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Analysis of Fatty Acid Content and Composition in Microalgae
Published on: October 1, 2013
Variability in nitrogen stable isotope ratios of macroalgae: consequences for the identification of nitrogen sources
María Julia Ochoa-Izaguirre1,2, Martín F Soto-Jiménez3
1Posgrado en Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México, Unidad Académica Mazatlán, Apdo. Postal 811, Mazatlán, Sinaloa, 82040, México.
Abstract:
In our research, we collected and analyzed numerous macroalgal specimens (738) for isotopic analysis sampled over a year at monthly intervals across 20 sites within the Urías lagoon complex, a typical subtropical coastal ecosystem located in the Gulf of California. We quantified and characterized (chemically and isotopically) the N loads received by Urías throughout a year. We studied the spatial-temporal variation of the chemical forms and isotopic signals of the available N in the water column, and we monitored in situ different environmental variables and other hydrodynamic parameters. Multiple N sources (e.g., atmospheric, sewage, seafood processing, agriculture and aquaculture effluents) and biogeochemical reactions related to the N cycle (e.g., ammonia volatilization, nitrification and denitrification) co-occurring across the ecosystem, result in a mixture of chemical species and isotopic compositions of available N in the water column. Increased variability was observed in the δ(15) N values of macroalgae (0.41‰-22.67‰). Based on our results, the variation in δ(15) N was best explained by spatio-temporal changes in available N and not necessarily related to the N sources. The variability was also explained by the differences in macroalgal biology among functional groups, species and/or individuals. Although the δ(15) N-macroalgae technique was a useful tool to identify N sources, its application in coastal ecosystems receiving multiple N sources, with changing environmental conditions influencing biogeochemical processes, and high diversity of ephemeral macroalgal species, could be less sensitive and have less predictive power.
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