Potential sources of variability in ocean acidification mesocosm experiments

Abstract

Mesocosm experiments on phytoplankton dynamics under high CO 2 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 distribution of the replicates exposed to the same treatment. In experiments with multiple unresolved factors and a suboptimal number of replicates, post-processing statistical inference tools may fail to detect an effect. In such cases, model-based data analyses are suitable tools to unearth potential responses to the treatment and to identify which uncertainties may give rise to the observed divergences. As test cases, we use data showing high variability from two independent mesocosm experiments, where, according to statistical inference tools, biomass appeared insensitive to changing CO 2 conditions. Our simulations, in stead, show earlier and more intense phytoplankton blooms in modeled replicates at high CO 2 concentrations and suggest 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 estimate the thresholds below which uncertainties do not escalate into high variability. This information may help to interpret controversial results about acidification and to design future mesocosm experiments.