Impacts of sample handling and storage conditions on archiving physiologically active soil microbial communities.

Soil microbial communities are fundamental to ecosystem processes and plant growth, yet community composition is seasonally and successionally dynamic, which interferes with long-term iterative experimentation of plant-microbe interactions. We explore how soil sample handling (e.g. filtering) and sample storage conditions impact the ability to revive the original, physiologically active, soil microbial community. We obtained soil from agricultural fields in Montana and Oklahoma, USA and samples were sieved to 2 mm or filtered to 45 µm. Sieved and filtered soil samples were archived at -20°C or -80°C for 50 days and revived for 2 or 7 days. We extracted DNA and the more transient RNA pools from control and treatment samples and characterized microbial communities using 16S amplicon sequencing. Filtration and storage treatments significantly altered soil microbial communities, impacting both species richness and community composition. Storing sieved soil at -20°C did not alter species richness and resulted in the least disruption to the microbial community composition in comparison to nonarchived controls as characterized by RNA pools from soils of both sites. Filtration significantly altered composition but not species richness. Archiving sieved soil at -20°C could allow for long-term and repeated experimentation on preserved physiologically active microbial communities.

Rhizosphere microbial community composition shifts diurnally and in response to natural variation in host clock phenotype.

Plant-associated microbial assemblages are known to shift at time scales aligned with plant phenology, as influenced by the changes in plant-derived nutrient concentrations and abiotic conditions observed over a growing season. But these same factors can change dramatically in a sub-24-hour period, and it is poorly understood how such diel cycling may influence plant-associated microbiomes. Plants respond to the change from day to night via mechanisms collectively referred to as the internal "clock," and clock phenotypes are associated with shifts in rhizosphere exudates and other changes that we hypothesize could affect rhizosphere microbes. The mustard Boechera stricta has wild populations that contain multiple clock phenotypes of either a 21- or a 24-hour cycle. We grew plants of both phenotypes (two genotypes per phenotype) in incubators that simulated natural diel cycling or that maintained constant light and temperature. Under both cycling and constant conditions, the extracted DNA concentration and the composition of rhizosphere microbial assemblages differed between time points, with daytime DNA concentrations often triple what were observed at night and microbial community composition differing by, for instance, up to 17%. While we found that plants of different genotypes were associated with variation in rhizosphere assemblages, we did not see an effect on soil conditioned by a particular host plant circadian phenotype on subsequent generations of plants. Our results suggest that rhizosphere microbiomes are dynamic at sub-24-hour periods, and those dynamics are shaped by diel cycling in host plant phenotype. IMPORTANCE We find that the rhizosphere microbiome shifts in composition and extractable DNA concentration in sub-24-hour periods as influenced by the plant host's internal clock. These results suggest that host plant clock phenotypes could be an important determinant of variation in rhizosphere microbiomes.

The rhizosphere microbiome and host plant glucosinolates exhibit feedback cycles in Brassica rapa.

The rhizosphere microbiome influences many aspects of plant fitness, including production of secondary compounds and defence against insect herbivores. Plants also modulate the composition of the microbial community in the rhizosphere via secretion of root exudates. We tested both the effect of the rhizosphere microbiome on plant traits, and host plant effects on rhizosphere microbes using recombinant inbred lines (RILs) of Brassica rapa that differ in production of glucosinolates (GLS), secondary metabolites that contribute to defence against insect herbivores. First, we investigated the effect of genetic variation in GLS production on the composition of the rhizosphere microbiome. Using a Bayesian Dirichlet-multinomial regression model (DMBVS), we identified both negative and positive associations between bacteria from six genera and the concentration of five GLS compounds produced in plant roots. Additionally, we tested the effects of microbial inoculation (an intact vs. disrupted soil microbiome) on GLS production and insect damage in these RILs. We found a significant microbial treatment × genotype interaction, in which total GLS was higher in the intact relative to the disrupted microbiome treatment in some RILs. However, despite differences in GLS production between microbial treatments, we observed no difference in insect damage between treatments. Together, these results provide evidence for a full feedback cycle of plant-microbe interactions mediated by GLS; that is, GLS compounds produced by the host plant "feed-down" to influence rhizosphere microbial community and rhizosphere microbes "feed-up" to influence GLS production.

