International Journal of Aquaculture, 2025, Vol.15, No.6, 275-286 http://www.aquapublisher.com/index.php/ija 279 The morphometric data were transformed in natural logarithm to determine the length-weight relationship using the formula: xLog W – log a + b Log L Where W = weight (g), L = length (cm), a = constant (the point at which the regression line intersects the y-axis), and b = slope (the growth coefficient). The condition factor of the fish was calculated using the formula: K= 100W L 3 Where K= condition factor; W= weight; L= Total Length. This metric helps to evaluate whether the fish were in good condition or under ecological stress, and has been successfully applied in recent studies assessing fish health in polluted tropical aquatic ecosystems (Ndome et al., 2020; Adewumi et al., 2021; Yakubu et al., 2023). 2.4 Data analysis The length and weight data were log-transformed to normalize distributions and stabilize variance prior to regression analysis. The linearized model was expressed as: logW=loga+blogL\log W = \log a + b \log L to estimate the regression parameters (a and b) and assess growth patterns. Linear regression was applied to determine the correlation between length and weight, while deviations of the exponent (b) from the isometric growth value of 3 were tested using Student’s t-test at a 95% confidence level. To evaluate spatial differences across sampling stations (Ayetoro, Bijimi, Idiogba, Asumogha), mean values of length, weight, and condition factor were compared using one-way Analysis of Variance (ANOVA). Where significant differences were detected, Tukey’s post hoc test was conducted to separate means. Statistical analyses were performed with SPSS version 16.0, and significance was set at p < 0.05. This approach ensured that variations in growth and condition factor could be objectively linked to environmental stressors, particularly pollution intensity, as supported by recent methodological applications in African fisheries research (Okomoda et al., 2021; Olopade et al., 2022; Eyo et al., 2023). 3 Results and Discusion 3.1 Morphometric data (length and weight) The morphometric measurements of Ethmalosa fimbriata and Chrysichthys macropogon across the four stations in Ilaje coastal waters (Ondo State, Nigeria) showed notable spatial differences (Tables 1 and Table 2). For E. fimbriata, the lowest mean weight was recorded at Asumogha in group A (27.25 ± 2.09 g), while the highest was observed at Idiogba in group D (184.90 ± 13.29 g). At Ayetoro, the group D specimens showed a maximum weight of 164.50 ± 0.00 g. Mean weights varied significantly across stations: Asumogha (66.36 ± 41.49 g) and Bijimi (62.18 ± 34.30 g) were not significantly different (p > 0.05), while Ayetoro (86.40 ± 48.73 g) and Idiogba (92.78 ± 52.31 g) differed significantly (p < 0.05). Such variations may be attributed to food resource distribution, habitat quality, and fishing pressure across sampling areas (Bolarinwa and Popoola, 2022; Yakubu et al., 2023). Length data for E. fimbriata also showed differences among stations. Asumogha (18.00 ± 4.64 cm) and Bijimi (16.72 ± 4.40 cm) had the shortest mean lengths, while Ayetoro (19.85 ± 6.43 cm) and Idiogba (20.20 ± 4.94 cm) recorded longer specimens. These findings are in line with reports that E. fimbriata populations display size variation across West African coastal habitats due to seasonal productivity and ecological conditions (Ekunwe et al., 2021; Okorie et al., 2022). For C. macropogon, the smallest mean weight occurred at Asumogha (20.72 ± 9.86 g), while the largest was at Bijimi (177.85 ± 10.75 g). Mean weights at Asumogha (65.28 ± 41.28 g) and Ayetoro (71.53 ± 38.42 g) were not
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