Microbial feedbacks optimize ocean iron availability

Abstract

Iron is the limiting factor for biological production over a large fraction of the surface ocean because free iron is rapidly scavenged or precipitated under aerobic conditions. Standing stocks of dissolved iron are maintained by association with organic molecules (ligands) produced by biological processes. We hypothesize a positive feedback between iron cycling, microbial activity, and ligand abundance: External iron input fuels microbial production, creating organic ligands that support more iron in seawater, leading to further macronutrient consumption until other microbial requirements such as macronutrients or light become limiting, and additional iron no longer increases productivity. This feedback emerges in numerical simulations of the coupled marine cycles of macronutrients and iron that resolve the dynamic microbial production and loss of iron-chelating ligands. The model solutions resemble modern nutrient distributions only over a finite range of prescribed ligand source/sink ratios where the model ocean is driven to global-scale colimitation by micronutrients and macronutrients and global production is maximized. We hypothesize that a global-scale selection for microbial ligand cycling may have occurred to maintain "just enough" iron in the ocean.

Keywords: colimitation; dissolved iron; macronutrients; ocean productivity; organic ligands.

Fig. 1.

Schematic of the “ligand–iron–microbe” feedback. Free iron ($Fe\prime$) is added to the ocean from external sources like dust and sedimentary mobilization, and lost due to scavenging, precipitation, and burial. Iron retention is increased by complexation with organic ligands ($L_T$; $F{e}_T=Fe\prime +FeL$ and $L_T=L\prime +FeL$). Microbial production of biomass ($B$) is dependent on iron, and is a source of ligands, for example, by siderophore production, excretion of organic carbon, and release of cell detritus during remineralization. The production of ligands retains a greater concentration of iron, fueling more microbial production, and so on, until the other resources that microbial production requires, such as macronutrients, become limiting. Thus the “ligand–iron–microbe” feedback maintains “just enough” iron in the ocean to match the availability of other resources, resulting in colimitation.

Fig. 2.

Schematic of the idealized three-box ocean biogeochemistry model. See text and Materials and Methods for further details.

Fig. 3.

Illustration of the “ligand–iron–microbe” feedback: 10,000-y time series (note the log-scale axis) of (A) macronutrient, (B) dissolved iron, and (C) ligand concentrations in the “Southern Ocean” box (yellow), “Atlantic Ocean” box (green), and Deep Ocean box (blue), and (D) globally integrated export production. Steady state is reached after 1,000 y. The model is initialized with uniform 33 mmol N$\cdot {\mathrm{m}}^{-3}$ of macronutrients and no iron and ligands, with $\gamma /\lambda \approx$ 4,500 s ($\gamma =5\times 10^{-5}$ mol $L\cdot$(mol C)⁻¹ and $1/\lambda \approx 3$ y from the observationally constrained range).

Fig. 4.

Ensemble of 10,000 box model simulations. (A) Range of prescribed $\gamma$ and $\lambda$ values, (B) envelope of resulting surface nitrate (blue), iron (red), and ligand (green) concentrations, and (C) envelope of export production rates for the “Southern Ocean” box (blue), “Atlantic Ocean” box (green), and global total (red). Envelopes are calculated from median values of simulations with equal $\gamma /\lambda$ ratios, that is, averaged within colored contours in Fig. 4A $\pm$ the median absolute deviation (note the log scales). The range of $\gamma /\lambda$ informed by oceanic observations is indicated by arrows on the x axis. Guided by these data, the experiments can be partitioned into three regimes: 1) iron-replete (macronutrient limited) simulations, 2) iron-limited (macronutrient replete) simulations, and 3) iron and macronutrient colimited simulations (region shaded gray)—see text for details.

Fig. 5.

Ensemble of 10,000 box model simulations. (A) Model–data comparison scores ($S$; Eq. 6), with gray contours of macronutrient usage efficiency (note the uneven contour interval). (B) Envelope of model–data comparison scores and (C) envelope of macronutrient use efficiency, both calculated from the median of simulations with equal $\gamma /\lambda \hspace{0.17em}\pm$ the median absolute deviation. The range of $\gamma /\lambda$ informed by oceanic observations is indicated by arrows on the x axis. Best-fit simulations have an optimal $\gamma /\lambda$ (4,398 s) marked by the vertical black dashed line. The optimum ratio lies within the data-constrained range of $\gamma /\lambda$, and within the iron and macronutient colimited regime identified in Fig. 4. The horizontal dashed line in B is a benchmark model–data comparison score (0.69) calculated from an instance where ligand concentration is a fixed, uniform, 1 $\mathrm{\mu }$mol$\cdot {\mathrm{m}}^{-3}$. The horizontal dashed line in C is at the emergent value of macronutrient usage efficiency (0.29) at the optimal $\gamma /\lambda$ ratio.