Corrigendum: A mechanistic model of macromolecular allocation, elemental stoichiometry, and growth rate in phytoplankton.

[This corrects the article DOI: 10.3389/fmicb.2020.

Carbon isotope budget indicates biological disequilibrium dominated ocean carbon storage at the Last Glacial Maximum.

Understanding the causes of the  ~90 ppmv atmospheric CO swings between glacial and interglacial climates is an important open challenge in paleoclimate research. Although the regularity of the glacial-interglacial cycles hints at a single driving mechanism, Earth System models require many independent physical and biological processes to explain the full observed CO signal. Here we show that biologically sequestered carbon in the ocean can explain an atmospheric CO change of 75 ± 40 ppmv, based on a mass balance calculation using published carbon isotopic measurements.

A model of time-dependent macromolecular and elemental composition of phytoplankton.

Phytoplankton Chl:C:N:P ratios are important from both an ecological and a biogeochemical perspective. We show that these elemental ratios can be represented by a phytoplankton physiological model of low complexity that includes major cellular macromolecular pools. In particular, our model resolves time-dependent intracellular pools of chlorophyll, proteins, nucleic acids, carbohydrates/lipids, and N and P storage.

The Mid-Pleistocene Transition: a delayed response to an increasing positive feedback?

Glacial-interglacial cycles constitute large natural variations in Earth's climate. The Mid-Pleistocene Transition (MPT) marks a shift of the dominant periodicity of these climate cycles from to  kyr. Recently, it has been suggested that this shift resulted from a gradual increase in the internal period (or equivalently, a decrease in the natural frequency) of the system.

Linear scaling between microbial predator and prey densities in the global ocean.

It has been proposed that microbial predator and prey densities are related through sublinear power laws. We revisited previously published biomass and abundance data and fitted Power-law Biomass Scaling Relationships (PBSRs) between marine microzooplankton predators (Z) and phytoplankton prey (P), and marine viral predators (V) and bacterial prey (B). We analysed them assuming an error structure given by Type II regression models which, in contrast to the conventional Type I regression model, accounts for errors in both the independent and the dependent variables.