EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 33 (2020) Aquat. Living Resour., 33 (2020) 17 References
Free Access
Issue
Aquat. Living Resour.
Volume 33, 2020
Article Number 17
Number of page(s) 11
DOI https://doi.org/10.1051/alr/2020019
Published online 16 November 2020
  • Arlot S, Celisse A. 2010. A survey of cross-validation procedures for model selection. Statist Surv 4: 40–79. [CrossRef] [MathSciNet] [Google Scholar]
  • Bo L, Wang L, Jiao L. 2006. Feature scaling for kernel fisher discriminant analysis using leave-one-out cross validation. Neural Comput 18: 961–978. [Google Scholar]
  • Brooks SP, Roberts GO. 1998. Convergence assessment techniques for Markov chain Monte Carlo. Stat Comput 8: 319–335. [Google Scholar]
  • Brosse S, Beauchard O, Blanchet S, Dürr HH, Grenouillet G, Hugueny B, Oberdorff T. 2013. Fish-SPRICH: a database of freshwater fish species richness throughout the World. Hydrobiologia 700: 343–349. [Google Scholar]
  • Bustos R, Luque Á, Pajuelo JG. 2009. Age estimation and growth pattern of the island grouper, Mycteroperca fusca (Serranidae) in an island population on the northwest coast of Africa. Sci Mar 73: 319–328. [CrossRef] [Google Scholar]
  • Cailliet GM, Smith WD, Mollet HF, Goldman KJ. 2006. Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environ Biol Fish 77: 211–228. [CrossRef] [Google Scholar]
  • Carlin BP, Louis TA. Bayesian Methods for Data Analysis. 3rd edn, CPC Press, Boca Raton, 2009. [Google Scholar]
  • Chen Y, Jiao Y, Chen L. 2003. Developing robust frequentist and Bayesian fish stock assessment methods. Fish Fish 4: 105–120. [CrossRef] [Google Scholar]
  • Cowles MK, Carlin BP. 1996. Markov chain Monte Carlo convergence diagnostics: a comparative review. J Am Stat Assoc 91: 883–904. [Google Scholar]
  • Eastwood S, Couture P. 2002. Seasonal variations in condition and liver metal concentrations of Yellow Perch (Perca flavescens) from a metal-contaminated environment. Aquat Toxicol 58: 43–56. [CrossRef] [PubMed] [Google Scholar]
  • Essington TE, Kitchell JF, Walters CJ. 2001. The von Bertalanffy growth function, bioenergetics, and the consumption rates of fish. Can J Fish Aquat Sci 58: 2129–2138. [Google Scholar]
  • Froese R, Thorson JT, Reyes RB. 2014. A Bayesian approach for estimating length-weight relationships in fishes. J Appl Ichthyol 30: 78–85. [Google Scholar]
  • Gelman A, Hwang J, Vehtari A. 2014. Understanding predictive information criteria for Bayesian models. Stat Comput 24: 997–1016. [Google Scholar]
  • Gelman A, Rubin DB. 1992. Inference from iterative simulation using multiple sequences. Stat Sci 21: 457–472. [Google Scholar]
  • Gompertz B. 1825. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Phil Trans of the Royal Soc 182: 513–585. [Google Scholar]
  • Haddon M. Modelling and Quantitative Methods in Fisheries . 2nd edn, Chapman and Hall, New York, 2010. [Google Scholar]
  • Haddon M, Mundy C, Tarbath D. 2008. Using an inverse-logistic model to describe growth increments of blacklip abalone (Haliotis rubra) in Tasmania. FISH B-NOAA 106: 58–71. [Google Scholar]
  • He JX, Rudstam LG, Forney JL, VanDeValk AJ, Stewart DJ. 2005. Long-term patterns in growth of Oneida Lake Walleye: a multivariate and stage-explicit approach for applying the von Bertalanffy growth function. J Fish Biol 66: 1459–1470. [Google Scholar]
  • Helser TE, Lai HL. 2004. A Bayesian hierarchical meta-analysis of fish growth: with an example for North American Largemouth Bass, Micropterus salmoides . Ecol Model 178: 399–416. [CrossRef] [Google Scholar]
