Accessible Requires Authentication Published by De Gruyter May 30, 2017

Artificial Neural Networks approach in morphometric analysis of crayfish (Astacus leptodactylus) in Hirfanlı Dam Lake

Semra Benzer, Recep Benzer and Aysel Çağlan Günal
From the journal Biologia


This study aims to compare the growth estimation of narrow-clawed crayfish (Astacus leptodactylus Eschscholtz, 1823) obtained from two methods which are length–weight relations and Artificial Neural Networks (ANNs) from Hirfanlı Dam Lake in 2013 and 2014. The growth estimation of 325 crayfish was carried out with both methods and the obtained results were compared. Then, the estimated values found via both methods were examined. Correlation coefficient (r2), sum square error (SSE), mean absolute percentage error performance criteria (MAPE) were used for comparison of artificial neural network and linear regression models goodness of fit. The results of the current study show that compared to linear regression models, ANNs is a superior estimation tool. Thus, as an outcome of the present study, ANNs can be considered as a more efficient method especially in the growth estimation of the species in biological systems. Another outcome of this study is that crayfish of Hirfanlı Dam Lake well accommodates itself to the ecologic features of the environment and so its growth features are similar to the values of other water systems.



We would like to express our gratitude to the reviewers for their help and support to our research with their comments and recommendations. This study has been accepted for presentation at Ecology Symposium 2015 (Sinop, Turkey).


Atar H.H. & Seçer S. 2003. Width/length–weight relationships of the blue crab (Callinectes sapidus Rathbun 1896) population living in Beymelek Lagoon Lake. Turk. J. Vet. Anim. Sci. 27 (2): 443–447. Search in Google Scholar

Balık S., Ustaoğlu M.R., Sari H.M. & Berber S. 2005. Determination of traits some growth and morphometric of crayfish (Astacus leptodactylus Eschscholtz, 1823) at Demirköprü Dam Lake (Manisa). Ege J. Fish. Aquat. Sci. 22 (1-2): 83–89. 10.12714/egejfas.2005.22.1.5000156891 Search in Google Scholar

Benzer S. & Benzer R. 2016. Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Appl. Ecol. Environ. Res. 14 (2): 543–554. 10.15666/aeer/1402_543554 Search in Google Scholar

Benzer S., Benzer R. & Gül A. 2016. Artificial Neural Network applications for biological systems: The case study of Pseudorasbora parva. Chapter 5, pp. 49–58. In: Efe R., Matchavariani L., Yaldir A. & Lévai L. (eds), Developments in Science and Engineering, St. Kliment Ohridski University Press, Sofia, 769 pp. ISBN: 978-954-07-4137-6 Search in Google Scholar

Benzer S., Karasu Benli Ç. & Benzer R. 2015. The comparison of growth with length–weight relation and artificial neural networks of crayfish, Astacus leptodactylus, in Mogan Lake. J. Black Sea Mediter. Environ. 21 (2): 208–223. Search in Google Scholar

Berber S. & Balik S. 2006. Determination of traits some growth and morphometric of crayfish (Astacus leptodactylus Eschscholtz, 1823) at Manyas Lake (Bali kesir). Ege J. Fish. Aquat. Sci. 23 (1-2): 83–91. 10.12714/egejfas.2006.23.1.5000156695 Search in Google Scholar

Berber S. & Balik S. 2009. The length–weight relationships, and meat yield of crayfish (Astacus leptodactylus Eschcholtz, 1823) population in Apolyont Lake (Bursa, Turkey). J. Fish. Sci. 3 (2): 86–99. 10.3153/jfscom.2009012 Search in Google Scholar

Brosse S., Guegan J., Tourenq J. & Lek S. 1999. The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecol. Modell. 120 (2-3): 299–311. 10.1016/S0304-3800(99)00110-6 Search in Google Scholar

Deniz T.B., Aydin C. & Ateş C. 2013. A study on some morphological characteristics of Astacus leptodactylus (Eschscholtz 1823) in seven different inland waters in Turkey. J. Black Sea Mediter. Environ. 19 (2): 190–205. Search in Google Scholar

DSİ. 1968. Limnological survey report of Hirfanli Dam Lake. Ankara, 216 pp. Search in Google Scholar

Ekici B.B. & Aksoy U.T. 1993. Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software 40 (5): 356–362. 10.1016/j.advengsoft.2008.05.003 Search in Google Scholar

Evans J.D. 1996. Straightforward Statistics for the Behavioral Sciences. Pacific Grove, CA: Brooks/Cole Publishing, 600 pp. ISBN: 0534231004, 9780534231002 Search in Google Scholar

