Research Article
PDF Zotero Mendeley EndNote BibTex Cite

Effect of different parametrization methods on von Bertalanffy growth model of Saurida lessepsianus (Russell, Golani & Tikochinski, 2015)

Year 2015, Volume 32, Issue 4, 205 - 208, 16.01.2016
https://doi.org/10.12714/egejfas.2015.32.4.05

Abstract

In this study, effect of different parametrization on the von Bertalannfy growth model of Saurida lessepsianus has been investigated. For this purpose, Galucci and Quinn parametrization, Mooij parametrization, Francis parametrization and Schnute parametrization were used. Reparametrizated models have been compared via Akaike Information Criterion (AIC), confidence intervals and parameter correlations. Hence Francis parametrization method has been found as the most suitable parametrization method.

References

  • Baty, F., Ritz, C., Charles, S., Brutsche, M., Flandrois, J.P., Delignette Muller, M.L., 2014. A toolbox for nonlinear regression in R: the package nlstools. Journal of Statistical Software, 66(5):1-21.
  • Beverton, R.J., 1994. Notes on the use of theoretical models in the study of the dynamics of exploited fish populations: from lectures by RJH Beverton presented at US Fishery Laboratory, Beaufort, North Carolina, Bureau of Commercial Fisheries, Vol. 1. Marine Fisheries Section, American Fisheries Society, USA, 159.
  • Beverton, R.J., Holt, S.J., 2012. On the dynamics of exploited fish populations Vol. 11. Springer Science Business Media, UK, 456
  • Bolker, B.M., Gardner, B., Maunder, M., Berg, C.W., Brooks, M., Comita, L., Ford, J., 2013. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods in Ecology and Evolution, 4(6), 501-512. doi: 10.1111/2041-210X.12044
  • Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical informationtheoretic approach, Springer Science Business Media, USA, 488
  • Cailliet, G.M., Smith, W.D., Mollet, H. F., Goldman, K.J., 2006. Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environmental Biology of Fishes, 77:211-228. doi: 10.1007/978-1-4020-5570-6_2
  • Francis, R., 1988. Are growth parameters estimated from tagging and agelength data comparable? Canadian Journal of Fisheries and Aquatic Sciences, 456: 936-942. doi: 10.1139/f88-115
  • Gallucci, V.F., Quinn, T.J., 1979. Reparameterizing, fitting, and testing a simple growth model. Transactions of the American Fisheries Society, 108(1): 14-25. doi: 10.1577/1548-
  • (1979)108<14:RFATAS>2.0.CO;2
  • Haddon, M., 2010. Modelling and quantitative methods in fisheries. CRC press, USA, 449
  • Helser, T.E., Lai, H.L., 2004. A Bayesian hierarchical metaanalysis of fish growth: with an example for North American largemouth bass, Micropterus salmoides. Ecological Modelling, 178(3): 399-416. doi: 10.1016/j.ecolmodel.2004.02.013
  • Katsanevakis, S., Maravelias, C.D., 2008. Modelling fish growth: multi‐model inference as a better alternative to a priori using von Bertalanffy equation. Fish and Fisheries, 92: 178-187. doi: 10.1111/j.1467-2979.2008.00279.x
  • Knight, W., 1968. Asymptotic growth: an example of nonsense disguised as mathematics. Journal of the Fisheries Board of Canada, 25(6): 1303-1307. doi: 10.1139/f68-114
  • Mooij, W., Van Rooij, J., Wijnhoven, S., 1999. Analysis and comparison of fish growth from small samples of lengthatage data: detection of sexual dimorphism in Eurasian perch as an example. Transactions of the American Fisheries Society, 128(3): 483-490. doi: 10.1577/1548-8659(1999)128<0483:AACOFG>2.0.CO;2
  • Ogle, D.H., 2012. FSA: Fisheries stock analysis. R package version 0.2–8
  • Ogle, D.H., 2013. fishR Vignette-von Bertalanffy growth models. Ashland, WI: Northland College, 54.
  • Ogle, D.H., 2015. Introductory Fisheries Analyses with R. CRC Press, USA, 317. doi: 10.1201/b19232-19
  • Quinn, T.J., Deriso, R.B., 1999. Quantitative fish Dynamics. Oxford University Press, UK, 542
  • Ritz, C., Streibig, J.C., 2008. Nonlinear regression with R. Springer Science Business Media, USA, 63
  • Roff, D.A., 1980. A motion for the retirement of the von Bertalanffy function. Canadian Journal of Fisheries and Aquatic Sciences, 37(1): 127-129. doi: 10.1139/f80-016
  • Schnute, J., Fournier, D., 1980. A new approach to lengthfrequency analysis: growth structure. Canadian Journal of Fisheries and Aquatic Sciences, 37(9) 1337-1351. doi: 10.1139/f80-172
  • Schnute, J., 1981. A versatile growth model with statistically stable parameters. Canadian Journal of Fisheries and Aquatic Sciences, 38:1128-1140. doi: 10.1139/f81-153
  • Team, R.C., 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Farklı parametrizasyon tekniklerinin Saurida lessepsianus (Russell, Golani & Tikochinski, 2015)’un von Bertalanffy büyüme parametrelerinin tahminine etkisi

