A trait-based approach to describe the elemental stoichiometry ofmarine phytoplankton and the regulation of the biological pump
A trait-based approach to describe the elemental stoichiometry ofmarine phytoplankton and the regulation of the biological pump
Abstract:
The regulation of the biological pump plays a central role in the global carbon cycle. Recent laboratory studies and field observations demonstrate considerable variation in C:N:P of both plankton growing under different environmental conditions and in particulate organic matter across regions.
Multiple distinct environmental drivers, including both temperature and inorganic nutrient concentrations, have been hypothesized as mechanisms responsible for these variations, each implying different feedbacks in the Carbon cycle. To reconcile the biological and geochemical measurements of C:N:P and to determine the extent to which temperature and nutrients control C:N:P, we here developed a trait-based model (ATOM, Adaptive Trait Optimization Model) designed to capture known
metabolic regulation of different cellular biochemical components and estimated variation in C:N:P along environmental gradients. Bayesian optimization of this
model against a newly compiled dataset of both C:N:P measurements and high-resolution measurements of inorganic N and P revealed that nutrient supply rates
followed by nutrient levels were the strongest regulators of C:N:P, with much less influence from temperature. Integrating ATOM with a global-scale model of
Nitrogen and Phosphate cycling led to improved an improved fit of carbon export to nutrient traps compared to both a static Redfield model and a model based on
nutrient concentrations only. Thus, the biological regulation of C:N:P captured a global rearrangement in carbon export with important implications for the carbon cycle.
Multiple distinct environmental drivers, including both temperature and inorganic nutrient concentrations, have been hypothesized as mechanisms responsible for these variations, each implying different feedbacks in the Carbon cycle. To reconcile the biological and geochemical measurements of C:N:P and to determine the extent to which temperature and nutrients control C:N:P, we here developed a trait-based model (ATOM, Adaptive Trait Optimization Model) designed to capture known
metabolic regulation of different cellular biochemical components and estimated variation in C:N:P along environmental gradients. Bayesian optimization of this
model against a newly compiled dataset of both C:N:P measurements and high-resolution measurements of inorganic N and P revealed that nutrient supply rates
followed by nutrient levels were the strongest regulators of C:N:P, with much less influence from temperature. Integrating ATOM with a global-scale model of
Nitrogen and Phosphate cycling led to improved an improved fit of carbon export to nutrient traps compared to both a static Redfield model and a model based on
nutrient concentrations only. Thus, the biological regulation of C:N:P captured a global rearrangement in carbon export with important implications for the carbon cycle.