Volume 6
Issue 2
Fisheries
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES
Available Online: http://www.ejpau.media.pl/volume6/issue2/fisheries/art03.html
ANALYSIS OF FISHERY EXPLOITATION PARAMETERS OF LAKES AND ASSESSMENT OF THEIR SUITABILITY FOR OBSERVATION OF TRENDS IN FISH CATCHES
Andrzej Skrzypczak, Andrzej Mamcarz
Longterm studies on catches from 757 lakes in northeastern Poland served to analyse fishery exploitation parameters available from the records in lakes books. Yield (kg^{.}ha^{1}), annual frequency of exploitation and relative catch (kg^{.}ha^{1.}month^{1}) were analysed in five size classes of lakes, together with the relations occurring between these parameters. Linear correlation was determined between yield versus the number of months of exploitation when the catches were made and relative catch. The latter parameter was characterised by a slightly smaller variability of real values and consequently by a higher concentration along the adjusted trend line compared to the catch per lake area unit.
Key words: fishery exploitation, yield, frequency of exploitation, relative catch, intensity of exploitation, lakes, northeastern Poland..
Analysis of fishery exploitation and its effects is performed, among other purposes, to find out the species composition and quantitative ratios of exploited fish populations [12]. Observation of results of longterm commercial catches often forms a basis for drawing conclusions on changes taking places within the exploited fish stocks [6, 13] or whole lake ecosystems [3, 8, 11, 35].
The system of organising inland fishery management which was established in Poland in the late 1940s functioned until 1994. It comprised a method for recording the effects of fishery exploitation, which made such analyses possible [17, 24]. In 1959 an instruction was issued containing guidelines on data collection for the purpose of lake exploitation analysis [10]. Several reports and publications appeared which specified principles underlying performance of fishery exploitation analysis, ways of reaching conclusions as well as their practical applications [4, 12, 13, 14, 15, 16, 20, 21].
A classical model of complete fishery exploitation analysis was based on three principal parameters: yield (kg·ha^{1}), intensity of exploitation (jm·ha^{1}) and effectiveness of exploitation (kg·jm^{1}). The term “jm” represents a coefficient of relative efficiency of a standard unit of various fishing gears [4]. A conventional fishing gear, catching one kg of fish daily, was taken as a standard unit of fishing effort for comparison. Then, based upon longterm catch data from different types of fishing gear and collected in various parts of Poland from hundreds of lakes, the average daily catch of the different types of fishing gear, expressed in kg of fish per day, was calculated. The values thus obtained were considered as standard fishing effort for each fishing gear. Thus, the fishing intensity of a given fishing gear is the product of its standard fishing effort multiplied by the number of days during which it is used. Subsequently, the catch per unit of area, or intensity per unit of area (standard units per ha), can be derived [20].
These principal parameters of fishery exploitation, which must be considered over the same time span, remain in a strong mutual relationship, thus creating the socalled exploitation system, which is analysed on different levels of detailedness [14]. Nonetheless, a fullscale analysis of exploitation requires detailed and properly gathered data, especially on fishing effort. The information necessary to determine intensity of exploitation, which was not included in lakes books, was technically very difficult and time consuming to reproduce, even for a short time period in the recent past [17]. Today it is absolutely impossible.
With incomplete data on intensity of exploitation in hand, the researchers have emphasised the possibility of using the socalled indices of exploitation intensity changes in comparative analyses. One of such indices, known as the utilisation of the fishing season, has been demonstrated to be a simple tool which in many cases facilitates good assessment of the changes in exploitation intensity [14]. It was derived from the number of months of exploitation in the fishing season assuming that a higher number of months corresponded to a larger total effect of catches and usually a larger and less monodirectional intensity of exploitation [12]. An undisputed advantage of the utilisation of the fishing season was, and still is, that this index is available from lakes books. The number of months of catches per year appeared as an element of analyses in reports on different aspects of eel management in Polish lakes [17, 19]. It was treated as an indirect index of the intensity of fishery exploitat ion (fishing effort) [17] or else as a reflection of the intensity of catches [19]. This simplified model for estimating intensity has been applied on the grounds that commercial catches are carried out regularly under stable conditions, and that the estimate is based on indices obtained from water reservoirs of similar size [17].
At present, in the vast majority of reports on observations of qualitative and quantitative changes in the exploited ichtiofauna components on the basis of longterm catch statistics, expressed in kg or kg·ha^{1}, the question of exploitation intensity is ignored following the assumption that its value has not changed.
The present paper reviews these exploitation parameters which are possible to be calculated and assessed on the basis of the data included in lakes books. Beside yield (kg·ha^{1}) and number of exploitation months per year, indicating the frequency of catches, relative catch was considered, which had previously been used in research on changes in distribution of pikeperch [27]. The objective of this study was to determine variability and mutual relationships between the above parameters and to assess their suitability for observation of time events in the exploited components of ichtiofauna.
The initial material for the research comprised the exploitation data from 757 lakes in northeastern Poland, collected in 19511994. The lakes books contained information on total annual fish catches expressed in kilograms and the number of months of exploitation in each year. The data on catches recorded in commercial years were converted to calendar years. The key factor that allowed the conversion lay in the association of a particular calendar year with the summer and autumn fishing season in a given commercial year. The number of exploitation months as a parameter of frequency of catches was assigned to this set. Yield (kg·ha^{1}) was calculated for each lake in every year. After dividing the yield by the appropriate number of months, the parameter of relative catch (kg·ha^{1}·month^{1}) was computed. The area of the lakes for computations was taken from the IR¦ database after Choiński [5].
All relations were analysed within five lake size categories established during the initial analysis of the lake size structure (Table 1). In the process of the analysis, values of the analysed parameters in each year of exploitation and for each lake were taken as cases within groups of variables. For the parameter of yield, outliers (infrequent observation), which have a profound influence on the power of relationships between groups of variables, were identified and removed according to the analysis of scatterplots and standard deviation of variables [31]. In practice, this procedure applied to the cases of yield exceeding 100 kg·ha^{1}, which were potentially burdened with the biggest random error. The years in which no catches were done were excluded from the analysis as invalid cases.
Table 1. Division and specification of the data sets included in the analysis of exploitation parameters 
Size class of lakes (ha) 
Category of lakes 
Number of lakes 
Outliers 

