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Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810)

Year 2023, Volume: 40 Issue: 2, 96 - 102, 15.06.2023

Semra Benzer , Recep Benzer

https://doi.org/10.12714/egejfas.40.2.02

Abstract

In this study, the growth parameters of big-scale sand smelt (Atherina boyeri Risso, 1810) in Iznik Lake has been determined with traditional (length weight relationships (LWRs), von Bertalanffy (VB), condition factor (CF)) and modern approaches (Artificial Neural Networks - ANNs). A total of 635 specimens (44.84% female and 55.16% male) were collected from the local fisherman during the hunting season between April 2018 to April 2019. Mean fork length (FL) (mm, min-max), mean W (g, min-max) and mean CF (value, min-max) were estimated as 67.31 mm (40.10 - 97.77 mm), 2.57 g (0.53 - 7.50 g), and 0.790 (0.170-1.520) for all individuals. The length weight relationships were determined W=0.00001437 L^2.8602 for female, W=0.00001570 L^2.8266 for male and W=0.00001328 L^2.8717 for all individuals. The von Bertalanffy equations were determined L_t=136.218 [1-e^(-0.240(t+0.51)) ] for female, L_t=155.042 [1-e^(-0.185(t+0.73)) ] for male, and L_t=146.916 [1-e^(-0.205(t+0.64)) ] for all individuals. The values in training (MSE (Mean Squared Error) 4.52559e-5, R (correlation coefficients) 9.09347e-1), verification (MSE 4.86111e-5, R2 9.00931e-1) and test data (MSE 3.391999e-5, R 9.43465e-1) were found in calculations made with ANNs. It was determined that ANNs could be an alternative for evaluating growth estimation.

Keywords

Artificial Neural Networks big-scale sand smelt growth parameters length weight relationships von Bertalanffy