Year 2015, Volume 32, Issue 4, 205 - 208, 16.01.2016
https://doi.org/10.12714/egejfas.2015.32.4.05

Abstract

Bu çalışmada Saurida lessepsianus’un von Bertalanffy büyüme modeli parametreleri tahminine, farklı parametrizasyon tekniklerinin etkisi incelenmiştir. Bu amaçla Galucci ve Quinn parametrizasyonu, Mooij parametrizasyonu, Francis parametrizasyonu ve Schnute parametrizasyonu kullanılmıştır. Akaike Bilgi Kriteri (AIC), güvenirlik aralıkları ve parametreler arası korelasyonlar yardımıyla modeller karşılaştırılmıştır. Buna göre en uygun parametrizasyon yönteminin Francis parametrizasyon yöntemi olduğu tespit edilmiştir.

References

  • Baty, F., Ritz, C., Charles, S., Brutsche, M., Flandrois, J.P., Delignette Muller, M.L., 2014. A toolbox for nonlinear regression in R: the package nlstools. Journal of Statistical Software, 66(5):1-21.
  • Beverton, R.J., 1994. Notes on the use of theoretical models in the study of the dynamics of exploited fish populations: from lectures by RJH Beverton presented at US Fishery Laboratory, Beaufort, North Carolina, Bureau of Commercial Fisheries, Vol. 1. Marine Fisheries Section, American Fisheries Society, USA, 159.
  • Beverton, R.J., Holt, S.J., 2012. On the dynamics of exploited fish populations Vol. 11. Springer Science Business Media, UK, 456
  • Bolker, B.M., Gardner, B., Maunder, M., Berg, C.W., Brooks, M., Comita, L., Ford, J., 2013. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods in Ecology and Evolution, 4(6), 501-512. doi: 10.1111/2041-210X.12044
  • Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical informationtheoretic approach, Springer Science Business Media, USA, 488
  • Cailliet, G.M., Smith, W.D., Mollet, H. F., Goldman, K.J., 2006. Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environmental Biology of Fishes, 77:211-228. doi: 10.1007/978-1-4020-5570-6_2
  • Francis, R., 1988. Are growth parameters estimated from tagging and agelength data comparable? Canadian Journal of Fisheries and Aquatic Sciences, 456: 936-942. doi: 10.1139/f88-115
  • Gallucci, V.F., Quinn, T.J., 1979. Reparameterizing, fitting, and testing a simple growth model. Transactions of the American Fisheries Society, 108(1): 14-25. doi: 10.1577/1548-
  • (1979)108<14:RFATAS>2.0.CO;2
