364 
Fishery Bulletin 115(3) 
Gislason, H., N. Daan, J. C. Rice, and J. G. Pope. 
2010. Size, growth, temperature and the natural mortal- 
ity of marine fish. Fish Fish. 11:149-158. 
Hamel, O. S. 
2015. A method for calculating a meta-analytical prior for 
the natural mortality rate using multiple life history cor- 
relates. ICES J. Mar. Sci. 72:62-69. 
Hewitt, D. A., and J. M. Hoenig. 
2005. Comparison of two approaches for estimating natu- 
ral mortality based on longevity. Fish. Bull. 103:433-437. 
Hofert, M., I. Kojadinovic, M. Maechler, and J. Yan. 
2015. Copula: multivariate dependence with copulas. R 
package vers. 0.999-13. [Available from website.] 
Lopez Quintero, F. O., J. E. Contreras-Reyes., R. Wiff, and R. 
B. Arellano-Valle. 
2017. Flexible Bayesian analysis of the von Bertalanffy 
growth function with the use of a log-skew-^ distribu- 
tion. Fish. Bull. 115:13-26. 
Marchenko, Y. V., and M. G. Genton. 
2010. Multivariate log-skew-elliptical distributions with 
applications to precipitation data. Environmetrics 
21:318-340. 
McAllister, M. K., E. K. Pikitch, A. E. Punt, and R. Hilborn. 
1994. A Bayesian approach to stock assessment and har- 
vest decisions using the sampling/importance resampling 
algorithm. Can. J. Fish. Aquat. Sci. 51:2673-2687. 
McNeil, A. J., R. Frey, and P. Embrechts. 
2005. Quantitative risk management: concepts, techniques 
and tools, 538 p. Princeton Univ. Press, Princeton, NJ. 
Millar, R. B., and R. Meyer. 
2000. Bayesian state-space modeling of age-structured 
data: fitting a model is just the beginning. Can. J. Fish. 
Aquat. Sci. 57:43-50. 
Montenegro, C., and M. Branco. 
2016. Bayesian state-space approach to biomass dynamic 
models with skewed and heavy-tailed error distribu- 
tions. Fish. Res. 181:48-62. 
Nelsen, R. B. 
2006. An introduction to copulas, 2nd ed., 272 p. Spring- 
er- Verlag, New York. 
Patterson, K. R. 
1999. Evaluating uncertainty in harvest control law 
catches using Bayesian Markov chain Monte Carlo vir- 
tual population analysis with adaptive rejection sam- 
pling and including structural uncertainty. Can. J. Fish. 
Aquat. Sci. 56:208-221. 
Pauly, D. 
1980. On the interrelationships between natural mortal- [ 
ity, growth parameters, and mean environmental temper- 
ature in 175 fish stocks. ICES J. Mar. Sci.39:175-192. 
Plummer, M. 
2003. JAGS: a program for analysis of Bayesian graphical ' 
models using Gibbs sampling. In Proceedings of the 3rd j 
international workshop on distributed statistical com- I 
puting (DSC 2003); Vienna, Austria, 20-22 March (K. 
Hornik, F. Leisch, and A. Zeileis, eds.), 10 p. [Available j 
from website.] 
Quiroz, J. C., R. Wiff, and B. Caneco. 
2010. Incorporating uncertainty into estimation of j 
natural mortality for two species of Rajidae fished in 
Chile. Fish. Res. 102:297-304. 
R Core Team. 
2014. R: a language and environment for statistical com- » 
puting. R Foundation for Statistical Computing, Vienna, J 
Austria. [Available from website, accessed April 2014.] | 
Sainsbury, K. J. 
1980. Effect of individual variability on the von Ber- i 
talanffy growth equation. Can. J. Fish. Aquat. Sci. j 
37:241-247. 
Siegfried, K. I., and B. Sanso. 
2006. Two Bayesian methods for estimating parameters f 
of the von Bertalanffy growth equation. Environ. Biol. 3 
Fish. 77:301-308. 
Vetter, E. F. 
1988. Estimation of natural mortality in fish stocks: a re- 1 
view. Fish. Bull. 86:25-43. 
Wiff, R., J. C. Quiroz, V. Ojeda., and M. A. Barrientos. 
2011. Estimation of natural mortality and uncertainty in j, 
pink cusk-eel ( Genypterus blacodes Schneider, 1801) in ( 
southern Chile. Lat. Am. J. Aquat. Res. 39:316-326. 
