Fig. 1.

Estimated Lyapunov exponent of the logistic model (A), and predictive accuracy (B) when the true model is the theta-logistic model (for r = 1.8, and q = 4.0). Similar to standard maximum likelihood estimates, the Lyapunov exponent estimates using the method proposed by Hartig and Dormann (2) are consistently biased toward stable dynamics (Lyapunov exponent < 0) when the true dynamics were often chaotic (Lyapunov exponent > 0). In addition, the state–space reconstruction (SSR) method exhibits substantially lower forecast error than the fitted logistic model (B). Forecast error is represented by standardized root mean-square error (RMSE); the shaded interval represents the 95% confidence interval.