Predictions of Taylor's power law, density dependence and pink noise from a neutrally modeled time series
Abstract
We investigated the ability of a neutral model to predict phenomena observed in single-population time series. We consider tests for density dependence, spectral density, and a relationship between the mean and variance of a fluctuating population (Taylor´s power law).
Our spectral analysis showed ‘pink noise’: a departure from a standard random walk dynamics in favor of the higher frequency fluctuations which is consistent with empirical data. We detected density dependence in local community time series but not in metacommunity time series.
The slope of the Taylor´s power law in the model was similar to the slopes observed in natural populations, but the fit was worse. We conclude that some of the phenomena observed in natural time series can emerge from neutral processes, as a result of random zero-sum death, birth and migration.
This suggests the neutral model would be a parsimonious null model for future studies of time series data.