Title: Connections between Taylor’s power law of fluctuation scaling and population synchrony
Abstract: Taylor’s law (TL) is a widely observed empirical pattern that relates the variances to the means of groups of nonnegative measurements via an approximate power law: variance_g ≈ a × mean_g^b, where g indexes the group of measurements. TL has had practical applications in many areas since its initial demonstrations for the population density of spatially distributed species in population ecology, including fisheries management, conservation, agriculture, finance, physics, and meteorology. Another widely observed aspect of populations is spatial synchrony, which is the tendency for time series of population densities measured in different locations to be correlated through time. Synchrony of populations increases the likelihood of large-scale pest or disease outbreaks and shortages of resources. Recent studies showed that patterns of population synchrony are changing, possibly as a consequence of climate change. We use mathematical, numerical, and empirical approaches to show that synchrony affects the validity and parameters of TL. Synchrony influenced TL in essentially all of our analytic, numerical, randomization-based, and empirical examples. Given the near ubiquity of synchrony in nature, it seems likely that synchrony influences the exponent of TL widely in ecologically and economically important systems.