Synchrony: quantifying variability in space and time
Article first published online: 10 MAY 2014
© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society
Methods in Ecology and Evolution
Volume 5, Issue 6, pages 524–533, June 2014
How to Cite
Gouhier, T. C., Guichard, F. (2014), Synchrony: quantifying variability in space and time. Methods in Ecology and Evolution, 5: 524–533. doi: 10.1111/2041-210X.12188
- Issue published online: 11 JUN 2014
- Article first published online: 10 MAY 2014
- Accepted manuscript online: 28 MAR 2014 07:09AM EST
- Manuscript Accepted: 6 MAR 2014
- Manuscript Received: 27 MAY 2013
- time-series analysis;
- spatial statistics;
- There is growing recognition that linking patterns to their underlying processes in interconnected and dynamic ecological systems requires data sampled at multiple spatial and temporal scales.
- However, spatially explicit and temporally resolved data sets can be difficult to analyze using classical statistical methods because the data are typically autocorrelated and thus violate the assumption of independence.
- Here, we describe the synchrony package for the R programming environment, which provides modern parametric and nonparametric methods for (i) quantifying temporal and spatial patterns of auto- and cross-correlated variability in univariate, bivariate, and multivariate data sets, and (ii) assessing their statistical significance via Monte Carlo randomizations.
- We illustrate how the methods included in the package can be used to investigate the causes of spatial and temporal variability in ecological systems through a series of examples, and discuss the assumptions and caveats of each statistical procedure in order to provide a practical guide for their application in the real world.