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Evaluation of virgin recruitment profiling as a diagnostic for selectivity curve structure in integrated stock assessment models
被引:27
|作者:
Wang, Sheng-Ping
[1
,5
,6
]
Maunder, Mark N.
[2
,5
]
Piner, Kevin R.
[3
]
Aires-da-Silva, Alexandre
[2
]
Lee, Hui-Hua
[4
]
机构:
[1] Natl Taiwan Ocean Univ, Dept Environm Biol & Fisheries Sci, Keelung 202, Taiwan
[2] Interamer Trop Tuna Commiss, La Jolla, CA 92037 USA
[3] Natl Ocean & Atmospher Adm, SW Fisheries Sci Ctr, La Jolla, CA 92037 USA
[4] Univ Hawaii, Joint Inst Marine & Atmospher Res, Honolulu, HI 96822 USA
[5] Univ Calif San Diego, Scripps Inst Oceanog, Ctr Adv Populat Assessment Methodol, La Jolla, CA 92093 USA
[6] Natl Taiwan Ocean Univ, Ctr Excellence Oceans, Keelung 202, Taiwan
关键词:
Virgin recruitment;
Likelihood profile;
Stock assessment;
Selectivity;
Diagnostic;
Bigeye tuna;
AGE DATA;
CATCH;
SIZE;
D O I:
10.1016/j.fishres.2013.12.009
中图分类号:
S9 [水产、渔业];
学科分类号:
0908 ;
摘要:
Virgin recruitment (R-0), the equilibrium recruitment in the absence of fishing, is an often used parameter in fisheries stock assessment for scaling population size. We describe and evaluate the use of the R-0 likelihood component profile to diagnose selectivity misspecification, using simulation analysis for bigeye tuna in the eastern Pacific Ocean. The profile is evaluated under two types of selectivity misspecification: (1) misspecified shape and (2) misspecified temporal variation. The results indicate that length-composition data can provide substantial information on R-0 estimation when the model is correctly specified, but can substantially bias estimates of absolute abundance when selectivity is misspecified. Although contradictory profiles for length-composition and abundance index data result from selectivity misspecification, they may not be useful in determining which survey or fishery selectivity is misspecified. The R-0 profile selectivity diagnostic is based on the influence of composition data on absolute abundance. However, perhaps a more problematic and difficult to detect issue is the impact of length-composition data on biomass trends. The age-structured production model diagnostic could be applied to identify bias in both absolute biomass and biomass trend caused by age- or length-composition data in the presence of model misspecification. (C) 2014 Published by Elsevier B.V.
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页码:158 / 164
页数:7
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