Monitoring of phytoplankton in a subtropical estuarine system through traditional taxonomic, functional diversity and microscopy-imaged-based classification tools.

12th International Phycological Congress, 2021 Também disponível em: (2021) 12th International Phycological Congress, Phycologia, 60:sup1, 74-74, ISSN: 0031-8884 DOI: 10.1080/00318884.2021.1922050Estuarine systems are under human activities pressure that may lead to changes in the structure of planktonic community. Given its importance as the basis of food webs and their rapid responses to environmental changes, phytoplankton is fundamental to understanding the effects of these changes on the general plankton community. The Santos Estuarine System (SES), Brazil, receives a high load of pollutants from petrochemical and fertilizer industries, as well as hosts one of the largest ports in Latin America. The present study aims to establish the bases for the implementation of long-term monitoring programs in this ecosystem combining classical monitoring methods (variation of chlorophyll biomass and taxonomic composition), with methodologies based on functional diversity (Convex hull) and on Microscopy-Imaged-based Classification Tools of plankton. Considering previous dataset from SES, computer vision techniques were employed to perform steps of object identification, filtering and feature extraction in order to obtain the final dataset. The developed software is open-source and available under the MIT license. From March 2020 (except in April and May due to COVID-19 pandemic), monthly surveys are being performed in four stations through the navigation channel of SES. Preliminary results showed dominance of filamentous cyanobacteria during raining/freshwater-influence periods and of diatoms under brackish and seawater-influence conditions. Salinity gradient was also a stressor condition resulting in changes in functional diversity index (FDis, FEve and FRic). Microscopy-Imaged-based Classification first results obtained a mean accuracy of 83.88% considering 4 classes, and 76.67% considering 13 classes.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)FAPESP: 2018/25816-