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

Photosynthesis reacts swiftly to environmental fluctuations, such as light variations, which subsequently influence the amount of carbon plants accumulate throughout their life cycle. By modeling genetic differences in photosynthesis over time, researchers can identify genetic variations that enhance photosynthetic efficiency. High-throughput chlorophyll fluorescence measurements provide detailed insight into photosynthetic processes over time; however, existing techniques for analyzing temporal and genetic variations in such data are lacking.

In this study, the authors integrated splines and functional principal component analysis (FPCA) to model genotype-specific fluorescence trait trajectories and identify the primary sources of variation within these time-series. This approach was applied to 412 Arabidopsis thaliana MAGIC lines subjected to daily sinusoidal and fluctuating light conditions.

The first component identified overall trait level differences, while subsequent components captured changes in trait values across days, particularly during the early growth phase. The scores derived from these components effectively predicted final dry weight (average r = 0.7224 under sinusoidal and 0.82 under fluctuating light).

The inclusion of day-to-day changes in the model significantly improved predictive accuracy when measurements were sparse. Time-varying coefficients revealed that the relationship between fluorescence traits and dry weight shifts over time, with the strongest associations occurring early in the growth period. This framework not only elucidates which photosynthetic processes are linked to biomass production but also clarifies when these processes become limiting factors.

By pinpointing these critical periods and traits, this research offers valuable guidance for selecting plant lines with enhanced photosynthetic performance.