High particulate iron(II) content in glacially sourced dusts enhances productivity of a model diatom

Abstract

Little is known about the bioavailability of iron (Fe) in natural dusts and the impact of dust mineralogy on Fe utilization by photosynthetic organisms. Variation in the supply of bioavailable Fe to the ocean has the potential to influence the global carbon cycle by modulating primary production in the Southern Ocean. Much of the dust deposited across the Southern Ocean is sourced from South America, particularly Patagonia, where the waxing and waning of past and present glaciers generate fresh glaciogenic material that contrasts with aged and chemically weathered nonglaciogenic sediments. We show that these two potential sources of modern-day dust are mineralogically distinct, where glaciogenic dust sources contain mostly Fe(II)-rich primary silicate minerals, and nearby nonglaciogenic dust sources contain mostly Fe(III)-rich oxyhydroxide and Fe(III) silicate weathering products. In laboratory culture experiments, Phaeodactylum tricornutum, a well-studied coastal model diatom, grows more rapidly, and with higher photosynthetic efficiency, with input of glaciogenic particulates compared to that of nonglaciogenic particulates due to these differences in Fe mineralogy. Monod nutrient accessibility models fit to our data suggest that particulate Fe(II) content, rather than abiotic solubility, controls the Fe bioavailability in our Fe fertilization experiments. Thus, it is possible for this diatom to access particulate Fe in dusts by another mechanism besides uptake of unchelated Fe (Fe') dissolved from particles into the bulk solution. If this capability is widespread in the Southern Ocean, then dusts deposited to the Southern Ocean in cold glacial periods are likely more bioavailable than those deposited in warm interglacial periods.

Keywords: diatoms; dust; iron bioavailability; iron mineralogy; particulate iron; subantarctic Southern Ocean.

Fig. 1. Sample locations in South America with the right panel focused on Patagonia.

Blue and red symbols represent samples of glaciogenic and nonglaciogenic origin, respectively. Triangles indicate samples used in the culture experiments. Shaded relief image is produced with the Matplotlib Basemap Toolkit for Python. The samples are described in table S1.

Fig. 2. XAS spectra and Fe(II) content of South American glaciogenic and nonglaciogenic sediments.

XAS spectra of all glaciogenic (blue) and nonglaciogenic (red) samples (bottom and left axes). Spectra corresponding to sediments used for culture experiments are in bold. Fe(II) content data (circles, gray top axis) are grouped as nonglaciogenic (red) and glaciogenic (blue) samples, offset for clarity. Values were calculated using PCA (open circles) and LCF (closed circles). Error bars represent SE and errors generated by the SIXPack interface (Monte Carlo simulations) for PCA and LCF fitting approaches, respectively. Images are of sediments used for culture experiments; gray color indicates reduced Fe and orange/yellow/red indicates oxidized Fe.

Fig. 3. Variable fluorescence (F_v/F_m), growth rates (μ), and cell densities of P. tricornutum used to evaluate particulate Fe bioavailability.

Symbol area is proportional to culture density in cells per milliliter close to the time of variable fluorescence measurement (14 days after inoculation). Variable fluorescence and cell counts were measured in triplicate. For F_v/F_m, error bars are based on the SE of 20 acquisitions per culture propagated for the triplicate cultures; for μ, error bars represent the SE of the slope of the natural log plot. Error is sometimes smaller than the symbol. Data from this experiment correspond to the circles in Fig. 4 (A to C).

Fig. 4. Monod model fits to normalized growth rates, r, as a function of three different classes of Fe species.

Cultures with glaciogenic sediments added are in shades of blue, and those with nonglaciogenic sediments added are in shades of red. Marker shape (circle, square, and triangle) corresponds to experiments run on three different dates. For glaciogenic particulate exposure, n = 5; for nonglaciogenic particulate exposure, n = 4. Error bars represent propagated SE for normalized rates (the same in all subplots). (A) Monod model fits (blue line is glaciogenic fit, R² = 0.82; red line is nonglaciogenic fit, R² = 0.94) as a function of total particulate Fe added to the cultures. Horizontal error bars are analytical error in particulate Fe concentration and are often smaller than the symbol size. (B) Attempted Monod fit (purple dashed line, R² = 0.22) as a function of [Fe′], with Fe′ defined as the unchelated Fe (mononuclear hydrolysis species) thought to control bioavailability of Fe in the ocean. This is an inappropriate fit because the very low K_S value implies that 3 × 10⁻¹⁹ M Fe′ can support phytoplankton growth and fully alleviate Fe limitation, which is not supported by the literature. (C) Monod model fit (purple line is the fit to all data, R² = 0.87) as a function of solid-phase Fe(II) calculated using LCF with standard spectra. The glaciogenic and nonglaciogenic data collapsing to the same curve suggest that particulate Fe(II) controls particulate Fe uptake in these cultures. Horizontal error bars are analytical error in particulate Fe(II) concentration and are often smaller than the symbol size. Inset zooms in on lower concentrations for clarity.