Abstract
The identification of changes in observational data relating to human induced climate change remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. Analyses based on the BAYES approach here offer new possibilities to describe long-term phenological time series. The first example of this chapter will focus on the model comparison option of the Bayesian approach that was used to compare three different types of models (constant, linear, and one change point) for the analysis of three species in Germany. In addition to the functional behaviour, rates of change in terms of days per year were also calculated. The second example of this chapter illustrates the application of the Bayesian method to several phases throughout the year in two different countries. Thus we particularly investigate phenological changes of different phases and seasons.
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