Synchrony: quantifying variability in space and time

Abstract

Summary 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.

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