期刊
FISH AND FISHERIES
卷 23, 期 2, 页码 462-477出版社
WILEY
DOI: 10.1111/faf.12627
关键词
elasmobranchs; empirical methods; Euler-Lotka; fishing mortality; maximum sustainable yield; spawning potential ratio
类别
资金
- WCPFC
- CSIRO
Sharks typically have long lifespans, slow growth rates, and low fecundity, making them vulnerable to fishing. Developing management reference points for elasmobranchs based on their life-history information is more feasible than traditional stock assessments. Evaluating natural mortality rate estimators is crucial for developing reference points, with different methods producing varying results for estimating fishing mortality reference points.
Sharks (class Chondrichthyes, subclass Elasmobranchii) typically have a long lifespan, slow growth rate and low fecundity, leading to low productivity and hence relatively high vulnerability to fishing. Information for managing fisheries catches of elasmobranchs is often lacking, because elasmobranchs are usually non-target species, often with low abundance. It is more feasible to develop management reference points for elasmobranchs based on their life-history information than through traditional stock assessments. The natural mortality rate (M) is the leading life-history parameter (LHP) required by many methods for developing reference points and is itself often derived indirectly from other LHPs. In this paper, we evaluate nine M estimators, using 15 shark stocks in the Western and Central Pacific Ocean as examples. We then compare four methods for developing fishing mortality reference points F-msy, including empirical modelling, the Euler-Lotka equation, an M-productivity-based method and the spawning potential rate converted method. Our analyses show that popular M estimators developed mainly from teleost data resulted in large deviations from the average and were not suitable for elasmobranchs. All four methods for estimating F-msy performed similarly. However, the empirical method is very simple and cost-effective and tended to produce smaller deviations from the average than the other three methods. Nonetheless, it is recommended that multiple methods should be used to minimize possible bias and reflect uncertainty, if the required LHPs are available.
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