Modélisation de la dynamique de population du Requin-Taupe commun de l'Atlantique Nord-Est - Barreau, Julie (2024)
Since the first landing of porbeagle sharks in 1926, their exploitation in Europe has intensified, beginning in France in 1972. By the late 1990’s, a decline in catches led to management measures and evaluations starting in the 2000’s, culminating in a total fishing ban in 2010. An evaluation in 2022 showed a relative improvement in the stock, although it remains degraded, and highlighted the need to improve models for more accurate forecasts. To address these challenges, this study explores French fishery data from 1972 to 2009 to enhance knowledge of the Northeast Atlantic porbeagle stock and provide valuable information for future management. New biomass indices were developed, including several on bycatch standardized using a generalized additive model, enabling a new assessment via the SPiCT biomass surplus model. The results show a relative improvement since 2010, although the stock remains degraded. Long-term projections indicate that reopening fishing at FMSY would return the stock to biomass at maximum sustainable yield in over a hundred years. Additionally, biases in the OSA residuals of the new indices suggest a possible overestimation of biomass. The study underscores the importance of estimating discards since the fishing ban for accurate assessments. It also highlights the need for more reliable data collection, through increased collaboration between fishers and scientists and scientific campaigns, to better understand the biology and ecology of the porbeagle and improve future stock management. Depuis le premier de barquement de requin-taupe en 1926, son exploitation en Europe s'est intensifiée, débutant en France en 1972. A la fin des années 1990, une diminution des captures a entrainé des mesures de gestion et des évaluations dès les années 2000, culminant avec l'arrêt total de la pêche en 2010. Une évaluation en 2022 a montré une amélioration relative du stock, bien que celui-ci reste de gradé, et la nécessite d’améliorer les modèles pour la précision des prévisions. Pour répondre à ces ...