Differences in initial abundances reveal divergent dynamic structures in Gause's predator-prey experiments

Area of Science:

• Ecology
• Ecological Dynamics
• Mathematical Ecology

Background:

• Forecasting complex ecological dynamics is limited by the difficulty in distinguishing between system complexity and stochasticity.
• Predictability declines in ecosystems can stem from stochasticity, nonlinearity, or chaotic behavior, posing a challenge for management and prediction.
• Quantifying the drivers of unpredictability is crucial for advancing ecological forecasting.

Purpose of the Study:

• To develop a method for quantifying the contributions of stochasticity, nonlinearity, and chaos to prediction error in ecological systems.
• To apply this method to Georgii Gause's classic predator-prey microcosm experiments.
• To investigate how initial abundances influence the interplay between these factors and prediction error.

Main Methods:

• Utilized Georgii Gause's predator-prey microcosm experiments with replicate populations differing only in initial abundances.
• Quantified the relative contributions of stochasticity, nonlinearity, and chaos to prediction error.
• Analyzed the interaction between initial abundances and dynamic factors (stochasticity, nonlinearity, chaos).

Main Results:

• Demonstrated a method to disentangle the effects of stochasticity, nonlinearity, and chaos on prediction error.
• Showed that initial abundances significantly interact with these dynamic factors, influencing prediction error.
• Identified specific impacts of these interactions on the predictability of ecological systems.

Conclusions:

• Jointly analyzing replicate time series from multiple starting points is essential for understanding complex ecological dynamics.
• The proposed quantification method aids in differentiating between complexity and stochasticity in ecological forecasting.
• Improved understanding of these factors can lead to better management strategies for future ecosystem states.