4.1 Analysis of catchrates
The time series analysed included one low water recession period with no fishery from 19961997. This recession occurred 33 years after the previous major recession in 1968, which was extensively documented in Kalk, McLachlan and HowardWilliams (1979). Another minor recession occurred in 197374. No catchrate data are available for the years 1976 and 1977. The results of the analysis of variance and subsequent trend analysis are summarized in Table 2 and Figures 2 to 6.
4.2 Variability in catchrates is extreme and possibly administratively induced
The variability in catchrates, expressed as coefficientofvariation (CV = standard deviation/mean) of the original (nontransformed) data, was extremely high. For instance catchrates in gillnets, the series with lowest variance (variance = ^{10}log(s^{2}) = 0.14), the coefficient of variation can be estimated through CV= Ö(10^{2.303*variance}  1) at 1.04. For other fisheries, daily catch variability (i.e. basic uncertainty as variability with trend and seasonality removed) expressed by CV ranges from 0.1 to 0.5 for trawlers to >1 in sport fisheries and some marine light fisheries. Therefore the aggregated monthly CV in the Chilwa data was about as high as the daily variability that individual sport fishermen experience (Densen, 2001). Individual gillnet fishermen experience a much lower day to day variability in catchrates, with CV’s of around 0.5 to 0.8 (Densen, 2001). The extreme variability in the Chilwa data was even more surprising taking into consideration that the catchrate series represented aggregations over strata, fishermen and month: the effect of aggregation is that variability is reduced. A disaggregation over month and fishermen to daily catchrates would result in a coefficientofvariation that is outside the experience even of fisheries exhibiting high daily variability, for example whale fishing (Densen, 2001), sport fishing on pikeperch (CV = 1.2, van Densen, 2001), and the Bagan light fishery on small marine pelagics in Ambon, Indonesia (CV = 2.4, Oostenbrugge, 2001). The variability in the Chilwa data is definitely outside the range of any gillnet or seine net fishery known from inland fisheries.
Apart from possible effects of trend and seasonality, discussed later, the extreme variability probably is caused by the method of raising the daily catch and effort data to arrive at the estimates of monthly catch and catchrate. Conversion factors are used to arrive at estimated total catches per standard gear by stratum. After that the effort and estimated catch figures collected during the month are each added to obtain a total catch and total effort. The monthly catchrate (CPUE) used in our analysis is calculated from these data. This summation procedure induces variability that is not present in the original data collected at the beach. Apart from that, the procedure makes it impossible to detect outliers and typing errors. In other words, much of the variability encountered in the Chilwa time series  and by extension the time series from other fisheries as the same system is used throughout Malawi  is “administratively induced error”. However, as the procedure has been maintained over the years, and there is no reason to believe that the administratively induced error changes over time (i.e. it can be considered random), it is still possible to proceed with our intended analyses of trends and their alleged causes. The enormous variability in the data has important consequences for the detection of trends and the analysis of causation: trends and fluctuations will be lost in “noise”, most of which unfortunately is administratively induced.
4.3 All gears except traps are selective
The variation in total catchrates was lowest in gillnets with a factor^{[19]} (F) around the geometric mean of F = 5.6 followed by traps (F = 9.4), seines (F = 16.9) and longlines (F = 20.3) (Table 2, Figure 2). On a species level lowest variation is seen in Oreochromis shiranus (F = 7.6) and Clarias gariepinus (F = 6.5) in gillnet catchrates. For most other speciesand gear combinations the variation is around a factor 20 or higher. Aggregation of catch over species thus leads to a reduction in variation. However, only in traps does the aggregation of various species to total catches lead to a significant reduction in variation. This indicates that it is the only truly “multispecies” gear in its target: all species are caught in more or less the same amounts over the same period of time. The main target for gillnets is O. shiranus, for a seine is Barbus paludinosus and to a lesser extent O. shiranus, while longlines target C. gariepinus. Other nontarget species only reduce the variance in total catches slightly. In the case of longlines this reduction of F is just three percent.
4.4 Annual variability is high
Annual variability in catchrates was high, and significant interannual differences explained much of the total variance (Table 2, Figures 2 and 3). As a result the unexplained factor around the mean was lowered by 50  75 percent in 14 out of 20 cases. In the remaining six cases, which were all nontarget species for the various gears, no variation at all could be explained by temporal analysis, and catchrate data of these speciesgear combinations on the aggregated level of the lake by month indicated pure chance.
FIGURE 2. The amount of variability expressed as factor around the geometric mean explained by trends (as linear regression), annual, monthly and residual variation. The residual variation is also expressed as standard deviation (s) at the bottom of each column. The arrow indicates the target species of a gear. Basic uncertainty (see text) is the variability remaining when trend and seasonality are subtracted from the total variation.
TABLE 2. Results of Analysis of Variance and regression analysis on monthly catchrates of Lake Chilwa by gear and species groups as contained in the CEDRS of Malawi (see text for further explanation)
Total catches 
Fishtrap 
Gillnets 


