Models to predict the start of the airborne pollen season

Aerobiological data can be used as indirect but reliable measures of flowering phenology to analyze the response of plant species to ongoing climate changes. The aims of this study are to evaluate the performance of several phenological models for predicting the pollen start of season (PSS) in 7 spring-flowering trees (Alnus glutinosa, Acer negundo, Carpinus betulus, Platanus occidentalis, Juglans nigra, Alnus viridis and Castanea sativa) and in two summer-flowering herbaceous species (Artemisia vulgaris and Ambrosia artemisiifolia) by using a 26-year aerobiological data set collected in Turin (Northern Italy). Data showed a reduced interannual variability of the PSS in the summer flowering species compared to the spring flowering ones. Spring Warming models with photoperiod limitation performed best for the greater majority of the studied species, while Chilling class models were selected only for the early spring flowering species. For Ambrosia and Artemisia, Spring Warming models were also selected as the best models, indicating that temperature sums are positively related to flowering. However, the poor variance explained by the models suggests that further analyses have to be carried out in order to develop better models for predicting the PSS in these two species. Modeling the pollen season start on a very wide data set provided a new opportunity to highlight the limits of models in elucidating the environmental factors driving the pollen season start when some factors are always fulfilled, as chilling or photoperiod or when the variance is very poor and is not explained by the models.