  • Heyer CJ, Miller TJ, Binkowski FP, Caldarone EM, Rice JA. 2001. Maternal effects as a recruitment mechanism in Lake Michigan Yellow Perch (Perca flavescens). Can J Fish Aquat Sci 58: 1477–1487. [Google Scholar]
  • Jessop BM. 2010. Geographic effects on American eel (Anguilla rostrata) life history characteristics and strategies. Can J Fish Aquat Sci 67: 326–346. [Google Scholar]
  • Jiao Y, Neves R, Jones J. 2008. Models and model selection uncertainty in estimating growth rates of endangered freshwater mussel populations. Can J Fish Aquat Sci 65: 2389–2398. [Google Scholar]
  • Jiao Y, Reid K, Nudds T. 2006. Variation in the catchability of Yellow Perch (Perca flavescens) in the fisheries of Lake Erie using a Bayesian error-in-variable approach. ICES J Mar Sci 63: 1695–1704. [Google Scholar]
  • Jiao Y, Reid K, Nudds T. 2010. Consideration of uncertainty in the design and use of harvest control rules. Sci Mar 74: 371–384. [CrossRef] [Google Scholar]
  • Jonsson B, Jonsson N. 2009. A review of the likely effects of climate change on anadromous Atlantic salmon Salmo salar and brown trout Salmo trutta, with particular reference to water temperature and flow. J Fish Biol 75: 2381–2447. [PubMed] [Google Scholar]
  • Kang B, Deng J, Wu Y, Chen L, Zhang J, Qiu H, He D. 2014. Mapping China's freshwater fishes: diversity and biogeography. Fish Fish 15: 209–230. [CrossRef] [Google Scholar]
  • Katsanevakis S. 2006. Modelling fish growth: model selection, multi-model inference and model selection uncertainty. Fish Res 81: 229–235. [Google Scholar]
  • Katsanevakis S, Maravelias CD. 2008. Modelling fish growth: multi-model inference as a better alternative to a priori using von Bertalanffy equation. Fish Fish 9: 178–187. [CrossRef] [Google Scholar]
  • Kimura DK. 1980. Likelihood methods for the von Bertalanffy growth curve. Fisheries Bulletin 77: 765–774. [Google Scholar]
  • Kuikka S, Vanhatalo J, Pulkkinen H, Mäntyniemi S, Corander J. 2014. Experiences in Bayesian inference in Baltic Salmon management. Stati Sci 29: 42–49. [MathSciNet] [Google Scholar]
  • Langaas M, Lindqvist BH, Ferkingstad E. 2005. Estimating the proportion of true null hypotheses, with application to DNA microarray data. J R Stat Soc B 67: 555–572. [CrossRef] [MathSciNet] [Google Scholar]
  • Liu C, Wan R, Jiao Y. 2017. Exploring non-stationary and scale-dependent relationships between walleye (Sander vitreus) distribution and habitat variables in Lake Erie. Mar Freshwater Res 68: 270–281. [CrossRef] [Google Scholar]
  • Michielsens CG, McAllister MK. 2004. A Bayesian hierarchical analysis of stock recruit data: quantifying structural and parameter uncertainties. Can J Fish Aquat Sci 61: 1032–1047. [Google Scholar]
  • Misra R.K. 1986. Fitting and comparing several growth curves of the generalized von Bertalanffy type. Can J Fish Aquat Sci 43: 1656–1659. [Google Scholar]
  • Nylander JA, Wilgenbusch JC, Warren DL, Swofford DL. 2008. AWTY (are we there yet?): a system for graphical exploration of MCMC convergence in Bayesian phylogenetics. Bioinformatics 24: 581–583. [CrossRef] [PubMed] [Google Scholar]
  • O'Hara RB, Sillanpää MJ. 2009. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal 4: 85–117. [Google Scholar]
  • Ohnishi S, Yamakawa T, Okamura H, Akamine T. 2012. A note on the von Bertalanffy growth function concerning the allocation of surplus energy to reproduction. Fish B-NOAA 110: 223–229. [Google Scholar]
  • Panhwar SK. Some aspects of fish population dynamics of the commercial fish species in Pakistan. PhD Thesis. Ocean University of China, Qingdao, 2012. [Google Scholar]
  • Pannell DJ. 1997. Sensitivity analysis: strategies, methods, concepts, examples. Agric Econ 16: 139–152. [Google Scholar]
  • Pardo SA, Cooper AB, Reynolds JD, Dulvy NK. 2018. Quantifying the known unknowns: estimating maximum intrinsic rate of population increase in the face of uncertainty. ICES J Mar Sci 75: 953–963. [Google Scholar]
  • Pilling GM, Kirkwood GP, Walker SG. 2002. An improved method for estimating individual growth variability in fish, and the correlation between von Bertalanffy growth parameters. Can J Fish Aquat Sci 59: 424–432. [Google Scholar]
  • Quinn TJ, Deriso RB. Quantitative Fish Dynamics . 1st edn, Oxford University Press, Oxford, 1999. [Google Scholar]
  • Román-Román P, Romero D, Torres-Ruiz F. 2010. A diffusion process to model generalized von Bertalanffy growth patterns: Fitting to real data. J Theor Biol 263: 59–69. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • Schneider JC. Manual of fisheries survey methods II: with periodic updates. Michigan Department of Natural Resources, Fisheries Special Report 25, Michigan, USA, 2000. [Google Scholar]
  • Schnute J, Richards LJ. 1990. A unified approach to the analysis of fish growth, maturity, and survivorship data. Can J Fish Aquat Sci 47: 24–40. [Google Scholar]
  • Soriano M, Moreau J, Hoenig JM, Pauly D. 1992. New functions for the analysis of two-phase growth of juvenile and adult fishes, with application to Nile Perch. T Am Fish Soc 121: 486–493. [CrossRef] [Google Scholar]
  • Stockwell JD, Yule DL, Gorman OT, Isaac EJ, Moore SA. 2006. Evaluation of bottom trawls as compared to acoustics to assess adult Lake Herring (Coregonus artedi) abundance in Lake Superior. J Great Lakes Res 32: 280–292. [Google Scholar]
  • Spiegelhalter DJ, Best NG, Carlin BP, Linde A. 2014. The deviance information criterion: 12 years on. J R Stat Soc B 76: 485–493. [CrossRef] [MathSciNet] [Google Scholar]
  • Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. 2002. Bayesian measures of model complexity and fit. J R Stat Soc B 64: 583–639. [CrossRef] [MathSciNet] [Google Scholar]
  • Szalai EB, Fleischer GW, Bence JR. 2003. Modelling time-varying growth using a generalized von Bertalanffy model with application to bloater (Coregonus hoyi) growth dynamics in Lake Michigan. Can J Fish Aquat Sci 60: 55–66. [Google Scholar]
  • Tang M, Jiao Y, Jones JW. 2014. A hierarchical Bayesian approach for estimating freshwater mussel growth based on tag-recapture data. Fish Res 149: 24–32. [Google Scholar]
  • Vilizzi L, Copp GH. 2017. Global patterns and clines in the growth of common carp Cyprinus carpio . J Fish Biol 91: 3–40. [CrossRef] [PubMed] [Google Scholar]
  • Vrugt JA, Ter Braak CJ, Gupta HV, Robinson BA. 2009. Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modelling? Stoch Env Res Risk A 23: 1011–1026. [CrossRef] [Google Scholar]
  • Wilberg MJ, Bence JR. 2008. Performance of deviance information criterion model selection in statistical catch-at-age analysis. Fish Res 93: 212–221. [Google Scholar]
  • Zhu X, Tallman RF, Howland KL, Carmichael TJ. 2016. Modeling spatiotemporal variabilities of length-at-age growth characteristics for slow-growing subarctic populations of Lake Whitefish, using hierarchical Bayesian statistics. J Great Lakes Res 42: 308–318. [Google Scholar]
  • Zellner A. 1971. Bayesian and non-bayesian analysis of the log-normal distribution and log-normal regression. J Am Stat Assoc 66: 327–330. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.