The rice-crayfish (RC) integrated breeding model is an important and special agricultural ecosystem that provides a unique ecological environment for exploring the microbial diversity, composition, and functional capacity. To date, little is known about the effect of the breeding model on microbiome assembly and breeding model-specific microbiome composition and the association of the microbiome with water quality and crayfish growth. In the present study, we assessed the taxonomic shifts in gut and water microbiomes and their associations with water quality and crayfish growth in the RC and crayfish monoculture (CM) breeding models across six time points of rice growth, including seedling (a), tillering and jointing (b), blooming (c), filling (d), fruiting (e), and rotting of rice residues (f). Dominant bacterial phyla, such as Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes, were detected in both gut and water microbiomes across breeding models. Notably, the diversity and structure of the gut and water microbiomes significantly (P < 0.001) differed between the RC and CM models, with higher microbial diversity being noted in the RC model than in the CM model. The taxa enriched in the RC model included Bacillus sp., Streptomyces sp., Lactobacillus sp., Prevotella sp., Rhodobacter sp., Bifidobacterium sp., Akkermansia sp., and Lactococcus sp., some of which are potentially beneficial to animals. However, opportunistic pathogens, such as Citrobacter sp. and Aeromonas sp., were depleted in the RC model. Furthermore, in the RC model, the enriched taxa that formed complex cooccurrence networks showed a significant positive correlation with water quality and crayfish growth, whereas the depleted taxa showed a significant negative correlation with water quality and crayfish growth. These results suggest that the RC model has a better microbiome composition and that RC model-specific microbes could play important roles in improving crayfish growth and water quality. IMPORTANCE The present study comprehensively compared two different breeding models in terms of their microbiome composition and the associations of the microbiomes with crayfish growth and water quality. RC model-specific microbiome composition was identified; these microbes were found to have a positive association with water quality and crayfish growth. These results provide valuable information for guiding microbial isolation and culture and for potentially harnessing the power of the microbiome to improve crayfish production and health and water quality.
Keywords: cooccurrence networks; correlationship; crayfish growth; functional prediction; high-throughput sequence; keystone; microbiome structure; rice-crayfish integrated breeding model; water quality.