Acclimatization of the crustose coralline alga Porolithon onkodes to variable pCO2

Ocean acidification (OA) has important implications for the persistence of coral reef ecosystems, due to potentially negative effects on biomineralization. Many coral reefs are dynamic with respect to carbonate chemistry, and experience fluctuations in pCO2 that exceed OA projections for the near future. To understand the influence of dynamic pCO2 on an important reef calcifier, we tested the response of the crustose coralline alga Porolithon onkodes to oscillating pCO2. Individuals were exposed to ambient (400 µatm), high (660 µatm), or variable pCO2 (oscillating between 400/660 µatm) treatments for 14 days. To explore the potential for coralline acclimatization, we collected individuals from low and high pCO2 variability sites (upstream and downstream respectively) on a back reef characterized by unidirectional water flow in Moorea, French Polynesia. We quantified the effects of treatment on algal calcification by measuring the change in buoyant weight, and on algal metabolism by conducting sealed incubations to measure rates of photosynthesis and respiration. Net photosynthesis was higher in the ambient treatment than the variable treatment, regardless of habitat origin, and there was no effect on respiration or gross photosynthesis. Exposure to high pCO2 decreased P. onkodes calcification by >70%, regardless of the original habitat. In the variable treatment, corallines from the high variability habitat calcified 42% more than corallines from the low variability habitat. The significance of the original habitat for the coralline calcification response to variable, high pCO2 indicates that individuals existing in dynamic pCO2 habitats may be acclimatized to OA within the scope of in situ variability. These results highlight the importance of accounting for natural pCO2 variability in OA manipulations, and provide insight into the potential for plasticity in habitat and species-specific responses to changing ocean chemistry.

Size-partitioned phytoplankton carbon concentrations retrieved from ocean color data, links to data in netCDF format

Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2 µm in diameter), nanophytoplankton (2-20 µm) and microphytoplankton (20-50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield - 0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations.