Volume 1
Issue 1
Fisheries
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES
Available Online: http://www.ejpau.media.pl/volume1/issue1/fisheries/art03.html
GROWTH RATE OF BREAM [ABRAMIS BRAMA (L.)] IN LAKE DˇBIE.
Włodzimierz Załachowski, Kazimierz Więski
Length and weight growth rates of 206 bream individuals caught in 1992 and 1995 were backcalculated. Different types (nonlinear vs. linear) of a relationship between the scale caudal radius and body length were revealed to prevail in the two years. The 1992 bream grew very rapidly during the first four years of life, the growth rate slowing down later on. On the other hand, the bream growth in 1995 was more uniform over time. No sex or agedependent differences in growth rate were found. The Lake Dąbie bream population belongs to the fast growing populations of the species.
Key words: bream, rate of growth, lengthweight relationship.
The Lake D±bie bream growth rate had been already studied by Kompowski [6, 7] in 19741977 and 19851986 and by AbdelBaky [1] in 1981. Results reported by those authors showed the growth indices to vary, which might have been related to habitat variability within a large water body on the one hand and to variations in climatic or ecological conditions on the other. It was therefore thought purposeful to follow up those studies several years later. The bream growth rate study reported in this paper was a part of a more comprehensive project, supported by the Committee for Scientific Research's funds for statutory activitie,s on "Fish growth rate under nonstable conditions of the River Odra mouth area".
A total of 206 bream individuals caught within 1992 (101 individuals) and 1995 (105 individuals) were examined. In 1992, the population was sampled four times from July until November, while all the 1995 individuals were collected in November. Only the 1992 fish were sexed. A detailed summary of the materials studied is given in Table 1. Figs 1 and 2 show the length and age distributions. Domination of the 6yrold fish is typical of both years; however, a higher number of older, hence larger, bream was examined in 1995.
Table 1. Description of the samples examined [l.c. (cm); W (g)] 
Date of capture 
Females 
Males 
Indet. sex 
Total 

n 
range 
n 
range 
n 
range 
n 
range 

17 July 1992 
14 
l.c. 
29.540.5 
5 
l.c. 
30.537.0 
 
l.c. 
19 
l.c. 
29.540.5 

W 
5241283 
W 
582954 
W 
W 
5241283 

28 August 1992 
13 
l.c. 
29.539.0 
10 
l.c. 
30.034.5 
2 
l.c. 
12.012.5 
25 
l.c. 
12.039.0 
W 
5351108 
W 
540838 
W 
20 
W 
201108 

17 October 1992 
17 
l.c. 
30.034.0 
18 
l.c. 
29.536.0 
 
l.c. 
35 
l.c. 
29.536.0 

W 
640950 
W 
6001000 
W 
W 
6001000 

30 November 1992 
8 
l.c. 
29.543.0 
14 
l.c. 
30.041.0 
 
l.c. 
22 
l.c. 
29.543.0 

W 
5001650 
W 
5501200 
W 
W 
5001650 

Total 1992 
52 
l.c. 
29.543.0 
47 
l.c. 
29.541.0 
2 
l.c. 
12.012.5 
101 
l.c. 
12.043.0 
W 
5001650 
W 
5401200 
W 
20 
W 
201650 

