AN ANALYSIS OF THE FISH POPULATIONS BY USING ANN AND WAVELET TECHNIQUES

Abstract

Air - sea climate, environmental and biological conditions show various differences on several spatio-temporal scales. Climate change associated with anthropogenic activity and natural global multi-decadal climate variations effects on air-sea interactions and water surface–atmosphere–biosphere climate system. In the first part of this paper is related with Artificial Neuro Network analyses for prediction of fish stocks in Marmara and Black Sea. The second part of this study is based on wavelet analyses and, the results were compared with former wavelet and harmonic analyses to explain seasonal effects of NAO and ENSO on fish population. The influence of climatic oscillations (based on NAO and ENSO) on monthly catch rates of fish population such as sea bass, Atlantic bonito,blue fish sea (pomatomus population between 1991-2012) in Black Sea and Marmara have been analyzed by discrete wavelet transform (DWT) with Meyer and Daubechie's. Wavelet analysis is an efficient method of time series analysis to study non-stationary data. Wavelet analyses allowed us to quantify both the pattern of variability in the time series and non-stationary associations between fish population and climatic signals. Phase analyses were carried out to investigate dependency between the two signals. We reported strong relations between fish stock and climate series for the 4- and 5-yr periodic modes, i.e. the periodic band of the El Niño Southern Oscillation signal propagation in the Black Sea and Marmara Sea. These associations were non-stationary, evidenced from 1995 to 2012. It is recognized that other factors in small, meso and large scales may modulate fish stocks beginning from 1995 and more clearly from 2005

Keywords

• Fish Stocks Management, Climate Signals, ANN, Wavelet Analysis, ENSO, NAO

References

1. Aslan,Z, G. Erdemir and A. Tokgozlu, “Analyses of Climate Change Impacts on Wind Speed and Energy Potential by Using ANN”, ICIAM, 17-21 July, Vancover, Canada. 2011.
2. Botsford LW, Castilla JC, Peterson CH The management of fisheries and marine ecosystems. Science 277: 509–515. 1997.
3. Coifman, R. R. and Donoho, D. L.: Translation- invariant denoising, Wavelets and Statistics, Springer Lecture Notes in Statistics 103, pp. 125- 150, Springer-Verlag, 1995.
4. Coifman, R. R. and Donoho, D. L.: Translation- invariant denoising, Wavelets and Statistics, Springer Lecture Notes in Statistics 103, pp. 125- 150, Springer-Verlag, 1995.
5. Durif CMF, Gjosaeter J, Vollestad LA Influence of oceanic factors on Anguilla anguilla (L.) over the twentieth century in coastal habitats of the Skagerrak, southern Norway. Proceedings of the Royal Society B: Biological Sciences 278: 464– 473, 2010.
6. Gutierrez J.C., Silva C., Yaez E., Rodriguez N., Pulido I., Monthly catch forecasting of anchovy engraulis ringens in the north area of Chile: Nonlinear univariate approach, Fisheries Research, vol. 86, pages 188-200, 2007.
7. Hsieh CH, Reiss CS, Hunter JR, Beddington JR, May RM, et al. (2006) Fishing elevates variability in the abundance of exploited species. Nature 443: 859–862. [8] Jury, Mark R.,
8. Influences on Central Benguela Fish Catch , Earth Interact., 16, 1–15.,2012. [9] Z.Aslan ,

9. Detection of Fish Population”The Fifth Symposium on Wavelet Application to the World Problems, 7-8 June 2010, Istanbul,Turkey. [10] Z.Aslan ,
10. Detection of Fish Population” Aydın Üniversitesi Fen Bilimleri Dergisi,Sayı I, (52-659, 2011. [11] Kahramaner
11. Effects of Atmospheric Parameters on Fish Population: Part I, Agriculture and Food Safety within the Context of European Union Directive Semposium,14 - 15 July 2005.
12. Kuo-Wei Lan and Ming-An Lee Climatic and marine environmental variations associated with fishing conditions of tuna species in the Indian Ocean, Department of Environmental Biology and Fisheries
13. University International Workshop on Climate and Ocean Fisheries, 2011. Taiwan
14. Ocean [13] Lehodey P, Alheit J, Barange M, Baumgartner T, Beaugrand G, et al. Climate variability, fish, and fisheries. Journal of Climate 19: 5009–5030. 2006. [14] Marcogliese LA, Casselman JM Long-term trends in size and abundance of juvenile American eel (Anguilla rostrata) in the St. Lawrence River. American Fisheries Society Symposium 58: 197– 206, 2009.
15. Maria Jose Juan-Jorda, John A. Barth, M. E. Clarke, W. W. Wakefield, Ground fish Species Associations With Distinct Oceanographic Habitats in the Northern California Current, Fisheries Oceanography, Volume 18 Issue 1, Pages 1-19, 2 Sept 2008
16. Menard,F.,Marsac,F.,BellierE.,CAzelles,B.,Cli matic oscillations and tuna catch rates in the Indian Ocean: a wavelet approach to time series analysis, Fisheries Oceonography,Volume 16, Issue 1, pages 95–104, January 2007.
17. Rouyer T, Fromentin JM, Stenseth NC, Cazelles B Analysing multiple time series and extending significance testing in wavelet analysis. Marine Ecology Progress Series 359: 11–23. 2008. [18]
18. applications.New York: Springer-Verlag , 2002
19. Tokgözlü A., Özdemir,G.,Kahramaner Y. ile birlikte
20. Concentration".Thirteenth Annual Conference and First International Conference of Gwalior Academy of
21. Symposium on Mathematical
22. Engineering and Biosciences,Anand Engineering College,Agra,India, January 10-13, 2008.
23. Tsukamoto K, Chow S, Otake T, Kurogi H, Mochioka N, et alOceanic spawning ecology of freshwater eels in the western North Pacific. Nature Communications 2: 179 ,2011. [21]
24. CazellesComplex interplays among population dynamics, environmental forcing, and exploitation in fisheries, 2008.
25. Tian Y, Kidokoro H, Fujino T Interannual- decadal variability of demersal fish assemblages in the Tsushima Warm Current region of the Japan Sea: Impacts of climate regime shifts and trawl fisheries with implications for ecosystem-based management. Fisheries Research 112: 140– 153,2011.
26. Thomas Brune,R.,Jean Bouche Long-term trends in fish recruitment in the north-east Atlantic related to climate change, Fisheries Oceanography, Vol. 16, No. 4. pp. 336-349, 2007.
27. Percival, D. and Walden, A.: Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge UK, 2000 Theory and " ANN Aplication on
28. Sediment Mathematical Sciences(GAMS)
29. with Modelling in T. Rouyer, J.-M. Fromentin,
30. B. [25] Pesquet, J.-C., Krim, H. and Carfantan, H.: Time-invariant
31. representations, IEEE Trans. on Signal Processing, vol. 44, no. 8, pp.1964-1970, 1996.
32. Polanco, J.,Ganzedo, U.,CAballero-Alfonso, A.M., Castro Hernandez, J.J.,Wavelet analysis of correlation among Canary Islands octopus captures per unit effort, sea-surface temperatures and the North Atlantic Oscillation, Fisheries Research, Volume 107, Issues 1–3, January 2011, Pages 177– 183.
33. http://ulcar.uml.edu/~iag/CS/Intro-to- ANN.html(27.9.2012)
34. Tourre, Y.M., S. E., Lluch-Cotaand W. B. White, (2007): “Global multi-decadal ocean climate and small-pelagic fish population”, Environ. Res. Lett. 2 (July-September
35. doi:10.1088/1748-9326/2/3/034005
36. wavelet 2007) 034005,