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A spectral and Bayesian approach for analysis of fluctuations and synchrony in ecological datasets
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-7856-2925
University of New S Wales, Australia .
Skovde University, Sweden .
Linköping University, Department of Physics, Chemistry and Biology, Ecology. Linköping University, The Institute of Technology.
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2012 (English)In: Methods in Ecology and Evolution, ISSN 2041-210X, Vol. 3, no 6, 1019-1027 p.Article in journal (Refereed) Published
Abstract [en]

Autocorrelation within ecological time series and synchrony between them may provide insight into the main drivers of observed dynamics. We here present methods that analyse autocorrelation and synchrony in ecological datasets using a spectral approach combined with Bayesian inference. 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 gamma approximate to 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. We argue that this method is a promising approach for linking theoretical models with empirical data.

Place, publisher, year, edition, pages
Wiley-Blackwell , 2012. Vol. 3, no 6, 1019-1027 p.
Keyword [en]
1/f noise; co-fluctuation; environmental fluctuations; Flicker noise; hierarchical Bayesian modelling; periodogram
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-88346DOI: 10.1111/j.2041-210X.2012.00240.xISI: 000312462000008OAI: diva2:602853
Available from: 2013-02-04 Created: 2013-02-04 Last updated: 2016-08-31

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