A semiparametric Bayesian approach to estimating maximum reproductive rates at low population sizes

Ecological Applications

Volumn 23, Issue 4, 2013, Pages 699-709

  Sugeno, Masatoshi ^a   Munch, Stephan B ^b  

^a STONY BROOK UNIVERSITY   (United States)

^b SOUTHWEST FISHERIES SCIENCE CENTER   (United States)

Author keywords

Allee effects; Depensation; Gaussian process; Maximum annual reproductive rate; Semiparametric Bayesian modeling; Slope at the origin; Stock recruitment relationship

Indexed keywords

ACCURACY ASSESSMENT; ALLEE EFFECT; BAYESIAN ANALYSIS; DATA SET; FISHERY MODELING; FISHING MORTALITY; GAUSSIAN METHOD; POPULATION SIZE; PRECISION; RECRUITMENT (POPULATION DYNAMICS); REPRODUCTIVE PRODUCTIVITY; SUSTAINABILITY;

ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; COMPUTER SIMULATION; FISH; PHYSIOLOGY; POPULATION DENSITY; REPRODUCTION;

ANIMALS; BAYES THEOREM; COMPUTER SIMULATION; FISHES; MODELS, BIOLOGICAL; POPULATION DENSITY; REPRODUCTION;

EID: 84878076592     PISSN: 10510761     EISSN: None     Source Type: Journal    
DOI: 10.1890/12-0453.1     Document Type: Article

References (49)

