the recovery of photosynthesis helped to predict spatial and inter-annual variability of snow depths, soil warming, water uptake and net primary productivity during spring within different Scots pine stands across the landscape. This could provide a better basis for a more frostconscious forest management. The studies have confirmed the importance of low soil temperatures in combination with aboveground climate for root water uptake and net carbon assimilation during spring, when soil warming occurs after the start of the growing season. The studies have also confirmed that earlier, controlled laboratory studies on the inhibiting effects of low soil temperature on water relations and gas exchange for seedlings or saplings also hold true on mature trees in the field. The experimental data served well as the basis for model analyses of the interaction between belowground and aboveground conditions on water use and net photosynthesis. The results of the field studies and model analyses suggest that the effect of soil temperature on tree water uptake and net photosynthesis during spring, in conjunction with aboveground conditions, are factors that need to be considered in forest management in areas susceptible to soil frost and low soil temperatures

SIMULATING OZONE EFFECTS ON FOREST PRODUCTIVITY: INTERACTIONS AMONG LEAF‐, CANOPY‐, AND STAND‐LEVEL PROCESSES

Ozone pollution in the lower atmosphere is known to have adverse effects on forest vegetation, but the degree to which mature forests are impacted has been very difficult to assess directly. In this study, we combined leaf‐level ozone response data from independent ozone fumigation studies with a forest ecosystem model in order simulate the effects of ambient ozone on mature hardwood forests. Reductions in leaf carbon gain were determined as a linear function of ozone flux to the leaf interior, calculated as the product of ozone concentration and leaf stomatal conductance. This relationship was applied to individual canopy layers within the model in order to allow interaction with stand‐ and canopy‐level factors such as light attenuation, leaf morphology, soil water limitations, and vertical ozone gradients. The resulting model was applied to 64 locations across the northeastern United States using ambient ozone data from 1987 to 1992. Predicted declines in annual net primary production ranged from 3 to 16% with greatest reductions in southern portions of the region where ozone levels were highest, and on soils with high water‐holding capacity where drought stress was absent. Reductions in predicted wood growth were slightly greater (3–22%) because wood is a lower carbon allocation priority in the model than leaf and root growth. Interannual variation in predicted ozone effects was small due to concurrent fluctuations in ozone and climate. Periods of high ozone often coincided with hot, dry weather conditions, causing reduced stomatal conductance and ozone uptake. Within‐canopy ozone concentration gradients had little effect on predicted growth reductions because concentrations remained high through upper canopy layers where net carbon assimilation and ozone uptake were greatest. Sensitivity analyses indicate a trade‐off between model sensitivity to available soil water and foliar nitrogen and demonstrate uncertainties regarding several assumptions used in the model. Uncertainties surrounding ozone effects on stomatal function and plant water use efficiency were found to have important implications on current predictions. Field measurements of ozone effects on mature forests will be needed before the accuracy of model predictions can be fully assessed