Supporting Institution

Gazi University Project Resources Center Fund

Project Number

BAP Project Number: 04/2018-05

References

  • Bagenal, T.B., & Tesch, F.W. (1978). Age and growth. In T. Bagenal (Ed.), Methods for Assessment of Fish Production in Freshwaters (pp 101-136). Oxford: Blackwell Science Publications.
  • Bartulovic, V., Glamuzina, B., Conides, A., Gavriloviç, A., & Dulçiç, J. (2006). Maturation, reproduction, and recruitment of the sand smelt, Atherina boyeri Risso, 1810 (Pisces: Atherinidae) in the estuary of Mala Neretva River (Southeastern Adriatic, Croatia). Acta Adriatica, 47(1), 5-11.
  • Benzer, S. (2016). Growth Characteristics of Atherina boyeri Risso, 1880 in Mogan Lake. International Conference on Biological Sciences, Konya, Türkiye.
  • Benzer, S. (2020). Artificial Neural Networks Approach to Growth Properties Atherina boyeri Risso 1810 in Yamula Dam Lake Turkey. Fresenius Environmental Bulletin, 29(2), 1145-1152.
  • Benzer, S., & Benzer, R. (2016). Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Applied Ecology and Environmental Research, 14(2), 543-554. https://doi.org/10.15666/aeer/1402_543554
  • Benzer, S., & Benzer, R. (2017). Comparative growth models of big scale sand smelt Atherina boyeri Risso 1810 sampled from Hirfanlı Dam Lake Kırsehir Ankara Turkey. Computational Ecology and Software, 7(2), 82-90. https://doi.org/10.0000/issn-2220-721x-compuecol-2017-v7-0007
  • Benzer, S., & Benzer, R. (2019). Alternative growth models in fisheries: Artificial Neural Networks. Journal of Fisheries, 7(3), 719-725.
  • Benzer, S., & Benzer, R. (2020a). Growth Properties of Pseudorasbora parva in Süreyyabey Reservoir: Traditional and Artificial Intelligent Methods. Thalassas, 36(1), 149-156. https://doi.org/10.1007/s41208-020-00192-1
  • Benzer, S., & Benzer, R. (2020b). Artificial Neural Networks Model Biometric Features of Sand Smelt Atherina Boyeri Risso 1810 In İznik Lake, Fresenius Environmental Bulletin, 29(7), 5966-5973.
  • Benzer, S., & Benzer, R (2020c). Artificial Neural Networks Model Biometric Features of Sand Smelt Atherina boyeri Risso, 1810 In İznik Lake, Fresenius Environmental Bulletin, 29(2), 1145–1152.
  • Benzer, S., Garabaghi, F. H., Benzer, R. & Mehr, H.D. (2022). Investigation of some machine learning algorithms in fish age classification. Fisheries Research, 245, 106151. https://doi.org/10.1016/j.fishres.2021.106151
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  • Boudinar, A.S., Chaoui, L., & Kara, M.H. (2016). Age, growth and reproduction of the sand smelt Atherina boyeri Risso, 1810 in Mellah Lagoon (Eastern Algeria). Journal of Applied Ichthyology, 2(2), 302-309. https://doi.org/10.1111/jai.12992
  • Çetinkaya, S., Uysal, R., Yegen, V., Cesur, M., & Bostan, H. (2011). The growth characteristics of sand smelt (Atherina boyeri, Risso 1810) in Lake İznik (Türkiye). Turkish Journal of Fisheries and Aquatic Sciences, 11, 641-648. https://doi.org/10.4194/1303-2712-v11_4_19
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  • Gençoğlu, L., & Ekmekçi, F.G. (2016). Growth and reproduction of a marine fish (Atherina boyeri, Risso 1810) in a freshwater ecosystem. Turkish Journal of Zoology, 40, 534-542. https://doi.org/10.3906/zoo-1406-42
  • Gilliland, M. (2010). The Business Forecasting Deal: Exposing Myths, Eliminating Bad Practices, Providing Practical Solutions. John Wiley & Sons. https://doi.org/10.1002/9781119199885
  • Hekayati, J., & Rahimpour, M.R. (2017). Estimation of the saturation pressure of pure ionic liquids using MLP artificial neural networks and the revised is fugacity criterion. Journal of Molecular Liquids, 230, 85-95. https://doi.org/10.1016/j.molliq.2016.12.119
  • Hyun, K., Song, M.Y., Kim, S., & Chon, T.S. (2005). Using an artificial neural network to patternize long-term fisheries data from South Korea. Aquatic Sciences, 67(3),382-389. https://doi.org/10.1007/s00027-005-0771-8
  • İlhan, A., & İlhan, D. (2018). Length-Weight Relationship and Condition of Big-Scale Sand Smelt (Atherina boyeri Risso, 1810) from Marmara Lake (Manisa) and Homa Lagoon (İzmir). The Black Sea Journal of Sciences, 8(1), 25-34. https://doi.org/10.31466/kfbd.403014
  • Kottelat, M., & Freyhof, J. (2007). Handbook of European Freshwater Fishes. Kottelat, Cornol, Switzerland and Freyhof, Berlin, Germany.
  • Kuriakose, S. (2017). Estimation of length weight relationship in fishes. ICAR-Central Marine Fisheries Research Institute. Course Manual ICAR funded Summer School on Advanced Methods for Fish Stock Assessment and Fisheries Management, 215-220.
  • Küçük, F., Gülle, İ., Güçlü, S.S., Gümüş, E., & Demir, O. (2007). Effect on Fishery and lake ecosystem of non-native sand smelt (Atherina boyeriRİSSO, 1810) in Eğirdir Lake. Proceedings of the I. National Fisheries and Reservoir Management Symposium, 7-9 February 2007 Antalya, pp.119-128 (in Turkish with English abstract).
