Competition between cyanobacteria and green algae at low versus elevated CO2: who will win, and why?

Abstract

Traditionally, it has often been hypothesized that cyanobacteria are superior competitors at low CO2 and high pH in comparison with eukaryotic algae, owing to their effective CO2-concentrating mechanism (CCM). However, recent work indicates that green algae can also have a sophisticated CCM tuned to low CO2 levels. Conversely, cyanobacteria with the high-flux bicarbonate uptake system BicA appear well adapted to high inorganic carbon concentrations. To investigate these ideas we studied competition between three species of green algae and a bicA strain of the harmful cyanobacterium Microcystis aeruginosa at low (100 ppm) and high (2000 ppm) CO2. Two of the green algae were competitively superior to the cyanobacterium at low CO2, whereas the cyanobacterium increased its competitive ability with respect to the green algae at high CO2. The experiments were supported by a resource competition model linking the population dynamics of the phytoplankton species with dynamic changes in carbon speciation, pH and light. Our results show (i) that competition between phytoplankton species at different CO2 levels can be predicted from species traits in monoculture, (ii) that green algae can be strong competitors under CO2-depleted conditions, and (iii) that bloom-forming cyanobacteria with high-flux bicarbonate uptake systems will benefit from elevated CO2 concentrations.

Keywords: CO2-concentrating mechanism; Carbon dioxide; Microcystis aeruginosa; climate change; competition model; cyanobacteria; green algae; harmful algal blooms; lakes.

Fig. 1.

Monoculture experiments at low pCO₂ (100 ppm). (A–D) Population density (expressed as biovolume) and light intensity I_out penetrating through the chemostat. (E–H) CO₂(aq), bicarbonate and carbonate concentrations. (I–L) Dissolved inorganic carbon (DIC) and pH. Different panels represent different species: (A, E, I) Monoraphidium; (B, F, J) Microcystis; (C, G, K) Scenedesmus; (D, H, L) Chlorella. Symbols indicate experimental data, and lines indicate model fits. Parameter values of the model are provided in Table 1 and Table 2.

Fig. 2.

Monoculture experiments at high pCO₂ (2000 ppm). (A–D) Population density (expressed as biovolume) and light intensity I_out penetrating through the chemostat. (E–H) CO₂(aq), bicarbonate and carbonate concentrations. (I–L) Dissolved inorganic carbon (DIC) and pH. Different panels represent different species: (A, E, I) Monoraphidium; (B, F, J) Microcystis; (C, G, K) Scenedesmus; (D, H, L) Chlorella. Symbols indicate experimental data, and lines indicate model fits. Parameter values of the model are provided in Table 1 and Table 2.

Fig. 3.

R* values of the species. (A, B) R* values for CO₂(aq) (A) and bicarbonate (B) of the species, indicative of their competitive abilities for inorganic carbon. R* values were estimated from the CO₂ and bicarbonate concentrations measured in the steady-state monocultures at low pCO₂. (C) Critical light intensities (I*_out) of the species, indicative of their competitive abilities for light. Critical light intensities were estimated from the light intensities penetrating through the steady-state monocultures at high pCO₂. All estimates are based on the mean±SD of the last five data points of each monoculture experiment. Sce, Scenedesmus; Chl, Chlorella; Mic, Microcystis; Mon, Monoraphidium.

Fig. 4.

Competition experiments. The competition experiments were performed at low pCO₂ (100 ppm; left panels) and high pCO₂ (2000 ppm; right panels). (A, B) Competition between Monoraphidium and Scenedesmus. (C, D) Competition between Monoraphidium and Chlorella. (E, F) Competition between Monoraphidium and Microcystis. (G, H) Competition between Microcystis and Scenedesmus. (I, J) Competition between Microcystis and Chlorella. Dynamic changes in light conditions, carbon speciation and pH during the competition experiments are presented in Supplementary Figs S1 and S2. Symbols indicate experimental data, and lines indicate model predictions. Parameter values of the model are provided in Tables 1 and 2.