  • Haddon, M., 2010. Modelling and quantitative methods in fisheries. CRC press, USA, 449
  • Helser, T.E., Lai, H.L., 2004. A Bayesian hierarchical metaanalysis of fish growth: with an example for North American largemouth bass, Micropterus salmoides. Ecological Modelling, 178(3): 399-416. doi: 10.1016/j.ecolmodel.2004.02.013
  • Katsanevakis, S., Maravelias, C.D., 2008. Modelling fish growth: multi‐model inference as a better alternative to a priori using von Bertalanffy equation. Fish and Fisheries, 92: 178-187. doi: 10.1111/j.1467-2979.2008.00279.x
  • Knight, W., 1968. Asymptotic growth: an example of nonsense disguised as mathematics. Journal of the Fisheries Board of Canada, 25(6): 1303-1307. doi: 10.1139/f68-114
  • Mooij, W., Van Rooij, J., Wijnhoven, S., 1999. Analysis and comparison of fish growth from small samples of lengthatage data: detection of sexual dimorphism in Eurasian perch as an example. Transactions of the American Fisheries Society, 128(3): 483-490. doi: 10.1577/1548-8659(1999)128<0483:AACOFG>2.0.CO;2
  • Ogle, D.H., 2012. FSA: Fisheries stock analysis. R package version 0.2–8
  • Ogle, D.H., 2013. fishR Vignette-von Bertalanffy growth models. Ashland, WI: Northland College, 54.
  • Ogle, D.H., 2015. Introductory Fisheries Analyses with R. CRC Press, USA, 317. doi: 10.1201/b19232-19
  • Quinn, T.J., Deriso, R.B., 1999. Quantitative fish Dynamics. Oxford University Press, UK, 542
  • Ritz, C., Streibig, J.C., 2008. Nonlinear regression with R. Springer Science Business Media, USA, 63
  • Roff, D.A., 1980. A motion for the retirement of the von Bertalanffy function. Canadian Journal of Fisheries and Aquatic Sciences, 37(1): 127-129. doi: 10.1139/f80-016
  • Schnute, J., Fournier, D., 1980. A new approach to lengthfrequency analysis: growth structure. Canadian Journal of Fisheries and Aquatic Sciences, 37(9) 1337-1351. doi: 10.1139/f80-172
  • Schnute, J., 1981. A versatile growth model with statistically stable parameters. Canadian Journal of Fisheries and Aquatic Sciences, 38:1128-1140. doi: 10.1139/f81-153
  • Team, R.C., 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

-

Year 2015, Volume 32, Issue 4, 205 - 208, 16.01.2016
https://doi.org/10.12714/egejfas.2015.32.4.05

