Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

Regeneration in gap models: priority issues for studying forest responses to climate change

Recruitment algorithms in forest gap models are examined with particular regard to their suitability for simulating forest ecosystem responses to a changing climate. The traditional formulation of recruitment is found limiting in three areas. First, the aggregation of different regeneration stages (seed production, dispersal, storage, germination and seedling establishment) is likely to result in less accurate predictions of responses as compared to treating each stage separately. Second, the relatedassumptions that seeds of all species are uniformly available and that environmental conditions are homogeneous, are likely to cause overestimates of future species diversity and forest migration rates. Third, interactions between herbivores (ungulates and insect pests) and forest vegetation are a big unknown with potentially serious impacts in many regions. Possible strategies for developing better gap model representations for the climate-sensitive aspects of each of these key areas are discussed. A working example of a relatively new model that addresses some of these limitations is also presented for each case. We conclude that better models of regeneration processes are desirable for predicting effects of climate change, but that it is presently impossible to determine what improvements can be expected without carrying out rigorous tests for each new formulation

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

Assessment of the potential impacts of plant traits across environments by combining global sensitivity analysis and dynamic modeling in wheat

A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites x 125 years), management practices (3 sowing dates x 2 N fertilization) and $CO_2$ (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait x environment x management landscape ($\sim$ 82 million individual simulations in total). The patterns of parameter x environment x management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identifcation of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement.Comment: 22 pages, 8 figures. This work has been submitted to PLoS On

The Effects of Disturbance Architecture on Landscape-Level Population Dynamics

Phenomena such as disturbance play a major role in structuring ecological systems by producing a spatiotemporal mosaic of patches at different successional states. The distribution of species within the resulting mosaic depends upon an interaction between species\u27 life history traits and the spatial and temporal structure of the ecological processes controlling species\u27 distributions. We have used a spatially explicit simulation model (Jasper) of a serpentine grassland to examine the importance of some of these relationships, focusing primarily on the role of disturbance. The model Jasper is hierarchical in design and was developed to simulate the population dynamics of three interacting plant species: Bromus mollis, Calycadenia multiglandulosa, and Plantago erecta. Population dynamics were modeled as occurring within local sites, which were then arranged in a square array to form a landscape. Connections among sites within a landscape were made primarily through seed dispersal. Several components of disturbance architecture were varied systematically among model runs to determine their impact on population dynamics at the scale of the landscape. We considered three levels of organization in modeling disturbance: (1) overall rate of disturbance, (2) size of individual disturbances, and (3) temporal and spatial autocorrelation among individual disturbances. The results demonstrate that the impact of disturbance depends upon a complex interaction between the life history characteristics of the species making up the community and the spatial and temporal structure of the disturbance regime. For example, we found that the biggest impact on species abundance occurred in response to a shift in the temporal autocorrelation structure of the disturbance regime. Also, species diversity was found to increase at intermediate levels of disturbance (as has been shown in several other studies). However, what can be considered an intermediate level of disturbance depends as much upon the temporal autocorrelation structure of the disturbance regime as it does upon the absolute rate of disturbance. These results suggest that predicting the impact of disturbance on ecological communities will require an explicit understanding of at least some aspects of the spatial and temporal architecture of the disturbance regime

Delimitation of Major Lineages within \u3cem\u3eCuscuta\u3c/em\u3e Subgenus \u3cem\u3eGrammica\u3c/em\u3e (Convolvulaceae) using Plastid and Nuclear DNA Sequences

Subgenus Grammica, the largest and most diverse group in the parasitic genus Cuscuta, includes ~130 species distributed primarily throughout the New World, with Mexico as its center of diversity. To circumscribe the subgenus ans assess the relationships among its major lineages, we conducted the first phylogenetic study of Grammica using plastid trnL F and nrITS sequences from a wide taxonomic sampling covering its morphological, physiological, and geographical diversiity. With the exception of of one species belonging elsewhere, the subgenus was found to be monophyletic. The results further indicate the presence of 15 well supported major clades within Grammica. Some of those lineages correspond partially to earlier taxonomic treatments, but the majority of groups are identified in this study for the first time. The backbone relationships among major clades, however, remain weakly supported or unresolved in some cases. The phylogenetic results indicate that the fruit dehiscence character is homoplastic, thus compromising its value as a major taxonomic and evolutionary feature. While several striking cases of long distance dispersal are inferred, vicariance emerges as the most dominant biogeographical pattern for Cuscuta. Species placed within one of the caldes with a predominantly South American distribution are hypothesized to have substantially altered plastid genomes

Are plant species able to keep pace with the rapidly changing climate?

Future climate change is predicted to advance faster than the postglacial warming. Migration may therefore become a key driver for future development of biodiversity and ecosystem functioning. For 140 European plant species we computed past range shifts since the last glacial maximum and future range shifts for a variety of Intergovernmental Panel on Climate Change (IPCC) scenarios and global circulation models (GCMs). Range shift rates were estimated by means of species distribution modelling (SDM). With process-based seed dispersal models we estimated species-specific migration rates for 27 dispersal modes addressing dispersal by wind (anemochory) for different wind conditions, as well as dispersal by mammals (dispersal on animal's coat – epizoochory and dispersal by animals after feeding and digestion – endozoochory) considering different animal species. Our process-based modelled migration rates generally exceeded the postglacial range shift rates indicating that the process-based models we used are capable of predicting migration rates that are in accordance with realized past migration. For most of the considered species, the modelled migration rates were considerably lower than the expected future climate change induced range shift rates. This implies that most plant species will not entirely be able to follow future climate-change-induced range shifts due to dispersal limitation. Animals with large day- and home-ranges are highly important for achieving high migration rates for many plant species, whereas anemochory is relevant for only few species