Comparison of six statistical approaches in the selection of appropriate fish growth models

Lixin Zhu; Lifang Li; Zhenlin Liang

doi:10.1007/s00343-009-9236-6

Your Location：

Home >

Browse articles >

Comparison of six statistical approaches in the selection of appropriate fish growth models

OriginalPaper | Updated：2023-05-16

- Comparison of six statistical approaches in the selection of appropriate fish growth models
- Comparison of six statistical approaches in the selection of appropriate fish growth models
- 海洋湖沼学报（英文） 2009年27卷第3期页码：457-467
- Affiliations：
  
  1. Marine College Shandong University at Weihai
  2. Fisheries College Ocean University of China
- Author bio：
- Funds：
  
  Supported by the High Technology Research and Development Program of China (863 Program, No. 2006AA100301)
- DOI：10.1007/s00343-009-9236-6
  中图分类号：
- 网络首发：2009-11-07，
  
  纸质出版：2009
- Accepted：
Scan QR Code
Comparison of six statistical approaches in the selection of appropriate fish growth models[J]. 海洋湖沼学报（英文）, 2009,27(3):457-467.

Zhu, L., Li, L. & Liang, Z. Comparison of six statistical approaches in the selection of appropriate fish growth models., Chin. J. Ocean. Limnol. 27, 457–467 (2009). https://doi.org/10.1007/s00343-009-9236-6
Comparison of six statistical approaches in the selection of appropriate fish growth models[J]. 海洋湖沼学报（英文）, 2009,27(3):457-467. DOI： 10.1007/s00343-009-9236-6.

Zhu, L., Li, L. & Liang, Z. Comparison of six statistical approaches in the selection of appropriate fish growth models., Chin. J. Ocean. Limnol. 27, 457–467 (2009). https://doi.org/10.1007/s00343-009-9236-6 DOI：

摘要

Abstract

The performance of six statistical approaches

which can be used for selection of the best model to describe the growth of individual fish

was analyzed using simulated and real length-at-age data. The six approaches include coefficient of determination (R 2)

adjusted coefficient of determination (adj.-R 2)

root mean squared error (RMSE)

Akaike’s information criterion (AIC)

bias correction of AIC (AIC c ) and Bayesian information criterion (BIC). The simulation data were generated by five growth models with different numbers of parameters. Four sets of real data were taken from the literature. The parameters in each of the five growth models were estimated using the maximum likelihood method under the assumption of the additive error structure for the data. The best supported model by the data was identified using each of the six approaches. The results show that R 2 and RMSE have the same properties and perform worst. The sample size has an effect on the performance of adj.-R 2

AIC

AIC c and BIC. Adj.-R 2 does better in small samples than in large samples. AIC is not suitable to use in small samples and tends to select more complex model when the sample size becomes large. AIC c and BIC have best performance in small and large sample cases

respectively. Use of AIC c or BIC is recommended for selection of fish growth model according to the size of the length-at-age data.

关键词

Keywords

references

Akaike H. 1973. Information theory as an extension of the maximum likelihood principle. In: Petrov B N and Csaki F (eds.), Second International Symposium on Information Theory. Akademiai Kiado, Budapest. p. 267–281.

Beverton R J H, Holt S J. 1957. On the dynamics of exploited fish populations. Chapman and Hall, London, Facsimile reprint 1993. p. 1–533.

Burnham K P, Anderson D R. 2002. Model Selection and Multimodel Inference: A Practical Information-theoretic Approach, 2nd ed. Springer, New York. p. 49–89.

Chen W K, Chen P C, Liu K M, Wang S B. 2007. Age and growth estimates of the whitespotted bamboo shark, Chiloscyllium plagiosum, in the Northern waters of Taiwan. Zoological Studies, 46(1): 92–102.

Deng J Y, Zhao C Y. 1991. Marine Fishery Biology. Agricultural Press, Beijing. p. 180–185. (in Chinese)

Fei H N, Zhang S Q. 1990. Science of Fisheries Resources. China Science and Technology Press, Beijing. p. 281–285. (in Chinese)

Ghosh J K, Samanta T. 2001. Model selection- an overview. Current Science, 80(9): 1 135–1 144.