  • Kyritsi, S., & Kokkinakis, A.K. (2020). Age, growth, reproduction and fecundity of roach Rutilus rutilus from Volvi Lake, Northern Greece. Turkish Journal of Fisheries and Aquatic Sciences, 20(10), 717-726. https://doi.org/10.4194/1303-2712-v20_10_01
  • Lagler, K.F. (1966). Freshwater Fishery Biology. WMC Brown Company, Dubuque. 1966.
  • Leonardos, I. (2001). Ecology and exploitation pattern of a landlocked population of sand smelt, Atherina boyeri (Risso 1810) in Trichonis Lake (Western Greece). Journal of Applied Ichthyology, 18, 262-266. https://doi.org/10.1046/j.1439-0426.2001.00296.x
  • Leonardos, I., & Sinis, A. (2000). Age, growth and mortality of Atherina boyeri Risso, 1810 (Pisces: Atherinidae) in the Mesolongi and Etolikon lagoons (W.Greece). Fisheries Research, 45, 81-91. https://doi.org/10.1016/S0165-7836(99)00097-1
  • Lorenzoni, M., Giannetto, D., Carosi, A., Dolciami, R., Ghetti, L., & Pompei, L. (2015). Age, growth and body condition of big-scale sand smelt Atherina boyeri Risso, 1810 inhabiting a freshwater environment: Lake Trasimeno (Italy). Knowledge and Management of Aquatic Ecosystems, 416. https://doi.org/10.1051/kmae/2015005
  • Masoud, N. (2014). Predicting direction of stock prices index movement using artificial neural networks: The case of Libyan financial market. Journal of Economics, Management and Trade, 597-619. https://doi.org/10.9734/BJEMT/2014/5519
  • Munro, J.L. and Pauly, D. (1983) A Simple Method for Comparing Growth of Fishes and Invertebrates. ICLARM Fishbyte, 1, 5-6.
  • Nümann, W. (1958). Researches in terms of limnology and fisheries in various lakes of Anatolia and a special study on carp living in these lakes. Istanbul University Faculty of Science Hydrobiology Research Institute Publications, No. 7, 114p, İstanbul. (in Turkish).
  • Özcan, E.I. (2019). Artificial neural networks (a new statistical approach) method in length-weight relationships of Alburnus mossulensis in Murat River (Palu-Elaziğ) Turkey. Applied Ecology and Environmental Research, 17(5), 10253-10266. https://doi.org/10.15666/aeer/1705_1025310266
  • Özcan, E.İ. & Serdar, O. (2018). Artifical neural networks as new alternative method to estimating some population parameters of Tigris Loach (Oxynoemacheilus tigris (Heckel, 1843)) in the Karasu River, Turkey. Fresenius Environmental Bulletin, 27, (12B/2018), 9840-9850.
  • Özcan, E.I., & Serdar, O. (2019). Evaluation of a new computer method (ANNs) and traditional methods (LWRS and VBGF) in the calculation of some growth parameters of two cyprinid species. Fresenius Environmental Bulletin, 28(10), 7644-7654.
  • Özeren, S.C. (2004). Taxonomy of İznik Lake fishes and investigation of the bio-ecological dimension of Cyprinus carpio Linnaeus, 1758 (carp), Rutilus frisii Nordmann, 1840 (black sea roach) and Atherina boyeri Risso, 1810 (big-scaled sand-smelt). Doctoral Thesis, Hacettepe University, Ankara, Turkey.
  • Özeren, S.C. (2009). Age, growth and reproductive biology of the sand smelt Atherina boyeri, Risso 1810 (Pisces: Atherinidae) in Lake Iznik, Turkey. Journal of Fisheries International, 4, 34-39.
  • Patimar, R., Yousefi, M., & Hosieni, S.M. (2009). Age, growth and reproduction of the sand smelt Atherina boyeri Risso, 1810 in the Gomishan wetland–southeast Caspian Sea. Estuarine, Coastal and Shelf Science, 81(4), 457-462. https://doi.org/10.1016/J.ECSS.2008.12.007
  • Petrakis, G., & Stergiou, K.I. (1995). Weight–length relationships for 33 fish species in Greekwaters. Fisheries Research, 21, 465-469. https://doi.org/10.1016/0165-7836(94)00294-7
  • Pombo, L., Elliott, M., & Rebelo, J.E. (2005). Ecology, age and growth of Atherina boyeri and Atherina presbyter in the Ria de Aveiro, Portugal. Cybium, 29(1),47-55. https://doi.org/10.26028/cybium/2005-291-006
  • Sayfullin, R.R., & Shakirova, F.M. (2014). Age structure and linear growth rate of the population of ide Leuciscus idus (Linnaeus, 1788) in the Kuibyshev Reservoir in 2004–2005. Inland Water Biology, 7(4), 381-384. https://doi.org/10.1134/S1995082914040154
  • Sparre, P., & Venema, C.S. (1992). Introduction to Tropical Fish Stock Assessment, FAO Fisheries Technical Paper, 306/1, Rev. 1, London.
  • Türeli Bilen, C., Kokcu, P., & Ibrikci, T. (2011). Application of artificial neural networks (ANNs) for weight predictions of blue crabs (Callinectes sapidus Rathbun, 1896) using predictor variables. Mediterranean Marine Science, 12(2), 439-446. https://doi.org/10.12681/MMS.43
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Gümüş balığının (Atherina boyeri Risso, 1810) geleneksel ve yapay sinir ağları yöntemleriyle büyüme parametreleri