Model 
df 
MSE 
Factor 
r2 
P 
Model 
Df 
MSE 
Factor 
r2 
P 
Total variance 

207 
0.238 
9.4 



212 
0.139 
5.6 


After Year 
Year 
189 
0.117 
4.8 
0.55 
*** 
Year 
194 
0.060 
3.1 
0.61 
*** 
Trend 
Linear 
206 
0.164 
6.2 
0.31 
*** 
Linear 
211 
0.090 
3.9 
0.36 
*** 

Polynomial 
205 
0.160 
6.3 
0.33 
*** 
Polynomial 
210 
0.086 
3.9 
0.39 
*** 

(quadratic term takes 5.8% of total explained variance) 
(quadratic term takes 7.5% of total explained variance) 


Longtime 
Matemba seine 

Total variance 

210 
0.427 
20.3 



207 
0.377 
16.9 


After Year 
Year 
192 
0.193 
7.6 
0.59 
*** 
Year 
189 
0.141 
5.6 
0.66 
*** 
After Month 
 
 
 
 
 
ns 
Year + Month 
178 
0.127 
5.2 
0.71 
*** 
Trend 
Linear 
209 
0.279 
10.9 
0.35 
*** 
Linear 
206 
0.274 
10.6 
0.27 
*** 

Polynomial 
208 
0.271 
11.0 
0.37 
*** 
Polynomial 
205 
0.224 
8.8 
0.41 
*** 

(quadratic term explains 5.8% of total explained variance) 
(quadratic term takes 33% of total explained variance) 

Oreochromis 
Fishtrap 
Gillnets 

Total variance 

194 
0.410 
19.1 



208 
0.193 
7.6 


After Year 
Year 
177 
0.287 
11.8 
0.36 
*** 
Year 
190 
0.063 
3.2 
0.70 
*** 
After Month 
Year + Month 
166 
0.269 
10.9 
0.43 
A 
 
 
 
 
 
Ns 
Trend 
Linear 
193 
0.377 
16.0 
0.08 
*** 
Linear 
207 
0.136 
5.3 
0.30 
*** 

Polynomial 
192 
0.323 
13.7 
0.22 
*** 
Polynomial 
206 
0.121 
5.0 
0.34 
*** 

(quadratic term explains 62% of total explained variance) 
(quadratic term explains 42% of total explained variance) 


Longtime 
Matemba seine 

Total variance 

42 
0.743 
53.0 



204 
0.423 
20.0 


After Year 
 
 
 
 
 
ns 
Year 
186 
0.147 
5.9 
0.68 
*** 
Trend 
 
 
 
 
 
ns 
Linear 
203 
0.329 
13.3 
0.23 
*** 







Polynomial 
202 
0.261 
10.5 
0.39 
*** 







(quadratic term explains 42% of total explained variance) 

Barbus 
Fishtrap 
Gillnets 

Total variance 

204 
0.594 
34.8 



59 
0.919 
82.7 


After Year 
Year 
187 
0.338 
14.6 
0.48 
*** 
 
 
 
 
 
Ns 
Trend 
Linear 
203 
0.513 
25.4 
0.14 
*** 
 
 
 
 
 
Ns 

Longline 
Matemba seine 

Total variance 

19 
1.587 
330.9 



204 
0.432 
20.6 


After Year 
 
 
 
 
 
ns 
Year 
186 
0.165 
6.5 
0.65 
*** 
After Month 
 
 
 
 
 
ns 

175 
0.155 
6.1 
0.69 
A 
Trend 
 
 
 
 
 
ns 
Linear 
203 
0.292 
11.5 
0.33 
*** 







Polynomial 
202 
0.260 
10.5 
0.40 
*** 


(quadratic term explains 19% of total explained variance) 

