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Global distribution and evolutionary transitions of floral symmetry in angiosperms

Citation

Wang, Yunyun (2022), Global distribution and evolutionary transitions of floral symmetry in angiosperms, Dryad, Dataset, https://doi.org/10.5061/dryad.ghx3ffbrh

Abstract

Floral symmetry is a key trait that has played a major role in the diversification of angiosperms, yet the spatiotemporal variations in angiosperm floral symmetry and the drivers of these variations remain poorly explored. A key outstanding question is whether variation in flower symmetry is associated with variation in environmental conditions on geological timescales. Here, using newly compiled data on floral symmetry and global distributions of 259,261 angiosperm species, we mapped global patterns of floral symmetry variation, reconstructed floral symmetry evolution during the Cenozoic and analyzed the impact of climate on the geographical and evolutionary variations of this key trait. We found that the frequency of actinomorphy (radial symmetry) increased with latitude and temperature variability, indicating that species with actinomorphic flowers could better adapt to unstable climates compared with those with zygomorphic flowers (bilateral symmetry). Evolutionary transitions towards zygomorphy dominated floral symmetry evolution, although this transition decreased through the Cenozoic associated with decreasing temperature. Our study provides novel insights into the ecology and evolution of floral symmetry of angiosperms and suggests that climate change may influence species distribution via its effect on floral symmetry.

Funding

Strategic Priority Research Program of Chinese Academy of Sciences, Award: XDB31000000

National Key Research and Development Program of China, Award: 2017YFA0605101

National Key Research and Development Program of China, Award: 2018YFA0606104

National Natural Science Foundation of China, Award: 32125026

National Natural Science Foundation of China, Award: 31901216

National Natural Science Foundation of China, Award: 31988102

Natural Science Foundation of Hunan Province, Award: 2020JJ5977

Norwegian Metacenter for Computational Science, Award: NN9601K