Bayesian calibration of the Unified budburst model in six temperate tree speciesBayesian calibration of the Unified budburst model in six temperate tree species
Faculty of Sciences. Biology
Plant and Vegetation Ecology (PLECO)
International journal of biometeorology. - Amsterdam
56(2012):1, p. 153-164
University of Antwerp
Numerous phenology models developed to predict the budburst date of trees have been merged into one Unified model (Chuine, 2000, J. Theor. Biol. 207, 337347). In this study, we tested a simplified version of the Unified model (Unichill model) on six woody species. Budburst and temperature data were available for five sites across Belgium from 1957 to 1995. We calibrated the Unichill model using a Bayesian calibration procedure, which reduced the uncertainty of the parameter coefficients and quantified the prediction uncertainty. The model performance differed among species. For two species (chestnut and black locust), the model showed good performance when tested against independent data not used for calibration. For the four other species (beech, oak, birch, ash), the model performed poorly. Model performance improved substantially for most species when using site-specific parameter coefficients instead of across-site parameter coefficients. This suggested that budburst is influenced by local environment and/or genetic differences among populations. Chestnut, black locust and birch were found to be temperature-driven species, and we therefore analyzed the sensitivity of budburst date to forcing temperature in those three species. Model results showed that budburst advanced with increasing temperature for 13 days °C−1, which agreed with the observed trends. In synthesis, our results suggest that the Unichill model can be successfully applied to chestnut and black locust (with both across-site and site-specific calibration) and to birch (with site-specific calibration). For other species, temperature is not the only determinant of budburst and additional influencing factors will need to be included in the model.