Clarias 
Fishtrap 
Gillnets 

Total variance 

181 
0.714 
49.0 



212 
0.164 
6.5 


After Year 
Year 
163 
0.551 
30.5 
0.31 
*** 
Year 
194 
0.116 
4.8 
0.36 
*** 
After Month 
 
 
 
 
 
ns 
Year + Month 
183 
0.102 
4.3 
0.46 
*** 
Trend 
 
 
 
 
 
ns 
Linear 
211 
0.140 
5.4 
0.15 
*** 

Longline 
Matemba seine 

Total variance 

210 
0.437 
21.0 



207 
0.454 
22.2 


After Year 
Year 
192 
0.200 
7.8 
0.58 
*** 
Year 
189 
0.193 
7.6 
0.61 
*** 
After Month 
 
 
 
 
 
ns 
Year + Month 
178 
0.175 
6.9 
0.67 
** 
Trend 
Linear 
209 
0.282 
11.0 
0.36 
*** 
Linear 
206 
0.360 
15.3 
0.20 
*** 

Polynomial 
208 
0.277 
11.3 
0.53 
*** 
Polynomial 
205 
0.350 
15.2 
0.24 
*** 

(quadratic term explains 4% of total explained variance) 
(quadratic term explains 16% of total explained variance) 

Other spp. 
Fishtrap 
Gillnets 

Total variance 

193 
0.662 
42.4 



36 
0.640 
39.8 


After Year 
Year 
176 
0.312 
13.1 
0.57 
*** 
 
 
 
 
 
Ns 
After Month 
Year + Month 
165 
0.280 
11.4 
0.64 
** 
 
 
 
 
 
Ns 
Trend 
Linear 
192 
0.400 
17.4 
0.40 
*** 
 
 
 
 
 
Ns 

Longline 
Matemba seine 

Total variance 

28 
0.676 
44.1 



141 
1.015 
103.4 


After Year 
 
 
 
 
 
ns 
Year 
124 
0.600 
35.4 
0.48 
*** 
After Month 
 
 
 
 
 
ns 
Year + Month 
113 
0.548 
30.2 
0.57 
A 
Trend 
 
 
 
 
 
ns 
Linear 
 
 
 
