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According to prevailing theory, air temperature is the main environmental factor regulating the timing of bud burst of boreal and temperate trees. Air temperature has a dual role in this regulation. First, after the cessation of growth in autumn, prolonged exposure to chilling causes rest completion, i.e., removes the physiological growth-arresting conditions inside the bud. After rest completion, prolonged exposure to warm conditions causes ontogenetic development leading to bud burst or flowering. During the past three decades, several simulation models based on chilling and forcing have been developed and tested. In recent modeling studies of the timing of bud burst in mature trees, the simpler thermal-time models that assume forcing starts on a fixed date in the spring have outperformed the chilling-forcing models. We hypothesize that this discrepancy may be due to some element missing from the chilling-forcing models.We tested two new model formulations by introducing reversing, temperature-driven elements that precede forcing and by fitting the models to seven historical time series of data of flowering and leaf bud burst of common boreal tree species. In these tests, both of the new models were generally more accurate in predicting the timing of bud burst than a classical chilling-forcing model, but less accurate than the simple thermal-time model. We therefore conclude that besides chilling, other environmental factors are involved in the regulation of the timing of bud burst. Further work is needed to determine if the regulatory factors derive from air temperature or from some other environmental condition such as changes in light conditions, like day length or night length. [ABSTRACT FROM PUBLISHER]

Timing of bud burst and frost damage risk for leaves of Betula spp. in response to climatic warming in Finland was examined with two models. In the first model, ontogenetic development in spring was triggered by an accumulation of chilling temperatures. The second model assumed an additional signal from the light climate. The two models gave radically different estimates of frost damage risk in response to climate warming. [ABSTRACT FROM PUBLISHER]

We tested three theories predicting the timing of bud burst in mature birch (Betula pendula Roth) trees utilizing a 60-year phenological time series together with meteorological temperature observations. Predictions of the timing of bud burst based on light conditions in addition to temperature were more accurate than predictions based on dormancy development and temperature (prediction standard error of 2.4 days versus 4.3 days). The signal from light conditions, represented by fixed calendar date, determined the start of bud ontogenesis rather than dormancy release. We suggest that models developed to predict the timing of bud burst be utilized in the analysis of plant responses to climate change and of climate change itself. [ABSTRACT FROM PUBLISHER]

Collecting phenological data is a slow process. Although such data have been collected by a number of organizations, the reliability of these data is not known because the data-generating process cannot be repeated. No further observations to improve the reliability can be obtained. However, the data usually consist of several overlapping observation series and this overlap can be utilized to construct a combined phenological time series and to improve its reliability. We have developed two techniques for selecting the most reliable observations or observation series and thereby improve the reliability of the combined time series. Both techniques require that the method used to combine the separate phenological time series adjusts the individual series to eliminate possible systematic differences between them. A data set of bud burst in Betula pendula Roth collected in Central Finland during 1896–1955 was adjusted and used to test both techiques. Both techniques considerably improved the reliability of the combined time series; the mean of the confidence intervals of the annual means decreased by 12%. Despite the improvement in reliability, the resulting changes in the annual values of the combined time series were small, the largest change being 2.5 days. Removing outliers was the most effective method of improving reliability, i.e., it resulted in the greatest improvement with the smallest number of discarded observations. [ABSTRACT FROM PUBLISHER]