## 2 Results
### Biological, Gut Morphological and Physiological Responses in Different Reproductive Stages
terminalis_ metagenomes (999 permutations with adjusted \(p-\)value) using Bray–Curtis distance matrices for bacterial species–based metagenomes; (**D–F**) co-occurrence networks of gut microbiota among three groups distributed across the various reproductive stages of _M. terminalis_. The analyses were conducted utilizing the top 50 most abundant genera of the intestinal microbial community across the various groups. The significance level of the data presented in the figure was \(p<0.05\), with r \(\geq 0.6\). The abundance is proportional to the size of the circle representing a species. The correlation between two genera is symbolized by the line’s hue, with red denoting a positive correlation and green representing a negative correlation. The intensity of the correlation is indicated by the thickness of the edge. Note: NRS. non-reproductive stage; RS. reproductive stage; PRS. post-reproductive stage. Co-occurrence networks generated based the on significantly correlated genus (with Spearman coefficients \(r>0.6\) and \(p<0.5\); Figure 3D-F) revealed potential changes in microbial interactions among the three groups. Co-occurrence network analyses showed that the correlations exhibited different patterns among the three groups, with more connections in microbial communities from the NRS group (475 edges, positive 85.1% and negative 14.9%) and PRS (326 edges, positive 99.7% and negative 0.03%) compared to the RS group (216 edges, positive 79.2% and negative 21.8%) (Figure 3D-F). The average degree value, which indicates network complexity, was significantly greater in NRS (9.87) and PRS (6.77) compared to RS (4.57) based on the computed network topological properties. In addition, the LEFse analysis revealed several microbial taxa that were significantly enriched in the various groups, as shown in Figure 4A. The consensus characteristic seen in the gut microbiota was a high enrichment of the phylum Firmicutes in the PRS samples (Figure 4A). For example, _Clostridium_, _Romboutsia_, _Fusobacterium_, and _Paenicolstridium_ could be considered as microbiological markers to differentiate the PRS group from the other groups (Figure 4A). On the other hand, we found that phylum Proteobacteria in the NRS group presented much higher LDA scores than those in the other groups (Figure 4A). Fewer microbial taxa were identified as significantly enriched in the RS groups. The LDA scores of the genera _Marinifulum_, _Streptococcus_, _Oceaniculis_, _Escherichia_, and _Klebsiella_ were significantly higher in the RS group than in other groups (Figure 4A). Further investigation focused on six genera (_Clostrium_, _Fusobacterium_, _Bacteroides_, _Cellilosylticum_, _Ruminococcus_, _Romboutsia_) closely associated the carbohydrate metabolism indicated that the PRS group had a significantly higher relative abundance compared to the RS group (Kruskal-Wallis test, \(p<0.05\); Figure 5B). Moreover, only the relative abundance of _Clostrium_, _Fusobacterium_, _Cellilosylticum_ of PRS samples was significantly higher in the NRS samples (Figure 4B). Figure 4: _Cont._  
Gut Microbiota Functional Differences among Non-Reproductive, Reproductive and Post-Reproductive Groups  
In the current study, we assessed the functional diversity of the gut microbiome in three groups using the Chao1 index and KEGG functional gene annotation at level 3. The Chao1 index was significantly lower in the RS group compared to the NRS and PRS groups (Kruskal-Wallis test, \(p<0.001\); Figure 5A). Differences in the KEGG functional pathways (level 4) of the gut microbiota were specifically noted between the RS group and the NRS and PRS groups (Adions \(R^{2}=0.602\), \(p=0.002\); Figure 5B). Similarly, a difference in carbohydrate-active enzyme (CAZyme) function categories was noted across three groups (Adions \(R^{2}=0.496\) and \(p=0.002\); Figure 5C).