Number of cases 
Share 

< 50 
I 
359 
387 
3.8 
50  100 
II 
149 
111 
2.1 
100  200 
III 
116 
35 
0.8 
200  500 
IV 
86 
24 
0.7 
> 500 
V 
47 
3 
0.2 
When elaborating the results of the analyses, Statistica 6.0 PL and Microsoft Excel 2000 software packages were applied. The basic statistical computations relied on descriptive statistics such as an arithmetic mean, standard deviation and mean standard error. In order to compare the significance of statistical differences of exploitation parameters between the lake size categories, analysis of variance (ANOVA) and posthoc comparison of means (Tukey’s ttest) were performed. Preliminary tests were done on groups of variables to determine whether they fulfilled the basic requirements of ANOVA and were therefore suitable for performing the necessary statistical analyses. A hypothesis on homogeneity of variances in the groups of variables was tested using Levene and BrownForsythe tests. Although the hypothesis was discarded on the basis of the significance of these tests (p<0.05), the results obtained by ANOVA were not undermined [31]. Parametric tests were applied, hence the hypot hesis of the conformity of variables to the normal distribution was tested in each case with the help of ShapiroWilks’ W test, which is a preferable and powerful test of normality [31]. In none of the cases significant values of W statistics were attained (p<0.05), which entitled us to accept the hypothesis that the distribution of variables was normal [31].
The power of relations between the analysed parameters was determined by linear regression analysis and Pearson’s correlation coefficients [30]. The validity of tests and analyses was accepted at the level of significance a=0.05.
Approximation of time changes of mean annual values of exploitation parameters was done using functions of 2degree and 3degree polynomials. The values of the coefficient of determination (Rsquared) constituted a measure of trend line adjustment [32].
Analysis of the mean values of the fishery exploitation parameters of lakes in the years 1951–1994 revealed their variation within the lake size categories (Table 2). The mean total yield varied in the range of values from 21.87 kg·ha^{1} in lake size category III to 28.4 kg·ha^{1} in lake size category V. Statistical differences (p<0.05) between the mean values of this parameter were not found within the first three size categories of lakes. All the size categories significantly differed from each other in the annual number of months of exploitation. The larger the lake size, the higher the mean value of this parameter, rising from 2.64 for lakes of size category I to 10.25 for reservoirs over 500 ha large. A reverse distribution was obtained for the mean relative catch. The lowest relative catch value was determined for lakes in category V (2.77 kg·ha^{1}·month^{1}), being slightly higher for lakes in category IV (3.4 kg·ha^{1}·month^{1}), with no statistically significant differences found between these two groups of lakes. The highest mean relative catch was characteristic of lakes measuring less than 50 ha, for which it amounted to 10.81 kg·ha^{1}·month^{1}.
Table 2. Results of comparisons of the means of lake exploitation parameters obtained in 1951 – 1994 (mean±SEM). Critical value F=2.37 (df 4, 24988; a=0.05). Indexed (*) F test statistics values are statistically significant at p<0.05. Means of the parameters marked with different letter superscripts (in rows) are statistically different at p<0.05 
Parameters 
Fratio 
Category of lakes 

