Can steepness of the stock-recruitment relationship be estimated in fishery stock assessment models?

被引:97
作者
Lee, Hui-Hua [1 ,3 ]
Maunder, Mark N. [2 ]
Piner, Kevin R. [3 ]
Methot, Richard D. [4 ]
机构
[1] Univ Hawaii, Joint Inst Marine & Atmospher Res, Honolulu, HI 96822 USA
[2] Interamer Trop Tuna Commiss, La Jolla, CA 92037 USA
[3] NOAA Fisheries, Pacific Isl Fisheries Sci Ctr, Honolulu, HI 96822 USA
[4] NOAA Fisheries, Natl Marine Fisheries Serv, Seattle, WA 98112 USA
关键词
Pacific Coast groundfish stocks; Steepness; Stock assessment; Stock-recruitment; Stock synthesis; POPULATION-DYNAMICS; NATURAL MORTALITY; MANAGEMENT; INFERENCE;
D O I
10.1016/j.fishres.2012.03.001
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
Steepness of the stock recruitment relationship is one of the most uncertain and critical quantities in fishery stock assessment and management. Steepness is defined as the fraction of recruitment from a virgin population obtained when the spawners are at 20% of the virgin level. Steepness directly relates to productivity and yield and is an important element in the calculation of many management reference points. Stock recruitment relationships have traditionally been estimated from time series of recruitment and spawning biomass, but recently interest has arisen regarding the ability to estimate steepness inside fishery stock assessment models. We evaluated the ability to estimate steepness of the Beverton-Holt stock recruitment relationship using simulation analyses for twelve US Pacific Coast fish stocks. A high proportion of steepness estimates from the simulated data and the original data occur at the bounds for steepness and the proportion decreased as the true steepness decreased. The simulation results indicate that, in most cases, steepness was estimated with moderate to low precision and moderate to high bias. The poorly estimated steepness indicates that often there is little information in the data about this quantity. However, reliable estimation is attainable with a good contrast of spawning stock biomass for relatively unproductive stocks when the model is correctly specified. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:254 / 261
页数:8
相关论文
共 28 条
[1]  
[Anonymous], 2001, In all likelihood: statistical modelling and inference using likelihood
[2]  
Beverton R.J. H., 1993, On the dynamics of exploited fish populations
[3]   When can we reliably estimate the productivity of fish stocks? [J].
Conn, Paul B. ;
Williams, Erik H. ;
Shertzer, Kyle W. .
CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES, 2010, 67 (03) :511-523
[4]  
Dorn MW, 2002, N AM J FISH MANAGE, V22, P280, DOI 10.1577/1548-8675(2002)022<0280:AOWCRH>2.0.CO
[5]  
2
[6]   AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models [J].
Fournier, David A. ;
Skaug, Hans J. ;
Ancheta, Johnoel ;
Ianelli, James ;
Magnusson, Arni ;
Maunder, Mark N. ;
Nielsen, Anders ;
Sibert, John .
OPTIMIZATION METHODS & SOFTWARE, 2012, 27 (02) :233-249
[7]   The reliability of estimates of natural mortality from stock assessment models [J].
Francis, R. I. C. Chris .
FISHERIES RESEARCH, 2012, 119 :133-134
[9]  
He X, 2006, FISH B-NOAA, V104, P428
[10]  
HILBORN R, 1992