A novel μCT analysis reveals different responses of bioerosion and secondary accretion to environmental variability

Corals build reefs through accretion of calcium carbonate (CaCO3) skeletons, but net reef growth also depends on bioerosion by grazers and borers and on secondary calcification by crustose coralline algae and other calcifying invertebrates. However, traditional field methods for quantifying secondary accretion and bioerosion confound both processes, do not measure them on the same time-scale, or are restricted to 2D methods. In a prior study, we compared multiple environmental drivers of net erosion using pre- and post-deployment micro-computed tomography scans (μCT; calculated as the % change in volume of experimental CaCO3 blocks) and found a shift from net accretion to net erosion with increasing ocean acidity. Here, we present a novel μCT method and detail a procedure that aligns and digitally subtracts pre- and post-deployment μCT scans and measures the simultaneous response of secondary accretion and bioerosion on blocks exposed to the same environmental variation over the same time-scale. We tested our method on a dataset from a prior study and show that it can be used to uncover information previously unattainable using traditional methods. We demonstrated that secondary accretion and bioerosion are driven by different environmental parameters, bioerosion is more sensitive to ocean acidity than secondary accretion, and net erosion is driven more by changes in bioerosion than secondary accretion

Variation in fertilization as a function of flow.

<p>Mean percent fertilization (PF) is plotted as a function of rms(<i>u</i>_w) (m s⁻¹) in the four sampling locations: water column, wake eddy, substrate, and aboral surface. Symbols represent weighted mean of all the time points. Lines represent a nonlinear curve fit to the untransformed data. PF was strongly dependent on rms(<i>u</i>_w) (m s⁻¹) for all sampling locations (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0076082#pone-0076082-t001" target="_blank">Table 1</a>).</p

Diagram of the oscillatory water tunnel (OWT).

<p>The OWT chamber was used to determine the effects of oscillatory flow on fertilization of the green sea urchin <i>Strongylocentrotus droebachiensis</i>. The drive mechanism that attaches to the paddle (via two coupled hydraulic pistons driven by a flywheel attached to an electric motor powered by an adjustable frequency drive) has been omitted from the figure.</p

Stepwise regression analyses.

<p>Results of stepwise regression analysis for the four components of flow: current (<i>U</i> m s⁻¹), wave (rms(<i>u_w</i>) m s⁻¹), turbulent (rms(<i>u_t</i>) m s⁻¹), and period (<i>T</i> (s)), vs. arcsine-transformed values of PF at the four sampling locations. Stepwise regressions were done with <i>p</i><0.05 to enter and <i>p</i><0.10 to remain.</p

Importance of location for egg fertilization.

<p>Estimate of (A) the percentage of the total number of eggs spawned that were fertilized (PTF) and (B) the relative contribution to overall fertilization (RCO) at the aboral surface and water column as a function of rms(<i>u</i>_w) categories: LOW (<0.03 m s⁻¹), MEDIUM (>0.03<0.06 m s⁻¹) and HIGH (>0.06 m s⁻¹).</p

Hydrodynamic characterisation of the oscillatory water tunnel.

<p>(A) Sample wave form with underlying current (<i>u</i>, <i>v</i>, and <i>w</i> velocities (m s⁻¹)) from an ADV positioned with the <i>x</i> probe oriented with the dominant current direction and the sample volume 0.04 m above the substrate (corresponding to the average height of the female sea urchins used in the trials). (B) Range of hydrodynamic conditions explored in the experimental trials, with both rms(<i>u</i>_w) (m s⁻¹) and period (s) manipulated independently. (C) Test section velocity profiles in the absence of the female sea urchin. Mean longitudinal velocity (<i>ū</i>) normalized by mean longitudinal velocity of the maximum probe height (<i>ū</i>_m) from the tank floor (0.18 m) from three velocity profiles representative from each of the rms(<i>u</i>_w) categories: LOW [< 0.03 m s⁻¹], MEDIUM [> 0.03<0.06 m s⁻¹], HIGH [> 0.06 m s⁻¹]</p