AUTHOR=Gao Xinyue , Dai Junhu , Tao Zexing , Shahzad Khurram , Wang Huanjiong TITLE=Autumn phenology of tree species in China is associated more with climate than with spring phenology and phylogeny JOURNAL=Frontiers in Plant Science VOLUME=14 YEAR=2023 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1040758 DOI=10.3389/fpls.2023.1040758 ISSN=1664-462X ABSTRACT=

Both biotic and abiotic factors restrict changes in autumn phenology, yet their effects remain ambiguous, which hinders the accurate prediction of phenology under future climate change. In this study, based on the phenological records of 135 tree species at ten sites in China during 1979–2018, we first investigated the effects of climatic factors (temperature, precipitation, insolation and wind speed) and spring phenology on interannual changes in leaf coloring date (LCD) with the partial correlation analysis, and assessed the relative importance of phylogeny and native climate to LCD differences among species by using multivariate regression and phylogenetic eigenvector regression approach. The results showed that the effects of climate factors on interannual changes in LCD were more significant than spring phenology. In general, temperature played a more important role in cold regions (e.g. the northeast region), while the control of insolation on LCD was stronger in the warmer and wetter regions (e.g. the north, east and southwest regions). In addition, the effects of precipitation and wind speed were more evident in arid regions (e.g. the northwest region). We also found considerable effects of both native climate and phylogeny on the LCD differences among species, despite the contribution of native climate being almost 2~5 times greater than that of the phylogeny. Our findings confirmed and quantified the combined effects of climate, spring phenology and phylogeny on the autumn phenology of plants, which could help better understand the driving factors and influencing mechanism of plant phenology and provide a reference for the calibration and optimization of phenological models.