Year 2023, Volume: 40 Issue: 2, 96 - 102, 15.06.2023

Semra Benzer , Recep Benzer

https://doi.org/10.12714/egejfas.40.2.02

Abstract

Bu çalışmada İznik Gölü'nde yakalanan gümüş balığının (Atherina boyeri Risso, 1810) büyüme parametreleri geleneksel (boy ağırlık ilişkileri (LWR), von Bertalanffy (VB) ve kondisyon faktörü (CF)) ve modern yaklaşımlarla (yapay sinir ağları) incelenmiştir. Av sezonunda bölge balıkçılarından Nisan 2018-Nisan 2019 tarihleri arasında toplam 635 adet (%44,84 dişi ve %55,16 erkek) birey toplanmıştır. Tüm populasyon için ortalama çatal boy (FL) 67,31 mm (40,10 - 97,77 mm), ortalama ağırlık (W) 2,57 g (0,53 - 7,50g) ve ortalama CF 0,790 (0,170-1,520) olarak bulunmuştur. LWR dişi popülasyon için W=0,00001437 L2,8602, erkek popülasyon için W=0,00001570 L2,8266 ve tüm popülasyon için W=0,00001328 L2,8717 olarak bulunmuştur. Von Bertalanffy dişi, erkek ve tüm popülasyonda sırasıyla L_t=136,218 [1-e^(-0,240(t+0,51)) ], L_t=155,042 [1-e^(-0,185(t+0,73)) ] ve L_t=146,916 [1-e^(-0,205(t+0,64)) ] olarak tespit edilmiştir. Yapay sinir ağları (ANN) ile yapılan hesaplamalarda eğitim, doğrulama ve test verilerindeki MSE (Ortalama Kare Hata) ve R (korelasyon katsayısı) değerleri sırasıyla 4.52559e-5 9.09347e-1, 4.86111e-5 9.00931e-1 ve 3.391999e-5 9.43465e-1 olarak bulunmuştur. YSA’ların büyüme tahmininin değerlendirilmesinde bir alternatif olabileceği belirlenmiştir.

Keywords

Yapay sinir ağları Gümüş Balığı büyüme parametreleri boy ağırlık ilişkileri von Bertalanffy

