Bootstrap methods for estimating spatial synchrony of fluctuating populations

Abstract

E. 2005. Bootstrap methods for estimating spatial synchrony of fluctuating populations. Á/ Oikos 109: 342 Á/350.We describe and examine methods for estimating spatial correlations used in population ecology. We base our analyses on a hypothetical example of a species that has been censured at 30 different locations for 20 years. We assume that the population fluctuations can be described by a simple linear model on logarithmic scale. Stochastic simulations is utilized to check how seven different ways of resampling perform when the goal is to find nominal 95% confidence intervals for the spatial correlation in growth rates at given distances. It turns out that resampling of locations performs badly, with true coverage level as low as 30 Á/40%, especially for small correlations at long distances. Resampling of timepoints performs much better, with coverage varying from 80 to 90%, depending on the strength of density regulation and whether the spatial correlation is estimated for the response variable or for the error terms in the model. Assuming that the underlying model is known, the best results are achieved for parametric bootstrapping, a result that strongly emphasize the importance of defining and estimating a proper population model when studying spatial processes.Synchronous fluctuations in size of populations that are geographically separated from each other have now been demonstrated in many taxa, including birds (Ranta et al.

Measurements of spatial population synchrony: influence of time series transformations

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Population synchrony of a native fish across three Laurentian Great Lakes: evaluating the effects of dispersal and climate

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Spatial dynamics of Microtus vole populations in continuous and fragmented agricultural landscapes

“…Ranta et al 1995b;Bjørnstad et al 1999b;Koenig 1999), we executed a bootstrap procedure to estimate 95% conWdence limits for the mean level of synchrony in each landscape, and for the coeYcient of correlation between pairwise synchrony and distance between sites. As advocated by Lillegård et al (2005), we drew 1,000 bootstrap replicates of the population growth rate series by resampling (with replacement) time points instead of sites. Thus, each bootstrap replicate randomly generated a new time series of population growth rates for each site from the original growth rate values.…”