Naturally segregating genetic variation in circadian period exhibits a regional elevational and climatic cline.

Circadian clocks confer adaptation to predictable 24-h fluctuations in the exogenous environment, but it has yet to be determined what ecological factors maintain natural genetic variation in endogenous circadian period outside of the hypothesized optimum of 24 h. We estimated quantitative genetic variation in circadian period in leaf movement in 30 natural populations of the Arabidopsis relative Boechera stricta sampled within only 1° of latitude but across an elevation gradient spanning 2460-3300 m in the Rocky Mountains. Measuring ~3800 plants from 473 maternal families (7-20 per population), we found that genetic variation was of similar magnitude among versus within populations, with population means varying between 21.9 and 24.9 h and maternal family means within populations varying by up to ~6 h. After statistically accounting for spatial autocorrelation at a habitat extreme, we found that elevation explained a significant proportion of genetic variation in the circadian period, such that higher-elevation populations had shorter mean period lengths and reduced intrapopulation ranges. Environmental data indicate that these spatial trends could be related to steep regional climatic gradients in temperature, precipitation, and their intra-annual variability. Our findings suggest that spatially fine-grained environmental heterogeneity contributes to naturally occurring genetic variation in circadian traits in wild populations.

Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth.

Microbial communities in the rhizosphere are distinct from those in soils and are influenced by stochastic and deterministic processes during plant development. These communities contain bacteria capable of promoting growth in host plants through various strategies. While some interactions are characterized in mechanistic detail using model systems, others can be inferred from culture-independent methods, such as 16S amplicon sequencing, using machine learning methods that account for this compositional data type. To characterize assembly processes and identify community members associated with plant growth amid the spatiotemporal variability of the rhizosphere, we grew Brassica rapa in a greenhouse time series with amended and reduced microbial treatments. Inoculation with a native soil community increased plant leaf area throughout the time series by up to 28%. Despite identifying spatially and temporally variable amplicon sequence variants (ASVs) in both treatments, inoculated communities were more highly connected and assembled more deterministically overall. Using a generalized linear modeling approach controlling for spatial variability, we identified 43 unique ASVs that were positively or negatively associated with leaf area, biomass, or growth rates across treatments and time stages. ASVs of the genus Flavobacterium dominated rhizosphere communities and showed some of the strongest positive and negative correlations with plant growth. Members of this genus, and growth-associated ASVs more broadly, exhibited variable connectivity in networks independent of growth association (positive or negative). These findings suggest host-rhizobacterial interactions vary temporally at narrow taxonomic scales and present a framework for identifying rhizobacteria that may work independently or in concert to improve agricultural yields. IMPORTANCE The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques. We identified 43 specific bacterial sequences associated with the growth of the plant Brassica rapa in different soil microbial treatments and at different stages of plant development. Most associations between bacterial abundances and plant growth were positive, although similar bacterial groups sometimes had different effects on growth. Why this happens will require more research, but overall, this study provides a way to identify native bacteria from plant roots that might be isolated and applied to boost agricultural yields.

Whole-Genome Duplication and Host Genotype Affect Rhizosphere Microbial Communities.

