In base of these results:
Body shape analysis: Specimens collected from the Central subarea 27.9.a exhibited the largest mean sizes, whereas the southern population (South) displayed the smallest mean sizes (KW test; H = 230.3, P = 0.001) (Table 1, Figure 5). A similar trend was observed for otolith length (KW test: H = 212.04, P = 0.001) (Table 1, Figure 5). The normality (P > 0.001; Supplementary Table 1) and homogeneity (F = 152.97, P 95% of the variance for both analysis (see Supplementary Table 3). The first principal component (PC1) explained 34.70% of the variance among origins, which explained the body curvature, from a dorsal convexity body shape, accompanied by notable outward deformations in the landmarks corresponding to the anterior and posterior insertions of the first and last spines of both the first and second dorsal fins and a ventral mouth (PC1 negative scores) to a more dorsal concave shape, accompanied by inwards deformations in the same landmarks referred previously and superior positioning of the mouth. The second principal component accounted for 15.44% of the total variance differentiating a more (negative PC2 scores) or less wide (positive PC2 scores) body shape on the ventral side. For the Fresh vs. Frozen samples (Figure 6B), the PC1 accounted 31.75% of the total variation while the PC2 explained 11.32%. The first principal component highlighted differences in body curvature and mouth position, ranging from a more convex dorsal body with a ventrally positioned mouth (negative PC1 scores) to a more concave body shape with a superiorly positioned mouth (positive PC1 scores). The second principal component (PC2) mainly reflected variations in the midpoint between the base of the second dorsal fin and the anterior attachment of the dorsal membrane from the caudal fin (LM6). Positive PC2 scores indicated a dorsally more elongated body, whereas the negative PC2 scores indicated the opposite (dorsally less elongated).
The first canonical variate axis explained 100% of the variance in mean shapes associated with localities, highlighting a strong and positive correlation of both PC1 and PC2 components and the canonical variable (Figure 7A). Regarding the variable contributing the most to group separations (PC2), variations between populations are linked to inward (N) or outward (S) deformations in the grid at the insertion points of the pelvic and anal fins, and to a slightly more elongated head (N) or shorter head (S). (Figure. 7A). The overall classification accuracy per origin was 96.28%, showing a misclassification rate of 3. The Cohen’s kappa (κ) was 0.87, indicating that the classification efficiency was 87% better than would have occurred by chance alone (Table 3).
For the analysis of Fresh vs. Frozen samples (Figure 7B), the first canonical variate axis also accounted for 100%, highlighting a strong and positive correlation between PC2 and the canonical variable. Variations between fresh and frozen samples are associated with deformations of the landmark 6, located midway between the base of the second dorsal fin and the anterior attachment of the dorsal membrane from the caudal fin, resulting in a more (fresh) or less (frozen) elongated dorsal part of the body (Figure 7B). The average classification accuracy based on the state of the fish was 95.71%, with a misclassification rate of 4.3%. Cohen’s Kappa was 0.9143, indicating that the classification was 91% better than what would have occurred by chance alone (Table 3). Otolith analysis: The variability solely attributed to chance was accounted by the first 21 principal components (90.55%) (Supplementary Table 2). However, the explanatory power of these PCs was relatively modest. In particular, PC1 contributed 12.86% to the overall variance, facilitating the differentiation among the three populations within ICES subarea 27.9.a. (Figure 8A). The reconstructed overall average otolith shape for each region revealed notable inter- and intra-population variability, particularly in the antirostrum, dorsal margin curvature and posterior part of the otolith (Figure 9A). Specifically, otoliths obtained from the Northern subregion exhibit a significantly more pronounced and wider notch, contrasting with the less conspicuous notch observed in both the South and Central subregions (Figure 9B). Additionally, the southern population displayed a more lanceolate shape, with the posterior shape more acute than the northern population. The PERMANOVA analysis revealed a significant regional differentiation (F = 7.36, P = 0.001).
When considering the average phenotype per origin, the classification accuracy of regional populations achieved 100%, with a Cohen’s kappa of 1 (Table 4). The accuracy remained consistently high across all three analyzed populations, with each population attaining 100% accuracy. The relative importance of PC components was examined. Specifically, PC1 showed a strong correlation with the notch region, while PC2 was associated to irregularities in the curvature of the dorsal margin. Furthermore, PC9 was associated with the rostrum and PC6 with the posterior zone (Figure 10).
identification of morphotypes: The agglomeration method ward-D enabled the differentiation of, at least, four morphotypes (M1-4), which were clearly separated in the morphospace (Figure 8B, Supplementary Figure 2). The PC1 axis (12.86%) represented the variability in the antirostrum and notch regions, spanning from the less conspicuous antirostrum and notch of M1 and M2 (negative PC1 scores) to a more pronounced antirostrum and notch observed in M3 and M4 morphotypes, consequently resulting in an enlarged rostrum (positive PC1 scores) (Figures 8B and 11). The PC2 axis (12.18% variance) allowed the differentiation of morphotypes M1 and M4 (positive PC2 scores) from M2 and M3 (negative PC2 scores) (Figures 8B and 11), mostly attributed to variations in notch wide and posterior region shape. Morphotypes M1 and M4 exhibit a wider notch compared to the narrower notch observed in M2 and M3. Additionally, M4 displays a more rounded posterior region, contrasting with the more lanceolate morphology of M1, M2 and M3 (Figures 8B and 11). The PERMANOVA analysis revealed distinct regional distinctions (F = 14.383, p = 0.001). The proportion and morphotypes distributions varied across regions. In the northern subarea, morphotype M4 (55.56%) was the most abundant, followed by M3 (36.11%) and M2 (8.33%). In the Central and South regions, the predominant phenotype was M2 (44.07 and 42.86%, respectively), followed by M1 (30.51 and 31.43%, respectively) and by M3 (25.42 and 25.71%, respectively) (Figure 12).
Regarding the classification accuracy, high values were achieved (>98%) for morphotypes M1, M2 and M3 (Table 4). The morphotype M4 was not classified because it was exclusively found in North subregion.”
could you structure the discussion in such a way that: in the first paragraph talk in general about my results, in the second paragraph about the stocks in the different regions I talk about (if you can add existing citations better), in the third paragraph you can talk about the results of the body shape section and comparing them with other results of other Trachurus trachurus works (with existing citations please), in the fourth paragraph about the results of otolith analysis and comparing them with other results of other works of Trachurus trachurus using Fourier or Wavelets as in my case, and a last paragraph about the results obtained in identification of morphotypes and comparing it with other results of other works of Trachurus trachurus using correct and existing citations. My research is to find differents stock of Trachurus trachurus applaying morphometrics with landmarks (truss network) and otolith analysis using wavelets
