Mechanisms driving Antarctic microbial community responses to ocean acidification: a network modelling approach

• Published in: Polar Biology
• Main Authors: Subramaniam, RC, Melbourne-Thomas, J, Davidson, A, Corney, SP
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer-Verlag 2016
• Subjects: Biological Sciences; Ecology; Marine and estuarine ecology (incl. marine ichthyology); Antarctic; Southern Ocean; Antarc*; Ocean acidification; Polar Biology
• Online Access: https://doi.org/10.1007/s00300-016-1989-8; http://ecite.utas.edu.au/109959

Rising atmospheric CO 2 concentrations and the subsequent changes to ocean chemistry may have pronounced effects on marine microbial communities, particularly for the cold Southern Ocean. Changes to the microbial community in this region could affect the way nutrients are cycled, impact the efficiency of carbon drawdown, and cause shifts in food supply to higher trophic levels. Increased CO 2 could affect the bioavailability of iron to phytoplankton. Fertilisation experiments show that iron can influence phytoplankton community composition, favouring large phytoplankton species in iron-replete conditions. The potential interactive effects of CO 2 and iron bioavailability are currently poorly understood but are likely to be important in determining CO 2 -induced changes to the microbial community. We employ a qualitative network modelling approach to evaluate alternative hypotheses regarding the effects of elevated CO 2 on Antarctic microbial communities in incubation experiments. We used a sequential approach to model development and testing, where we first formulated a base model for microbial community interactions, and then sequentially added direct and indirect effects of elevated CO 2 on particular groups. We found that model simulations were most consistent with observations from incubation experiments when we assumed an indirect effect of CO 2 on phytoplankton. In particular, when we assumed a negative effect of elevated CO 2 on the uptake of iron by large phytoplankton, as a result of a decrease in iron bioavailability. Our findings show that qualitative network models can be used to test hypotheses relating to results from experimental studies, and help identify key processes to target in future studies.