The composition of microbial communities found in association with plants is influenced by host phenotype and genotype. However, the ways in which specific genetic architectures of host plants shape microbiomes are unknown. Genome duplication events are common in the evolutionary history of plants and influence many important plant traits, and thus, they may affect associated microbial communities. Using experimentally induced whole-genome duplication (WGD), we tested the effect of WGD on rhizosphere bacterial communities in Arabidopsis thaliana. We performed 16S rRNA amplicon sequencing to characterize differences between microbiomes associated with specific host genetic backgrounds (Columbia versus Landsberg) and ploidy levels (diploid versus tetraploid). We modeled relative abundances of bacterial taxa using a hierarchical Bayesian approach. We found that host genetic background and ploidy level affected rhizosphere community composition. We then tested to what extent microbiomes derived from a specific genetic background or ploidy level affected plant performance by inoculating sterile seedlings with microbial communities harvested from a prior generation. We found a negative effect of the tetraploid Columbia microbiome on growth of all four plant genetic backgrounds. These findings suggest an interplay between host genetic background and ploidy level and bacterial community assembly with potential ramifications for host fitness. Given the prevalence of ploidy-level variation in both wild and managed plant populations, the effects on microbiomes of this aspect of host genetic architecture could be a widespread driver of differences in plant microbiomes. IMPORTANCE Plants influence the composition of their associated microbial communities, yet the underlying host-associated genetic determinants are typically unknown. Genome duplication events are common in the evolutionary history of plants and affect many plant traits. Using Arabidopsis thaliana, we characterized how whole-genome duplication affected the composition of rhizosphere bacterial communities and how bacterial communities associated with two host plant genetic backgrounds and ploidy levels affected subsequent plant growth. We observed an interaction between ploidy level and genetic background that affected both bacterial community composition and function. This research reveals how genome duplication, a widespread genetic feature of both wild and crop plant species, influences bacterial assemblages and affects plant growth.

Plastic response to early shade avoidance cues has season-long effect on Beta vulgaris growth and development.

Early-emerging weeds are known to negatively affect crop growth but the mechanisms by which weeds reduce crop yield are not fully understood. In a 4-year study, we evaluated the effect of duration of weed-reflected light on sugar beet (Beta vulgaris L.) growth and development. The study included an early-season weed removal series and a late-season weed addition series of treatments arranged in a randomized complete block, and the study design minimized direct resource competition. If weeds were present from emergence until the two true-leaf sugar beet stage, sugar beet leaf area was reduced 22%, leaf biomass reduced 25%, and root biomass reduced 32% compared to sugar beet grown season-long without surrounding weeds. Leaf area, leaf biomass, and root biomass was similar whether weeds were removed at the two true-leaf stage (approximately 330 GDD after planting) or allowed to remain until sugar beet harvest (approximately 1,240 GDD after planting). Adding weeds at the two true-leaf stage and leaving them until harvest (~1,240 GDD) reduced sugar beet leaf and root biomass by 18% and 23%, respectively. This work suggests sugar beet responds early and near-irreversibly to weed presence and has implications for crop management genetic improvement.

Time outweighs the effect of host developmental stage on microbial community composition.

Thousands of microbial taxa in the soil form symbioses with host plants, and due to their contribution to plant performance, these microbes are often considered an extension of the host genome. Given microbial effects on host performance, it is important to understand factors that govern microbial community assembly. Host developmental stage could affect rhizosphere microbial diversity while, alternatively, microbial assemblages could change simply as a consequence of time and the opportunity for microbial succession. Previous studies suggest that rhizosphere microbial assemblages shift across plant developmental stages, but time since germination is confounded with developmental stage. We asked how elapsed time and potential microbial succession relative to host development affected microbial diversity in the rhizosphere using monogenic flowering-time mutants of Arabidopsis thaliana. Under our experimental design, different developmental stages were present among host genotypes after the same amount of time following germination, e.g. at 76 days following germination some host genotypes were flowering while others were fruiting or senescing. We found that elapsed time was a strong predictor of microbial diversity whereas there were few differences among developmental stages. Our results support the idea that time and, likely, microbial succession more strongly affect microbial community assembly than host developmental stage.

Soil Microsite Outweighs Cultivar Genotype Contribution to Brassica Rhizobacterial Community Structure.

Microorganisms residing on root surfaces play a central role in plant development and performance and may promote growth in agricultural settings. Studies have started to uncover the environmental parameters and host interactions governing their assembly. However, soil microbial communities are extremely diverse and heterogeneous, showing strong variations over short spatial scales. Here, we quantify the relative effect of meter-scale variation in soil bacterial community composition among adjacent field microsites, to better understand how microbial communities vary by host plant genotype as well as soil microsite heterogeneity. We used bacterial 16S rDNA amplicon sequencing to compare rhizosphere communities from four Brassica rapa cultivars grown in three contiguous field plots (blocks) and evaluated the relative contribution of resident soil communities and host genotypes in determining rhizosphere community structure. We characterize concomitant meter-scale variation in bacterial community structure among soils and rhizospheres and show that this block-scale variability surpasses the influence of host genotype in shaping rhizosphere communities. We identified biomarker amplicon sequence variants (ASVs) associated with bulk soil and rhizosphere habitats, each block, and three of four cultivars. Numbers and percent abundances of block-specific biomarkers in rhizosphere communities far surpassed those from bulk soils. These results highlight the importance of fine-scale variation in the pool of colonizing microorganisms during rhizosphere assembly and demonstrate that microsite variation may constitute a confounding effect while testing biotic and abiotic factors governing rhizosphere community structure.

Aquatic Macrophytes Are Associated With Variation in Biogeochemistry and Bacterial Assemblages of Mountain Lakes.

In aquatic systems, microbes likely play critical roles in biogeochemical cycling and ecosystem processes, but much remains to be learned regarding microbial biogeography and ecology. The microbial ecology of mountain lakes is particularly understudied. We hypothesized that microbial distribution among lakes is shaped, in part, by aquatic plant communities and the biogeochemistry of the lake. Specifically, we investigated the associations of yellow water lilies (Nuphar polysepala) with the biogeochemistry and microbial assemblages within mountain lakes at two scales: within a single lake and among lakes within a mountain range. We first compared the biogeochemistry of lakes without water lilies to those colonized to varying degrees by water lilies. Lakes with >10% of the surface occupied by water lilies had lower pH and higher dissolved organic carbon than those without water lilies and had a different microbial composition. Notably, cyanobacteria were negatively associated with water lily presence, a result consistent with the past observation that macrophytes outcompete phytoplankton and can suppress cyanobacterial and algal blooms. To examine the influence of macrophytes on microbial distribution within a lake, we characterized microbial assemblages present on abaxial and adaxial water lily leaf surfaces and in the water column. Microbial diversity and composition varied among all three habitats, with the highest diversity of microbes observed on the adaxial side of leaves. Overall, this study suggests that water lilies influence the biogeochemistry and microbiology of mountains lakes.

Correction: Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development.

[This corrects the article DOI: 10.1371/journal.pgen.1008367.].

Multiple Loci Control Variation in Plasticity to Foliar Shade Throughout Development in Arabidopsis thaliana.

The shade avoidance response is a set of developmental changes exhibited by plants to avoid shading by competitors, and is an important model of adaptive plant plasticity. While the mechanisms of sensing shading by other plants are well-known and appear conserved across plants, less is known about the developmental mechanisms that result in the diverse array of morphological and phenological responses to shading. This is particularly true for traits that appear later in plant development. Here we use a nested association mapping (NAM) population of Arabidopsis thaliana to decipher the genetic architecture of the shade avoidance response in late-vegetative and reproductive plants. We focused on four traits: bolting time, rosette size, inflorescence growth rate, and inflorescence size, found plasticity in each trait in response to shade, and detected 17 total QTL; at least one of which is a novel locus not previously identified for shade responses in Arabidopsis Using path analysis, we dissected each colocalizing QTL into direct effects on each trait and indirect effects transmitted through direct effects on earlier developmental traits. Doing this separately for each of the seven NAM populations in each environment, we discovered considerable heterogeneity among the QTL effects across populations, suggesting allelic series at multiple QTL or interactions between QTL and the genetic background or the environment. Our results provide insight into the development and variation in shade avoidance responses in Arabidopsis, and emphasize the value of directly modeling the relationships among traits when studying the genetics of complex developmental syndromes.

A Nested Association Mapping Panel in Arabidopsis thaliana for Mapping and Characterizing Genetic Architecture.

