An optimality model of photoadaptation in contrasting aquatic light regimes

Abstract

To investigate photoacclimation of phytoplankton adapted to different aquatic light regimes, a physiologically explicit phytoplankton optimality model was applied in two contrasting environments: constant irradiance vs. dynamic irradiance associated with oceanic mixed layers. Nitrogen was assumed to be partitioned between cellular components associated with light harvesting, carbon fixation, biosynthesis, and photoprotection. The model was used to predict how resources are (re)distributed to optimize growth in the different environments. Optimal intracellular nitrogen allocation in dynamic environments was associated with constitutive investment in Calvin cycle enzymes; in contrast, in the static environment Calvin cycle allocation was reduced at low light. Furthermore, reduced allocation to components associated with photoprotection in static environments led to heavily inhibited photosynthesis-irradiance response consistent with that of Prochlorococcus adapted to relatively stable oligotrophic gyres. In contrast, photosynthetic response in the diatom Skeletonema costatum was better explained by maintenance of photoprotection components across a range of integrated light doses. Limited range of chlorophyll : C in Thalassiosira pseudonana was consistent with optimization of resource allocation to light-harvesting components in dynamic environments, in contrast to the relatively wide range in allocation to light harvesting predicted by the model in static environments and chlorophyll cell 21 observed in high-light-adapted Prochlorococcus. The model was used to explain variability of the photosynthesis-irradiance response of samples from the Celtic and Irish Seas. Photoacclimation state is a consequence of optimization of resource allocation to the set of environmental parameters (e.g., surface irradiance, depth of mixing, and light attenuation) that influence light variability.

Photoacclimation of natural phytoplankton communities

“…Model results yielded a photoacclimation response curve with a more limited range in light harvesting for a mixed light environment (2.5-fold) than for a static light environment (8-fold) (see Fig. 10 in Talmy et al 2013). These model results reflect a higher investment of resources into carbon fixation and photoprotection compared to pigment synthesis under mixed light conditions (Talmy et al 2013), suggesting alternative strategies to pigment synthesis when cells are in a low mean but highly variable light environment.…”

“…10 in Talmy et al 2013). These model results reflect a higher investment of resources into carbon fixation and photoprotection compared to pigment synthesis under mixed light conditions (Talmy et al 2013), suggesting alternative strategies to pigment synthesis when cells are in a low mean but highly variable light environment. Importantly, the chl:C phyto response from the dynamic light model is very similar to the photoacclimation response derived from satellite data (Fig.…”

“…Laws & Bannister 1980, Geider et al 1997). An optimality model of resource allocation to light harvesting for cells grown under dynamic and static light conditions was recently described by Talmy et al (2013). Model results yielded a photoacclimation response curve with a more limited range in light harvesting for a mixed light environment (2.5-fold) than for a static light environment (8-fold) (see Fig.…”

“…Many ecosystem models and satellite algorithms specify relationships between chl:C phyto and E g to Talmy et al 2013), and a steady-state nutrient-replete culture of Dunaliella tertiolecta (dashed grey line, after Behrenfeld et al 2005). Data normalized to the minimum chl:C phyto at high light determine phytoplankton μ and net primary production (NPP) (e.g.…”

An Exact Solution For Modeling Photoacclimation of the Carbon-to-Chlorophyll Ratio in Phytoplankton

“…In fact, acclimation from one light level to another will take place over a finite period, with Geider et al (1986) and Raven and Geider (2003) suggesting that the appropriate time scale for acclimation is of the order of hours to days, implying that balanced growth would hold on daily time scales. Moore et al (2003Moore et al ( , 2006 have provided examples where photoacclimation timescales were longer than those for surface mixing, and Talmy et al (2013) highlighted the importance of surface irradiance, depth of mixing, and light attenuation using a resource allocation based model of photoacclimation. It is also apparent that when numerical models are run at short time steps (less than an hour), it will be increasingly important to account in some manner for non-balanced growth during the transition phase.…”