Taylor′s law (TL), a commonly observed and applied pattern in ecology, describes variances of population densities as related to mean densities via log(variance)=log(a)+b*log(mean). Variations among datasets in the slope, b, have been associated with multiple factors of central importance in ecology, including strength of competitive interactions and demographic rates. But the mechanisms producing these associations are poorly understood. TL is thus a ubiquitously used indicator in ecology the mechanisms behind which are still opaque. Using theoretical analysis, numeric simulations, and 82 multi-decadal population datasets, we here propose, test, and apply two proximate statistical determinants of TL slopes which we argue can become key tools for understanding the nature and ecological causes of TL slope variation.
Results/Conclusions
Measures based on population skewness, coefficient of variation, and synchrony are strongly correlated with TL slopes, are simpler than the slope itself, are more readily linked to ecological mechanisms, and thus are effective proximate determinants. Greater synchrony typically decreases the exponent b of spatial TL while increases that of temporal TL. 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. We also apply the two proximate determinants by using them to help explain ranges and taxonomic heterogeneity of slopes, and covariation in slopes of spatial and temporal TL. This study provides and applies tools for understanding how TL is linked to ecological mechanisms.