Avoiding tipping points in fisheries management through Gaussian process dynamic programming.

Area of Science:

• Ecological modeling
• Bayesian statistics
• Stochastic dynamic programming

Background:

• Model uncertainty and limited data challenge effective human intervention in natural systems.
• Ecological systems may possess tipping points, critical thresholds beyond which collapse is imminent.
• The location and existence of these tipping points are often unknown prior to collapse.

Purpose of the Study:

• To develop a robust management framework addressing model uncertainty and data limitations in ecological systems.
• To investigate the efficacy of a Bayesian non-parametric approach for ecological management.
• To compare the performance of Gaussian process dynamic programming (GPDP) against standard model selection methods.

Main Methods:

• Utilized a Bayesian non-parametric approach with a Gaussian process (GP) prior for flexible uncertainty representation.
• Embedded GPs within a stochastic dynamic programming framework to generate robust management predictions.
• Evaluated the GPDP approach through simulations, comparing it with traditional model selection techniques.

Main Results:

• Standard model selection favored models without tipping points, leading to policies that caused population extinction.
• Gaussian process dynamic programming (GPDP) significantly outperformed standard approaches.
• GPDP-based management avoided population crashes by appropriately accounting for uncertainty outside observed data.

Conclusions:

• The GPDP approach provides a more reliable method for managing ecological systems with unknown tipping points and limited data.
• Traditional model selection is inadequate for handling the inherent uncertainties in ecological dynamics, potentially leading to detrimental management outcomes.
• Bayesian non-parametric methods offer a promising avenue for robust decision-making in complex environmental management scenarios.