I 
II 
III 
IV 
V 

Yield 
69.46* 
22.45^{a }±0.21 
22.23^{a }±0.26 
21.87^{a }±0.25 
25.90^{b }±0.28 
28.40^{c }±0.31 
months of exploitation 
6633.19* 
2.64^{a }±0.02 
4.10^{b }±0.03 
5.72^{c }±0.04 
7.84^{d }±0.04 
10.25^{e }±0.05 
relative catch 
807.93* 
10.81^{a }±0.13 
6.55^{b }±0.10 
4.31^{c }±0.06 
3.47^{d }±0.05 
2.77^{d }±0.03 
The mean annual number of months of exploitation was compared for consecutive 11yearlong periods of exploitation (Table 3). The results of the analysis reveal a general tendency for the mean values of this parameter in each size category of lakes to decline from 1951 to 1994. In all the five size classes there were statistically significant differences (p<0.05) between the penultimate and ultimate time period compared. Moreover, for the lakes in size classes I, II, III and IV a statistically proven decrease in the means of the analysed parameter was observed as early as between the second and third 11yearlong period of analysis, while for the smallest lakes (size less than 50 ha) such a decline also occurred between the first two periods. At each stage of the analysis, a higher annual number of months of exploitation was typical of a higher size category of lakes.
Table 3. Results of comparisons of the mean annual number of exploitation months of the analysed lakes in elevenyearlong periods in 19511994 (mean±SEM). Critical values F=2.61 (df 3, 102481915; a=0.05). Indexed (*) F test statistics values are statistically significant at p<0.05. Means of the parameters with different letter superscripts (in rows) are statistically different at p<0.05. 
Category of lakes 
Fratio 
Years 

195161 
196272 
197383 
198494 

I 
143.09* 
3.21^{a }±0.05 
2.87^{b }±0.04 
2.56^{c }±0.04 
2.13^{d }±0.03 
II 
123.75* 
4.82^{a }±0.08 
4.60^{a }±0.06 
4.02^{b }±0.07 
3.14^{c }±0.06 
III 
195.16* 
6.68^{a }±0.08 
6.43^{a }±0.07 
5.56^{b }±0.08 
4.26^{c }±0.07 
IV 
132.79* 
8.46^{a }±0.09 
8.55^{a }±0.08 
8.00^{b }±0.08 
6.50^{c }±0.09 
V 
51.77* 
10.78^{a }±0.08 
10.58^{ab }±0.08 
10.37^{b }±0.08 
9.34^{c }±0.11 
Table 4. Results of the estimation of linear regression analysis of the dependent variable (y) total annual yields (kg·ha^{1}) and independent variable (x) number of exploitation months per year. Critical value t=1.96 (df 98521917; a=0.05). Indexed (*) F test statistics values are statistically significant at p<0.05. Values of the correlation coefficient marked with different letter superscript (in the column) are statistically different at p<0.05. 
Category of lakes 
Parameters 
Estimate 
Standard 
t value 
Correlation 
Std. Error 
I 
Intercept 
12.8819 
0.3361 
38.3232* 
0.3327^{a} 
19.4213 
Slope (x) 
3.6168 
0.1033 
35.0124* 

II 
Intercept 
8.2940 
0.4575 
18.1305* 
0.4396^{b} 
17.1946 
Slope (x) 
3.3945 
0.0954 
35.5775* 

III 
Intercept 
6.8085 
0.5061 
13.4531* 
0.4439^{bc} 
14.7166 
Slope (x) 
2.6382 
0.0797 
33.0958* 

IV 
Intercept 
5.7915 
0.8159 
7.0980* 
0.4033^{de} 
15.0178 
Slope (x) 
2.5638 
0.0988 
25.9416* 

