Chromatic acclimation shapes phytoplankton biogeography

Abstract

Marine photoautotrophs have evolved to exploit the ocean's variable light conditions, with chromatic acclimators being able to adjust their pigment content to better match the ambient light color. The impact of chromatic acclimation on phytoplankton distribution and competition is not well understood despite its global importance. This study explores chromatic acclimation's role in shaping the biogeography of Synechococcus, a widespread cyanobacterium. We integrated three pigment types into a global ecosystem model: a green-light specialist, a blue-light specialist, and a chromatic acclimator. Laboratory studies defined each type's specific absorption properties. Our results indicate that chromatic acclimation offers an evolutionary advantage by enabling Synechococcus to adapt to varying light environments. This ability to mimic blue- and green-light specialists and enhance absorption at intermediate states, particularly in areas with high seasonal light variations, increases Synechococcus distribution and biomass. Thus, chromatic acclimation affects ecosystem functioning and biogeochemical processes in the ocean.

Fig. 1.. Surface global distribution of Synechococcus PTs.

(A) Relative gene abundance of Synechococcus PTs in surface waters estimated on the basis of metagenomic analysis of Tara Oceans samples (13) and grouped as described elsewhere (42). The “NA” label refers to the Synechococcus percentage that could not be assigned to any specific PT by the metagenomic analysis, while the “No Syn” label indicates the absence of Synechococcus in the Tara Oceans data and model simulation. (B) Relative abundance of Synechococcus PTs in surface waters computed from Darwin simulation biomass for the month at which the samples were collected at each location along the Tara Oceans transect. The pie charts circled in yellow represent consistent results in terms of dominant PT between the metagenomic analysis and the modeled biomass. Note that only qualitative comparison is possible because Tara Oceans data represent gene abundance, while the model provides biomass. Only the Tara Oceans stations within the model domain are displayed (given the coarse resolution, some coastal stations were not resolved in the model). The white dots in both panels depict the coordinates of the Tara stations when the pie charts needed to be repositioned to prevent overlap.

Fig. 2.. Total and photosynthetic chlorophyll a–specific absorption spectra of simulated Synechococcus PTs.

(A) Total (solid lines) and photosynthetic absorption (dashed lines) spectra of GSs (green lines), BSs (blue lines), and CAs acclimated to blue and green light (indicated by purple and pink lines, respectively). (B) Total (solid) and photosynthetic (dashed) absorption spectra of the CA acclimated to blue and green light (purple and pink lines, respectively) and intermediate acclimation states. The blue and green vertical dotted lines depict the absorption peak of PUB (495 nm) and PEB (545 nm), respectively. “Total absorption” refers to the absorption by all pigments, and “photosynthetic absorption” refers to absorption by only pigments used for photosynthesis (see Materials and Methods and the Supplementary Materials for details).

Fig. 3.. Annual patterns of modeled oceanographic variables.

The global patterns of (A) MLD, (B) integrated primary production (IPP), (C) euphotic zone depth, and (D) average 0- to 100-m blue light–to–green light ratio that emerged in the simulated ocean. The yellow triangle, red circle, and orange square indicate the position of the example upwelling, subtropical, and temperate locations discussed in the text (Figs. 4 and 5).

Fig. 4.. Biogeography of simulated Synechococcus PTs.

(A to C) Annual average biomass distribution integrated from the surface to 200 m (mg C m⁻²) of (A) the BS, (B) GS, and (C) CA. (D to F) Annual average percentage contribution relative to the total Synechococcus biomass integrated within the first 200 m for the BS (D), GS (E), and CA (F). The three markers on the maps (yellow triangle, red circle, and orange square) indicate the position of the example upwelling, subtropical, and temperate locations discussed in the text (Figs. 4 and 5).

Fig. 5.. Spatiotemporal variability of the modeled planktonic community.

(A to F) Each column in the figure corresponds to the January and July biomass distribution of plankton functional groups (mg C m⁻³) within the water column for a different selected location. The size of the bubbles is proportional to the biomass of the respective groups. The solid horizontal line represents the MLD, while the depths at which light intensity corresponds to 1 and 0.1% of surface PAR are indicated by the dashed and dotted lines, respectively.

Fig. 6.. Plasticity of CA absorption properties.

(A) Monthly vertical profiles of the blue light–to–green light ratio for each regime. The ratio is computed from the modeled light field using the available light at 495 and 545 nm, which correspond to the absorption peaks of PUB and PEB, respectively. (B to D) Monthly vertical profiles of the acclimation states for the upwelling (B), subtropical (C), and temperate (D) regimes. Color shading represents the best acclimation state: the state most efficient in harvesting the available light for a given portion of the water column and month of the year (i.e., representing the acclimation target for all CAs in different states). The number outside the brackets in each box shows how many acclimation states coexist in the depth bin, while the number inside the brackets indicates the dominant acclimation state in terms of biomass. See fig. S7 for a more detailed breakdown of the percentage of each acclimation state. The grid lines represent the time and depth resolution of the model output. The thick solid line represents the MLD, while the depths at which light intensity corresponds to 1 and 0.1% of surface PAR are indicated by the dashed and dotted lines, respectively.