It is understood that there is an association between productivity and microbial diversity in aquaculture systems. For example, changes in bacterial microbiomes are implicated in animal performance, in disease development associated with both bacterial and viral origin, and in dysbiosis (disequilibrium in microbial communities) triggered by environmental stressors or diet choice. With the increasing development of novel DNA sequencing technologies , new concepts and cost-effective applications have emerged to better understand the role of microbial communities in the growth and health of farmed vertebrates and invertebrates. Evidence of any of the three main mechanisms that lead to dysbiosis, such as the decrease in diversity, the loss of beneficial bacteria and the expansion of pathogens or potentially harmful microorganisms, can be used as an indicator tool for productivity monitoring/forecasting purposes. This work establishes a methodology and a pipeline for microbiome characterization in three commercial shrimp ponds in Ecuador . Also, we c ompare the merit of two types of samples (water and sediment) and d etect patterns that allow inference on water quality in the ponds and evaluation of productive performance of the shrimp.
Briefly, the samples were filtered to concentrate, remove debris, and capture microbes present. DNA was extracted from the concentrated microbes sequenced to look at the type of bacteria present and the relative amount of each type. This was successful, with clear profiles produced along with some information on pathogen presence. Alpha diversity is a measurement of the microbial diversity of each sample. All sediment samples from the three ponds were very diverse with alpha diversity scores of over 600. Water samples were markedly less diverse with alpha diversity scores generally less than 250. For one of the ponds, alpha diversity was relatively high and over 250. For another pond alpha diversity was less than 100, and a single bacterial species made up over 50% of the bacteria detected in all water samples. In addition, for some water samples potentially pathogenic Vibrio species were identified .
Overall, this information reveals that monitoring the microbiome of production ponds over a grow-out cycle has the real chance to deliver data meaningful for pond management and pond performance. Possible application of this technology/analysis are 1) assessing g enotype by environment effects in shrimp performance, 2) understanding of the impact of the microbes in the growth, development, and survival of shrimp, 3) evaluation of the effectiveness of probiotics or diets, and 4) predicting pond performance by evaluation of variations in microbial composition at different timepoints in the production cycle.