The comparison of growth with length-weight relation and artificial neural networks of crayfish, Astacus leptodactylus, in Mogan Lake

Abstract

AbstractThis study aimed to determine some morphological characteristics of freshwater crayfish, Astacus leptodactylus Eschscholtz 1823, populations in Mogan Lake, Turkey. Samplings were done between 2 July and 30 October in 2006 and 2007 with a random method. We present the relationships between total length (TL), carapace length (CL), chelae length (ChL), abdomen length (AL) and total weight (TW) for A. leptodactylus from Mogan Lake. Study was conducted in 112 individuals (14 female, 98 male). The research was found as 87.5 % male, 12.5 % female of crayfish thought investigation female and male ratios was of determined as to 0.14 /1.00. Avarage total length was 108.71 mm for female, 102.93 mm for male, average total weight was 28.64 g for female, 32.49 g for male. Length-weight relation equation was found for females W=0.0022 L2.01 for males W=0.00095 L2.23. The results obtained by artificial neural networks and length-weight relation equation are compared to those obtained by the growth rate of the crayfish caught from Mogan Lake. Length-weight relation and artificial neural network MAPE (mean absolute percentage error) results were examined. Artificial neural networks gives better results than length-weight relation. Artificial neural networks can be alternative as a evaluated for growth estimation.

Keywords

• Keywords: artificial neural networks, crayfish, estimated, length-weight relation, Mogan Lake

References

1. Anonymous (1989) Turkey's Wetlands. Environmental Problems Foundation of Turkey. 82 pp.
2. Atar, H.H., Seçer, S. (2003) Width/length˗weight relationships of the blue crab
3. (Callinectes sapidus Rathbun 1896) population living in Beymelek Lagoon
4. Lake. Turk. J. Vet. Anim. Sci. 27: 443-447. Ateş, M., Aksu Ö. (2013) The effects of traps (fyke-net) having hexagonal and diamond mesh sizes over catch efficiencies and sex compositions. Tunceli
5. University Journal of Science and Youth1(2):71-82. (in Turkish). Balık, S., Ustaoğlu, M.R., Sarı, H.M., Berber, S. (2005) Determination of traits some growth and morphometric of crayfish (Astacus leptodactylus Eschscholtz, 1823) at Demirköprü Dam Lake (Manisa). Ege Journal of Fisheries and Aquatic Sciences 22(1-2): 83-89.(in Turkish).
6. Berber, S., Balık, S. (2006) Determination of traits some growth and morphometric of crayfish (Astacus leptodactylus Eschscholtz, 1823) at Manyas
7. Lake (Balıkesir). Ege Journal of Fisheries and Aquatic Sciences 23(1-2):83-91. Berber, S., Balık, S. (2009) The lenght-weight relationships, and meat yield of crayfish (Astacus leptodactylus Eschcholtz, 1823) population in Apolyont Lake
8. (Bursa, Turkey). Journal of Fisheries Sciences 3(2): 86-99. (in Turkish). 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. Ecological Modelling 120(2-3):299-311.

