The Impact of Variable Phytoplankton Stoichiometry on Projections of Primary Production, Food Quality, and Carbon Uptake in the Global Ocean

Abstract

Ocean biogeochemical models are integral components of Earth system models used to project the evolution of the ocean carbon sink, as well as potential changes in the physical and chemical environment of marine ecosystems. In such models the stoichiometry of phytoplankton C:N:P is typically fixed at the Redfield ratio. The observed stoichiometry of phytoplankton, however, has been shown to considerably vary from Redfield values due to plasticity in the expression of phytoplankton cell structures with different elemental compositions. The intrinsic structure of fixed C:N:P models therefore has the potential to bias projections of the marine response to climate change. We assess the importance of variable stoichiometry on 21st century projections of net primary production, food quality, and ocean carbon uptake using the recently developed Pelagic Interactions Scheme for Carbon and Ecosystem Studies Quota (PISCES‐QUOTA) ocean biogeochemistry model. The model simulates variable phytoplankton C:N:P stoichiometry and was run under historical and business‐as‐usual scenario forcing from 1850 to 2100. PISCES‐QUOTA projects similar 21st century global net primary production decline (7.7%) to current generation fixed stoichiometry models. Global phytoplankton N and P content or food quality is projected to decline by 1.2% and 6.4% over the 21st century, respectively. The largest reductions in food quality are in the oligotrophic subtropical gyres and Arctic Ocean where declines by the end of the century can exceed 20%. Using the change in the carbon export efficiency in PISCES‐QUOTA, we estimate that fixed stoichiometry models may be underestimating 21st century cumulative ocean carbon uptake by 0.5–3.5% (2.0–15.1 PgC).

Quantifying photosynthetic performance of phytoplankton based on photosynthesis–irradiance response models

“…Phytoplankton are a key functional component of aquatic ecosystems and play a pivotal role in biogeochemical cycles [1]. In particular, marine phytoplankton, as the principal driving force of ocean carbon cycles and energy flows, fix approximately 50 gigatons of inorganic carbon annually, almost half of the total global primary production [2,3]. They show higher CO 2 fixation rates and higher biomass productivity than any other photosynthetic organisms [3].…”

“…In particular, marine phytoplankton, as the principal driving force of ocean carbon cycles and energy flows, fix approximately 50 gigatons of inorganic carbon annually, almost half of the total global primary production [2,3]. They show higher CO 2 fixation rates and higher biomass productivity than any other photosynthetic organisms [3]. As the increase of CO 2 concentration in the atmosphere and global warming, an accurate estimate of photosynthetic productivity of phytoplankton becomes ever more important for modelling primary production and structure changes of phytoplankton communities in aquatic ecosystems, especially eutrophic lakes (e.g., Taihu, Erie, Winnipeg lake) and estuaries (e.g., Yangtze River).…”

A meta-analysis on environmental drivers of marine phytoplankton C : N : P

“…Recent global biogeochemical models are therefore starting to incorporate a more realistic representation of plankton physiology, which includes flexible phytoplankton C:N:P (e.g., Buchanan et al, 2018). Modeling studies with flexible phytoplankton stoichiometry have demonstrated that proliferation of C-rich phytoplankton under future climate scenario has the potential to buffer expected future decline in carbon export and net primary productivity caused by increased stratification (Kwiatkowski et al, 2018;Tanioka and Matsumoto, 2017). This buffering effect cannot be simulated by biogeochemical models with fixed phytoplankton C:N:P.…”

Optimality-Based Non-Redfield Plankton-Ecosystem Model (OPEMv1.0) in the UVic-ESCM 2.9. Part II: Sensitivity Analysis and Model Calibration

“…The subsistence quota was first introduced by Droop (1968) in phytoplankton growth models. While it has been applied in Earth System Models (Kwiatkowski et al, 2018;Wang et al, 2019), a sensitivity analysis similar to the present study has not been done before. A higher Q N 0, phy implies that more nitrogen is required for phytoplankton growth, but it also can be interpreted as a lessening of carbon fixation for a given nitrogen supply.…”