Linking dynamic photosynthetic traits to biomass: a functional data framework that pinpoints when photosynthesis limits growth

Abstract

Photosynthesis responds rapidly to environmental changes, such as light fluctuations, and these responses affect how much carbon plants gain over time. Modelling genetic variation in photosynthesis over time opens a route for selection on photosynthetic performance. High-throughput chlorophyll fluorescence has enabled measurements of photosynthetic processes at high time resolution, yet methods to model the temporal and genetic variation in these time-series data remain scarce. Here, we combined splines and functional principal component analysis (FPCA) to model genotype-specific trajectories of fluorescence traits and capture their main variation in a few components. We applied this approach to 412 Arabidopsis thaliana MAGIC lines under daily sinusoidal and fluctuating light. The first component captured differences in overall trait level, whereas subsequent components captured differences in how trait values changed across days, mainly in the early growing period. These component scores predicted final dry weight reasonably well (average r = 0.7224 under sinusoidal and 0.82 under fluctuating light). Information on changes across days added most predictive value when measurements were limited to a few days. Time-varying coefficients revealed that the associations between fluorescence traits and dry weight changed over time. For most traits, these associations were strongest early in the growing period. Our framework thus shows not only which photosynthetic processes are associated with biomass, but also when. This insight will point to the period in which photosynthesis limits growth and to the traits beneficial to select for.