Estimating population models from uncertain observations is an important problem in ecology. Perretti et al. observed that standard Bayesian state-space solutions to this problem may provide biased parameter estimates when the underlying dynamics are
chaotic. Consequently, forecasts based on these estimates showed poor predictive accuracy compared to simple model-free methods, which lead Perretti et al. to conclude that Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data. However, a simple modification of the statistical methods also suffices to remove the bias and reverse their results.
