Fifty Years of Sporadic Coral Reef Calcification Estimates at One Tree Island, Great Barrier Reef: Is it Enough to Imply Long Term Trends?

Introduction

Coral reefs are threatened by climate change and other anthropogenic activities. Conditional stressors such as ocean warming, acidification, and eutrophication are predicted to increase in severity for the foreseeable future (Borges and Gypens, 2010; Andersson and Gledhill, 2013; Manzello et al., 2013; Hughes et al., 2018). Understanding how stressors and their interactions are driving losses of habitat, biodiversity, and economic value provided by coral reefs is required to safeguard these ecosystems. In particular, investigations of the major metabolic processes can provide valuable information on the long-term response of coral reef ecosystems to anthropogenic stressors (Albright et al., 2013; Cyronak et al., 2018).

Carbon cycling in coral reefs is driven by two main metabolic processes: inorganic, which includes calcification (C) and dissolution (D) of calcium carbonate coral skeletons and calcifying algae, and organic–photosynthesis (P) and respiration (R) by marine algae. The rates of inorganic (NEC or G_net = C–D) and organic (NEP or P_net = P–R) production in coral reef ecosystems may be used as a proxy for overall ecosystem health (Venti et al., 2014; DeCarlo et al., 2017). Inorganic and organic production can be estimated by measuring changes in seawater carbon chemistry, namely total alkalinity (TA) and dissolved inorganic carbon (DIC). There is considerable temporal and spatial variability within chemistry-based ecosystem metabolism estimates (Gattuso et al., 1999; Falter et al., 2001; Atkinson and Falter, 2003), which is driven by seasonality, depth, water residence times, species composition, benthic cover, and carbonate chemistry (Demicco and Hardie, 2002; McCulloch et al., 2012; Andersson and Gledhill, 2013; Falter et al., 2013; Camp et al., 2017). Declining rates of calcification have been demonstrated on coral reefs off Australia (Silverman et al., 2012, 2014; Kwiatkowski et al., 2016) and Hawaii (Shamberger et al., 2011). Field results support ocean acidification and coral bleaching predictions for ecosystem metabolism in a changing ocean (Kayanne et al., 2005; Kleypas et al., 2011; Albright et al., 2016, 2018) consistent with an observed increase in coral reef carbon dioxide and decrease in the aragonite saturation state (Cyronak et al., 2014a).

The ecosystem metabolism of the reef flat at One Tree Island (OTI, Capricorn Bunker region of the Southern GBR, Australia) has been studied on four occasions since the late 1960s, providing an opportunity to potentially assess long-term changes in coral reef ecosystem G_net and P_net. Springtime calcification at the original study site (hereafter referred to as DK-13) was estimated to be ∼ 125 mmol m^-2 d^-1 in the late 1960s and early 1970s (Kinsey, 1972). Silverman et al. (2012) provided a 40-year perspective on Kinsey’s early work, suggesting a 45% drop in net ecosystem calcification associated with a 66% reduction in coral cover. More recent observations in 2012 and 2014 imply further community degradation, with reductions in G_net of up to 75% and increases in P_net by >300% over the last 45 years (Shaw et al., 2015; Kwiatkowski et al., 2016). If these trends are consistent over time, we would expect G_net to be approaching zero.

Here, we quantify G_net and P_net rates at three locations on the OTI reef flat. Our estimates are compared with prior estimates to investigate long-term trends to the ecosystem metabolism at the reef flat in the last 50 years. Specifically, the objectives of this study were (i) to investigate spatial variability in estimated rates of ecosystem metabolism within the same reef flat, and (ii) to analyze potential long-term trends in ecosystem metabolism. This paper not only replicates earlier observations at two sites (Kinsey, 1972; Silverman et al., 2012; Shaw et al., 2014; Kwiatkowski et al., 2016), but also covers a new location to assess a potential terrestrial effect on nearshore metabolic rates.

Materials and Methods

Field Sampling

One Tree Island is a pseudo-atoll located approximately 20 km southwest of Heron Island in the Capricorn Bunker region of the Southern GBR (23°30′30″S, 152°15′30″E). Two sites, DK-13 and SHAW, were originally chosen based on the locations of previous studies investigating community metabolism at OTI. DK-13 has been previously described by Kinsey (1972), Kinsey (1980) and Silverman et al. (2012) and the SHAW site has been described in Shaw et al. (2013). During field investigations, we observed groundwater seepages from the coral rubble at low tide ∼10 m from the SHAW site. To assess whether the observed groundwater seepage interfered with G_net, a third site, DAVIS, was also included here, situated approximately 30 m west of SHAW (Figure 1). This site was chosen due to its similar cross-reef position, benthic community, and depth (0.81 m at DAVIS versus 0.60 at SHAW and 0.65 at DK-13), but away from the immediate influence of groundwater seepage from the island. Therefore, the inclusion of a third site allows us to assess the potential influence on ecosystem metabolism of groundwater-derived inputs of carbon, alkalinity, or nutrients that can be important in coral reefs (Cyronak et al., 2014a; McMahon and Santos, 2017). Water samples of five shallow groundwater seeps closest to the SHAW site were taken at low tide for TA, DIC, and nutrients, as described below.

