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Mathematical Modeling and Intensive Simulations Assess Chances for Recovery of the Collapsed Azov Pikeperch Population

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  • Yuri V. Tyutyunov

    (Southern Scientific Centre of the Russian Academy of Sciences (SSC RAS), Chekhov Street, 41, Rostov-on-Don 344006, Russia)

  • Inna Senina

    (The Pacific Community (SPC), BP D5, Noumea 98848, New Caledonia)

Abstract

The main objective of the study is to evaluate the recovery potential of the collapsed semi-anadromous pikeperch population ( Sander lucioperca L.) in the Azov Sea during 2021–2030. We use a Ricker-based age-structured model that accounts for the effects of salinity and temperature on reproduction. In earlier work, the model predicted and explained the pikeperch stock collapse as the consequence of salinity and temperature exceeding the species’ tolerance limits. To assess the probability of stock recovery, we conducted a long-term retrospective validation and ran Monte Carlo projections under alternative climate scenarios with supplemental management actions. The results confirm that the dynamics of the pikeperch population in the Azov Sea are essentially environment-driven and negatively impacted by the large positive anomalies in both water temperature and salinity. Simulations suggest that either a substantial and persistent artificial restocking of juvenile recruits, or mostly unlikely scenarios of simultaneous reduction in salinity and temperature combined with additional restocking can provide conditions for the stock restoration within the decade considered. Based on these projections, we recommend a suite of urgent restoration measures to create the conditions required for future stock recovery.

Suggested Citation

  • Yuri V. Tyutyunov & Inna Senina, 2025. "Mathematical Modeling and Intensive Simulations Assess Chances for Recovery of the Collapsed Azov Pikeperch Population," Mathematics, MDPI, vol. 13(19), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:19:p:3232-:d:1767123
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    References listed on IDEAS

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    1. Jacobs, Bas & Tobi, Hilde & Hengeveld, Geerten M., 2024. "Linking error measures to model questions," Ecological Modelling, Elsevier, vol. 487(C).
    2. Vitaly G. Il’ichev & Dmitry B. Rokhlin, 2022. "Internal Prices and Optimal Exploitation of Natural Resources," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    3. Revutskaya, Oksana & Neverova, Galina & Frisman, Efim, 2025. "Fluctuations in real population sizes: Mathematical modeling and estimation of demographic parameters," Ecological Modelling, Elsevier, vol. 507(C).
    4. Aleksandr Abakumov & Yuri Izrailsky, 2022. "Optimal Harvest Problem for Fish Population—Structural Stabilization," Mathematics, MDPI, vol. 10(6), pages 1-16, March.
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