Project Number

BAP Project Number: 04/2018-05

References

  • Bagenal, T.B., & Tesch, F.W. (1978). Age and growth. In T. Bagenal (Ed.), Methods for Assessment of Fish Production in Freshwaters (pp 101-136). Oxford: Blackwell Science Publications.
  • Bartulovic, V., Glamuzina, B., Conides, A., Gavriloviç, A., & Dulçiç, J. (2006). Maturation, reproduction, and recruitment of the sand smelt, Atherina boyeri Risso, 1810 (Pisces: Atherinidae) in the estuary of Mala Neretva River (Southeastern Adriatic, Croatia). Acta Adriatica, 47(1), 5-11.
  • Benzer, S. (2016). Growth Characteristics of Atherina boyeri Risso, 1880 in Mogan Lake. International Conference on Biological Sciences, Konya, Türkiye.
  • Benzer, S. (2020). Artificial Neural Networks Approach to Growth Properties Atherina boyeri Risso 1810 in Yamula Dam Lake Turkey. Fresenius Environmental Bulletin, 29(2), 1145-1152.
  • Benzer, S., & Benzer, R. (2016). Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Applied Ecology and Environmental Research, 14(2), 543-554. https://doi.org/10.15666/aeer/1402_543554
  • Benzer, S., & Benzer, R. (2017). Comparative growth models of big scale sand smelt Atherina boyeri Risso 1810 sampled from Hirfanlı Dam Lake Kırsehir Ankara Turkey. Computational Ecology and Software, 7(2), 82-90. https://doi.org/10.0000/issn-2220-721x-compuecol-2017-v7-0007
  • Benzer, S., & Benzer, R. (2019). Alternative growth models in fisheries: Artificial Neural Networks. Journal of Fisheries, 7(3), 719-725.
  • Benzer, S., & Benzer, R. (2020a). Growth Properties of Pseudorasbora parva in Süreyyabey Reservoir: Traditional and Artificial Intelligent Methods. Thalassas, 36(1), 149-156. https://doi.org/10.1007/s41208-020-00192-1
  • Benzer, S., & Benzer, R. (2020b). Artificial Neural Networks Model Biometric Features of Sand Smelt Atherina Boyeri Risso 1810 In İznik Lake, Fresenius Environmental Bulletin, 29(7), 5966-5973.
  • Benzer, S., & Benzer, R (2020c). Artificial Neural Networks Model Biometric Features of Sand Smelt Atherina boyeri Risso, 1810 In İznik Lake, Fresenius Environmental Bulletin, 29(2), 1145–1152.
  • Benzer, S., Garabaghi, F. H., Benzer, R. & Mehr, H.D. (2022). Investigation of some machine learning algorithms in fish age classification. Fisheries Research, 245, 106151. https://doi.org/10.1016/j.fishres.2021.106151
  • Bon, A.T., & Hui, H.S. (2017). Industrial engineering solution in the industry:artificial neural network forecasting approach. Proceedings of the International Conference on Industrial Engineering and Operations Management Rabat, Morocco, April 11-13.
  • Boudinar, A.S., Chaoui, L., & Kara, M.H. (2016). Age, growth and reproduction of the sand smelt Atherina boyeri Risso, 1810 in Mellah Lagoon (Eastern Algeria). Journal of Applied Ichthyology, 2(2), 302-309. https://doi.org/10.1111/jai.12992
  • Çetinkaya, S., Uysal, R., Yegen, V., Cesur, M., & Bostan, H. (2011). The growth characteristics of sand smelt (Atherina boyeri, Risso 1810) in Lake İznik (Türkiye). Turkish Journal of Fisheries and Aquatic Sciences, 11, 641-648. https://doi.org/10.4194/1303-2712-v11_4_19
  • Clarke, T.A. (1983). Sex ratios and sexual differences in size among mesopelagic fishes from the central Pacific Ocean. Marine Biology, 73(2), 203-209. https://doi.org/10.1007/BF00406889
  • Gaygusuz, Ö. (2006). Reproduction and growth biology of the sandsmelt (Atherina boyeri Risso, 1810) living in the İznik Lake. Doctoral Thesis Istanbul University, Science Institute, İstanbul, Turkey.
  • Geldiay, R., & Balık, S. (1996). Freshwater Fishes. Ege University Press. No:46, İzmir.