 
Ns 







Quadratic 
140 
0.790 
59.9 
0.23 
*** 
Significance level is indicated by asterixes: * p< = 0.05, ** p< = 0.01, ***p< = 0.001
FIGURE 3. Annual variation in total catchrates by gear. Vertical bars represent 95 percent confidence limits. Note ^{10}log scale on vertical axes.
FIGURE 4. Monthly variation in total catchrates by gear. Vertical bars represent 95 percent confidence limits. The scale on vertical axes represents a multiplication factor of the of the ^{10}log annual mean catchrates.
FIGURE 5. Annual variation (bars) and polynomial trends (thick line) in geometric mean annual total catchrates by gear in lake Chilwa. Trends are shown with 95 percent confidence limits (broken lines). The thin line is the relative mean annual water level of the lake.
4.5 No seasonality is present
Generally, no seasonality was observed in the catchrate data, as no significant differences between months were found in 14 out of 20 series. This indicates that the catchrate data do not contain a clear seasonal signal for most species(groups) examined (Table 2, Figure 4). Significant differences between months were found in six cases: of Oreochromis and of “Other” species caught in fish traps; of Clarias caught in Matemba seines and in gillnets; of “Other” species caught in Matemba seines; and in Matemba seines on the aggregated level of total catches. The clearest seasonal signal was seen in traps where 11% of the total variation in catchrates is explained by the significant differences between months. Overall catchrates of Oreochromis are higher from January to April during rising water levels (Figure 10), however only May, July and December had significantly lower catchrates compared to the months February to April. “Other” species had significantly lower catchrates in December and January, the season with lowest water levels, compared to the remaining year. But only 7% of the total variation was explained by this difference. Clarias catchrates of Gillnets and Matemba seines were slightly elevated during the low water period in December and January, which explained 10% and 6% of the total variation. Differences between monthly catches of “Other” species in Matemba seines explained only six percent of the total variation, and were caused by lowered catch in December compared to the period between March to June and October. On an aggregated level by gear only Matemba seines showed monthly differences: February was slightly elevated compared to much of the period from June to December, but the signal was weak as it explained merely 5 percent of the total variation.
4.6 All observed trends are declining
All but six out of 20 timeseries revealed a substantial downward trend in average catchrates ranging in speed from a factor 4 in Clarias catchrates of gillnets to a dramatic factor 120 in catchrates of “Other” species in fish traps (Table 2, Figures 5 and 6). Polynomial trends indicated that all of the downward trend legs of the curves commenced before 1986 and most even before 1982. Trends were linear or concave downward in ten series. The quadratic term of only two of the seven concave downward series contributed more than 10 percent to the total variation explained by the trend, indicating that the linear downward component was dominant in all cases. Four series had a dome shaped trend, but with just a slight upward slope and a strong downward slope after the peak. Three of the peaks within these four series were in 1982 (total catches in Matemba seine), 1983 and 1984 (Oreochromis catches in fish traps and Matemba seine). “Other” species caught in Matemba seines peaked in 1986 and this was the only series without a linear component. Thus most of the downward trends could be sufficiently explained by the linear component and the remaining analysis will be done using linear trends.
FIGURE 6. Annual variation (bars) and polynomial trends (thick line) in geometric mean annual catchrates by species and by gear in lake Chilwa. Trends are shown with 95 percent confidence limits (broke lines). The thin line is the relative mean annual water level of the lake.
The trend component in the variation is strong: 44 percent to 88 percent of the observed annual variation was explained by the downward trends (Table 2, Figure 2). Both for fish traps except in case of Barbus  gillnets and longlines more than 60 percent of the annual variation was explained by trends. The highest amount of total variation explained by a linear trend was in “Other” species caught by fish traps (40 percent). Linear trends in total catchrates explained between 27 percent (seines) and 36 percent (gillnets) of the variation.
4.7 Basic uncertainty is high
Unexplained variation is the amount of variation that remains after all significant year and month effects (trends) are subtracted from the monthly catchrate timeseries. The unexplained variation is lowest in gillnets (F = 3.1), followed by fish traps (F = 4.8), seines (F = 5.2) and Longlines (F = 7.6). Except in traps, the unexplained variation is about the same or somewhat higher for the separate target species of the various gears compared to the total catchrates (Table 2, Figure 2). The basic uncertainty, or the uncertainty remaining after removing the trend, is a factor F = 4 for gillnets and F = 6.5 for traps. Basic uncertainty in catchrates of seines and longlines is excessively high (F = 11), indicating strong interannual variation.
TABLE 3. Trend, trendtonoise ratio and number of months data needed to detect the observed trends with and without autocorrelation (persistence).
Species 
Gear 
df 
Trend 
Standard deviation 
Trend/noise 
N 
Autocorrelation coefficient 
N 
B 
S 
B/s 
(months) 
r 
(months) 

Total 
Gillnet 
211 
0.04 
0.30 
0.13 
19 
0.45 
21 
Longline 
209 
0.07 
0.53 
0.13 
20 
0.40 
21 

Seine 
206 
0.06 
0.57 
0.10 
24 
0.58 
27 

Trap 
206 
0.05 
0.41 
0.12 
21 
0.36 
22 

Oreochromis 
Gillnet 
207 
0.04 
0.37 
0.12 
23 
0.57 
26 
Longline 

n.s. 






Seine 
203 
0.06 
0.57 
0.10 
24 
0.61 
28 

Trap 
193 
0.05 
0.72 
0.07 
31 
0.41 
33 

Barbus 
Gillnet 

n.s. 





Longline 

n.s. 






Seine 
203 
0.07 
0.54 
0.13 
20 
0.52 
29 

Trap 
203 
0.03 
0.62 
0.06 
35 
0.42 
38 

Clarias 
Gillnet 
211 
003 
037 
008 
29 
025 
30 
Longline 
209 
0.07 
0.53 
0.13 
20 
0.37 
21 

Seine 
206 
0.06 
0.60 
0.09 
25 
0.55 
29 

Trap 

n.s. 






Other 
Gillnet 

n.s. 





Longline 

n.s. 






Seine 

n.s. 






