Wadge Bank trawl fishery studies. Pt. 4. An analysis of the length frequency measurements of the sea bream (Lethrinus nebulosus) made in 1949 and 1953 to 1958

Length frequency distributions of the sea bream collected during the period 1953 to 1958 have been analysed. The increase in average sizes of the sea bream with depth suggests a movement to deeper waters with increase in size. By numbers, the sea bream is more abundant between 21 and 30 fathoms than in deeper areas. The recruitment was continuous and regular. There is no sign of entry or progression of a dominant brood throughout the period under study. Length frequency distribution shows three distinct modes. The first mode occurs regularly but does not progress beyond 40cm, recruitment being balanced by natural and fishing mortality. The other two which are not regular are probably the result of fishing outside regular areas. Short sections of “growth” lines which fit into one another when extrapolated, are evident. The larger lines obtained by extrapolation are parallel to one another. These tentative "growth lines" indicate that this species which enters the fishing grounds, when 15 cm or larger in length are exploited by the trawl fishery for a period of three to four years. This species appears to be six months old when it enters the fishing grounds and increases in length by about 37.5 cm in the next 30 months. Later growth slows down. The average size of the specimens sampled continued to get smaller from 1953 till 1957. It is shown that this reduction in size is due to increased fishing effort.

On the problem of nonadditivity in two-way analysis of variance of data for comparing the efficiency of fishing gears

To bring out the relative efficiency of various types of fishing gears, in the analysis of catch data, a combination of Tukey's test, consequent transformation and graphical analysis for outlier elimination has been introduced, which can be advantageously used for applying ANOVA techniques, Application of these procedures to actual sets of data showed that nonadditivity in the data was caused by either the presence of outliers, or the absence of a suitable transformation or both. As a corollary, the concurrent model: X sub(ij) = µ + α sub(i) + β sub(j) + λ α sub(i) β sub(j) + E sub(ij) adequately fits the data.