International Journal of Biometeorology

, Volume 59, Issue 7, pp 837–848 | Cite as

Models to predict the start of the airborne pollen season

  • Consolata Siniscalco
  • Rosanna Caramiello
  • Mirco Migliavacca
  • Lorenzo Busetto
  • Luca Mercalli
  • Roberto Colombo
  • Andrew D. Richardson
Original Paper

Abstract

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 seven 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.

Keywords

Airborne pollen Ambrosia artemisiifolia Artemisia vulgaris Chilling units Forcing units Phenology models Winter dormancy Turin 

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Copyright information

© ISB 2014

Authors and Affiliations

  • Consolata Siniscalco
    • 1
  • Rosanna Caramiello
    • 1
  • Mirco Migliavacca
    • 2
  • Lorenzo Busetto
    • 3
  • Luca Mercalli
    • 4
  • Roberto Colombo
    • 5
  • Andrew D. Richardson
    • 6
  1. 1.Department of Life Sciences and Systems BiologyUniversity of TorinoTorinoItaly
  2. 2.Max Planck Institute for BiogeochemistryJenaGermany
  3. 3.CNR-IREAMilanItaly
  4. 4.Società Meteorologica ItalianaBussolenoItaly
  5. 5.The Remote Sensing of Environmental Dynamics LaboratoryUniversity of Milano-BicoccaMilanItaly
  6. 6.Department of Organismic and Evolutionary BiologyHarvard UniversityCambridgeUSA

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