%0 Generic %A Garassino, Francesco %D 2023 %T Supplementary information: Analysis of natural variation in photosynthesis in a panel of Brassicaceae species %U %R 10.4121/79a62b5f-2881-4520-b031-e03334c02aad.v1 %K Brassicaceae %K Natural variation %K Photosynthesis %K High-throughput phenotyping %X
This data supports the conclusions derived from an high- and low-throughput investigation of natural variation in photosynthetic light-use efficiency (LUE), and a number of traits potentially correlated to it, in a panel of ten Brassicaceae species. In this study, I performed an analysis of photosynthetic efficiency at high irradiance in ten species that reflect key evolutionary events within the Brassicaceae family: Arabidopsis thaliana, Brassica oleracea, Brassica nigra, Brassica rapa, Brassica tournefortii, Erucastrum littoreum, Hirschfeldia incana, Sinapis alba, Sisymbrium irio, and Zahora ait-atta. I made use of high-throughput phenotyping techniques to measure photosynthetic efficiency, and integrated these measurements with other image-based parameters, such as the Excess Green Index (ExGI) and the Normalized Difference Vegetation Index (NDVI), as well as a range of anatomical and biochemical characteristics that potentially influence photosynthetic efficiency. I then explored the resulting multivariate dataset using various statistical methods to identify trends across species and investigated if more species within the Brassicaceae family show high-photosynthetic LUE at high irradiance. Furthermore, I assessed the alignment of these trends with the evolutionary history of the Brassicaceae family. This study delivers a detailed description of inter-specific variation in photosynthetic parameters for the Brassicaceae family, completed by a selection of anatomical and biochemical characteristics that may play a role in supporting high photosynthetic LUE under high irradiance. The gained insights will be important in developing strategies to enhance the photosynthetic LUE at high irradiance of crop species.
%I 4TU.ResearchData