Standard Presentation 2024 Australian Marine Sciences Association Annual Meeting combined with NZMSS

Assessing iron-mediated phytoplankton dynamics in East Antarctic coastal polynyas based on photophysiological metrics (#110)

Jakob Weis 1 , Sophie Bestley 1 2 , Christina Schallenberg 3 , Delphine Lannuzel 1 4 , Mark Hindell 1 4 , Esther Portela 1 5 , Esmee van Wijk 3 , Clive R McMahon 6 7 , Sara Labrousse 8 , Fabien Roquet 9 , Christophe Guinet 8
  1. IMAS, University of Tasmania, Hobart, TAS, Australia
  2. AAPP, Hobart, TAS, Australia
  3. CSIRO, Hobart, TAS, Australia
  4. ACEAS, Hobart, TAS, Australia
  5. LOPS, Plouzane, France
  6. SIMS, Mosman, NSW, Australia
  7. IMOS, Hobart, TAS, Australia
  8. CNRS, Paris, France
  9. University of Gothenburg, Gothenburg, Sweden

Within the Antarctic sea-ice zone, coastal polynyas are areas of high phytoplankton productivity, and associated carbon export, supporting productive food webs foraged upon by higher trophic levels. This biological activity is notable in the Southern Ocean, where low iron and light typically co-limit phytoplankton growth elsewhere. Identifying iron sources and phytoplankton responses is key to understanding impacts on carbon sequestration and polar ecosystems along the Antarctic margins now and in the future. This study investigates the relationship between iron limitation and phytoplankton physiology in East Antarctic polynyas using animal-borne chlorophyll fluorescence and light sensors. Using recently established relationships between iron limitation and non-photochemical quenching (NPQ), the sensor data will be used to estimate the level of iron limitation in East Antarctic polynyas. We expect that factors such as seasonal sea-ice dynamics, glacier melt rates, nutrient intrusions, and sediment resuspension lead to disparate iron availability, reflected in the NPQ metric. These results will improve our understanding of the spatial and temporal variability of iron supply across different polynyas and within individual polynyas over time and help reduce uncertainty about likely future states in these important systems.