V 
Intercept 
1.6826 
1.4254 
1.1805 
0.3992^{bce} 
12.1861 
Slope (x) 
2.6003 
0.1364 
19.0619* 
Based on the model of linear regression analysis, a relationship between total yield as a dependent variable (y) and number of months of exploitation as an independent variable (x) within the size categories of lakes was determined (Table 4). In each case, a statistically significant (p<0.05) positive correlation was obtained, in which an increase in the number of exploitation months caused an increase in the mean annual yield. The values of estimation (B) of the intercept and coefficient of regression (slope) allowed us to construct a regression equation describing the analysed relationship for each size category:
 I  II  III  IV  V 
y = 12.88 + 3.62x y = 8.29 + 3.39x y = 6.81 + 2.64x y = 5.79 + 2.56x y = 1.68 + 2.60x 
These equations imply that onemonth extension of the period of exploitation (in an interval of 1 to 12 months) caused an average increase in total yield in the range of 2.56 kg·ha^{1} in lakes from size category IV to 3.62 kg·ha^{1} in lakes measuring less than 50 ha. Comparison of the significance of statistical differences (p<0.05) in the values of the correlation coefficient showed no differences between the largest lakes (size class V) versus the lakes from categories II, III and IV, or between the lakes from category II versus category III. Coefficient r in the interval of values from 0.33 and 0.44 suggests that the correlation level of the analysed relations is on an average level.
It was found that yield was statistically significantly correlated with relative catch in all the size categories of the analysed lakes, and the values of Pearson’s linear correlation coefficient (r) obtained from the analysis confirmed that this relation was positive (Table 5). Strong correlation between the two parameters was observed for lakes in categories I, II, III and IV, where r coefficient was contained in the interval between 0.567 and 0.673. On the other hand, a very strong relation between yield and relative catch (r=0.864) was found for the largest lakes (area over 500 ha). Analysis of the regression equation constructed for this size category of lakes reveals that an increment in relative catch by 1 kg·ha^{1}·month^{1} was accompanied by the largest mean increase in yield equal 9.13 kg·ha^{1}. As regards the other four size categories of lakes, an increment in the mean value of this parameter was smaller for smaller lakes down to 1.10 kg·ha^{1} for lakes less than 50 ha large. No statistical differences were found in the recorded values of Pearson’s coefficient between categories I and IV or II and III.
Table 5. The coefficient of linear correlation r (Pearson’s) between the variable (y) total annual yields (kg·ha^{1}) and the variable (x) relative catch (kg·ha^{1}·month^{1}). Critical value t=1.96 (df 98521917; a=0.05). Indexed (*) t test statistics values are statistically significant at p<0.05. Values of the correlation coefficient r marked with different letter superscripts (in the column) are statistically different at p<0.05. 
Category 
N 
t value 
Coefficient 
Regression equation 
I 
9854 
90.2666* 
0.6729^{a} 
y=10.56 + 1.10x 
II 
5287 
50.0608* 
0.5672^{b} 
y=12.80 + 1.44x 
III 
4463 
46.2026* 
0.5689^{b} 
y=11.84 + 2.33x 
IV 
3467 
50.8644* 
0.6538^{a} 
y=12.28 + 3.93x 
V 
1919 
75.2595* 
0.8642^{c} 
y=3.11 + 9.13x 
Based on the mean annual values of total yield and relative catch, simulated approximation of time series changes in these parameters for each size category of the analysed lakes in 1951–1994 was conducted. Curvilinear regression equations for virtual trend lines were obtained by adjusting 2degree and 3degree polynomial functions. Appropriate formulas of equations and values of their Rsquared determination coefficients are presented in Table 6. The smallest function adjustment was observed at the second order regression equation for yield in category I lakes (R^{2}=0.001), while the highest determination coefficient (0.797) and the best adjustment appeared at the third order equation for relative catch parameter in category III lakes. The values of Rsquared coefficient at all levels of analysis were statistically different (p<0.05). Naturally, better adjustment of a polynomial function to actual values at a higher value of determination coefficient was o btained for 3order equations. However, for either form of the polynomial, approximation of changes in fish catches based on relative catch generally resulted in a higher value of Rsquared coefficient for nearly all the size categories of lakes except the largest lakes (size over 500 ha), where the 2degree polynomial function was slightly better adjusted to total yield rather than to relative catch.
Table 6. Regression equation and determination coefficient in the polynomial adjustment of trend lines at approximation of time series changes in annual values of exploitation parameters for the analysed lakes in 1951–1994 
Category 
Polynomial 
Parameters of exploitation 

yield (kg× ha^{1}) 
relative catch (kg× ha^{1× }month^{1}) 

