Spatio-temporal modelling and assessment of within-species phenological variability using thermal time methods.

Phenological observations of flowering date, budding date or senescence provide very valuable time series. They hold out the prospect for relating plant growth to environmental and climatic factors and hence for engendering a better understanding of plant physiology under natural conditions. The statistical establishment of associations between time series of phenological data and climatic factors provides a means of aiding forecasts of the biological impacts of future climatic change. However, it must be kept in mind that plant growth and behaviour vary spatially as well as temporally. Environmental, climatic and genetic diversity can give rise to spatially structured variation on a range of scales. The variations extend from large-scale geographical (clinal) trends, through medium-scale population and sub-population fluctuations, to micro-scale differentiation among neighbouring plants, where spatially close individuals are found to be genetically more alike than those some distance apart. We developed spatio-temporal phenological models that allow observations from multiple locations to be analysed simultaneously. We applied the models to the first-flowering dates of Prunus padus and Tilia cordata from localities as far apart as Norway and the Caucasus. Our growing-degree-day approach yielded a good fit to the available phenological data and yet involved only a small number of model parameters. It indicated that plants should display different sensitivities to temperature change according to their geographical location and the time of year at which they flower. For spring-flowering plants, we found strong temperature sensitivities for islands and archipelagos with oceanic climates, and low sensitivities in the interiors of continents.