Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling

Classical budburst models (Spring Warming, Sequential, Parallel and Alterna ting) are unable to fully predict external data, partly because of the meth ods of optimization used to adjust them. The purpose of this study was to e xamine different assumptions of budburst models and select those which are best supported by the data, defining new models able to predict external da ta. Eight models, each differing in one assumption, were fitted and tested using external data. The dataset used to test the models was deduced from a eropalynological data at two stations in France. The results show that some of the models proposed are able to accurately predict external dates of fl owering of most of the studied species. The assumptions of those models hav e been individually tested and shown to improve the models accuracy. Robust estimates of the best predictor models of 12 tree species are presented. T he analysis of hypothetical provenance transfer of two species, Buxus sempe rvirens and Platanus acerifolia, between the two study sites, shows that P. acerifolia estimates are similar in both environments whereas B. sempervir ens estimates are variable. This result, which agrees with the genetic char acteristics of both species, shows that local adaptation of phenology can a lso be studied through modelling approaches.