Articles | Volume 14, issue 7
https://doi.org/10.5194/bg-14-1883-2017
https://doi.org/10.5194/bg-14-1883-2017
Research article
 | 
06 Apr 2017
Research article |  | 06 Apr 2017

Potential sources of variability in mesocosm experiments on the response of phytoplankton to ocean acidification

Maria Moreno de Castro, Markus Schartau, and Kai Wirtz

Abstract. Mesocosm experiments on phytoplankton dynamics under high CO2 concentrations mimic the response of marine primary producers to future ocean acidification. However, potential acidification effects can be hindered by the high standard deviation typically found in the replicates of the same CO2 treatment level. In experiments with multiple unresolved factors and a sub-optimal number of replicates, post-processing statistical inference tools might fail to detect an effect that is present. We propose that in such cases, data-based model analyses might be suitable tools to unearth potential responses to the treatment and identify the uncertainties that could produce the observed variability. As test cases, we used data from two independent mesocosm experiments. Both experiments showed high standard deviations and, according to statistical inference tools, biomass appeared insensitive to changing CO2 conditions. Conversely, our simulations showed earlier and more intense phytoplankton blooms in modeled replicates at high CO2 concentrations and suggested that uncertainties in average cell size, phytoplankton biomass losses, and initial nutrient concentration potentially outweigh acidification effects by triggering strong variability during the bloom phase. We also estimated the thresholds below which uncertainties do not escalate to high variability. This information might help in designing future mesocosm experiments and interpreting controversial results on the effect of acidification or other pressures on ecosystem functions.

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Short summary
Observations from different mesocosms exposed to the same treatment level typically show variability that hinders the detection of potential treatments effects. To unearth relevant sources of variability, we developed and performed a data-based model analysis that simulates uncertainty propagation. With this method we investigate the divergence in the outcomes due to the amplification of differences in experimentally unresolved ecological factors within replicates of the same treatment level.
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