Main goals of time series analysis: (a) identifying the nature of the phenomenon studied (b) forecasting (predicting future values of the time series.

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Presentation transcript:

Main goals of time series analysis: (a) identifying the nature of the phenomenon studied (b) forecasting (predicting future values of the time series variable)

The objectives (i) Weather the observed variations of the indicator reflect adequately the real interannual variability of the sprat food supply? (ii) In what degree the observed variations of the indicator are connected to long-term environment variability? (iii) Can we predict the fat content in Black Sea sprat population using available data?

Annual dynamics of the Black sea sprat fat content (M ± SD) spawningfeeding periodspawning IIIIIIIVVVIVIIVIIIIXXXIXIII Month F a t c o n t e n t, %

Sample locations of the Black sea sprat (after Minyuk et al., 1997) 1 3 2

Summary of spatial variability in the sprat fat content data (single factor analysis of variance)

Summary of interannual variability in the sprat fat content data (single factor analysis of variance) Spatial variability exceeds 56 % of total variability Standard error of single observation amounts 1.3

Long-term dynamics of sprat fat content from 1960 to 2001 M=11.74 SD=1.71 Fat content, % +SD -SD

P C F A T, % P C F A T, % Long-term dynamics of sprat fat content compared with two first principal components of environmental variability (acordingly to Daskalov, 2003) Sprat fat content Principal components (Daskalov, 2003 )

All data: R = Except data with < 3 points per year : R = Except data with < 5 points per year : R = Sprat fat content dynamics (detrended) in comparison with the 2 nd principal component of the Black sea ecosystem variability (acordingly to Daskalov, 2003)

+ Sea level pressure + Total river inflow + Inorganic phosphorus + Phytoplankton + Whiting recruitment + Anchovy recruitment + Horse mackerel recruitment + Hypoxia zone + Hypoxia zone (?) - Hydrogen sulphide - Zooplankton (E) - Pleurobrachia pileus - Phytoplankton during bloom - Mytilus biomass Main loadings of the input variables to the 2 nd principal component (according to Daskalov, 2003)

Summary of autocorrelation test Partial autocorrelationAutocorrelation function Time lag, years

Variables Correlation coefficient Sprat fat content (t-1) 0.43 Sprat fat content (t-3) 0.42 Sprat biomass (t-1) 0.34 Sprat biomass (t-2) 0.35 Sprat biomass (t-3) 0.36 Mean annual SST(t-4) – 0.46 Mean winter SST(t-2) – 0.31 Phytoplankton NW (t-1) 0.52 Phytoplankton NW (t-2) 0.39 Phytoplankton E (t-4) 0.48 Summary of correlation tests for sprat fat content Only significant correlation coefficients (p<0.05) are presented

Observed and predicted indices of sprat fat content (linear model) r = 0.69 (R 2 = 0.47) 8,0 9,0 10,0 11,0 12,0 13,0 14,0 15,0 16, Observed Predicted ? ? FAT t = f (FAT t-1, FAT t-3, SST t-4 )

Conclusion We are need long-term series data. Let they will be good data.

References Afifi A. A., Azen S. P. (1979). Statistical analysis. A computer oriented approach. Academic Press, New York, San Francisco, London. Daskalov G. M. (2003). Long-term changes in fish abundance and indices in the Black Sea. Mar. Ecol. Prog. Ser., 255, 259– 270. Daskalov G.M., Grishin A., Mihneva V. Ecosystem time-series analyses in the Black sea // Mediterranean biological time series. CIESM Workshop monographs. – Monaco, – P. 31–36. Minyuk G. S., Shulman G. E., Shchepkin V. Ya., Yuneva T. V. (1997). Black Sea sprat: the relationship between lipid dynamics, biology and fishery. Ekosi-Hydrophysica, Sevastopol, Ukraine (in Russian) Shulman G. E. (1974). Life cycles of fish. Physiology and biochemistry. Hulsted Press, John Wiley and Sons, New York. Shulman G. E., Chashchin A. K., Minyuk G. S., Shchepkin V. Ya., Nikolsky V. N., Dobrovolov I. S., Dobrovolova S. G., Zhigunenko A. S. (1994). Long-term monitoring of Black Sea sprat condition. Doklady Akademii Nauk, 335, 124–126 (in Russian). Shulman G. E., Love R. M. (1999). The Biochemical Ecology of Marine Fishes. In: Advances in marine biology, vol. 36, Academic Press, London. Shulman G. E, Nikolsky V. N, Yuneva T. V., Minyuk G. S., Shchepkin V. Ya., Shchepkina A. M, Ivleva E. V., Yunev O. A., Dobrovolov I. S., Bingel F., Kideys A. E. (2005). Fat content of Black Sea sprat as an indicator of fish and ecosystem condition. Mar.Ecol.Prog.Ser., 293, 201–212. Zar J.H. (1984). Biostatistical analysis. 2nd edn., Prentice Hall, Englewood Cliffs, NJ.