Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data.

Area of Science:

• Ecology
• Ecological Modeling
• Time Series Analysis

Background:

• Accurate species abundance prediction is a major ecological challenge, often hindered by nonlinear population dynamics.
• Recent advances offer statistical techniques for fitting complex mechanistic models and simpler model-free forecasting methods.

Purpose of the Study:

• To evaluate if correctly specified mechanistic models, fitted using standard techniques, outperform simple model-free methods for ecological forecasting.
• To assess the accuracy of mechanistic models versus model-free approaches in predicting species abundance in systems with noisy nonlinear dynamics.

Main Methods:

• Utilized four control models and seven experimental time series data from flour beetle populations.
• Applied Markov chain Monte Carlo (MCMC) procedures for fitting mechanistic models.
• Employed a model-free state-space reconstruction method for forecasting.

Main Results:

• MCMC procedures frequently resulted in parameter estimates that deviated significantly from known parameters for mechanistic models.
• Consequently, these mechanistic models produced inaccurate forecasts and misleading ecological inferences.
• The model-free state-space reconstruction method yielded the most accurate short-term predictions, even with limited data.

Conclusions:

• Standard statistical fitting of mechanistic ecological models can lead to poor predictive performance and incorrect inferences.
• Model-free forecasting approaches, particularly state-space reconstruction, demonstrate superior accuracy for short-term ecological predictions.
• Flexible model-free methods are recommended for advancing ecological predictions and supporting ecosystem-based management.