Trap 
192 
0.09 
0.63 
0.15 
18 
0.36 
19 
4.8 Trendtonoise: the capacity to detect trends
All linear downward trends were detectable as statistically significant between 19 and 24 months for the total catchrates of the four gears (Table 3, Figure 7). Persistence (= nonrandom residuals) had little effect: it increased the number of data points needed to detect the observed trends with two to three months (Figure 7). Trendtonoise ratio was highest in “Other” species in traps, and lowest in Barbus caught by traps. The effect of persistence in the species(groups) was an increase in the data points needed, but all observed trends were detectable from 21 to 38 months of data.
A longterm negative trend for total catchrates in gillnets and longlines became statistically significant in 1987 (Figure 8a), in both cases around seven years after the peak in catchrates was reached (Figure 5). The negative trend in fish traps was significant in 1988, or around five years after the peak. Matemba seines gave a different signal: the negative trend was significant in 1992 or around two, six and nine years after peaks observed in average annual catchrates and about nine years after the estimated peak in catchrates through polynomial regression (Figure 5). Reversals in trends seen in all catchrate timeseries took about three to four years during which only the decision of no longterm trend could be made. For example, this was the case from 1984 to 1986 in gillnets (Figure 8a).
Shortterm trends, taken over five years, are obviously much more erratic (Figure 8b), but nevertheless gave fairly consistent signals over time. For instance a strong positive trend (b/s = 0.88) seen in 1980 after three years (plus two missing years) of gillnet data, reversed into a fairly strong negative trend (b/s = 0.40) in 1981. From 1981 to 1988 the shortterm trends remained negative, though becoming less strong. Between 1988 and 1991 no shortterm trends were seen. During this period of higher water levels, catchrates levelled out (Figure 5 and Figure 9A). After that shortterm trends became significant and negative again. This picture is confused by the behaviour of shortterm trends in other gears. Fish traps and longlines did not exhibit negative shortterm trends until after 1983 and Matemba seines not even before 1989. Shortterm trends in fish traps remained negative until 1995 the year before the lake dried up, while all other shortterm trends remained negative until the end of the series in 1998. Furthermore, reversals in shortterm trends take place more often in these gears and result in relatively high absolute values of the trendtonoise ratio.
FIGURE 7. The relation of the trendtonoise ratio to the number of months of data needed to detect a trend in total catchrates and catchrates by species/gear combinations of including the effect of autocorrelation (persistence)
FIGURE 8 A. Development of the trendtonoise ratio from five years of catchrate data in 1980 onwards with successive addition by year of monthly catchrate data for gillnets, Matemba seines, fish traps and longlines. 1980 is the trendtonoise over 1976 to 1980; 1981 is b/s of 1977  1981 etc. B. Development of trendtonoise over five year moving periods, each indicated by the last year
4.9 Water levels, fishing effort and catchrates: immediate effect of changes in water level on catchrates
Average annual water levels increased significantly from 1986 to 1991 compared to the periods before and after. Water levels dropped from 1992 onwards to the drought of 1995 and 1996 when the lake was largely dry. After that the average water level increased to pre1986 levels (Figure 9). This means that during the period over which we have data on catchrates (19791998) a significant fluctuation in water levels took place providing the necessary contrast to detect the effect of increased effort with changing water levels. In all cases except catchrates of “Other” species in Matemba seines, which peaked in 1986, catchrates peaked before the onset of the higher water levels. This can be clearly seen in Figures 5 and 6.
Changing water levels have, in all but one case, an “immediate” effect on annual average catchrates: fluctuations in water levels are reflected in the same or the following year in the catchrate levels (Table 4). Detrended annual catchrates in gillnets positively correlated best with either mean or maximum average lake levels and significant lags varied between zero years for O. shiranus in gillnets and seine nets, to four years for C.gariepinus in seine nets. In contrast, regressions between catchrates by species or gear and water levels with lags higher than one year were not significant, except for total catchrates in seines. The correlations indicating longterm effects of water levels on catchrates (i.e. inducing generations of strong and weak year classes of longer lived species) are thus rather weak signals in the variation in catchrates as observed through the CEDRS data.
FIGURE 9. A. Annual average water level (thick line) and mean monthly water levels (thin line) in Lake Chilwa from 1979 onwards. B. Long term average monthly water levels and minimum (excluding periods of recession) and maximum water levels measured by month
TABLE 4. Crosscorrelation between residuals of detrended annual average catchrates (“anomalies”) and annual mean, minimum and maximum water levels in Lake Chilwa. Analysis is done on total catchrates by gear and the main target species (groups) of the various gears. Trend is a linear regression on annual average catchrates. Regressions of anomalies on water levels are done on lags with the highest significant correlation. N = number of observations, r^{2 }= proportion of explained variation, b = trend parameter. Significance values are denoted by asterixes: * p<0.05, ** p<0.01, *** p<0.001
Gear 
Catch 
Trend 
Cross correlations 
Regression on lags with highest correlation 

