Search

A comparative assessment of bud and wood phenology could aid a better understanding of tree growth dynamics. However, the reason for asynchronism or synchronism in leaf and cambial phenology remains unclear. To test the assumption that the temporal relationship between the budburst date and the onset date of wood formation is due to their common or different responses to environmental factors, we constructed a wood phenology dataset from previous literature, and compared it with an existing bud phenology dataset in Europe. We selected three common conifers (Larix decidua Mill. Picea abies (L.) H. Karst. and Pinus sylvestris L.) in both datasets and analyzed 909 records of the onset of wood formation at 47 sites and 238,720 records of budburst date at 3051 sites. We quantified chilling accumulation (CA) and forcing requirement (FR) of budburst and onset of wood formation based on common measures of CA and FR. We then constructed negative exponential CA–FR curves for bud and wood phenology separately. The results showed that the median, variance and probability distribution of CA–FR curves varied significantly between bud and wood phenology for three conifers. The different FR under the same chilling condition caused asynchronous bud and wood phenology. Furthermore, the CA–FR curves manifested that wood phenology was more sensitive to chilling than bud phenology. Thus, the FR of the onset of wood formation increases more than that of budburst under the same warming scenarios, explaining the stronger earlier trends in the budburst date than the onset date of woody formation simulated by the process-based model. Our work not only provides a possible explanation for asynchronous bud and wood phenology from the perspective of organ-specific responses to chilling and forcing, but also develops a phenological model for predicting both bud and wood phenology with acceptable uncertainties. [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]

• 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]

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]