  • Gençoğlu, L., & Ekmekçi, F.G. (2016). Growth and reproduction of a marine fish (Atherina boyeri, Risso 1810) in a freshwater ecosystem. Turkish Journal of Zoology, 40, 534-542. https://doi.org/10.3906/zoo-1406-42
  • Gilliland, M. (2010). The Business Forecasting Deal: Exposing Myths, Eliminating Bad Practices, Providing Practical Solutions. John Wiley & Sons. https://doi.org/10.1002/9781119199885
  • Hekayati, J., & Rahimpour, M.R. (2017). Estimation of the saturation pressure of pure ionic liquids using MLP artificial neural networks and the revised is fugacity criterion. Journal of Molecular Liquids, 230, 85-95. https://doi.org/10.1016/j.molliq.2016.12.119
  • Hyun, K., Song, M.Y., Kim, S., & Chon, T.S. (2005). Using an artificial neural network to patternize long-term fisheries data from South Korea. Aquatic Sciences, 67(3),382-389. https://doi.org/10.1007/s00027-005-0771-8
  • İlhan, A., & İlhan, D. (2018). Length-Weight Relationship and Condition of Big-Scale Sand Smelt (Atherina boyeri Risso, 1810) from Marmara Lake (Manisa) and Homa Lagoon (İzmir). The Black Sea Journal of Sciences, 8(1), 25-34. https://doi.org/10.31466/kfbd.403014
  • Kottelat, M., & Freyhof, J. (2007). Handbook of European Freshwater Fishes. Kottelat, Cornol, Switzerland and Freyhof, Berlin, Germany.
  • Kuriakose, S. (2017). Estimation of length weight relationship in fishes. ICAR-Central Marine Fisheries Research Institute. Course Manual ICAR funded Summer School on Advanced Methods for Fish Stock Assessment and Fisheries Management, 215-220.
  • Küçük, F., Gülle, İ., Güçlü, S.S., Gümüş, E., & Demir, O. (2007). Effect on Fishery and lake ecosystem of non-native sand smelt (Atherina boyeriRİSSO, 1810) in Eğirdir Lake. Proceedings of the I. National Fisheries and Reservoir Management Symposium, 7-9 February 2007 Antalya, pp.119-128 (in Turkish with English abstract).
  • Kyritsi, S., & Kokkinakis, A.K. (2020). Age, growth, reproduction and fecundity of roach Rutilus rutilus from Volvi Lake, Northern Greece. Turkish Journal of Fisheries and Aquatic Sciences, 20(10), 717-726. https://doi.org/10.4194/1303-2712-v20_10_01
  • Lagler, K.F. (1966). Freshwater Fishery Biology. WMC Brown Company, Dubuque. 1966.
  • Leonardos, I. (2001). Ecology and exploitation pattern of a landlocked population of sand smelt, Atherina boyeri (Risso 1810) in Trichonis Lake (Western Greece). Journal of Applied Ichthyology, 18, 262-266. https://doi.org/10.1046/j.1439-0426.2001.00296.x
  • Leonardos, I., & Sinis, A. (2000). Age, growth and mortality of Atherina boyeri Risso, 1810 (Pisces: Atherinidae) in the Mesolongi and Etolikon lagoons (W.Greece). Fisheries Research, 45, 81-91. https://doi.org/10.1016/S0165-7836(99)00097-1
  • Lorenzoni, M., Giannetto, D., Carosi, A., Dolciami, R., Ghetti, L., & Pompei, L. (2015). Age, growth and body condition of big-scale sand smelt Atherina boyeri Risso, 1810 inhabiting a freshwater environment: Lake Trasimeno (Italy). Knowledge and Management of Aquatic Ecosystems, 416. https://doi.org/10.1051/kmae/2015005
  • Masoud, N. (2014). Predicting direction of stock prices index movement using artificial neural networks: The case of Libyan financial market. Journal of Economics, Management and Trade, 597-619. https://doi.org/10.9734/BJEMT/2014/5519
  • Munro, J.L. and Pauly, D. (1983) A Simple Method for Comparing Growth of Fishes and Invertebrates. ICLARM Fishbyte, 1, 5-6.
