Abstract
Sustainable management of natural resources requires a good understanding of ecosystems components and their interrelationships. Statistics is essential for understanding the structure and behaviour of ecological processes and provides the basis of predictive modelling. Mostly, physical, chemical, and biological variables are recorded across time and space. They serve as indicators, giving information concerning the state and changes of ecosystems. Most of monitored ecological indicators are non-stationary in time structure. The classical static statistical methods revealed the presence of trends and long memories in these data sets. On the other hand, modern dynamic statistical methods indicate the presence of long-term cycling processes. The Fourier polynomial is a technique for approximating periodic functions by sums of cosine and sine periodic functions, shifted and scaled. Therefore, it may be suitable for approximating cycling processes with a fixed frequency as portrayed by some ecological indicators. Wavelet analysis can be used to investigate the timescale behaviour of ecological processes. This analysis reveals the long-term evolution of an ecological indicator at different resolutions, the dominant scale of variability in the data set, and its correlation and cross-correlation with other ecological indicators on a scale by scale basis.
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Gnauck, A., Li, BL.L., Feugo, J.D.A., Luther, B. (2010). The Role of Statistics for Long-Term Ecological Research. In: Müller, F., Baessler, C., Schubert, H., Klotz, S. (eds) Long-Term Ecological Research. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8782-9_8
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