Classical budburst models (Spring Warming, Sequential, Parallel and Alternating) are unable to fully predict external data, partly because of the methods of optimization used to adjust them. The purpose of this study was to examine different assumptions of budburst models and select those which are best supported by the data, defining new models able to predict external data. Eight models, each differing in one assumption, were fitted and tested using external data. The dataset used to test the models was deduced from aeropalynological data at two stations in France. The results show that some of the models proposed are able to accurately predict external dates of flowering of most of the studied species. The assumptions of those models have been individually tested and shown to improve the models accuracy. Robust estimates of the best predictor models of 12 tree species are presented. The analysis of hypothetical provenance transfer of two species, Buxus sempervirens and Platanus acerifolia, between the two study sites, shows that P. acerifolia estimates are similar in both environments whereas B. sempervirens estimates are variable. This result, which agrees with the genetic characteristics of both species, shows that local adaptation of phenology can also be studied through modelling approaches.
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