Content of inorganic solutes in lettuce grown with brackish water in different hydroponic systems

A B S T R A C T The objective of this study was to evaluate the growth and accumulation of ions in lettuce grown in different hydroponic systems and recirculation frequencies. The experimental design was randomized blocks with 8 treatments and 4 replicates. The evaluated hydroponic systems were Nutrient Flow Technique (NFT) and an adapted Deep Flow Technique (DFT), the latter with recirculation frequencies of 0.25, 2 and 4 h. Both systems used fresh water and brackish water. Plant growth, accumulation of inorganic solutes (Na + , K + , Cl -and NO 3 -) and the correlation between dry matter production and Na + /K + and Cl -/NO 3 -were evaluated. The salinity of the water used to prepare the nutrient solution caused decrease in growth and K + and NO 3 -levels, and increased contents of Na + and Cl -in the plants. When using fresh water the highest dry matter production was obtained in the NFT system. In case of brackish water the adapted DFT system increased the production, in relation to NFT system (at same recirculation frequency: 0.25 h). It was found that the choice of the type of hydroponic system and recirculation interval for the cultivation of lettuce depends on the quality of the water used to prepare the nutrient solution. Teor de solutos inorgânicos em alface cultivada com água salobra em diferentes sistemas hidropônicos . A salinidade da água utilizada no preparo da solução nutritiva proporcionou diminuição no crescimento e nos teores de K + e NO 3 -e aumentou os teores de Na + e Cl -nas plantas. Quando foi empregada água doce, o sistema NFT proporcionou maior produção de massa de matéria seca. No caso de água salobra o sistema DFT adaptado aumentou a produção, em relação ao sistema NFT (na mesma frequência de recirculação: 0,25 h). Verificou-se que a escolha do sistema hidropônico e do intervalo de recirculação para o cultivo da alface depende da qualidade da água utilizada no preparo da solução nutritiva. R E S U M

Nycthemeral and Monthly Occupation of the Fish Assemblage on a Sheltered Beach of Baía Norte, Florianópolis, Santa Catarina State, Brazil

Interpreting fish community records is challenging for several reasons, including the lack of past ichthyofauna data, the cyclical temporal variations in the community, and the methodology employed, which usually underestimates fish assemblages. The objective of this study was to describe short-scale and meso-scale (nycthemeral period and months, respectively) temporal variations in the ichthyofauna composition and structure of a sheltered beach of Baía Norte (Florianópolis, Santa Catarina state, Brazil), using a capéchade net. Samples were collected monthly for a period of 48 hours. During the period from December 2010 to November 2011, a total of 19,302 individuals belonging to 89 species and 39 families were captured. The number of individuals that were sampled during the day and/or night was dependent on the sampling month. On average, the daytime assemblage was more abundant and different in structure and composition than the nighttime assemblage. Of the eight species that had the highest Index of Relative Importance (%IRI), five had higher variations (ANOVA F) between the day and night than between the months. This finding reinforced the need for sampling during both the day and night. The capéchade net effectively captured demersal and pelagic individuals in a broad range of sizes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio