The Underlying Ecological Processes of Gut Microbiota Among Cohabitating Retarded, Overgrown and Normal Shrimp

Abstract

Increasing evidence of tight links among the gut microbiota, obesity, and host health has emerged, but knowledge of the ecological processes that shape the variation in microbial assemblages across growth rates remains elusive. Moreover, inadequately control for differences in factors that profoundly affect the gut microbial community, hampers evaluation of the gut microbiota roles in regulating growth rates. To address this gap, we evaluated the composition and ecological processes of the gut bacterial community in cohabitating retarded, overgrown, and normal shrimps from identically managed ponds. Gut bacterial community structures were distinct (P = 0.0006) among the shrimp categories. Using a structural equation modeling (SEM), we found that changes in the gut bacterial community were positively related to digestive activities, which subsequently affected shrimp growth rate. This association was further supported by intensified interspecies interaction and enriched lineages with high nutrient intake efficiencies in overgrown shrimps. However, the less phylogenetic clustering of gut microbiota in overgrown and retarded subjects may offer empty niches for pathogens invasion, as evidenced by higher abundances of predicted functional pathways involved in disease infection. Given no differences in biotic and abiotic factors among the cohabitating shrimps, we speculated that the distinct gut community assembly could be attributed to random colonization in larval shrimp (e.g., priority effects) and that an altered microbiota could be a causative factor in overgrowth or retardation in shrimp. To our knowledge, this is the first study to provide an integrated overview of the direct roles of gut microbiota in shaping shrimp growth rate and the underlying ecological mechanisms.

Keywords: Functional pathways; Interspecies interaction; Phylogenetic clustering; Shrimp gut microbiota; Structural equation modeling.