11 November 1995 
105 
l.c. 
22.747.0 

W 
2452675 
Fig. 1. Fish length distribution in samples studied 
Fig. 2. Fish age distribution in samples studied 
The fish age was determined from scales collected from the midpart of the body above the lateral line. There were large within and betweensample differences in legibility of seasonal growth zones on the scales. In about 10% of the individuals, due to the presence of larval ring, the existence of the first anual ring was doubtful. Due to the high density of the fringe seasonal rings in those individuals aged 10+, age of the oldest fish can be treated as hypothetical only, although the difference relative to the actual age should not exceed 12 years.
The body length (longitudo corporis) growth rate was backcalculated from scale caudal radius measurements. The scale caudal radiusfish body length relationship is shown in Fig. 3. The relationship differed between the years. In 1992, the equation describing the line plotted from empirical data representing mean values for adult individuals (body length > 30 cm) contained a free term equal to 23.9. In this case, the Vovk method [3] was used and an auxiliary line was plotted from 1.32 cm [4] to the nearest empirical data point, as shown in Fig. 3B. The location of empirical data points representing the two juvenile fish in the sample confirms that the broken line plotted in this way and indicative of a nonlinear relationship fits the data well.
Fig. 3. Fish body length–scale caudal radius relationship 
The relationship describing the scale radiusbody length relationship in the fish caught in 1995 contains a free term equal to 6.5 cm. In this case, a shift of the line to 1.32 cm on the length axis (which, according to Heese [4], concerned samples containing juveniles) produced no clear reduction in the coefficient of determination (Fig. 3C). Therefore, back calculations on the 1995 data were performed with the Rosa Lee equation, corrected by 1.32 cm.
The backcalculated lengths were used to compute the von Bertalanffy equations; thereupon, after the lengthweight relationships were calculated with the power function, modified von Bertalanffy equations describing the weight growth rate were derived. To compare growth rates of fish belonging to different age groups, the GL growth coefficients [8] were calculated for each group, which necessitated the use of a binomial equation with respect to change of length with time. The GL values were calculated as definite integrals within age limits from 0 to 10 years (rather than to tmx, as in Szypuła).
Tables 2 and 3 contain backcalculated body lengths in age groups. The mean values indicate the bream caught in 1992 to have grown at a faster rate. Those fish attained higher annual length increments over the first 4 years of life. Older fish grew faster in 1995, but they became longer as of the age of 8 years. This is illustrated by Fig. 4 showing both backcalculated mean length and curves plotted from the von Bertalanffy model. The 1995 curve shows a better fit to the empirical data. On the other hand, a stronger levelling off of the growth rate past the age of 4 is observed in 1992. Consequently, a considerably lower asymptotic length and a higher katabolic coefficient k were obtained in that year. Noteworthy is also the fact that the 1992 data set produced decidedly higher coefficients of variation, which evidences a substantial body length variability within the population (Tables 2 and 3).
Table 2. Length growth rate in different bream age groups in 1992 (l.c., mean body length; dl, length increment; SD, standard deviation; v, coefficient of variation; n, sample size) 
Age group 
n 
Age 

l_{1} 
l_{2} 
l_{3} 
l_{4} 
l_{5} 
l_{6} 
l_{7} 
l_{8} 
l_{9} 
l_{10} 
l_{11} 
l_{12} 

I 
2 
7.75 

IV 
9 
8.15 
15.45 
22.50 
27.80 

V 
21 
8.41 
14.86 
21.63 
26.52 
29.79 

VI 
36 
7.00 
11.78 
17.55 
23.99 
28.78 
31.30 

VII 
6 
7.71 
14.19 
20.20 
26.28 
30.02 
32.07 
33.36 

VIII 
6 
8.06 
13.63 
20.45 
25.48 
29.63 
32.02 
33.29 
34.00 

IX 
10 
8.24 
15.45 
22.82 
28.08 
30.97 
32.58 
33.81 
34.64 
35.27 

X 
9 
9.40 
16.10 
23.06 
29.68 
32.07 
33.37 
34.59 
35.48 
36.27 
36.97 

XI 
1 
10.02 
16.01 
23.08 
27.64 
31.29 
32.32 
33.37 
34.70 
35.86 
36.39 
36.87 

XII 
1 
11.32 
20.22 
25.46 
29.27 
31.98 
33.63 
35.42 
36.13 
38.01 
38.85 
39.52 
40.10 
l.c. 
7.93 
13.92 
20.37 
26.12 
29.79 
31.93 
33.88 
34.84 
35.86 
37.09 
38.2 
40.10 

dl 
7.93 
5.99 
6.45 
5.75 
3.67 
2.14 
1.95 
0.96 
1.02 
1.23 
1.11 
1.90 

SD 
1.75 
3.24 
4.41 
4.24 
3.31 
2.45 
1.82 
2.07 
2.38 
2.64 
1.87 

n 
101 
99 
99 
99 
90 
69 
33 
27 
21 
11 
2 
1 

v 
22.10 
23.30 
21.70 
16.20 
11.10 
7.70 
5.40 
5.90 
6.60 
7.10 
4.90 
Table 3. Length growth rate in different bream age groups in 1995 (l.c., mean body length; dl, length increment; SD, standard deviation; v, coefficient of variation; n, sample size) 
Age group 
n 
Age 

l_{1} 
l_{2} 
l_{3} 
l_{4} 
l_{5} 
l_{6} 
l_{7} 
l_{8} 
l_{9} 
l_{10} 
l_{11} 
l_{12} 
l_{13} 
l_{14} 
l_{15} 

