Synchrony: quantifying variability in space and time
- 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.