Consumers mediate the effects of experimental ocean acidification and warming on primary producers

It is well known that ocean acidification can have profound impacts on marine organisms. However, we know little about the direct and indirect effects of ocean acidification and also how these effects interact with other features of environmental change such as warming and declining consumer pressure. In this study, we tested whether the presence of consumers (invertebrate mesograzers) influenced the interactive effects of ocean acidification and warming on benthic microalgae in a seagrass community mesocosm experiment. Net effects of acidification and warming on benthic microalgal biomass and production, as assessed by analysis of variance, were relatively weak regardless of grazer presence. However, partitioning these net effects into direct and indirect effects using structural equation modeling revealed several strong relationships. In the absence of grazers, benthic microalgae were negatively and indirectly affected by sediment-associated microalgal grazers and macroalgal shading, but directly and positively affected by acidification and warming. Combining indirect and direct effects yielded no or weak net effects. In the presence of grazers, almost all direct and indirect climate effects were nonsignificant. Our analyses highlight that (i) indirect effects of climate change may be at least as strong as direct effects, (ii) grazers are crucial in mediating these effects, and (iii) effects of ocean acidification may be apparent only through indirect effects and in combination with other variables (e.g., warming). These findings highlight the importance of experimental designs and statistical analyses that allow us to separate and quantify the direct and indirect effects of multiple climate variables on natural communities.

On the role of circulation and mixing in the ventilation of oxygen minimum zones with a focus on the eastern tropical North Atlantic

Ocean observations carried out in the framework of the Collaborative Research Center 754 (SFB 754) "Climate-Biogeochemistry Interactions in the Tropical Ocean" are used to study (1) the structure of tropical oxygen minimum zones (OMZs), (2) the processes that contribute to the oxygen budget, and (3) long-term changes in the oxygen distribution. The OMZ of the eastern tropical North Atlantic (ETNA), located between the well-ventilated subtropical gyre and the equatorial oxygen maximum, is composed of a deep OMZ at about 400 m depth with its core region centred at about 20° W, 10° N and a shallow OMZ at about 100 m depth with lowest oxygen concentrations in proximity to the coastal upwelling region off Mauritania and Senegal. The oxygen budget of the deep OMZ is given by oxygen consumption mainly balanced by the oxygen supply due to meridional eddy fluxes (about 60%) and vertical mixing (about 20%, locally up to 30%). Advection by zonal jets is crucial for the establishment of the equatorial oxygen maximum. In the latitude range of the deep OMZ, it dominates the oxygen supply in the upper 300 to 400 m and generates the intermediate oxygen maximum between deep and shallow OMZs. Water mass ages from transient tracers indicate substantially older water masses in the core of the deep OMZ (about 120-180 years) compared to regions north and south of it. The deoxygenation of the ETNA OMZ during recent decades suggests a substantial imbalance in the oxygen budget: about 10% of the oxygen consumption during that period was not balanced by ventilation. Long-term oxygen observations show variability on interannual, decadal and multidecadal time scales that can partly be attributed to circulation changes. In comparison to the ETNA OMZ the eastern tropical South Pacific OMZ shows a similar structure including an equatorial oxygen maximum driven by zonal advection, but overall much lower oxygen concentrations approaching zero in extended regions. As the shape of the OMZs is set by ocean circulation, the widespread misrepresentation of the intermediate circulation in ocean circulation models substantially contributes to their oxygen bias, which might have significant impacts on predictions of future oxygen levels.