III 
1 
7.70 
13.19 
19.90 

IV 
6 
6.42 
12.58 
18.22 
23.12 

V 
13 
6.38 
11.82 
16.70 
21.55 
26.23 

VI 
36 
6.45 
11.73 
17.51 
21.75 
25.30 
28.91 

VII 
8 
6.13 
12.07 
17.48 
22.10 
26.70 
29.83 
33.85 

VIII 
7 
5.60 
10.39 
14.74 
18.84 
23.21 
26.82 
30.01 
33.36 

IX 
5 
6.32 
11.48 
15.60 
20.10 
24.88 
29.26 
32.32 
34.71 
37.06 

1X 
6 
7.03 
12.27 
16.73 
22.60 
26.95 
31.19 
35.46 
37.82 
39.91 
41.92 

XI 
8 
6.60 
13.06 
17.88 
22.82 
26.61 
30.27 
33.09 
35.54 
37.77 
39.81 
41.47 

XII 
8 
6.14 
11.42 
16.17 
20.70 
25.34 
29.93 
33.15 
36.68 
38.84 
40.85 
42.55 
44.13 

XIII 
5 
6.47 
11.37 
15.91 
20.52 
24.31 
27.62 
30.32 
33.74 
35.94 
38.11 
39.84 
41.20 
42.83 

XIV 
1 
6.38 
10.09 
14.59 
17.10 
20.11 
25.22 
28.14 
29.53 
31.62 
34.17 
37.61 
39.51 
40.76 
42.48 

XV 
1 
5.40 
8.51 
11.25 
15.27 
18.06 
20.31 
23.43 
26.54 
30.94 
34.80 
37.49 
40.22 
42.58 
43.76 
45,49 
l.c. 
6,38 
11.77 
16.91 
21.44 
25.40 
29.08 
32.42 
35.04 
37.64 
39.87 
41.15 
42.59 
42.50 
43.12 
45.59 

dl 
6,38 
5.39 
5.14 
4.53 
3.96 
3.68 
3.34 
2.62 
2.60 
2.23 
1.28 
1.44 
0.09 
0.62 
2.47 

SD 
0,88 
1.88 
2.42 
2.60 
2.60 
2.96 
2.99 
2.94 
2.88 
2.63 
2.35 
2.35 
1.60 
0.64 

n 
105 
105 
105 
105 
98 
85 
49 
41 
34 
29 
23 
15 
7 
2 
1 

v 
13,79 
15.97 
14.31 
12.13 
10.24 
10.18 
9.22 
8.39 
7.65 
6.60 
5.71 
5.52 
3.76 
1.48 
Fig. 4. Growth rate of bream caught in 1992 and 1995 
No significant sexdependent differences in growth rate were revealed in 1992 (Fig. 5). The male and female curves took similar shapes, and the von Bertalanffy equation parameters were similar.
Fig. 5. Growth rate of bream males and females caught in 1992 
As shown by the analysis of agedependent differences in growth rate (Table 4), in neither of the two years did the Rosa Lee phenomenon occur. No regular pattern of changes in GL could be detected, which evidences a similar growth rate of fish differing in age. The higher mean value obtained in 1992 confirms the faster growth of the bream caught in that year. By the same token, the similar GL values of males and females demonstrate that both sexes were growing at a similar rate.
Table 4. Age–specific length growth rate in bream 
1992 
1995 

