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Understanding how spring phenology of temperate trees responds to global change is essential for assessing the vegetation dynamics in the future. Besides chilling and forcing temperatures, the influences of other environmental and biotic factors on leaf unfolding date (LUD) are still unclear. Based on long-term records (1960–2015) of LUD for 6 typical temperate tree species at 4242 stations in Europe, we first used a classic process-based model (the Unified model) to describe the relationship between chilling and forcing requirements of LUD. Furthermore, we used 3 explainable machine learning (ML) algorithms (RF, EBM, and GAMI-Net) to quantify the influences of 45 biotic and environmental factors on the LUD. The root-mean-square error (RMSE) of the ML-based models averaged from all species (7.03 to 7.33 days) was lower than the Unified model (8.73 days). The ML algorithms detected 2 biotic (elevation and previous leaf senescence date) and 3 temperature-related variables (chilling accumulation, freezing days in March and annual temperature range) with high importance for most species. The trees adapted to a higher elevation or with later leaf senescence date in the previous year exhibited later LUD. The increase in chilling accumulation, freezing days in March and the annual temperature range could reduce the forcing requirement of LUD. Water-related and light-related variables were only important for one or two species. The increase in relative humidity in February could advance LUD, while soil moisture in March had the opposite effect. The increase in March day length and downward surface shortwave radiation exerted an advancing influence on the LUD of light-sensitive species. Our results suggest that incorporating multiple biotic and environmental factors into ML algorithms not only could effectively improve the accuracy of phenological prediction but also enhance our understanding of how spring phenology responds to each factor.

Over the past half-century, there has been a significant rise in the frequency and intensity of extreme climate events in dryland ecosystems. While vegetation phenology acts as an important indicator of how ecosystems respond to climate change, the impact of extreme climate events on autumn phenology in dryland ecosystems remains uncertain. In this study, we firstly assessed the temporal variations in the end of growing season (EOS) in Central Asia using the Normalized Difference Vegetation Index and enhanced vegetation index over 2001–2018. Then, we assessed the impacts of eight climate indices, which capture the average and extreme conditions of the local climate, on EOS across diverse vegetation types using linear mixed models. Finally, we conducted partial correlation analysis to explore how the primary influencing factors affect EOS along an elevational gradient. Our results suggested increased occurrences of drought and heat events in the pre-growing season (April–September) in Central Asia. Under the background, a general trend towards earlier EOS (ca. 1.01 days/decade) was observed for grassland. This trend was mostly attributed to the changes in extreme climate indices (CIextreme), especially heavy rainfall and heat stress. The total explanatory power of CIextreme was almost twice that of mean climate indices (CImean). However, the impact of CIextreme was less pronounced in regions with elevations exceeding 1500 m. For forests and shrublands, the EOS exhibited delaying trend of 1.29 days/decade and advancing trend of 1.21 days/decade, respectively. Contrary to grasslands, the variations in EOS of forests and shrublands were largely influenced by the shift of CImean, especially mean minimum temperature and accumulated precipitation. The contribution of CIextreme accounted for only 1/6 of the contribution of CImean, likely attributed to the adaptation of forests to cold conditions and shrublands to arid conditions. The results provide valuable insights into the phenological response to extreme climate events and serve as a reference for ecosystem management in arid regions.

The effects of climate change on plant phenology have been widely recognized around the world. However, the effect of plant internal factors (such as phylogeny) on the variations in phenology among plant species remains unclear. In this study, we investigated the phylogenetic conservatism in spring phenological traits using phylogenetic signal and evolutionary models, including Brownian motion (BM) model, Ornstein–Uhlenbeck (OU) model and white noise (WN) model, based on the phenological data of 48 temperate plant species in Northeast China. We also explored the relative contributions of phylogeny and adaptation to native climate (i.e., the climate in native range of species) to the variations in the phenological traits among species using phylogenetic eigenvector regression and variance partitioning analysis. The results showed thatspring phenological traits conformed to the OU model, indicating thatspring traits were phylogenetically conserved. The effect of phylogeny on flowering traits was stronger than that on leaf-out traits. Additionally, the adaptation to native climate contributed more to the variations in spring phenological traits among species than phylogeny, and adaptation to native climate explained more variations in leaf-out traits than in flowering traits. Our results suggested that the spring phenological traits were constrained by both phylogeny and adaptation to native climate. However, the adaptation to native climate had a stronger effect on the variations in phenological traits than phylogeny. Therefore, the degree of similarity in spring phenological traits across closely related species depends on the degree of similarity in the environmental conditions where these close relatives are distributed.