Füreder L., Oberkofler B., Hanel R., Leiter J. & Thaler B. 2003. The freshwater crayfish Austropotamobius pallipes in South Tyrol: Heritage species and bioindicator. [l’écrevisse Austropotamobius pallipes dans le Tyrol du Sud : espèce patrimoniale et bioindicateur]. Bull. Fr. Pêche Piscic. 370-371: 79–95. 10.1051/kmae:2003005 Search in Google Scholar

Gentry T.W., Wiliamowski B.M. & Weatherford L.R. 1995. A comparison of traditional forecasting techniques and neural networks, pp. 765–770. In: Dagli C.H., Akay M., Chen C.L.P., Fernández B.R. & Ghosh J. (eds), Intelligent Engineering Systems Through Artificial Neural Networks Vol. 5, Fuzzy Logic and Evolutionary Programming, American Society of Mechanical Engineers (ASME), 1056 pp. ISBN-10: 0791800482 Search in Google Scholar

Gillet C. & Laurent P.J. 1995. Tail length variations among noble crayfish (Astacus astacus (L)) populations. Freshwater Crayfish 10: 31–36. Search in Google Scholar

Hald A. 1952. Statistical Theory with Engineering Applications. Wiley, New York, 783 pp. ISBN-10: 0471340561 Search in Google Scholar

Harlioğlu M.M. 1999. The relationships between length–weight, and meat yield of freshwater crayfish, Astacus leptodactylus Eschscholtz, in the Ağin Region of Keban Dam Lake. Turk. J. Zool. 23 (EK3): 949–958. Search in Google Scholar

Harlioğlu M.M. & Harlioğlu A.G. 2005. Eğirdir, İznik Gölleri ve Hirfanli Baraj Gölünden Avlanan Tatli Su İstakozu Astacus leptodactylus (Eschscholtz, 1823)’un Morfometrik Analizleri ile Et Verimlerinin Karşilaştirilmasi [The comparison of morphometric analysis and meat yield contents of freshwater crayfish, Astacus leptodactylus (Esch 1823) caught from İznik, Eğirdir Lakes and Hirfanli Dam Lake]. Firat Üniversitesi Mühendislik Fakültesi Dergisi [Science and Engineering Journal of Firat University] 17 (2): 412–423. Search in Google Scholar

Haykin S. 1999. Neural Networks: A Comprehensive Foundation, Perenctice Hall, New Jersey, 842 pp. ISBN: 0132733501, 9780132733502 Search in Google Scholar

Hopgood A.A. 2000. Intelligent Systems for Engineers and Scientists. 2nd edn. CRC Press, Forida, 488 pp. ISBN: 0-8493-0456-3 Search in Google Scholar

Krenker A., Bešter J. & Kos A. 2011. Introduction to the Artificial Neural Networks, pp. 3–18. 10.5772/15751. In: Suzuki K. (ed.), Artificial Neural Networks – Methodological Advances and Biomedical Applications, 362 pp. ISBN: 978-953-307-243-2 Search in Google Scholar

Lewis C.D. 1982. Industrial and Business Forecasting Methods. London: Butterworths, 144 pp. 10.1002/for.3980020210 Search in Google Scholar

Lindqvist O.V. & Lahti E. 1983. On the sexual dimorphism and condition index in the crayfish Astacus astacus L. in Finland. Freshwater Crayfish 5: 3–11. Search in Google Scholar

Maravelias C.D., Haralabous J. & Papaconstantinou C. 2003. Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Mar. Ecol. Prog. Ser. 255: 249–258. 10.3354/meps255249 Search in Google Scholar

Mastrorillo S., Lek S., Dauba F. & Belaud A. 1997. The use of artificial neural networks to predict the presence of smallbodied fish in river. Freshwater Biol. 38 (2): 237–246. 10.1046/j.1365-2427.1997.00209.x Search in Google Scholar

Mendes B., Fonseca P. & Campos A. 2004. Weight–length relationships for 46 fish species of the Portuguese west coast. J. Appl. Ichthyol. 20 (5): 355–361. 10.1111/j.1439-0426.2004.00559.x Search in Google Scholar

Morato T., Afonso P., Lourinho P., Barreiros J.P., Santos R.S. & Nash R.D.M. 2001. Length–weight relationships for 21 coastal fish species of the Azores, north-eastern Atlantic. Fish. Res. 50 (3): 297–302. 10.1016/S0165-7836(00)00215-0 Search in Google Scholar

Obach M., Wagner R., Werner H. & Schmidt H.H. 2001. Modelling population dynamics of aquatic insects with artificial neural networks. Ecol. Modell. 146 (1–3): 207–217. 10.1016/S0304-3800(01)00307-6 Search in Google Scholar

