• 1/f noise;
  • co-fluctuation;
  • environmental fluctuations;
  • Flicker noise;
  • hierarchical Bayesian modelling;
  • periodogram


  1. Autocorrelation within ecological time series and synchrony between them may provide insight into the main drivers of observed dynamics.
  2. We here present methods that analyse autocorrelation and synchrony in ecological datasets using a spectral approach combined with Bayesian inference.
  3. To exemplify, we implement the method on dendrochronological data of the pedunculate oak (Quercus robur). The data consist of 110 years of growth of 10 live trees and seven trees that died during a synchronized oak death in Sweden in c. 2002–2007. We find that the highest posterior density is found for a noise colour of tree growth of γ ≈ 0·95 (i.e. ‘pink noise’) with little difference between trees, suggesting climatic variation as a driving factor. This is further supported by the presence of synchrony, which we estimate based on phase-shift analysis. We conclude that the synchrony is time-scale dependent with higher synchrony at larger time-scales. We further show that there is no difference between the growth patterns of the alive and dead tree groups. This suggests that the trees were driven by the same factors prior to the synchronized death.
  4. We argue that this method is a promising approach for linking theoretical models with empirical data.