Table 1.
Information regarding the locations of sampling sites and corresponding sample identifiers.
Table 2.
Summary of alpha diversity metrics (mean ± standard error) by sample type.
Fig 1.
Microbial Alpha Diversity Analysis of Studied Communities.
Alpha diversity, a measure of within-sample microbial richness and evenness, was evaluated across sample types using four complementary indices: observed richness, Chao1 (estimating total richness), Shannon, and Inverse Simpson. The distribution of these metrics is presented in boxplots with overlaid individual data points. Pairwise comparisons between sample types were performed using the Wilcoxon rank-sum test. Statistical significance is denoted on the figures as follows: ***p < 0.001, **p < 0.01, *p < 0.05; “n.s.” indicates non-significant differences. The x-axis represents the sample types, while the y-axis corresponds to the value of each alpha diversity index.
Fig 2.
Microbial Beta Diversity Across Sample Types.
Principal Coordinate Analysis (PCoA) based on Bray–Curtis dissimilarity illustrates distinct clustering of microbial communities according to sample type. PERMANOVA confirms that sample type explains a significant portion of the observed variation in community structure (R² = 0.473, p = 0.001). Each point represents a sample, colored by type, and the analysis was performed with 999 permutations.
Fig 3.
Microbial Community Composition at Phylum and Genus Level.
Stacked bar charts display the relative abundance of the (A) top 22 microbial phyla and (B) top 28 microbial genera across all samples. Samples are grouped by type along the x-axis. The y-axis represents the percentage of sequences assigned to each taxon. Taxa are ordered from bottom to top by their mean relative abundance across the dataset.
Table 3.
Organism Counts and Percentages from Experimental (Wet Lab) and Computational (Dry Lab) Analyses.
Fig 4.
Antibiotic Resistance Profiles.
This figure presents the antibiotic resistance profiles of bacterial isolates, highlighting high resistance levels to various antibiotic classes such as Tetracyclines, Fluoroquinolones, and Penicillins. The results underscore the prevalence of antibiotic-resistant strains like Escherichia coli and Proteus mirabilis, raising concerns about antibiotic use and resistance in integrated fish farming.
Fig 5.
Distribution of Pathway Enrichment Across Samples.
Fig 5A presents a clustering analysis of KEGG pathways, revealing distinct patterns among the samples (Feed, Chicken Gut, Droppings, Fish Intestine, and Sediment), with amino acid and carbohydrate metabolism pathways enriched in Feed, while translation and replication pathways are more prominent in Chicken Gut and Droppings. Fig 5B indicates that metabolism pathways are the most abundant, particularly amino acid (67.56%) and carbohydrate metabolism (65.70%). In contrast, human diseases and organismal systems pathways are less dominant. Fig 5C highlights that Feed exhibits elevated metabolic pathways, especially amino acid metabolism (15.27%), while Sediment shows greater representation of environmental and genetic processing pathways.
Table 4.
Xenobiotics Biodegradation and Metabolism Pathways Across Samples.
Fig 6.
Antibiotics resistance genes (ARGs) detected different samples and their Spearman’s correlation with top 50 dominant microbial genera.
This figure presents a correlation matrix, a visual representation of the relationships between top 50 dominant bacterial genera (listed on the left) and a set of AMR genes (listed across the top). The numbers display the Spearman’s correlation coefficient (r). Vivid Orange and Moss Green indicate positive and negative correlation, respectively. The intensity of the color, along with the size of the circles, reflected the strength of these relationships, where larger and darker circles denoted stronger correlations. *Significant level (*p < 0.05; **p < 0.01; ***p < 0.001).