  • Nümann, W. (1958). Researches in terms of limnology and fisheries in various lakes of Anatolia and a special study on carp living in these lakes. Istanbul University Faculty of Science Hydrobiology Research Institute Publications, No. 7, 114p, İstanbul. (in Turkish).
  • Özcan, E.I. (2019). Artificial neural networks (a new statistical approach) method in length-weight relationships of Alburnus mossulensis in Murat River (Palu-Elaziğ) Turkey. Applied Ecology and Environmental Research, 17(5), 10253-10266. https://doi.org/10.15666/aeer/1705_1025310266
  • Özcan, E.İ. & Serdar, O. (2018). Artifical neural networks as new alternative method to estimating some population parameters of Tigris Loach (Oxynoemacheilus tigris (Heckel, 1843)) in the Karasu River, Turkey. Fresenius Environmental Bulletin, 27, (12B/2018), 9840-9850.
  • Özcan, E.I., & Serdar, O. (2019). Evaluation of a new computer method (ANNs) and traditional methods (LWRS and VBGF) in the calculation of some growth parameters of two cyprinid species. Fresenius Environmental Bulletin, 28(10), 7644-7654.
  • Özeren, S.C. (2004). Taxonomy of İznik Lake fishes and investigation of the bio-ecological dimension of Cyprinus carpio Linnaeus, 1758 (carp), Rutilus frisii Nordmann, 1840 (black sea roach) and Atherina boyeri Risso, 1810 (big-scaled sand-smelt). Doctoral Thesis, Hacettepe University, Ankara, Turkey.
  • Özeren, S.C. (2009). Age, growth and reproductive biology of the sand smelt Atherina boyeri, Risso 1810 (Pisces: Atherinidae) in Lake Iznik, Turkey. Journal of Fisheries International, 4, 34-39.
  • Patimar, R., Yousefi, M., & Hosieni, S.M. (2009). Age, growth and reproduction of the sand smelt Atherina boyeri Risso, 1810 in the Gomishan wetland–southeast Caspian Sea. Estuarine, Coastal and Shelf Science, 81(4), 457-462. https://doi.org/10.1016/J.ECSS.2008.12.007
  • Petrakis, G., & Stergiou, K.I. (1995). Weight–length relationships for 33 fish species in Greekwaters. Fisheries Research, 21, 465-469. https://doi.org/10.1016/0165-7836(94)00294-7
  • Pombo, L., Elliott, M., & Rebelo, J.E. (2005). Ecology, age and growth of Atherina boyeri and Atherina presbyter in the Ria de Aveiro, Portugal. Cybium, 29(1),47-55. https://doi.org/10.26028/cybium/2005-291-006
  • Sayfullin, R.R., & Shakirova, F.M. (2014). Age structure and linear growth rate of the population of ide Leuciscus idus (Linnaeus, 1788) in the Kuibyshev Reservoir in 2004–2005. Inland Water Biology, 7(4), 381-384. https://doi.org/10.1134/S1995082914040154
  • Sparre, P., & Venema, C.S. (1992). Introduction to Tropical Fish Stock Assessment, FAO Fisheries Technical Paper, 306/1, Rev. 1, London.
  • Türeli Bilen, C., Kokcu, P., & Ibrikci, T. (2011). Application of artificial neural networks (ANNs) for weight predictions of blue crabs (Callinectes sapidus Rathbun, 1896) using predictor variables. Mediterranean Marine Science, 12(2), 439-446. https://doi.org/10.12681/MMS.43
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There are 46 citations in total.

Details

Primary Language English
Subjects Limnology, Fish Biology
Journal Section Articles
Authors

Semra Benzer 0000-0002-8548-8994

Recep Benzer 0000-0002-5339-0554

Project Number BAP Project Number: 04/2018-05
Early Pub Date June 10, 2023
Publication Date June 15, 2023
Submission Date December 2, 2022
Published in Issue Year 2023Volume: 40 Issue: 2

Cite

APA Benzer, S., & Benzer, R. (2023). Growth parameters with traditional and artificial neural networks methods of big-scale sand smelt (Atherina boyeri Risso, 1810). Ege Journal of Fisheries and Aquatic Sciences, 40(2), 96-102. https://doi.org/10.12714/egejfas.40.2.02