Mathematical models for sustainable fisheries management in U.S. waters: Balancing economic growth and conservation

Sustainable fisheries management is critical for balancing the dual objectives of economic growth and environmental conservation, particularly in U.S. waters where overfishing, habitat degradation, and fluctuating marine ecosystems pose significant challenges. This review explores the role of mathematical models in addressing these challenges and guiding fisheries management decisions. Various models, including population dynamics, bioeconomic, and ecosystem-based approaches, are examined for their utility in setting sustainable catch limits, assessing stock health, and predicting ecosystem changes. Through case studies of U.S. fisheries, the paper highlights how these models have been implemented within the framework of federal regulations, such as the Magnuson-Stevens Act, to achieve sustainable yields while supporting economic growth. The review also identifies trade-offs between short-term economic gains and long-term conservation goals, emphasizing the need for adaptive management strategies. Finally, it explores emerging innovations in mathematical modeling techniques, such as artificial intelligence, and provides policy recommendations to enhance the integration of these models into sustainable fisheries management. This paper underscores the importance of mathematical models as essential tools in ensuring the future viability of U.S. fisheries, balancing economic prosperity with the preservation of marine ecosystems.

Authors

Publication Details

Journal
Magna Scientia Advanced Biology and Pharmacy
Published
2024-10-19
DOI
https://doi.org/10.30574/msabp.2024.13.1.0061
Citations
1
Primary Topic
Marine and fisheries research
Type
article
Field-Weighted Citation Impact
0.28
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article

Mathematical models for sustainable fisheries management in U.S. waters: Balancing economic growth and conservation

Francis Agbo Idoko, Josephine Biya Aladetan, Otugene Victor Bamigwojo
1 citations
Magna Scientia Advanced Biology and Pharmacy
Marine and fisheries research
0.28
article

Mathematical models for sustainable fisheries management in U.S. waters: Balancing economic growth and conservation

Francis Agbo Idoko, Josephine Biya Aladetan, Otugene Victor Bamigwojo
article en
1 citations

Abstract

Sustainable fisheries management is critical for balancing the dual objectives of economic growth and environmental conservation, particularly in U.S. waters where overfishing, habitat degradation, and fluctuating marine ecosystems pose significant challenges. This review explores the role of mathematical models in addressing these challenges and guiding fisheries management decisions. Various models, including population dynamics, bioeconomic, and ecosystem-based approaches, are examined for their utility in setting sustainable catch limits, assessing stock health, and predicting ecosystem changes. Through case studies of U.S. fisheries, the paper highlights how these models have been implemented within the framework of federal regulations, such as the Magnuson-Stevens Act, to achieve sustainable yields while supporting economic growth. The review also identifies trade-offs between short-term economic gains and long-term conservation goals, emphasizing the need for adaptive management strategies. Finally, it explores emerging innovations in mathematical modeling techniques, such as artificial intelligence, and provides policy recommendations to enhance the integration of these models into sustainable fisheries management. This paper underscores the importance of mathematical models as essential tools in ensuring the future viability of U.S. fisheries, balancing economic prosperity with the preservation of marine ecosystems.

Magna Scientia Advanced Biology and PharmacyVol. 13(1)
Openalex Percentile: Top 41%
Marine and fisheries research
0.28
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Mathematical models for sustainable fisheries management in U.S. waters: Balancing economic growth and conservation — Francis Agbo Idoko, Josephine Biya Aladetan, et al. · Magna Scientia Advanced Biology and Pharmacy (2024) | TGRS Research Map | TGRS