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Let us return to the question posed in the introduction on how the data collected through the Malawian CEDRS on Lake Chilwa could be utilized to gain information on the status of the fishery, and following the answer to that summarize our conclusions.

5.1 On the data collected

The catch-rate data contain an enormous variability that, to a large extent, we believe to be mostly administratively induced as caused by the method of raising the daily catch and effort data to arrive at the estimates of monthly catch and catch-rate. As a result, for instance, seasonality is hardly detectable. Seasonality is expected to be clearly visible in the data in a system with highly seasonal changes in productivity. Despite the high variability, it is still possible to significantly detect trends in the various catch-rates series within two to three years of monthly aggregated data. The time windows, over which short- and long-term trends in total catch-rates could be detected, are generally between two and three years of data, and occasionally lower than that. Taking into account that the average duration between periods of recession is six years, and that 30 percent of the variation in water levels is accounted for by these cycles, this is rather a long time-frame to evaluate the effects of any management measure that aims at improving catch-rates. The speed of change in fish stocks in Lake Chilwa appears to be much faster than can be detected through the present CEDRS.

5.2 On the effects of fluctuating water levels and increased effort

What does this mean for using the information gathered through the CEDRS? Our analysis has concentrated mainly on total catch-rates by gear, with an occasional excursion to individual species (groups). Long-term trends in total catch-rates for all gears all point in the same downward direction. As variation in aggregated total catch-rates by gear is lower than for the individual species, it will be more difficult to detect both long and short-term trends by species (groups), even on the aggregated level of the whole lake. Using the information gathered at lower levels of aggregation - for example at the level of main strata representing ecological areas, at village/beach level or at the level of individual fishermen or by species(group) - will be non-informative within a small time window but may be informative in a large time window. At present, different gears are generally targeting different species(groups), which means that gear specific trends can serve as an indicator for their respective targets species. As some gears - e.g. fish traps - are also fairly habitat specific, such trends will also provide information on changes in those habitats. In a first approximation, short-term trends by gear could be related to existing knowledge - both local-knowledge and scientific knowledge (e.g. Kalk, McLachlan and Howard-Williams, 1979) - of the effects of changing water levels related to the species and area specificity of the gear.

The immediate effect of changing water levels can be illustrated without resorting to sophisticated statistical methods. Peaks in total catch-rates and catch-rates of O. shiranus, Barbus paludinosus, Clarias gariepinus and “Other” species in Matemba seines all coincide with the significant increase in water levels in the same year (Figures 5 and 6). This would indicate that relative change in water level is probably a better indicator for changes in catch-rates (» stocks), than absolute lake-levels, also indicated by the fact that maximum water levels usually score better than mean or minimum water levels. Also in Kariba annual change in lake levels, reflecting the amount of new inundated land every year (= new nutrients) scored better than mean annual lake levels (Kolding, 1994; Karenge and Kolding, 1995).

By eliminating much of the perceived “administratively induced” variance the information contained in the data collected through the Malawian CEDRS could be made much more sensitive over the short-term to changes both in effort and in productivity. Then the present analysis could be easily extended at a lower aggregated level (by area and by species-gear combination). Furthermore, at a higher aggregated level, overall effects of management measures could be detected more quickly, even with the observed high variation in catch-rates caused by changing water levels. This could make the CEDRS a much better instrument to evaluate the biological effects of (co-) management measures in such an adaptive environment.

[20] That the effect of water levels on catch-rates can still be seen indicates that the assumption that "administratively induced error" is a random effect may be correct.

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