Equation 
Rsquared 
Equation 
Rsquared 

I 
2degree 
y = 0.0009x^{2}+0.04x+27.78 
0.001 
y = 0.0009x^{2}+0.25x+8.10 
0.524 
3degree 
y = 0.0019x^{3}+0.13x^{2}2.26x+36.86 
0.457 
y = 0.0011x^{3}+0.07x^{2}1.12x+13.49 
0.764 

II 
2degree 
y = 0.0055x^{2}+0.26x+22.54 
0.038 
y = 0.0018x^{2}+0.04x+4.97 
0.487 
3degree 
y = 0.0016x^{3}+0.10x^{2}–1.72x+30.38 
0.441 
y = 0.0008x^{3}+0.06x^{2}–0.92x+8.79 
0.797 

III 
2degree 
y = 0.0076x^{2}+0.33x+20.27 
0.099 
y = 0.0002x^{2}+0.06x+2.98 
0.537 
3degree 
y = 0.0014x^{3}+0.09x^{2}–1.43x+27.23 
0.524 
y = 0.0004x^{3}+0.03x^{2}–0.39x+4.76 
0.785 

IV 
2degree 
y = 0.0120x^{2}+0.48x+23.65 
0.185 
y = 0.0002x^{2}+0.02x+3.02 
0.231 
3degree 
y = 0.0014x^{3}+0.08x^{2}–1.25x+30.51 
0.453 
y = 0.0003x^{3}+0.02x^{2}–0.30x+4.27 
0.582 

