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Aim Location Time periods Major taxa studied Methods Results Main conclusions Controlled experiments are increasingly important for investigating how and to what degree plant phenology responds to global climate change. Current experiments underline that chilling and forcing temperatures are two major environmental cues shaping the budburst date of temperate species, but whether experiments could reflect the observed responses to chilling has rarely been examined.Europe and North America.1951–2021.Temperate trees and shrubs.Using an experimental database of budburst dates for 50 species derived from previous literature and observational data of the same species at 12,579 stations in Europe and 1469 stations in the USA, we compared the response of forcing requirement (FR) of the budburst date to chilling accumulation (CA) between observations and experiments using a common measure of FR and CA.The median, variance and probability distribution of CA‐FR curves differed significantly between experiments and observations in most cases. The distinction in chilling effects between experiments and observations could be attributed to the difference in thermal space, heat stress, genetic variation among provenances, different forcing treatments adopted and plant materials used in the experiments.Our results suggest that the uncertainty of phenological models based solely on the experimental data needs to be re‐evaluated when predicting future spring phenological responses across broad spatial scales. [ABSTRACT FROM AUTHOR]

Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1 ∘ and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at 10.57760/sciencedb.07995 (Zhu and Dai, 2023). [ABSTRACT FROM AUTHOR]

Winter temperature‐related chilling and spring temperature‐related forcing are two major environmental cues shaping the leaf‐out date of temperate species. To what degree insufficient chilling caused by winter warming would slow phenological responses to spring warming remains unclear. Using 27,071 time series of leaf‐out dates for 16 tree species in Europe, we constructed a phenological model based on the linear or exponential function between the chilling accumulation (CA) and forcing requirements (FR) of leaf‐out. We further used the phenological model to quantify the relative contributions of chilling and forcing on past and future spring phenological change. The results showed that the delaying effect of decreased chilling on the leaf‐out date was prevalent in natural conditions, as more than 99% of time series exhibited a negative relationship between CA and FR. The reduction in chilling linked to winter warming from 1951 to 2014 could offset about one half of the spring phenological advance caused by the increase in forcing. In future warming scenarios, if the same model is used and a linear, stable correlation between CA and FR is assumed, declining chilling will continuously offset the advance of leaf‐out to a similar degree. Our study stresses the importance of assessing the antagonistic effects of winter and spring warming on leaf‐out phenology. [ABSTRACT FROM AUTHOR]

In a natural ecosystem or as climatic climax community, grassland is relatively dry with a strongly seasonal climate, and sensitive to climatic changes. Most of the previous studies used remote sensing data to investigate the phenological response of grassland to climate change, while ground‐based studies covering a large geographic area were limited. In this study, using the long‐term phenological data (1981–2012) of 16 herbaceous species observed at 20 stations in Inner Mongolia, China, we first investigated the trends in three spring phenophases, including the dates of bud‐burst, first leaf unfolding, and 50% of leaf unfolding. Subsequently, multiple linear regressions between phenophases and four climatic factors (spring temperature, soil moisture, chilling temperature, and insolation) were performed to determine the relative importance of each factor on the spring phenology. To validate the resulted regression coefficients, we developed a controlled environment to investigate the factors regulating the leaf unfolding time of one dominant species (Leymus chinensis). The results showed that the study area became warmer and drier from 1981 to 2012. However, the overall changes in spring phenophases were not apparent, as there was a similar proportion of significant earlier or later trends. Such phenological changes were driven by multiple climatic factors. The warmer temperature would advance the spring phenophases, while lower soil moisture would delay them. The impact of soil moisture was significant in the experimental data, but not significant in the observation data. In addition, leaf unfolding became faster when L. chinensis experienced more chilling days, but this effect was difficult to be detected in observation data due to the weak sensitivity of the leaf unfolding time to chilling days. These findings can help us to understand how the spring phenology of typical herbaceous species responds to multiple climatic factors under the background of climate change. [ABSTRACT FROM AUTHOR]

• We used three machine learning (ML) algorithms to simulate leaf senescence date. • The ML-based models outperformed process-based models for six tree species. • Interpretable ML algorithms could decipher the effects of multiple driving factors. Predicting tree phenology accurately is essential for assessing the impact of climate change on ecosystems. However, the current process-based models are still hard to fully explain and quantify the effects of multiple biotic and environmental factors (e.g., spring phenology, local adaptation, productivity, climatic variables) on autumn phenology. Using leaf senescence data (1980-2015) of 232766 site-year records (3020 sites and 6 deciduous tree species) in Europe, we first calibrated and evaluated 4 process-based models developed by previous studies. Subsequently, by using different machine learning (ML) algorithms (RF, EBM, and GAMI-Net), we developed 3 ML-based models for predicting the leaf senescence date of the same species and quantifying the importance and response function of 63 biotic or environmental variables. We found that the root mean square error (RMSE) of process-based models (averaged from all species) for the test dataset ranged from 11.97 to 12.91 days. The ML-based models outperformed process-based models for all species, with RMSE ranging from 10.01 to 10.58 days. For most species, the recently developed ML algorithms (EBM and GAMI-Net) are more effective than the classical RF algorithm developed in the early 21th century. Besides the temperature and photoperiod in autumn, the geographic factors (especially elevation, longitude, and latitude) were identified as the most important variables in the ML-based models, implying that leaf senescence date is an adaptive trait. Furthermore, for most species investigated, earlier leaf-out dates tended to advance the leaf senescence date. Our results highlight that the ML algorithms not only could effectively improve the performance of the process-based models for predicting the leaf senescence date, but also help to understand the nonlinear and interactive effects of multiple driving factors on autumn phenology. [ABSTRACT FROM AUTHOR]

