The sprat fat content variability The sprat fat content variability in connection with long-term environmental changes in connection with long-term environmental changes

in the Black Seain the Black Sea 

V. N. Nikolsky and G. E. Shulman

Institute of Biology of the Southern Seas (IBSS), Nakhimov Av. 2, Sevastopol 99011, Ukraine

  

e-mail: nikolsky@ibss.iuf.net shulman@ibss.iuf.net

Main goals of time series analysis: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 objectivesThe 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 contentAnnual dynamics of the Black sea sprat fat content(M (M ± SD)± SD)

spawningfeeding periodspawning

I II III IV V VI VII VIII IX X XI XII IMonth

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

Fat

con

tent

, %

28.0 30.0 32.0 34.0 36.0 38.0 40.0

42.0

44.0

46.0

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

1

32

V a r i a n c e s Y e a r s A r e a s N o o f

s a m p l e s b e t w e e n a r e a s

w i t h i n a r e a s

F F –

c r i t i c a l P –

v a l u e

1 9 6 0 1 , 2 , 3 1 2 0 . 8 3 1 . 3 8 0 . 6 1 4 . 2 6 0 . 5 6 7 1 9 6 1 1 , 2 , 3 1 1 1 . 5 9 5 . 9 4 0 . 2 7 4 . 4 6 0 . 7 7 2 1 9 6 2 1 , 2 , 3 1 1 0 . 6 5 5 . 4 1 0 . 1 2 4 . 5 6 0 . 8 8 9 1 9 7 7 1 , 2 8 9 . 6 5 2 . 3 0 4 . 2 0 5 . 9 9 0 . 0 8 6 1 9 8 1 1 , 2 1 0 0 . 0 0 3 0 . 7 2 0 . 0 0 5 5 . 3 2 0 . 9 4 8 1 9 8 2 1 , 2 6 0 . 5 9 1 . 0 6 0 . 5 5 7 . 7 1 0 . 4 9 8 1 9 8 3 1 , 2 4 7 . 4 7 0 . 4 7 1 5 . 8 3 1 8 . 5 1 0 . 0 5 8 1 9 8 4 1 , 2 6 2 . 9 8 0 . 5 0 5 . 9 6 7 . 7 1 0 . 0 7 2 1 9 8 5 1 , 2 1 4 1 . 3 5 0 . 9 8 1 . 3 8 4 . 7 5 0 . 2 6 3 1 9 8 6 2 , 3 1 2 1 . 4 0 0 . 6 8 2 . 0 6 4 . 9 7 0 . 1 8 2 1 9 8 7 1 , 2 , 3 1 5 0 . 0 0 6 0 . 9 1 0 . 0 0 7 3 . 8 9 0 . 9 9 3

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

S o u r c e o f v a r i a t i o n S S d f M S F F

c r i t i c a l P

v a l u e

B e t w e e n g r o u p s ( y e a r s ) 3 3 1 . 1 3 2 4 1 3 . 8 0 7 . 5 3 1 . 5 9 < 0 . 0 0 1

W i t h i n g r o u p s ( y e a r s ) 2 6 0 . 2 8 1 4 2 1 . 8 3

T o t a l 5 9 1 . 4 1 1 6 6

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

Spatial variability exceeds 56 % of total variability

Standard error of single observation amounts 1.3

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

8

10

12

14

16

Long-term dynamics of sprat fat content Long-term dynamics of sprat fat content from 1960 to 2001from 1960 to 2001

M=11.74 SD=1.71

Fat

con

tent

, %

+SD

-SD

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

-3

-2

-1

0

1

2

3

PC

1

8

10

12

14

16

FA

T, %

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

-2

-1

0

1

2

3

PC

2

8

10

12

14

16

FA

T, %

Long-term dynamics of sprat fat content compared with two Long-term dynamics of sprat fat content compared with two first principal componentsfirst principal components of environmental variabilityof environmental variability

(acordingly to Daskalov, 2003)(acordingly to Daskalov, 2003)

Sprat fat contentPrincipal components (Daskalov, 2003)

All data: R = 0.548

Except data with < 3 points per year: R = 0.727

Except data with < 5 points per year : R = 0.871

Sprat fat content dynamics (detrended) in comparison with Sprat fat content dynamics (detrended) in comparison with the 2the 2ndnd principal component of the Black sea ecosystem variability principal component of the Black sea ecosystem variability

(acordingly to Daskalov, 2003)(acordingly to Daskalov, 2003)

1960 1970 1980 1990 2000

-2

-1

0

1

2

PC

2

-3

-2

-1

0

1

2

3

4

Fa

t co

nte

nt

an

om

alie

s, %

+ Sea level pressure+ Sea level pressure+ Total river inflow+ Total river inflow+ Inorganic phosphorus+ Inorganic phosphorus

+ Phytoplankton+ Phytoplankton

+ Whiting recruitment+ Whiting recruitment+ Anchovy recruitment+ Anchovy recruitment+ Horse mackerel recruitment+ Horse mackerel recruitment

+ Hypoxia zone+ Hypoxia zone (?)

- Hydrogen sulphide- Hydrogen sulphide- Zooplankton (E)- Zooplankton (E)- Pleurobrachia pileus- Pleurobrachia pileus- Phytoplankton during bloom- Phytoplankton during bloom- Mytilus biomass- Mytilus biomass

Main loadings of the input variables to the 2Main loadings of the input variables to the 2ndnd principal component principal component

(according to Daskalov, 2003)(according to Daskalov, 2003)

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

-2

-1

0

1

2

3

PC

2

8

10

12

14

16

FA

T, %

Sprat fat content

0 4 8 12 16

-0.4

-0.2

0

0.2

0.4

0 4 8 12 16

-0.4

-0.2

0

0.2

0.4

Summary of autocorrelation testSummary of autocorrelation test

Partial autocorrelationAutocorrelation function

Time lag, years 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 contentSummary of correlation tests for sprat fat content

Only significant correlation coefficients (p<0.05) are presented

Observed and predicted indices of sprat fat contentObserved and predicted indices of sprat fat content(linear model)(linear model)

r = 0.69 (R2 = 0.47)

8,0

9,0

10,0

11,0

12,0

13,0

14,0

15,0

16,0

1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004

Observed

Predicted

?

?

FATt = f (FATt-1, FATt-3, SSTt-4)

ConclusionConclusion

We are need long-term series data. We are need long-term series data.

Let they will be good data.Let they will be good data.

ReferencesReferences

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, 2003. – 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.