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Ecological significance of cyanobacteria pigment color acclimation in the ocean

Creative Commons 'BY' version 4.0 license

Chromatic acclimation by marine Synechococcus cyanobacteria is a colorful example of light-harvesting generalists, that change their phycoerythrin pigment to absorb blue (495 nm) or green (545 nm) light (type IV chromatic acclimation, or CA4). Though much is known about the molecular regulation of the trait, the ocean environment that selects for this acclimation strategy is not well understood. Similarly, the ecological costs of CA4 compared to Synechococcus strains with fixed pigment strategies (specialists) had not previously been investigated. This research used a combination of laboratory experiments, a competition model between Synechococcus generalists and specialists in simulated spectral environments, and comparisons with metagenomic and physical oceanographic in situ data from global ocean cruises, Argo floats, and satellites to address these gaps. Under experimental conditions, we show that blue-green generalists suffer an energetic cost across variable environments compared to specialists, and that all Synechococcus strains are twice as efficient in green light as in blue light. In model simulations, Synechococcus generalists had >70% correlation with deep mixed layers, but only 14% correlation with mixed layers in situ. Higher correlations were observed with low average mixed layer temperatures (< 14oC), low sea surface height, and large sea surface gradients, indicating upwelling zones and ocean fronts are important niches for generalists in the ocean. We propose that the main advantage of CA4 is not in direct competition with other Synechococcus strains, but in expanding the niche space of the genus into highly dynamic ocean environments.

In application of light color and pigment research to ecosystem management, the final chapter of this dissertation is a case study using the spectral shape produced by absorption and fluorescence of the marker pigment phycocyanin to detect cyanobacteria by field-based remote sensing. We test four algorithms to detect cyanobacteria blooms across a salinity gradient from freshwater to the Atlantic Ocean. We find that empirical algorithms centered around longer wavelengths in the red spectral region (~700 nm), such as Mean Peak Height for turbid waters, worked best for cyanobacteria detection and prediction. An expansion of this algorithm could be used to develop an early warning system for harmful cyanobacteria blooms in the region.

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