Scaling phenology from the local to the regional level: advances from species-specific phenological models

Plant phenology, the study of seasonal plant activity driven by environment al factors, has found a renewal in the context of global climate change. Ph enological events, such as leaf unfolding, exert strong control over season al exchanges of matter and energy between the land surface and the atmosphe re. Phenological models that simulate the start of the growing season shoul d be efficient tools to predict vegetation responses to climatic changes an d related changes in energy balance. Species-specific phenological models d eveloped in the eighties have not been used for global-scale predictions be cause their predictions were inaccurate in external conditions. Recent adva nces in phenology modelling at the species level suggest that prediction at a large scale may now be possible. In the present study, we tested the per formance of species-specific phenological models in time and space, looking at their ability (i) to predict regional phenology when previously fitted at a local scale, and (ii) to predict phenological trends, linked to climat e changes, observed over a long-term. For that task we used an historical p henological dataset from Ohio from the late ninetieth century and an airbor ne pollen dataset from Ontario, Quebec and Maryland from the late twentieth century. The results show that the species-specific phenological models us ed in this study were able to predict regional phenology even though they w ere fitted locally. The reconstruction of a phenological time series over t he twentieth century showed a significant advancement of 0.2 days per year in the date of flowering of Ulmus americana, but very weak trends for Fraxi nus americana and Quercus velutina.