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The addition of Navicula sp. to shrimp nurseries can improve the growth of Penaeus vannamei reared in biofloc systems. However, the contribution of microalgae to the biofloc formation and the effective contribution to shrimp nutrition remain unknown. In this study, Navicula sp. was added to biofloc nursery systems of P. vannamei at distinct time frequencies for evaluating its nutritional contribution to shrimp growth. Nursery rearing was carried out in bioflocs for 35 days at a stocking density of 3000 post-larvae m−3. Shrimp were fed using a commercial feed plus fresh culture of Navicula sp. at different frequencies: no addition of Navicula sp. (WN – control), the addition of 10 × 104 cells mL−1 of the diatom every 5, 10 and 15 days (N5, N10 and N15, respectively). Food sources relative contribution to P. vannamei development was estimated using a Bayesian mixture model. The isotopic discrimination factor (Δ15N and Δ13C) for each food source was determined experimentally. After 35 days of culture, survival (∼93 %) was similar across all treatments but there was a significant difference in weight gain and feed conversion ratio. The N10 treatment (0.50 ± 0.05 g, 0.99 ± 0.01) exhibited better growth parameters when compared to the WN treatment (0.33 ± 0.07 g, 11.46 ± 0.30). Biofloc was the food source most assimilated by shrimp followed by Navicula sp. and commercial feed. Contribution of Navicula sp. was higher in the N5 treatment. In the treatments with diatom addition, an inverse correlation was observed between the relative contributions of biofloc and Navicula sp., indicating that Navicula sp. is not in the biofloc composition, but it is directly consumed by P. vannamei post-larvae. Biofloc and Navicula sp. exhibited larger contributions to the growth of shrimp, reinforcing the importance of natural food sources to the aquaculture of P. vannamei post-larvae. [ABSTRACT FROM AUTHOR]

The floodplain lakes of the Amazon constitute one of the most important aquatic systems in terms of fish biodiversity. These ecosystems are influenced by the flood pulse, which in turn, affects the behaviour of fish communities within this lake ecosystem. In this study, we tested the hypothesis that the facets of taxonomic and functional diversity may be influenced by the conditions of 'patches' of internal habitats, considering the margins and central environments of a floodplain lake in the Amazon. Lake Cujubim, located on the right bank of the Madeira River, was evaluated via fisheries carried out in the high‐water and low‐water phases, during the day and night, and considering two location strata. The results suggest that the absence of significant differences in alpha taxonomic diversity between the two phases may be a possible seasonal mischaracterisation in Lake Cujubim attributed mainly to dam constructions along the Madeira River. Apparently, no major differences occur in the assembly of fish in terms of their taxonomic and functional characteristics, regardless of luminosity. In the strata of the locations, demonstrates noticeable habitat patches, with the margins being bioregions with high incidences of individuals of the Engraulidae family, confirmed by both taxonomic and functional diversity (alpha and beta). It can be seen that, even in connected lakes, there are differentiated metacommunities between the bioregions, thus forming patches of habitat, and that the shores of the lake, especially those located in proximity or distant from the river, are more suitable areas for food and refuge. [ABSTRACT FROM AUTHOR]

Small pelagic fish play a key role in human nutrition, especially in emerging countries, as they are affordable protein sources and provide income for fishing communities. Despite their nutritional benefits for human health, prices are the main factor when choosing seafood as diet components, which highlights the relevance of an economic analysis, since changes in fish prices might alter the feeding patterns of populations worldwide. This study analyzed the price dynamics of the Sardinella brasiliensis in one of the main markets in northeast Brazil and employed machine learning techniques to forecast future prices. The dataset was obtained from the Pernambuco Supply and Logistics Center website, and it was modeled using the FbProphet library in addition to a long short-term memory (LSTM) neural network in order forecast future prices. Both algorithms reached low error metrics, but LSTM performed significantly better, showing its usability in the economic context of marine products. [ABSTRACT FROM AUTHOR]