Preliminary results of oceanographic data from the G-NEO1 time series in the Nuevo Gulf, Argentina

DE CIAN, A.; CRESPI-ABRIL, A; HERNÁNDEZ MORESINO, R.; EPHERRA, L.; PISONI, JUAN PABLO; BARBIERI, E.

Plymouth

Simposio; Symposium Trends, Reflections, Evolution, and Visions in Ocean Research ? A celebration of the scientific life of Trevor Platt; 2023

Trevor Platt Science Foundation

Since coastal marine environments present a great diversity of natural processes, continuous monitoring over time is required to understand the balance of factors observed in it. The aim of this work is to analyze the seasonal variability of oceanographic data in a coastal area of the Golfo Nuevo, Argentina. Surface monthly samples were analyzed from July 2021 to June 2022 at the Luis Piedrabuena pier (750 m from the coastline). The samples belong to the time series "Golfo Nuevo Estación Oceanográfica 1" (G-NEO1), supported by NANO-DOAP Project. In G-NEO1 temperature has been continuously monitored since 2010, and discrete oceanographic parameters since 2018 such as salinity, nutrient concentration, chlorophyll-a, the composition of the planktonic community (phytoplankton and zooplankton) and atmospheric conditions (winds and relative humidity reported by the Meteorological Information Center, and atmospheric dust measured in situ). The station is in a temperate sea, so it presents high temperature variations during the year (between 9.7 to 20.5°C). During periods where the water column is stratified and the concentration of nutrients is low, the phytoplankton community is dominated by nanophytoplankton (nanoflagellates in October and ciliates in January/February). In summer, high concentrations of dinoflagellates and ciliates were observed, and in autumn a bloom of the genus Chaetoceros. Maintaining a time series requires a long-term investment in trained personnel, institutions, and infrastructure that provide repeatable oceanographic measurements. The time series of in situ observations allows to know the variability of the ecosystem, to detect extraordinary events and identify anthropogenic effects.