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. Philosophical Transactions of the Royal Society of London, Series A, 115: 515–585.

Granada V P, Masuda Y, Matsuoka T. 2004. Age and growth of the yellowbelly threadfin bream Nemipterus bathybius in Kagoshima Bay, southern Japan. Fisheries Science, 70: 497–506.

Haddon M. 2001. Modeling and quantitative methods in fisheries. Champman and hall/CRC, Florida. p. 88–92.

Hilborn R, Walters C J. 1992. Quantitative Fisheries Stock Assessment, Choice, Dynamics and Uncertainty. Chapman and Hall, New York. p. 409–433.

Imai C, Sakai H, Katsura K, Honto W, Hida Y, Takazawa T. 2002. Growth model for the endangered cyprinid fish Tribolodon nakamurai based on otolish analyses. Fisheries Science, 68: 843–848.

Johnson J B, Omlang K S. 2004. Model selection in ecology and evolution. Trends in Ecology and Evolution, 19(2): 101–108.

Katsanevakis S. 2006. Modelling fish growth: model selection, multi-model inference and model selection uncertainty. Fisheries Research, 81: 229–235.

Kimura D K. 1980. Likelihood methods for the von Bertalanffy growth curve. Fishery Bulletin, 77(4): 765–776.

Kullback S, Leibler R A. 1951. On information and sufficiency. Annals of Mathematical Statistics, 22: 79–86.

Pauly D. 1979. Gill size and temperature as governing factors in fish growth: a generalization of von Bertalanffy’s growth formula. Ber. Inst. Meereskd. Christian-Albrechts Univ. Kiel, 63, 1–156

Porch C E, Wilson C A, Nieland D L. 2002. A new growth model for red drum (Sciaenops ocellatus) that accommodates seasonal and ontogenic changes in growth rates. Fishery Bulletin, 100: 149–152.

Quinn II T J, Deriso R B. 1999. Quantitative Fish Dynamics. Oxford University Press, New York. p. 128–165.

Ricker W E. 1975. Computation and interpretation of biological statistics of fish populations. Bulletin of the Fisheries Research Board of Canada, 191: 1–382.

Romine J G, Grubbs R D, Musick J A. 2006. Age and growth of the sandbar shark, Carcharhinus plumbeus, in Hawaiian waters through vertebral analysis. Environmental Biology of Fish, 77: 229–239.

Schnute F J, Richards L J. 1990. A unified approach to the analysis of fish growth, maturity, and survivorship data. Canadian Journal of Fisheries and Aquatic Science, 47: 24–40.

Schwarz G. 1978. Estimating the dimension of a model. Annals of Statistics, 6: 461–464.

Shono H. 2000. Efficiency of the finite correction of Akaike’s information criteria. Fisheries Science, 66: 608–610.

Sun J S, Ma S Q. 2004. Econometrics. Tsinghua University Press, Beijing. p. 119–121. (in Chinese)

Urban H J. 2002. Modeling growth of different developmental stages in bivalves. Marine Ecology Progress Series, 238: 109–114.

von Bertalanffy L. 1938. A quantitative theory of organic growth (Inquiries on growth laws II). Human Biology, 10: 181–213.

Wang Y J, Liu Q. 2006. Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of stock-recruitment relationships. Fisheries Research, 77(2): 220–225.

Wise B S. 2005. Age composition and growth rates of selected fish species in Western Australia. Ph. D thesis, Murdoch University, Western Australia. p. 1–4.

Yang Y H. 2005. Can the strengths of AIC and BIC be shared? A conflict between model identification and regression estimation. Biometrikal, 92(4): 937–950.

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Construction of a growth model in the green alga Tetraselmis subcordiformis using a response surface approach

高压涡轮封严流与主流相互作用的机理研究

双转子轴间滑油颗粒运动轨迹参数化模型

齿轮传动对转桨扇发动机总体性能建模

一种涡轮基组合动力的整机低速风洞试验研究