V 
2degree 
y = 0.0115x^{2}+0.45x+25.90 
0.174 
y = 0.0009x^{2}+0.05x+2.35 
0.122 
3degree 
y = 0.0014x^{3}+0.09x^{2}–1.31x+32.87 
0.439 
y = 0.0002x^{3}+0.01x^{2}–0.16x+3.16 
0.557 
Fig. 1. Mean fishery yield and relative catch in all the analysed lakes (757) in northeastern Poland in the years 19511994. Error 'whiskers' represent standard deviation (SD) of the mean values of the lake exploitation parameters in the consecutive years. Trend lines were determined by adjusting the 3degree polynomial function 
Function of 3degree polynomial was used for approximation of time series changes in total yield and relative catch from all 757 lakes in the years 1951–1994 (Fig. 1). Due to a very large amount of actual data used to construct curvilinear regression equations (24 990 for each series of variables), means and standard deviation (SD) values were used for graphic interpretation of the analysed phenomena. The trend line delineated on the basis of relative catch was characterised by better adjustment to actual values at the determination coefficient equal 0.838.
The differences in mean yields and number of exploitation months between lake size categories observed in the present study are typical and have been demonstrated in reports by other authors [16, 25]. According to Nowak [25] the yield from 600 Polish lakes (excluding lakes of crucian carp type) ranged from 24 kg·ha^{1} to 29 kg·ha^{1} and was therefore lower than the yield assumed when creating fishing typology of lakes (3040 kg·ha^{1} without crucian carp lakes) [33]. Yield depends on lake fertility and exploitation intensity. Smaller reservoirs are characterised by lower levels of exploitation, which is due to the fact that they have been assigned lesser importance in the fishing economy [25]. Leopold [16] reported that an average yield obtained in reservoirs of an area >500 ha was higher than in lakes measuring <100 ha, although smaller lakes were demonstrated to show the largest range of differences in values of this parameter. Acco rding to this author, small lakes were also characterised by the largest range of exploitation intensity at the highest longterm mean value of this parameter (32 jm<·ha^{1}). This parameter for lakes of an area >500 ha was 22 jm·ha^{1}. In the light of these observations, the fishing management in lakes sized <100 ha was described as unstable, consisting of irregular catches executed with great fishing effort [16]. Assuming that the number of months of exploitation served as a fishing frequency index in the present report, a decrease in this parameter noticed for smaller lakes confirms the conclusions contained in the reports published by Leopold [16] and Nowak [25]. Months of exploitation for lakes in different size categories have been used to formulate conclusions about different levels of fishery exploitation [17].
Distribution of the computed mean values of relative catch, which is indicative of the mean volume of catch obtained per month of annual exploitation, reflects variation in frequency of fishery exploitation in size categories of lakes.
A decline in frequency of lake exploitation, demonstrated for all the five size categories of lakes, was most evident in the years 19731994. It is difficult to state categorically whether this event was connected with a depressed exploitation intensity. Should the number of months of exploitation be treated as an indirect index of fishing intensity [17, 19], such correlation ought to be accepted. Nevertheless, such an approach must account for the fact that no data are available on fishing effort in a particular month. In contrast, it needs to be emphasised that the general annual intensity of exploitation in the classical model depends directly on the number of type of fishing gears and frequency of their use [12, 13, 14, 15, 16, 20]. Irrespective of the fact that no detailed studies on the intensity of fishing exploitation in 1970s and 1980s have been conducted, some theories on causes of the general decline in exploitation intensity have been put forth while observing changes in catches of several fish species [33]. The decrease in the number of months of annual exploitation was most probably triggered by the change in lake management enforced by changes in lake ecosystems, associated with more intense anthropogenic pressures on the aquatic environment, of which the most important one was rapid eutrophication of water bodies [9]. Eutrophication contributed to changes in species composition of the ichtiofauna of Polish lakes, which has been broadly described in many publications [2, 22, 23, 28, 29].
As a rule, the restructuring of qualitative and quantitative composition of ichtiofauna is associated with a modification of exploitation type [12]. Eutrophication also has an effect on economic aspects of lake management and is connected with numerous obstacles to fishing exploitation, including inferior effectiveness of some fishing gears and their limited use [18]. Changes in fishing exploitation and extent of fishing season utilisation may have also been affected by the climatic changes observed. Studies carried out worldwide have determined that the effect of global warming on water environment mainly consists in elevated thermal capacity of lakes and is associated with warm, icefree winters [7, 26]. Climatic changes resulting in shorter ice cover and higher temperature of surface water layers in summer have also been recorded in Poland [1]. Weather anomalies can disrupt proper use of fishing gears drawn in late autumn and winter catches. That is precisely when they can be most effect ive [13]. The current studies on results of fishing exploitation of lakes have pointed to a considerable effect that, for instance, early and unstable ice cover can have on effects of annual catches [34].
The power of the relation between the total yield and number of months of exploitation during which the given catch was obtained varied within the size categories of lakes. The highest increase in mean yield attained at a onemonth extension of exploitation time was observed for lakes of an area to100 ha, that is for the reservoirs in which the extent of fishing season utilisation counted in months of exploitation was the lowest. Studies on relations between the effectiveness and intensity of exploitation versus the surface area of lakes were carried by Leopold [14, 16], who concluded that exploitation intensity maintained on an unchanged level resulted in better yield in larger lakes. Moreover, the researcher found out that as the intensity of exploitation increased, yield increment per unit of intensity decreased. Furthermore, the relation between the number of months, reflecting intensity of catches, and the catch obtained was analysed while studying the question of effectiveness of eel stocking [19]. The results then achieved revealed positive correlation at which a onemonth increase in fishing intensity resulted in a mean increase in eel catches by 0.38 kg·ha^{1}.
Conversion of yield obtained by referring yield to the number of months of exploitation when this catch was obtained and expressing it in the form of relative catch brings distinct advantages for the process of analysis. Relative catch shows strong and very strong correlation with fishery yield in each size category of lakes, describing the analysed events on a lower level of absolute values. It accounts for time changes in the way fishery exploitation is executed, which is reflected by depressed annual frequency of exploitation. Consequently, the values of this parameter can correct possible large variations in the catch, the variability of which is connected with a higher or lower number of months in which the exploitation took place. As a result, the actual values of relative catch in the course of years are characterised by a higher level of concentration along curvilinear trend lines and better adjustment. Analyses of longterm time changes in fish catches and the interpretation of the se changes are not infrequently performed without dividing water reservoirs into size categories. Whichever parameter is then analysed, it is always burdened with additional variability. Nevertheless, relative catch shows a much smaller level of variability and better adjustment of the trend line.
Lakes in the particular size categories were significantly different in annual exploitation frequency, which proves that they varied in the extent of fishery management of waters and the way of conducting commercial catches.
The decrease in annual fishery exploitation observed in the years 19501994 may have been caused by the changed lake fishery management enforced by the changes in the aqueous environment of natural water reservoirs.
Linear correlation was determined between yield and number of months of catches during an annual exploitation cycle, with the power of the relationship being highly variable within the size categories of the analysed lakes.
Strong correlations appeared between yield and relative catch, which is characterised by lower variability and higher concentration along the time trend line.
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Andrzej Skrzypczak, Andrzej Mamcarz
Department of Lake and River Fisheries
University of Warmia and Mazury in Olsztyn
M. Oczapowskiego 5, 10957 OlsztynKortowo, Poland
phone: +48 89 523 33 88, fax: +48 89 523 39 69
email: sandacz@uwm.edu.pl;
mamcarz@uwm.edu.pl
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