Mean water level 
Minimum\ water level 
Maximum water level 

N 
r^{2} 
s 
b 
p 
Lag 
Corr 
Lag 
Corr 
Lag 
Corr 
r^{2} 
P 

Gillnet 
Total 
18 
0.61 
0.23 
0.057 
*** 
1 
0.58 
1 
0.57 
1 
0.52 
0.23 
* 
Oreochromis 
18 
0.35 
0.79 
0.100 
*** 
0 
0.56 
0 
0.38 
0 
0.58 
0.36 
** 

Clarias 
18 
0.38 
0.22 
0.031 
*** 
4 
0.44 


4 
0.45 
n.s. 


Seine 
Total 
18 
0.45 
0.51 
0.081 
** 
3 
0.57 
3 
0.53 
3 
0.54 
0.22 
** 
Total 





0 
0.55 
0 
0.51 
0 
0.58 
0.36 
** 

Oreochromis 
18 
0.44 
0.64 
0.098 
** 
1 
0.7 
1 
0.66 
1 
0.67 
0.36 
** 

Oreochromis 





0 
0.72 
0 
0.68 
0 
0.78 
0.63 
*** 

Barbus 
18 
0.46 
0.61 
0.098 

0 
0.52 


0 
0.55 
0.32 
* 

Clarias 
18 
0.41 
0.42 
0.060 
** 
4 
0.88 
4 
0.88 
4 
0.88 

n.s. 

Clarias 





3 
0.81 
3 
0.75 
3 
0.82 



Longline 
Total 
18 
0.61 
0.35 
0.077 
*** 
0 
0.51 
0 
0.48 
0 
0.54 

n.s 
Clarias 
18 
0.62 
0.34 
0.077 
*** 
0 
0.48 
0 
0.44 
0 
0.51 
0.28 
* 

Fish trap 
Total 
17 
0.80 
0.18 
0.065 
*** 
4 
0.68 
4 
0.65 
4 
0.69 

n.s. 
Barbus 
17 
0.48 
0.40 
0.068 
** 
4 
0.79 
4 
0.73 
4 
0.75 

n.s. 

Barbus 





3 
0.6 
3 
0.57 
3 
0.6 

n.s. 