Linkage and association mapping populations are crucial public resources that facilitate the characterization of trait genetic architecture in natural and agricultural systems. We define a large nested association mapping panel (NAM) from 14 publicly available recombinant inbred line populations (RILs) of Arabidopsis thaliana, which share a common recurrent parent (Col-0). Using a genotype-by-sequencing approach (GBS), we identified single nucleotide polymorphisms (SNPs; range 563-1525 per population) and subsequently built updated linkage maps in each of the 14 RIL sets. Simulations in individual RIL populations indicate that our GBS markers have improved power to detect small effect QTL and enhanced resolution of QTL support intervals in comparison to original linkage maps. Using these robust linkage maps, we imputed a common set of publicly available parental SNPs into each RIL linkage map, generating overlapping markers across all populations. Though ultimately depending on allele frequencies at causal loci, simulations of the NAM panel suggest that surveying between 4 to 7 of the 14 RIL populations provides high resolution of the genetic architecture of complex traits, relative to a single mapping population.

Rapid Chlorophyll a Fluorescence Light Response Curves Mechanistically Inform Photosynthesis Modeling.

Crop improvement is crucial to ensuring global food security under climate change, and hence there is a pressing need for phenotypic observations that are both high throughput and improve mechanistic understanding of plant responses to environmental cues and limitations. In this study, chlorophyll a fluorescence light response curves and gas-exchange observations are combined to test the photosynthetic response to moderate drought in four genotypes of Brassica rapa The quantum yield of PSII (ϕ PSII ) is here analyzed as an exponential decline under changing light intensity and soil moisture. Both the maximum ϕ PSII and the rate of ϕ PSII decline across a large range of light intensities (0-1,000 µmol photons m-2 s-1; ß PSII ) are negatively affected by drought. We introduce an alternative photosynthesis model (ß PSII model) incorporating parameters from rapid fluorescence response curves. Specifically, the model uses ß PSII as an input for estimating the photosynthetic electron transport rate, which agrees well with two existing photosynthesis models (Farquhar-von Caemmerer-Berry and Yin). The ß PSII model represents a major improvement in photosynthesis modeling through the integration of high-throughput fluorescence phenotyping data, resulting in gained parameters of high mechanistic value.

Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development.

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find genetic variation for plasticity of growth rates and final sizes, but not the inflection point (transition from accelerating to decelerating growth) of growth curves. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.

The fitness benefits of genetic variation in circadian clock regulation.

Functional circadian clocks are essential for fitness in diverse ecosystems, facilitating detection of predictable light-dark and temperature cycles. The molecular basis of endogenous clocks is variable across the tree of life, but it has one omnipresent attribute: natural genetic diversity that manifests as variation for instance in circadian period length around the hypothesised optimum of 24 hours. Latitudinal variation in photoperiod alone is unlikely to account for the vast diversity documented in varied organisms, but we have yet to achieve a solid understanding of the interplay between clock variability and natural selection. Recent circadian studies sampling populations have drawn attention to the hierarchical structure of genetic diversity in the wild, unveiling pronounced genetic variation even on a scale of metres.

A framework for genomics-informed ecophysiological modeling in plants.

Dynamic process-based plant models capture complex physiological response across time, carrying the potential to extend simulations out to novel environments and lend mechanistic insight to observed phenotypes. Despite the translational opportunities for varietal crop improvement that could be unlocked by linking natural genetic variation to first principles-based modeling, these models are challenging to apply to large populations of related individuals. Here we use a combination of model development, experimental evaluation, and genomic prediction in Brassica rapa L. to set the stage for future large-scale process-based modeling of intraspecific variation. We develop a new canopy growth submodel for B. rapa within the process-based model Terrestrial Regional Ecosystem Exchange Simulator (TREES), test input parameters for feasibility of direct estimation with observed phenotypes across cultivated morphotypes and indirect estimation using genomic prediction on a recombinant inbred line population, and explore model performance on an in silico population under non-stressed and mild water-stressed conditions. We find evidence that the updated whole-plant model has the capacity to distill genotype by environment interaction (G×E) into tractable components. The framework presented offers a means to link genetic variation with environment-modulated plant response and serves as a stepping stone towards large-scale prediction of unphenotyped, genetically related individuals under untested environmental scenarios.