9. Deniz, T.B, Aydın, 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/Mediterranean Environment 19(2):190- 20
10. Füreder, L., Oberkofler, B., Hanel, R., Leiter, J., Thaler, B. (2003) The freshwater crayfish Austropotamobius pallipes in South Tyrol: Heritage species and bioindicator. Bull. Fr. Pêche Piscic. 370-371:79-95.
11. Gillet, C., Laurent, P.J. (1995) Tail length variations among noble crayfish
12. (Astacus astacus (L)) populations. Freshwater Crayfish 10: 31-36. Güner U. (2008) Some morphometric properties and growth parameter of crayfish at Kavaklı Lake (Edirne, Meriç). Research Journal of Biological
13. Sciences 181:37-42.(in Turkish). Güner, U., Harlıoğlu, M.M. (2010) Status of freshwater crayfish distribution in
14. Thrace region of Turkey. Reviews Fish. Sci. 18: 1-6. Harlıoğlu, M.M. (1999) The relationships between length-weight, and meat yield of freshwater crayfish, Astacus leptodactylus Eschscholtz, in the Ağın
15. Region of Keban Dam Lake. Tr. J. of Zool. 23: 949-958.(in Turkish). Harlıoğlu M.M. (2004) The present situation of freshwater crayfish, Astacus leptodactylus (Eschscholtz, 1823) in Turkey. Aquaculture 230:181-187.
16. Harlıoğlu, M.M., Harlıoğlu, A.G. (2005) The comparison of morphometric analysis and meat yield contents of freshwater crayfish, Astacus leptodactylus
17. (Esch 1823) caught from İznik, Eğirdir Lakes and Hirfanlı dam lake. Science and Engineering Journal of Fırat University 17/2: 412-423.
18. Harlıoğlu, M.M., Harlıoğlu, A.G. (2006) Threat of non-native crayfish introductions into Turkey: global lessons. Rev. Fish Biol. Fisher. 16:171-181.
19. Haykin S. (1999) Neural Networks: A Comprehensive Foundation, Perenctice Hall, New Jersey. 842 pp.
20. Hopgood A.A. (2000) Intelligent Systems for Engineers and Scientists. CRC Press, Florida, 461 pp.
21. Joy, K.M., Death, R.G. (2004) Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks.
22. Freswater Biology 49(8):1306-1052.
23. Karabatak, M., Tüzün, Ü. (1989) Some features of crayfish (Astacus leptodactylus, Esch, 1823) populations in Lake Mogan. Akdeniz University
24. Fisheries Engineering Journal 2: 1-34. (in Turkish). Köksal, G., Korkmaz, A.Ş., Kırkağaç, M. (2003) Investigation of the crayfish
25. (Astacus leptodactylus Esch., 1823) population in Ankara˗Dikilitaş¸ Irrigation
26. Reservoir. Journal of Agricultural Sciences 9(1): 51-58. (in Turkish). Krenker, A., Bešter, J., Kos, A. (2011) Introduction to the Artificial Neural
27. Networks, Artificial Neural Networks - Methodological Advances and Biomedical Applications, (ed., K. Suzuki). InTech. DOI: 10.5772/15751.
28. 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. 19
29. Maravelias, C.D., Haralabous, J., Papaconstantinou, C. (2003) Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Marine Ecology 255: 249-258.
30. Mastrorillo, S., Lek, S., Dauba, F., Belaud, A. (1997) The use of artificial neural networks to predict the presence of small-bodied fish in river. Freshwater Biology 38: 237-246.
31. Matlab (2006) The MathWorks, Inc. Matlab Help. MATLAB,127 pp.
32. Obach, M., Wagner, R., Werner, H., Schmidt, H.H. (2001) Modelling population dynamics of aquatic insects ith artificial neural networks. Ecological Modeling 146: 207-217.
33. 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 Research 37: 1749-1758.
34. 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: 67-75.
35. Rhodes, C.P., Holdich, D.M. (1979) On size and sexual dimorphism in
36. Austropotamobius pallipes (Lereboullet) - a step in assessing the commercial exploitation potential of the native British freshwater crayfish. Aquaculture 17: 345-3
37. Ricker, W.E. (1973) Linear regressions in fishery research. J. Fisheries
38. Research Board of Canada 30:409-434. Romaire, R. P., Forester, J. S., Avault, J. W. Jr. (1977) Length˗weight relationships of two commercially important crayfishes of the genus
39. Procambarus. Freshwater Crayfish 3: 463-470. Rumelhart, D.E., Hinton, G.E., Williams, R.J. (1986) Learning Internal
40. Representations by Error Propagation in Parallel Distributed Processing. Explorations in the Microstructure of Cognition MIT Press, 506 pp. Skurdal, J., Qvenild, T. (1986) Growth, maturity, and fecundity of Astacus astacus in Lake Steinfjorden, S.E. Norway. Freshwater Crayfish 6: 182-186.
41. Souty-Grosset, C., Holdrich, D. M., Noel, P. Y., Reynolds, J. D., Haffner, P. (2006) Atlas of crayfish in Europe. Publications Scientifiques du MNHN-Paris.
42. Sun, L., Xiao, H., Li, S., Yang, D. (2009) Forecating fish stock recruitment and planning optimal harvesting strategies by using neural network. Journal of Computers 4(11):1075-1082.
43. Suryanarayana, I., Braibanti, A., Rao, R.S., Ramamc, V.A., Sudarsan, D., Rao, G.N. (2008) Neural networks in fisheries research. Fisheries Research 92:115
44. Tesch, F.W. (1971) Age and growth. In: Methods for Assessment of Fish
45. Production in Fresh Waters (ed., W.E. Ricker). Blackwell Scientific Publications, Oxford, pp. 99-130. TKB (2002) Hunting Regulating Circular No. 34/1 in Seas and Inland Waters of
46. Commercial Fisheries 2000-2002 Hunting Season Republic of Turkey Ministry of Food, Agriculture and Livestock General Directorate of Protection and Control (TKB), Ankara. (in Turkish).
47. Tosunoğlu, Z., Aydın, C., Özaydın, O., Leblebici, S. (2007) Trawl codend mesh selectivity of braided PE material for Parapenaeus longirostris (Lucas, 1846)
48. (Decapoda, Penaeidae). Crustaceana 80: 1087-1094.
49. TUIK (2011) Fisheries Statistics Turkish Statistical Institute (TUIK). (in Turkish).
50. 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. Mediterranean Marine Science 12(2):439-446.
51. Yanez, E., Plaza, F., Gutierrezestrada, J.C., Rodriquez, N., Barbieri, M.A., Pulido-Calvo, I., Borquez, C. (2010) Anchovy (Engraulis ringens) and sardine
52. (Sardinops sagax) abundance forecast off northern Chile: a multivariate ecosystem neural network approach. Oceanography 87: 242-250.