Regression Plot

Y- 125393-0.314684X R-Sq=1.3%

! : : : • •----,—1

! « : ' : •

• , • • •

Figure 14 The relationship between the condition factor of the trout (a) and the salmon parr (b) and the total parasite abundance (transformed log10 1+ x).

As the regression equation, based on a plot of loge weight against loge length for the salmon parr was y = -3.79918 + 2.76135x the value of 2.76135 was used to calculate the condition factor (W/L2-76135). 100 where W = the weight in grams and L - the length in cm. Unlike the brown trout the condition factor showed no significant seasonal variation although the mean values tended to be slightly higher and more variable in January and March (Figure 9). Regression analyses, where the log10 (1 + jc) transformed values for parasite numbers are plotted on the y-axis against values for condition factors on the x-axis were undertaken. These reveal that with the exception of N. rutili, the condition factor tends to decline significantly with increases in numbers of C. farionis, C. metoecus, nematodes and the total parasites (P = 0.008, < 0.001, < 0.001 and 0.051, respectively) as the number of parasites increase (Table 20). In contrast, the condition factor tends to increase as numbers of N. rutili increase although this trend is not statistically significant. The tendency for the condition factor of the salmon parr to decline with increase in the total number of parasites is shown in Figure 14b. In contrast, the regression equations relating the adipose index to the transformed numbers of N. rutili and the total numbers of parasites indicate significant positive relationships (Table 21).

Table 20 The regression equations relating the transformed numbers of parasites (log10 (1 + x)) to the condition factor (CF) of the salmon parr

Regression equations

0 0

Post a comment