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As wild fish stocks decline worldwide, land-based fish rearing is likely to be of increasing relevance to feeding future human generations. Little is known about the structure and role of microbial communities in fish aquaculture, particularly at larval developmental stages where the fish microbiome develops and host animals are most susceptible to disease. We employed next-generation sequencing (NGS) of 16S rRNA gene reads amplified from total community DNA to reveal the structure of bacterial communities in a gilthead seabream (Sparus aurata) larviculture system. Early- (2 days after hatching) and late-stage (34 days after hatching) fish larvae presented remarkably divergent bacterial consortia, with the genera Pseudoalteromonas, Marinomonas, Acinetobacter, and Acidocella (besides several unclassified Alphaproteobacteria) dominating the former, and Actinobacillus, Streptococcus, Massilia, Paracoccus, and Pseudomonas being prevalent in the latter. A significant reduction in rearing-water bacterial diversity was observed during the larviculture trial, characterized by higher abundance of the Cryomorphaceae family (Bacteroidetes), known to populate microniches with high organic load, in late-stage rearing water in comparison with early-stage rearing-water. Furthermore, we observed the recruitment, into host tissues, of several bacterial phylotypes—including putative pathogens as well as mutualists—that were detected at negligible densities in rearing-water or in the live feed (i.e., rotifers and artemia). These results suggest that, besides host-driven selective forces, both the live feed and the surrounding rearing environment contribute to shaping the microbiome of farmed gilthead sea-bream larvae, and that a differential establishment of host-associated bacteria takes place during larval development.

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Computer-assisted sperm analysis (CASA) and clustering analysis have enabled to study sperm subpopulations in mammals, but their use in fish sperm has been limited. We have used spermatozoa from Senegalese sole (Solea senegalensis) as a model for subpopulation analysis in teleostei using two different activating solutions. Semen from six males was activated using 1100 mOsm/kg solutions: artificial seawater (ASW) or sucrose solution (SUC). Motility was acquired at 15, 30, 45, and 60 s post-activation. CASA parameters were combined into two principal components, which were used in a non-hierarchical clustering analysis, obtaining four subpopulations (CL): CL1 (slow/non-linear), CL2 (slow/linear), CL3 (fast/non-linear), and CL4 (fast/linear). We detected spermatozoa lysis, especially in ASW. Sperm motility was higher for SUC and decreased with time. The subpopulation proportions varied with time and activating treatment, showing both an increase in CL1 and CL2 and a decrease in CL3 and CL4 with time. Both CL3 and CL4 were higher in samples activated with SUC, at least in early postactivation. Proportions of CL3 and CL4 at 15 s were associated with higher quality at 60 s and with lower lysis. A second clustering analysis was conducted, classifying the males accordingly to their motility subpopulations. This analysis showed a high heterogeneity between samples. Subpopulation analysis of CASA data can be applied to Solea spermatozoa, allowing identification of potentially interesting sperm subpopulations. Future studies might benefit from these techniques to establish the relationship of these subpopulations with fish sperm quality and fertility, helping to characterize males according to their reproductive potential.