QTL Underlying Circadian Clock Parameters Under Seasonally Variable Field Settings in Arabidopsis thaliana.

The circadian clock facilitates coordination of the internal rhythms of an organism to daily environmental conditions, such as the light-dark cycle of one day. Circadian period length (the duration of one endogenous cycle) and phase (the timing of peak activity) exhibit quantitative variation in natural populations. Here, we measured circadian period and phase in June, July and September in three Arabidopsis thaliana recombinant inbred line populations. Circadian period and phase were estimated from bioluminescence of a genetic construct between a native circadian clock gene (COLD CIRCADIAN RHYTHM RNA BINDING 2) and the reporter gene (LUCIFERASE) after lines were entrained under field settings. Using a Bayesian mapping approach, we estimated the median number and effect size of genomic regions (Quantitative Trait Loci, QTL) underlying circadian parameters and the degree to which these regions overlap across months of the growing season. We also tested for QTL associations between the circadian clock and plant morphology. The genetic architecture of circadian phase was largely independent across months, as evidenced by the fact that QTL determining phase values in one month of the growing season were different from those determining phase in a second month. QTL for circadian parameters were shared with both cauline and rosette branching in at least one mapping population. The results provide insights into the QTL architecture of the clock under field settings, and suggest that the circadian clock is highly responsive to changing environments and that selection can act on clock phase in a nuanced manner.

The effect of rhizosphere microbes outweighs host plant genetics in reducing insect herbivory.

Rhizosphere microbes affect plant performance, including plant resistance against insect herbivores; yet, a direct comparison of the relative influence of rhizosphere microbes versus plant genetics on herbivory levels and on metabolites related to defence is lacking. In the crucifer Boechera stricta, we tested the effects of rhizosphere microbes and plant population on herbivore resistance, the primary metabolome, and select secondary metabolites. Plant populations differed significantly in the concentrations of six glucosinolates (GLS), secondary metabolites known to provide herbivore resistance in the Brassicaceae. The population with lower GLS levels experienced ~60% higher levels of aphid (Myzus persicae) attack; no association was observed between GLS and damage by a second herbivore, flea beetles (Phyllotreta cruciferae). Rhizosphere microbiome (disrupted vs. intact native microbiome) had no effect on plant GLS concentrations. However, aphid number and flea beetle damage were respectively about three- and seven-fold higher among plants grown in the disrupted versus intact native microbiome treatment. These differences may be attributable to shifts in primary metabolic pathways previously implicated in host defence against herbivores, including increases in pentose and glucoronate interconversion among plants grown with an intact microbiome. Furthermore, native microbiomes with distinct community composition (as estimated from 16s rRNA amplicon sequencing) differed two-fold in their effect on host plant susceptibility to aphids. The findings suggest that rhizosphere microbes, including distinct native microbiomes, can play a greater role than population in defence against insect herbivores, and act through metabolic mechanisms independent of population.

Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes.

Agronomists have used statistical crop models to predict yield on a genotype-by-genotype basis. Mechanistic models, based on fundamental physiological processes common across plant taxa, will ultimately enable yield prediction applicable to diverse genotypes and crops. Here, genotypic information is combined with multiple mechanistically based models to characterize photosynthetic trait differentiation among genotypes of Brassica rapa. Infrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods. Three advantages of Bayesian approaches are employed: a hierarchical model structure, the testing of parameter estimates with posterior predictive checks and a multimodel complexity analysis. In all, eight models of photosynthesis are compared for fit to data and penalized for complexity using deviance information criteria (DIC) at the genotype scale. The multimodel evaluation improves the credibility of trait estimates using posterior distributions. Traits with important implications for yield in crops, including maximum rate of carboxylation (Vcmax ) and maximum rate of electron transport (Jmax ) show genotypic differentiation. B. rapa shows phenotypic diversity in causal traits with the potential for genetic enhancement of photosynthesis. This multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.