1. Barrowman, N. J., and R. A. Myers. 2000. Still more spawner-recruitment curves: the hockey stick and its generalizations. Canadian Journal of Fisheries and Aquatic Sciences 57:665-676.
2. Barrowman, N. J., R. A. Myers, R. Hilborn, D. G. Kehler, and C. A. Field. 2003. The variability among populations of coho salmon in the maximum reproductive rate and depensation. Ecological Applications 13:784-793.
3. Bohonak, A. J. 1999. Dispersal, gene flow, and population structure. Quarterly Review of Biology 74:21-45.
4. Bravington, M. V., T. K. Stokes, and C. M. O'Brien. 2000. Sustainable recruitment: the bottom line. Marine Freshwater Research 51:465-75.
5. Brodziak, J., and C. M. Legault. 2005. Model averaging to estimate rebuilding targets for overfished stocks. Canadian Journal of Fisheries and Aquatic Sciences 62:544-562.
6. Campana, S. E. 1999. Chemistry and composition of fish otoliths: pathways, mechanisms and applications. Marine Ecology Progress Series 188:263-297.
7. Chen, D. G., N. B. Hargreaves, D. M. Ware, and Y. Liu. 2000. A fuzzy logic model with genetic algorithm for analyzing fish stock-recruitment relationships. Canadian Journal of Fisheries and Aquatic Sciences 57:1878-1887.
8. Chen, D. G., and J. R. Irvine. 2001. A semiparametric model to examine stock-recruitment relationships incorporating environmental data. Canadian Journal of Fisheries and Aquatic Sciences 58:1178-1186.
9. Chen, D. G., and D. M. Ware. 1999. A neural network model for forecasting fish stock recruitment. Canadian Journal of Fisheries and Aquatic Sciences 56:2385-2396.
10. Cook, R. M. 1998. A sustainability criterion for the exploitation of North Sea cod. ICES Journal of Marine Science 55:1061-1070.
11. Courchamp, F., L. Berec, and J. Gascoigne. 2008. Allee effects in ecology and conservation. Oxford University Press, New York, New York, USA.
12. Dietz, T., E. Ostrom, and P. C. Stern. 2003. The struggle to govern the commons. Science 302:1907-1912.
13. Evans, G. T., and J. C. Rice. 1988. Predicting recruitment from stock size without the mediation of a functional relation. ICES Journal of Marine Science 44:111-122.
14. Gelfand, A., and S. Ghosh. 1998. Model choice: A minimum posterior predictive loss approach. Biometrika 85(1):1-11.
15. Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 2003. Bayesian data analysis. Second edition. Chapman and Hall/ CRC Press, Boca Raton, Florida, USA.
16. He, X., M. Mangel, and A. MacCall. 2006. A prior for steepness in stock-recruitment relationships, based on an evolutionary persistence principle. Fisheries Bulletin 104:428-433.
17. Hilborn, R., and C. J. Walters. 1992. Quantitative fisheries stock assessment: choice, dynamics and uncertainty. Chapman and Hall, New York, New York, USA.
18. Hill, S. L., G. M. Watters, A. E. Punt, M. K. McAllister, C. L. Quere, and J. Turner. 2007. Model uncertainty in the ecosystem approach to fisheries. Fish and Fisheries 8:315-336.
19. Kehler, D. G., R. A. Myers, and C. A. Field. 2002. Measurement error and bias in the maximum reproductive rate for the Ricker model. Canadian Journal of Fisheries and Aquatic Sciences 59:854-864.
20. Ludwig, D., R. Hilborn, and C. Waters. 1993. Uncertainty, resource exploitation, and conservation: lessons from history. Science 260:17, 36.
21. Ludwig, D., and C. J. Walters. 1981. Measurement errors and uncertainty in parameter estimates for stock and recruitment. Canadian Journal of Fisheries and Aquatic Sciences 38:711-720.
22. Mantzouni, I., H. Sørensen, R. B. O'Hara, and B. R. MacKenzie. 2010. Hierarchical modelling of temperature and habitat size effects on population dynamics of North Atlantic cod. ICES Journal of Marine Science 67:833-855.
23. Millar, R. B. 2002. Reference priors for Bayesian fisheries models. Canadian Journal of Fisheries and Aquatic Sciences 59:1492-1502.
24. Millar, R. B., and R. Meyer. 2000. State-space modeling of non-linear fisheries biomass dynamics using the Gibbs sampler. Applied Statistics 49:327-342.
25. Munch, S. B., A. Kottas, and M. Mangel. 2005. Bayesian nonparametric analysis of stock-recruitment relationships. Canadian Journal of Fisheries and Aquatic Sciences 62:1808-1821.
26. Myers, R. A. 2001. Stock and recruitment: generalizations about maximum reproductive rate, density dependence, and variability using meta-analytic approaches. ICES Journal of Marine Science 58:937-951.
27. Myers, R. A., and N. J. Barrowman. 1995. Time series bias in the estimation of density-dependent mortality in stock-recruitment models. Canadian Journal of Fisheries and Aquatic Sciences 52:223-232.
28. Myers, R. A., K. G. Bowen, and N. J. Barrowman. 1999. Maximum reproductive rate of fish at low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56:2404-2419.
29. Myers, R. A., B. R. MacKenzie, K. G. Bowen, and N. J. Barrowman. 2001. What is the carrying capacity for fish in the ocean? A meta-analysis of population dynamics of North Atlantic cod. Canadian Journal of Fisheries and Aquatic Sciences 58:1464-1476.
30. Myers, R. A., and G. Mertz. 1998. The limits of exploitation: A precautionary approach. Ecological Applications 8:S165-S169.
31. Myers, R. A., G. Mertz, and P. S. Fowlow. 1997. Maximum population growth rates and recovery times for Atlantic cod, Gadus morhua. Fisheries Bulletin 95:762-772.
32. Myers, R. A., A. A. Rosenberg, P. M. Mace, N. Barrowman, and V. R. Restrepo. 1994. In search of thresholds for recruitment overfishing. ICES Journal of Marine Science 51:191-205.
33. Ostrom, E., J. Burger, C. B. Field, R. B. Norgaard, and D. Policansky. 1999. Revisiting the commons: local lessons, global challenges. Science 284:278-282.
34. Quinn, T. J., and R. B. Deriso. 1999. Quantitative fish dynamics. Oxford University Press, New York, New York, USA.
35. Rasmussen, C. E., and C. K. I. Williams. 2006. Gaussian processes for machine learning. MIT Press, Cambridge, Massachusetts, USA.
36. Rothschild, B. J., and A. J. Mullen. 1985. The information content of stock-and-recruitment data and its non-parametric classification. ICES Journal of Marine Science 42:116-124.
37. Schirripa, M. J., C. P. Goodyear, and R. M. Methot. 2009. Testing different methods of incorporating climate data into the assessment of US West Coast sablefish. ICES Journal of Marine Science 66:1605-1613.
38. Shelton, P. A., and B. P. Healey. 1999. Should depensation be dismissed as a possible explanation for the lack of recovery of the northern cod (Gadus morhua) stock? Canadian Journal of Fisheries and Aquatic Sciences 56:1521-1524.
39. Turner, R., M. P. Deisenroth, and C. E. Rasmussen. 2010. State-space inference and learning with Gaussian processes. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), May 13-15, 2010, Sardinia, Italy. JMLR Workshop and Conference Proceedings 9:868-875.
40. Walters, C. J. 1990. A partial bias correction factor for stock-recruitment parameter estimation in the presence of autocorrelated environmental effects. Canadian Journal of Fisheries and Aquatic Sciences 47:516-519.
41. Walters, C. J., and D. Ludwig. 1981. Effects of measurement errors on the assessment of stock-recruitment relationships. Canadian Journal of Fisheries and Aquatic Sciences 38:704-710.
42. Walters, C. J., and S. J. D. Martell. 2004. Fisheries ecology and management. Princeton University Press, Princeton, New Jersey, USA.
43. Wang, S., G. Morishima, R. Sharma, and L. Gilbertson. 2009. The use of generalized additive models for forecasting the abundance of Queets River coho salmon. North American Journal of Fisheries Management 29:423-433.
44. Wang, Y., and Q. Liu. 2006. Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock-recruitment relationships. Fisheries Research 77:220-225.
45. Ward, E. J. 2008. A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools. Ecological Modelling 211:1-10.
46. West, M., and J. Harrison. 1997. Bayesian forecasting and time series analysis. Second edition. Springer, New York, New York, USA.
47. Wood, S. N., and M. B. Thomas. 1999. Super-sensitivity to structure in biological models. Proceedings of the Royal Society B 266:565-570.
48. Ylvisaker, N. D. 1965. The expected number of zeros of a stationary Gaussian process. Annals of Mathematical Statistics 36:1043-1046.
49. Zhou, S. 2007. Discriminating alternative stock-recruitment models and evaluating uncertainty in model structure. Fisheries Research 86:268-279.