Modeling Climate-Driven Profit and Biomass Dynamics in Moroccan Sardine Fisheries: A Markov Chain Perspective

This study develops a stochastic hybrid bioeconomic framework to quantify daily profit variability in the Moroccan sardine (Sardina pilchardus) fisheries, a vital sector for the Atlantic coast of Agadir. The species is currently listed as Least Concern on the IUCN Red List of Threatened Species; however, regional stocks face increasing pressures from climate variability, overfishing, and habitat degradation, underscoring the urgency of adaptive management strategies. Moving beyond static annual averages, the model couples a continuous-time logistic growth process with a discrete-time Markov chain to simulate regime-switching environmental states. We define three operational weather regimes—Good (G), Medium (M), and Bad (B)—parameterized via seasonal transition matrices derived from longitudinal meteorological data (2018–2025). The deterministic analysis identifies an optimal Maximum Economic Yield (MEY) effort of 96.67 vessels, maintaining a conservation biomass buffer of 2.58 × 106 tons and generating a baseline annual profit of 8.41M MAD. Systematic sensitivity analysis reveals that these fisheries are biologically anchored, with economic rent being highly sensitive to carrying capacity (K) and growth rate (r) but resilient to operational cost shocks. Stochastic simulations at daily resolution demonstrate that seasonal weather patterns significantly dictate financial risk: spring and summer provide high stability and maximum liquidity, while winter regimes introduce significant volatility, reducing catchability by up to 90% during adverse storm fronts. By integrating environmental stochasticity directly into the profit function, this research provides a robust decision-support tool for adaptive fisheries management, ensuring that management strategies are grounded in both ecological sustainability and high-frequency operational reality. Importantly, the modeling framework is designed with modular flexibility, allowing for extension across multiple temporal scales (seasonal, annual, decadal) and to other fish species and geographic regions as data become available, thereby supporting comparative bioeconomic assessments globally. The findings directly contribute to the United Nations Sustainable Development Goals (SDGs).