l.c. = a + bt +ct^{2} 

Age group 
a 
b 
c 
GL 
a 
b 
c 
GL 
IV 
0.530 
9.100 
0.525 
275 
0.425 
7.149 
0.315 
248 
V 
0.204 
8.973 
0.589 
250 
0.932 
5.607 
0.111 
253 
VI 
0.364 
7.015 
0.272 
256 
0.005 
6.652 
0.308 
230 
VII 
1.304 
9.180 
0.600 
272 
0.111 
6.572 
0.251 
244 
VIII 
0.686 
8.703 
0.545 
247 
0.583 
5.133 
0.130 
219 
IX 
0.249 
8.990 
0.579 
259 
0.308 
5.892 
0.197 
232 
X 
2.271 
8.260 
0.493 
271 
0.060 
6.609 
0.240 
251 
XI 
0.812 
6.460 
0.260 
244 

XII 
0.124 
6.141 
0.201 
239 

XIII 
1.162 
5.498 
0.181 
226 

Total 
0.679 
7.798 
0.426 
255 
0.075 
5.993 
0.209 
231 
Females 
0.844 
7.748 
0.418 
257 

Males 
0.308 
7.981 
0.450 
252 
The lengthweight relationship (Fig. 6) turned out similar in both years. In spite of some betweensample differences in length distribution (more larger individuals and the absence of the smallest ones in 1995), the power function parameters are similar and the curves overlap. On the other hand, the weight growth curves (Fig. 7), although producing a pattern similar to the length growth curves of Fig. 4, show a larger difference past the age of 8 in favour of the bream caught in 1995 and conform to the principle of weight growing in proportion to the length cube. Due to the same reason, the difference between asymptotic weights was much larger than that between the asymptotic lengths. The two weight growth curves are sigmoid; their characteristic inflection points (where annual increments begin to decrease) fall at the age of 6 years in 1992 and as late as 11 years in 1995.
Fig. 6. Length–weight relationship of bream caught in 1992 and 1995 
Fig. 7. Weight growth rate of bream caught in 1992 and 1995 
This study demonstrated the Lake D±bie bream population to be nonhomogenous. Not only did the 1992 and 1995 samples differ substantially in the bream growth rate, but  characteristically  different types of scale caudal radiusbody length relationship were revealed to prevail in both years. The relationship was nonlinear in 1992, while in 1995 it was close to linearity. Although both types of the relationship had already been recorded in bream, they occurred in different water bodies. For example, Heese [4] found a linear relationship to occur in four out of the six water bodies he studied (including the Kamień and Szczecin Lagoons, adjacent to Lake D±bie), nonlinear relationships prevailing in the remaining two reservoirs. Interestingly, the equation describing the nonlinear relationship in the Lake Pierzchały (L = 1.66 + 7.52R  0.33R2), derived by Heese, almost exactly fits the 1992 data reconstructed in Fig. 3B. The linear relationship determined for the 1995 data may be compared with both the linea r relationship derived by Heese for the water bodies neighbouring Lake D±bie and linear relationships derived by AbdelBaky [1] for Lake D±bie as well. The regression coefficients obtained by the two authors referred to were similar (ranging within 4.274.99); however, values of their equations' free terms (1.322.48) were clearly lower. They examined scales collected from small individuals, which were absent in the 1995 sample of this study. A linear radiuslength relationship was reported also by Kompowski [6] in the River Regalica and Lake D±bie bream, but that author collected the scales from below the lateral line. Further studies should show if the type of the relationship (linear vs. nonlinear) changes throughout the year in relation to seasonal asynchrony in growth of fish body and scales.
The Lake D±bie bream growth rate variability has already been demonstrated in the literature. Kompowski [7] pointed to the fact that the growth of young fish (age < 8 years) became accelerated in the period between 1974/1979 and 1985/1986, a sloweddown rate being recorded at the same time in older bream. This was reflected by a change in the von Bertalanffy equation parameters: the asymptotic length dropped from 54.4 to 44.6 cm and the katabolic coefficient increased from 0.113 to 0.175, which corresponds to the difference found between the 1995 and 1992 samples of the present study (55.4 vs. 41.9 cm asymptotic length and 0.1251 vs. 0.2104 katabolic coefficient). It is evident that the changes described were not chronologous. Their irregularity is confirmed by data reported by AbdelBaky [1] who, based on materials collected in 1982, that is between the two series of data collected by Kompowski [7], observed a much slower growth rate (L = 47.28; k = 0.12; to = 0.0467).