Abstract

-

References

  • Baty, F., Ritz, C., Charles, S., Brutsche, M., Flandrois, J.P., Delignette Muller, M.L., 2014. A toolbox for nonlinear regression in R: the package nlstools. Journal of Statistical Software, 66(5):1-21.
  • Beverton, R.J., 1994. Notes on the use of theoretical models in the study of the dynamics of exploited fish populations: from lectures by RJH Beverton presented at US Fishery Laboratory, Beaufort, North Carolina, Bureau of Commercial Fisheries, Vol. 1. Marine Fisheries Section, American Fisheries Society, USA, 159.
  • Beverton, R.J., Holt, S.J., 2012. On the dynamics of exploited fish populations Vol. 11. Springer Science Business Media, UK, 456
  • Bolker, B.M., Gardner, B., Maunder, M., Berg, C.W., Brooks, M., Comita, L., Ford, J., 2013. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods in Ecology and Evolution, 4(6), 501-512. doi: 10.1111/2041-210X.12044
  • Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical informationtheoretic approach, Springer Science Business Media, USA, 488
  • Cailliet, G.M., Smith, W.D., Mollet, H. F., Goldman, K.J., 2006. Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environmental Biology of Fishes, 77:211-228. doi: 10.1007/978-1-4020-5570-6_2
  • Francis, R., 1988. Are growth parameters estimated from tagging and agelength data comparable? Canadian Journal of Fisheries and Aquatic Sciences, 456: 936-942. doi: 10.1139/f88-115
  • Gallucci, V.F., Quinn, T.J., 1979. Reparameterizing, fitting, and testing a simple growth model. Transactions of the American Fisheries Society, 108(1): 14-25. doi: 10.1577/1548-
  • (1979)108<14:RFATAS>2.0.CO;2
  • Haddon, M., 2010. Modelling and quantitative methods in fisheries. CRC press, USA, 449
  • Helser, T.E., Lai, H.L., 2004. A Bayesian hierarchical metaanalysis of fish growth: with an example for North American largemouth bass, Micropterus salmoides. Ecological Modelling, 178(3): 399-416. doi: 10.1016/j.ecolmodel.2004.02.013
  • Katsanevakis, S., Maravelias, C.D., 2008. Modelling fish growth: multi‐model inference as a better alternative to a priori using von Bertalanffy equation. Fish and Fisheries, 92: 178-187. doi: 10.1111/j.1467-2979.2008.00279.x
  • Knight, W., 1968. Asymptotic growth: an example of nonsense disguised as mathematics. Journal of the Fisheries Board of Canada, 25(6): 1303-1307. doi: 10.1139/f68-114
  • Mooij, W., Van Rooij, J., Wijnhoven, S., 1999. Analysis and comparison of fish growth from small samples of lengthatage data: detection of sexual dimorphism in Eurasian perch as an example. Transactions of the American Fisheries Society, 128(3): 483-490. doi: 10.1577/1548-8659(1999)128<0483:AACOFG>2.0.CO;2
  • Ogle, D.H., 2012. FSA: Fisheries stock analysis. R package version 0.2–8
  • Ogle, D.H., 2013. fishR Vignette-von Bertalanffy growth models. Ashland, WI: Northland College, 54.
  • Ogle, D.H., 2015. Introductory Fisheries Analyses with R. CRC Press, USA, 317. doi: 10.1201/b19232-19
  • Quinn, T.J., Deriso, R.B., 1999. Quantitative fish Dynamics. Oxford University Press, UK, 542
  • Ritz, C., Streibig, J.C., 2008. Nonlinear regression with R. Springer Science Business Media, USA, 63
  • Roff, D.A., 1980. A motion for the retirement of the von Bertalanffy function. Canadian Journal of Fisheries and Aquatic Sciences, 37(1): 127-129. doi: 10.1139/f80-016
  • Schnute, J., Fournier, D., 1980. A new approach to lengthfrequency analysis: growth structure. Canadian Journal of Fisheries and Aquatic Sciences, 37(9) 1337-1351. doi: 10.1139/f80-172
  • Schnute, J., 1981. A versatile growth model with statistically stable parameters. Canadian Journal of Fisheries and Aquatic Sciences, 38:1128-1140. doi: 10.1139/f81-153
  • Team, R.C., 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Details

Primary Language Turkish
Subjects Science
Journal Section Articles
Authors

Sedat Gündoğdu This is me


Makbule Baylan This is me

Publication Date January 16, 2016
Application Date January 16, 2016
Acceptance Date September 29, 2021
Published in Issue Year 2015, Volume 32, Issue 4

Cite

APA Gündoğdu, S. & Baylan, M. (2016). Farklı parametrizasyon tekniklerinin Saurida lessepsianus (Russell, Golani & Tikochinski, 2015)’un von Bertalanffy büyüme parametrelerinin tahminine etkisi . Ege Journal of Fisheries and Aquatic Sciences , 32 (4) , 205-208 . DOI: 10.12714/egejfas.2015.32.4.05