Plankton detection in the surface ocean by high-resolution sensor technology

• Grant number: 13/17633-6
• Support type: Regular Research Grants
• Duration: May 01, 2014 - October 31, 2016
• Field of knowledge: Physical Sciences and Mathematics - Oceanography - Biological Oceanography
• Principal Investigator: Rubens Mendes Lopes
• Grantee: Rubens Mendes Lopes
• Home Institution: Instituto Oceanográfico (IO). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Assoc. researchers: Filippo Ghiglieno ; Nina Sumiko Tomita Hirata ; Olga Tiemi Sato ; Paulo Simionatto Polito ; Roberto Hirata Junior

Abstract

Marine plankton communities play a pivotal role in various ecosystem services, including global climate regulation. We propose to study plankton distribution and biodiversity in the surface layer of the ocean by means of an innovative sampling approach based on high spatial resolution and broad geographic coverage. Different biogeographic domains of the Atlantic, Pacific and Indian Oceans will be covered in this research. Both in situ (environmental probes and optical imaging) and remote (satellite sensors) sensing techniques will be used to register and interpret plankton distribution patterns and related physical and biogeochemical processes. The initiative represents the first experience of this kind worldwide. Innovative plankton imaging systems were especially developed for this project. They use a camera for bright-field (or phase contrast) image acquisition and a lower frequency camera (10 to 25 frames per second) for recordings with the digital holography technique. Automatic segmentation and classification algorithms will be used on the captured images to generate taxonomic and abundance data. Probes will provide simultaneous environmental data including temperature, salinity, colored dissolved organic matter (CDOM) and phytoplankton pigments. Environmental data and plankton biodiversity and abundance will be correlated with data obtained from satellite sensors to interpret the effects of physical forcing on meso- and large-scale variability in plankton communities. Efforts will be pursued to analyze the dataset in the context of climate change research. In addition to scientific publications the project will provide distribution maps of plankton communities, a rich image database, novel software for automatic plankton identification, and a myriad of opportunities for the development of outreach programs. (AU)