The observations described above tend to support a conclusion that the Lake D±bie bream population is not homogenous. The lack of homogeneity can be noticed when examining the scales, as both their shapes and legibility of seasonal zones and proportions between annual rings differ, even between individuals of the same age and caught the same day. This is reflected in the high coefficients of individual variability (Tables 2 and  particularly  3). Without more indepth studies, it is difficult to pinpoint causes of the variability. Both the habitat variability within the large (56 km2) area of the D±bie and the species' migrations in the River Odra estuary (including the Pomeranian Bight) may be taken into account, in which case every sample collected from the D±bie can produce different data. To determine the population mean growth rate would, then, require a sufficiently abundant set of systematically collected samples. It ought to be mentioned here that the roac h, a species which, too, occurs in Lake D±bie and migrates into the Pomeranian Bight, shows growth rate which is stable over a long period of time [9].
Although having a variable mean growth rate, the bream examined showed some growthrelated characteristics to be constant. Within each sample analysed, the growth rate of individuals belonging to different age groups proved invariant. This may mean that the fish growth rate was not affected by environmental conditions changing over recent years. In the 1992 sample, no clear sexrelated difference in growth rate was found, which confirms earlier observations of AbdelBaky [1] concerning the same lake. Moreover, the lengthweight relationship, similar in the two samples of this study, did not deviate from that found by Kompowski [7] and AbdelBaky [1].
According to classification of Backiel and Zawisza [2], the Lake D±bie bream meets the criteria of "good" growth: l.c. = 31.5 cm when aged 9 years and l.c. = 37 cm before the age of 11. In spite of the clear variability, the above criteria are met by both the two samples of the present study and the bream studied by Kompowski [6, 7], and even  although close to the lower limit  the fish described by AbdelBaky [1]. The growth rate found in this study for 1992 can be even qualified as "very good", as the values obtained by bream during the first 8 years of life are comparable to those of very fast growing brackish water populations from southern Europe [5].
 The Lake D±bie bream show a fast growth rate.
 The Lake D±bie bream population is not homogenous. The fish caught in different years differ in their growth rates and in types of scale caudal radiusbody length relationship.
 No sex or agedependent differences in growth rate are observed.
 The weight growth rate is, in different samples, related only to the length growth rate as the lengthweight relationship is constant.
 AbdelBaky T. E., 1983: Some aspects on the Biology of bream (Abramis brama L.) in D±bie Lake, Doctors manuscript, Agricultural University of Szczecin.
 Backiel T., Zawisza J., 1968: Synopsis of biological data on the bream, Abramis brama (L.), FAO Fisheries Synopsis No. 36.
 Čugunova N. I., 1959: Rukovodstvo po izučeniu wozrasta i rosta ryb, Izd. AN SSSR, Moskwa.
 Heese T., 1992: Optimisation of fish growth rate determination with back calculations, Monograph of Faculty of Overland and Sanitary Engineering No. 42, Koszalin. [In Polish].
 Kazančeiev E. N., 1963: Ryby Kaspijskogo Moria, Moskwa.
 Kompowski A., 1982: On some aspects of biology of bream, Abramis brama (L. 1758), inhabiting the River Regalica and Lake D±bie, Acta Ichth. Pisc. 12 (1): 425.
 Kompowski A., 1988: Growth rate of bream, Abramis brama (L. 1758), in Lake D±bie and Szczecin Lagoon, Acta Ichth. Pisc. 18 (1): 3548.
 Szypuła J., 1977: Application of the binomial and the 3rd order polynomial to characterise fish growth, D.Sc. Thesis 52, Agricultural University of Szczecin. [In Polish].
 Załachowski W., Krzykawska I., 1995: Growth of roach, Rutilus rutilus (L.), in Lake D±bie, Acta Ichth. Pisc. 25 (1): 318.
Submited: June 1998
Włodzimierz Załachowski, Kazimierz Więski
Department of Fish Biology,
Agricultural University of Szczecin
4 Kazimierza Królewicza St.,71550 Szczecin, Poland
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