“Other” 
17 
0.86 
0.23 
0.101 
*** 








Where an effect of water level on catchrate could be detected, it explained 10 percent to 40 percent of the total variation in annual average catchrates. Approximately 23 percent of the residual catchrates of gillnets (amounting to approximately 14 percent of the total annual variation) and 36 percent of the residual catchrates of O. shiranus in seines (= 22 percent of total variation) were explained by the mean water level of previous years. In all other cases, the highest significant regressions between the residual catchrates were found with “this year’s” maximum water levels, which in case of O. shiranus in Matemba seines amounted to 63 percent of the residual variation (= 40 percent of total variation) explained. Residuals in total catchrates in Matemba seines were explained for 22 percent by the average water level of one year earlier (»10 percent of the total variation).
4.10 Effort increases fast and is population driven
All indicators of fishing effort increased significantly over the period examined: i.e. number of gear owners, assistants, boats and gears. Trends in numbers of gear owners, assistants, boats, gillnets and longlines indicate a threefold increase while, traps and Matemba seines increased five fold (Figure 10). In the 1980s the number of fishing operators  gear owners and ancillary workers  in Lake Chilwa ranged from 2 060 to 3 403 while the range was from 3 955 to 9 466 in 1998, just two years after refilling of the lake (Table 5). This latter figure represents the highest number of fishermen and assistants ever registered on Lake Chilwa. Apparently many new fishermen entered the fishery after the recession, mainly in the west and north. This increase could be attributed to return migration from South Africa due to phasing out of the working contracts in the mines. We assume that many of the returning migrants took up fishing, as the Phalombe plain does not receive much rain making agriculture around the upland area an unattractive option.
All gears, both the lowinvestment (traps, longlines and handlines) as well as highinvestment gears such as Matemba seines, continuously increase in units over the years. However, as the ratio of gear owners to assistants as well as number of boats and gears per owner exhibit a steady decrease over the period from 1983 to 1998 it is likely that low investment gears (traps, longlines, and nchomanga) become relatively more popular (Figure 11).
FIGURE 10. Development in effort expressed as number of gear owners, number of assistants and number of boats in Lake Chilwa. The bold line is the regression of the total numbers over time. The thin regression line refers to the numbers of stratum 1 and the broken line is the regression of numbers of stratum 2 over time.
Most fishing effort is suspended during recession periods. Many fishermen, especially those operating Matemba seines and gillnets, stop fishing or migrate to other water bodies, though such migrations recently have encountered resistance from resident fishermen (Njaya, pers. obs.). Other fishermen migrate to pools of water remaining in the inflowing rivers. Fishermen operating fish traps sometimes set their traps in rivers, often in conjunction with weirs.
4.11 Increased effort negatively affects catchrates despite high regenerative capacity
We concluded in the previous section that a significant part of the variation in catchrates is immediately explained by absolute water levels. This implies that catchrates should increase with higher water levels and vice versa. However, between 1984 and 1991, with elevated water levels, no positive short termtrends were observed though trends became less strongly negative over time or indicated stable levels (Figure 8b). Longterm trends reveal a stabilization in catchrates between 1984 and 1987 for gillnets, fish traps and longlines and between 1988 and 1991 for Matemba seines (Figure 8a). This would imply that the expected downward trend observed in catchrates is delayed by increase in production during high water levels. After 1992, during the years before the recession, increased effort and decreasing water levels push stocks in the same direction, as a result of which the decrease in catchrates will probably accelerate compared to a situation with low effort levels. However, closer examination of the trends versus catchrates (Figures 5 and 6) show an increase for species (groups) caught in fish traps and for Clarias gariepinus caught in Matemba seines in the two to three years before the complete recession, thus confounding the explanations on the general trend.
Multiple regression of water levels and catchrates confirms these observations: in all cases fishing effort had a significant negative effect on catchrates. In gillnets 2640 percent of the variation in annual catchrates was explained by the number of gillnets for both Oreochromis and Clarias; in seines this amounted to 5256 percent for all three species; longline effort explained 57 percent of the variation in annual catchrates in Clarias. In traps the highest significant effect was found with Clarias (47 percent), while effort explained only 16 percent in Oreochromis catchrates (Table 6). Water level was positively related to annual catchrates, but explained much less than fishing effort (729 percent). In all gears, except longlines, Oreochromis catchrates were affected most by water levels (2029 percent). Clarias catchrates were only significantly influenced in seines (15 percent) and longlines (7 percent), but not in gillnets and traps. Barbus catchrates were only influenced by water level in seines (13 percent), but otherwise the regression model could not explain variation in catchrates of Barbus.
TABLE 5. Number of fishermen, ancillary workers and craft operating in Lake Chilwa (198498)
Year 
Fishermen 
Assistants 
Total 
Boats 
Boats 
Dugout 
1984 
527 
1186 
1783 
8 
71 
1289 
1985 
750 
1312 
2062 
13 
75 
1180 
1986 
1167 
1962 
3129 
15 
112 
1802 
1987 
No frame survey 





1988 
1363 
2020 
3383 
10 
146 
1683 
1989 
1185 
1597 
2782 
11 
116 
1373 
1990 
1874 
2081 
3955 
4 
229 
1801 
1991 
2319 
2546 
4865 
24 
268 
2045 
1992 
2496 
3412 
5908 
5 
329 
1883 
1993 
1718 
3958 
5676 
27 
440 
1201 
1994 
1043 
5043 
6096 
0 
507 
1190 
1995 
No frame survey due to recession 