Panofsky H.A. & Brier G.W. 1968. Some Applications of Statistics to Meteorology. Pennsylvania State University, University Park, 224 pp. Search in Google Scholar

Park Y.S., Verdonschot P.F.M., Chon T.S. & Lek S. 2003. Patterning and predicting aquatic macro invertabrate diversities using artificial neural network. Water Res. 37 (8): 1749–1758. 10.1016/S0043-1354(02)00557-2 Search in Google Scholar

Primavera J.H., Parado-Estepa F.D. & Lebata J.L. 1998. Morphometric relationship of length and weight of giant tiger prawn Penaeus monodon according to life stage, sex and source. Aquaculture 164 (1-4): 67–75. 10.1016/S0044-8486(98)00177-X Search in Google Scholar

Rhodes C.P. & Holdich D.M. 1979. On size and sexual dimorphism in Austropotamobius pallipes (Lereboullet) – A step in assessing the commercial exploitation potential of the native British freshwater crayfish. Aquaculture 17 (4): 345–358. 10.1016/0044-8486(79)90089-9 Search in Google Scholar

Ricker W.E. 1973. Linear regressions in fishery research. J. Fish. Res. Board Can. 30 (3): 409–434. 10.1139/f73-072 Search in Google Scholar

Romaire R.P., Forester J.S. & Avault J.W. 1977. Length–weight relationships of two commercially important crayfishes of the genus Procambarus. Freshwater Crayfish 3: 463–470. Search in Google Scholar

Rumelhart D.E., Hinton G.E. & Williams R.J. 1986. Learning internal representations by error propagation, pp. 318–362. In: Parallel Distributed Processing. Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, MA, USA, 567 pp. ISBN: 0-262-18120-7 Search in Google Scholar

Sinovcic G., Franicevic M., Zorica B. & Ciles-Kec V. 2004. Length–weight and length-length relationships for 10 pelagic fish species from the Adriatic Sea (Croatia). J. Appl. Ichthyol. 20 (2): 156–158. 10.1046/j.1439-0426.2003.00519.x Search in Google Scholar

Skurdal J. & Qvenild T. 1986. Growth, maturity, and fecundity of Astacus astacus in lake Steinfjorden, S.E. Norway. Freshwater Crayfish 6: 182–186. Search in Google Scholar

Souty-Grosset C., Holdrich D.M., Noel P.Y., Reynolds J.D. & Haffner P. (eds). 2006. Atlas of Crayfish in Europe. Publications Scientifiques du Muséum national d’Histoire naturelle, Paris, Patrimoines naturels Vol. 64, 187 pp. ISBN: 978-2-85653-579-0 Search in Google Scholar

Sun L., Xiao H., Li S. & Yang D. 2009. Forecasting fish stock recruitment and planning optimal harvesting strategies by using neural network. Journal of Computers 4 (11): 1075–1082. 10.4304/jcp.4.11.1075-1082 Search in Google Scholar

Suryanarayana I., Braibanti A., Rao R.S., Ramamc V.A., Sudarsan D. & Rao G.N. 2008. Neural networks in fisheries research. Fish Res. 92 (2-3): 115–139. 10.1016/j.fishres.2008.01.012 Search in Google Scholar

Tesch F.W. 1971. Age and growth, p. 99–130. In: Ricker W.E. (ed.), Methods for Assessment of Fish Production in Fresh Waters, 2nd edn., Blackwell Scientific Publications, Oxford, 348 pp. ISBN-10: 0632084901 Search in Google Scholar

Tosunoğlu Z., Aydin C., Özaydin O. & Leblebici S. 2007. Trawl codend mesh selectivity of braided PE material for Parapenaeus longirostris (Lucas, 1846) (Decapoda, Penaeidae). Crustaceana 80 (9): 1087–1094. 10.1163/156854007782008649 Search in Google Scholar

Tureli 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. Medit. Mar. Sci. 12 (2): 439–446. 10.12681/mms.43 Search in Google Scholar

Witt S.F. & Witt C.A. 1992. Modeling and Forecasting Demand in Tourism. Londra: Academic Press, 195 pp. ISBN: 0-127-60740-4, Search in Google Scholar

Yanez E., Plaza F., Gutierrezestrada J.C., Rodriquez N., Barbieri M.A., Pulido-Calvo I. & Borquez C. 2010. Anchovy (Engraulis ringens) and sardine (Sardinops sagax) abundance forecast off northern Chile: A multivariate ecosystem neural network approach. Oceanography 87: 242–250. 10.1016/j.pocean.2010.09.015 Search in Google Scholar

Received: 2016-4-8
Accepted: 2017-4-13
Published Online: 2017-5-30
Published in Print: 2017-5-24

© 2017 Institute of Zoology, Slovak Academy of Sciences