Temperature sensitivity of plant phenology (S) is a determining factor of as to what degree climate change impacts on plant species. Fu et al . (Int J Biometeorol 60:1611-1613, 2016) claimed that long long-term linear trends mask phenological shifts. However, the decreased and increased S was both found in warming scenarios. The conceptual scheme telling the nonlinear relationship between spring temperature and leaf unfolding date proposed by Fu et al . (Int J Biometeorol 60:1611-1613, 2016) cannot be supported by observation data across Europe. Therefore, linking declined S to climate warming is misleading, and future S changes are more uncertain than they suggested. [ABSTRACT FROM AUTHOR]

Spring warming substantially advances leaf unfolding and flowering time for perennials. Winter warming, however, decreases chilling accumulation (CA), which increases the heat requirement (HR) and acts to delay spring phenology. Whether or not this negative CA-HR relationship is correctly interpreted in ecosystem models remains unknown. Using leaf unfolding and flowering data for 30 perennials in Europe, here we show that more than half (7 of 12) of current chilling models are invalid since they show a positive CA-HR relationship. The possible reason is that they overlook the effect of freezing temperature on dormancy release. Overestimation of the advance in spring phenology by the end of this century by these invalid chilling models could be as large as 7.6 and 20.0 days under RCPs 4.5 and 8.5, respectively. Our results highlight the need for a better representation of chilling for the correct understanding of spring phenological responses to future climate change. Climate warming is advancing spring leaf unfolding, but it is also reducing the cold periods that many trees require to break winter dormancy. Here, the authors show that 7 of 12 current chilling models fail to account for the correct relationship between chilling accumulation and heat requirement, leading to substantial overestimates of the advance of spring phenology under climate change. [ABSTRACT FROM AUTHOR]

Existing evidence of plant phenological change to temperature increase demonstrates that the phenological responsiveness is greater at warmer locations and in early-season plant species. Explanations of these findings are scarce and not settled. Some studies suggest considering phenology as one functional trait within a plant's life history strategy. In this study, we adapt an existing phenological model to derive a generalized sensitivity in space (SpaceSens) model for calculating temperature sensitivity of spring plant phenophases across species and locations. The SpaceSens model have three parameters, including the temperature at the onset date of phenophases (Tp), base temperature threshold (Tb) and the length of period (L) used to calculate the mean temperature when performing regression analysis between phenology and temperature. A case study on first leaf date of 20 plant species from eastern China shows that the change of Tp and Tb among different species accounts for interspecific difference in temperature sensitivity. Moreover, lower Tp at lower latitude is the main reason why spring phenological responsiveness is greater there. These results suggest that spring phenophases of more responsive, early-season plants (especially in low latitude) will probably continue to diverge from the other late-season plants with temperatures warming in the future. [ABSTRACT FROM AUTHOR]

The global climate change-induced advanced spring green-up dates (GUDs) of vegetation in the past decades have been well documented. However, anthropogenic effects on GUDs are still unclear, and the impacts of widespread cropland expansion on GUDs are rarely quantified. Here, we examine the differences in GUDs between agroecosystems and natural ecosystems using both satellite and in-situ observations across China's ecosystems with a latitude ≥ 30°N and explore the potential effects of cropland expansion on vegetation spring phenology. We find that agroecosystems had significantly later GUDs than adjacent natural ecosystems according to satellite observations, with a delay of 22.26 ± 16.32 (Mean ± 1STD) days and 18.11 ± 11.75 days compared with woodland and grassland ecosystems, respectively. The later GUDs for agroecosystems is more distinct in the Northeast China Plain and Xinjiang Oasis region (>30 days), where land reclamation was remarkable. We also find a distinct delayed GUD trend (> 5 day/yr, p < 0.001) during 2001–2014 in a cropland expansion region in Xinjiang Province, which is much higher than the usual advancement of GUDs due to warming (usually ≤ 1 day/yr). In-situ and satellite observations of delayed GUDs agree better in agroecosystems than in forest and grassland ecosystems. More attention is needed to better understand the important role of cropland expansion in driving inter-annual change of vegetation spring phenology in addition to climate change, and integration of realistic crop green-up timing into climate models is also needed for accurate climate simulations. • Differences in the vegetation green-up dates (GUDs) between agroecosystems and natural ecosystems are quantified. • Cropland expansion delayed vegetation GUDs greatly according to satellite and in-situ observations. • Effects of cropland expansion on spring phenology change trends cannot be overlooked. [ABSTRACT FROM AUTHOR]

• Spring phenological traits of plant species in Northeast China are analyzed. • Spring phenological traits of plants are phylogenetically conserved. • Reproductive phenology is more phylogenetically conserved than vegetative phenology. • Native climate has a stronger effect than phylogeny on spring phenological traits. 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. [ABSTRACT FROM AUTHOR]