1996 
No frame survey due to recession 





1997 






1998 
5396 
4070 
9466 
4 
582 
4090 
Source: Fisheries Department Frame Survey (198498)
FIGURE 11. Ratio of number of assistants and gear owners. The bold line is the regression of the total numbers over time. The thin regression line refers to the numbers of stratum 1 and the broken line is the regression of numbers of stratum 2 over time.
4.12 Efficiency of gears (catchability) increases with decreasing water levels
The interaction of water level and gears was significant and positively related to catchrates, except with seines. The lowest number of gears, counted at the beginning of the time series, as well as the highest number of gears at the end of the series coincided with low water levels, while highest water levels around 1990 are associated with a period of increasing fishing effort with all gears. The drop in water level towards the recession in 1996 and 1997 is thus associated with the highest numbers of gears counted in the time series of fishing effort. Theoretically increased fishing effort would be associated with decreased catchrates. However, the high proportion of variation explained by the interaction term indicates that the situation is more complex. During receding waters, with a likely subsequent concentration of the fish, some of the gears catch a number of species more efficiently thus maintaining relatively high catchrates despite increasing effort. This is the case with Oreochromis caught with gillnets (30 percent of variation explained by interaction term) and traps (21 percent), and with Clarias caught in gillnets (29 percent), traps (26 percent), longlines (18 percent) and to a lesser extent seines (7 percent). This sustains the notion of a crowding effect of these two species during receding water levels. It is particularly clear in the case of Clarias where relatively high catchrates are encountered during very low water levels (Figure 6). There are indications that Clarias become more “active” under extreme low water levels in an attempt to find their way out of the desiccating areas changing their catchability.
That the interaction effect does not play the same role in the case of Matemba seines may not be surprising. Seines are active gears used either from the shore, or from boats in and around submerged vegetation, in relatively shallow areas. They are used both with receding or increasing lake levels in the areas where concentrations of smaller species and juveniles of larger species are found. Changes in recruitment levels will therefore affect catchrates more than crowding effects, which is indicated by the high explanatory value of the water level in the statistical model where seining effort is the explanatory variable. In comparison, longlines show the reverse situation: in this case recruitment variation is much less important (7 percent of variation explained by the effect of water level) compared to the interaction effect (18 percent). Of all gears, the increase in effort of seines and longline explains the highest proportion of variation in their respective catchrates, indicating a comparatively strong effect on stocks.
TABLE 6. Proportion of variation in annual catchrates explained by the multiple regression model with lake water level (mean, minimum or maximum), with or without a lag phase of 1 year, fishing effort (number of gear) and their interaction as explanatory variables. Sign indicates the direction of the effect in the model. Left of the vertical line are the statistics of the multiple regression model. Analysis is done on total catchrates by gear and the main target species (groups) of the various gears. Only regression models explaining the highest amount of variation are shown. Df = degrees of freedom, SS = sum of squares, % = r^{2} = proportion of explained variation, sign denotes the direction of the effect in the statistical model. Significance values are denoted by asterixes: * p<0.05, ** p<0.01, *** p<0.001
Gear 
Species 
Water level Model (A) 
Statistics of model (B) 


Water level 
Gear 
Interaction 

Total error 
Residual error 
Total error explained by model 


Lag 
Sign 
% 
Sign 
% 
Sign 
% 
Df 
SS 
SS 
% 
P 

Gillnet 
Total 
Mean 
1 


 
38 
+ 
38 
16 
2.53 
0.60 
76 
*** 
Oreochrom 
Max 
0 
+ 
20 
 
26 
+ 
30 
17 
15.11 
3.67 
76 
*** 

Clarias 
Max 
0 


 
40 
+ 
29 
17 
5.01 
1.57 
69 
*** 

Seine 
Total 
Max 
0 
+ 
16 
 
54 


17 
7.58 
2.27 
70 
*** 
Oreochrom 
Max 
0 
+ 
29 
 
52 


17 
11.54 
2.08 
82 
*** 

Clarias 
Max 
0 
+ 
15 
 
56 
+ 
9 
17 
5.01 
1.02 
80 
*** 

Barbus 
Max 
0 
+ 
13 
 
55 


17 
10.89 
3.43 
68 
*** 

Longlin 
Total 
Max 
0 
+ 
9 
 
57 
+ 
20 
17 
5.04 
0.76 
85 
*** 
Clarias 
Max 
0 
+ 
7 
 
57 
+ 
18 
17 
5.01 
1.01 
82 
*** 

Trap 
Total 
Max 
0 
+ 
9 
 
49 
+ 
24 
17 
5.04 
0.91 
82 
*** 
Total 
Min 
0 
+ 
17 
 
43 
+ 
13 
17 
5.04 
1.34 
74 
*** 

Oreochrom 
Max 
0 
+ 
29 
 
16 
+ 
21 
17 
3.56 
1.19 
67 
** 

Clarias 
Max 
0 


 
47 
+ 
26 
17 
5.01 
1.36 
73 
*** 

Clarias 
Min 
0 


 
47 
+ 
14 
17 
5.01 
1.94 
61 
*** 
^{[19]} A factor F=5.6 means
that 95 percent of the data fall within the range of 5.6 times the (geometric)
mean and the mean divided by 5.6. 