인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Objective Little Tunny Euthynnus alletteratus is a commercially viable species, and its research has been prioritized by the International Commission for the Conservation of Atlantic Tunas. This study aimed to evaluate the stock status of Little Tunny in the Northeast Atlantic Ocean, the effectiveness of the life history priors, and the sensitivity of the model's predicted reference points to prior misspecification. The findings could help reduce uncertainties regarding the state of Little Tunny in the region. Methods We used empirically estimated life history parameters and purse-seine fish aggregating device (FAD) and free school fishing length-frequency data from 2000 to 2021, available in the International Commission for the Conservation of Atlantic Tunas database. We applied the length-based Bayesian biomass (LBB) and length-based spawning potential ratio assessment methods. Result The LBB's outputs indicate that Little Tunny was grossly overfished at the exerted fishing pressure. This was corroborated by length-based spawning potential ratio estimates of low spawning per recruit for the stock exploited by purse seines under FAD and free school sets. The reference points of both models were most sensitive to asymptotic length (L∞) prior misspecification, and including life history priors in LBB produced a narrower confidence interval of the estimated reference points. Conclusion The Northeast Atlantic Ocean Little Tunny fishery exploited by purse-seine fisheries under FAD and free school sets may be unsustainable. We maintain that user priors could reduce uncertainty in LBB, whereas accurate specification of priors, particularly L∞, is required to have less biased results using the models. We recommend catch limits through, for instance, spatial closures, but caution is advised due to uncertainty in this study's life history priors.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