FIGURE 1

Figure 1. (A) Map of Australia with white dotted square representing the Capricorn Bunker Group of the Southern GBR, (B) One Tree Island coral cay and lagoon with white dotted rectangle denoting area of sampling, and (C) sampling locations used in this study.

Sampling occurred at the southern reef flat of OTI during the austral spring, from 14 to 28 November 2017 (Figure 1). All estimates use the low tide slack water approach originally used by Kinsey (1972) and previously applied to OTI (Kinsey, 1980; Silverman et al., 2012; Shaw et al., 2015; Kwiatkowski et al., 2016). The isolation of the reef flat at periods of low tide prevents the mixing of oceanic water. Thus, changes in water chemistry in the enclosed water body are assumed to be a direct result of biological activity (Kinsey, 1977; Langdon et al., 2010). Daily sampling during low tide gives a 24-hour integration of community metabolism to estimate diel G_net and P_net based on changes of the overlying seawater chemistry. Water samples from each site were taken in 30–120-minute increments from the commencement to cessation of reef flat isolation (approximately 1 h before and after low tide, depending on tidal height). Samples were filtered through a 0.45 μm cellulose acetate filter and stored for total alkalinity (TA), dissolved inorganic carbon (DIC), and nutrients (dissolved ammonium, nitrate and nitrite (NO_x) and orthophosphate). 0.05% of saturated mercuric chloride (0.37 M HgCl₂) was added to DIC vials prior to sampling to prevent carbonate changes due to biological activity (Dickson et al., 2007).

Total alkalinity was determined by the Gran Titration method using a Metrohm Titrando with 0.01 M HCl, referenced with Dickson’s certified reference material (Batches 166 and 170). Triplicate TA analysis per sample yielded a standard deviation of 1.1 ± 0.8 μmol kg^-1. DIC was determined using a combined Airica/ Li-Cor 7000 system standardized using Dickson’s certified reference material (Batches 163, 166, and 170). Each analysis ran four replicates with the closest three averaged for final concentrations (see Call et al., 2017 and McMahon et al., 2019 for instrument and analysis specifications). Differences between duplicate analyses of each DIC sample were an average of 0.8 μmol kg^-1. Nutrient samples were processed using a Lachat flow-injection analysis (FIA) system (analytical precision of 0.03 μmol L^-1) (Eyre and Ferguson, 2005).

Water temperature, pH, and dissolved oxygen (DO) were determined at each sampling interval using a Hach Multiprobe, calibrated with NBS standards. A Hydrolab DS5X was stationed at SHAW to continuously monitor water temperature (±0.1°C), pH (±0.02), salinity (±1%), depth (±1%), conductivity (±1%), and DO (±1%) at 10 min intervals for the duration of the study. Benthic community composition and cover at the time of sampling were estimated at each site using the linear point-intercept method from five 25 m transects spaced ∼5 m apart to be comparable to previous studies performed at these sites (Kinsey, 1972; Silverman et al., 2012; Shaw et al., 2015). The benthos was classified into seven categories following Silverman et al. (2012). It is worth noting that the “dead coral” category actually represents the percentage of filamentous algae, as dead corals are colonized by turfing algae within days of death. For the sake of continuity and comparison, this category will remain labeled as “dead coral.”

Calculations

Net ecosystem calcification (G_net) was estimated for each low tide:

${\text{G}}_{\text{net}}=\frac{-0.5\Delta \text{TAd}p}{\Delta \text{t}}\mathrm{\left(}\mathrm{1}\mathrm{\right)}$

where a positive G_net denotes net ecosystem calcification and a negative G_net describes net dissolution. ΔTA is the incremental change in TA multiplied by -0.5 as two mol of TA are taken up to produce one mol of CaCO₃; d refers to water depth at time of sampling, p denotes water density as a function of temperature and salinity, and Δt is the change in time over which the samples were taken. Net ecosystem production (P_net) was estimated as follows:

${\text{P}}_{\text{net}}=\frac{-\Delta \text{DICdp}}{\Delta \text{t}}-G_{net}-{\text{F}}_{{\text{CO}}_{\text{2}}}\mathrm{\left(}\mathrm{2}\mathrm{\right)}$

where a positive P_net value denotes net production and a negative value describes net respiration. ΔDIC is the incremental change in measured DIC. G_net is subtracted to account for changes in DIC due to inorganic precipitation of CO₃^2-. F_CO2 refers to the water-to-atmosphere flux of CO₂ as described by Raymond and Cole (2001):

${\text{F}}_{{\text{CO}}_2}={\text{kK}}_0({\text{pCO}}_{{\text{2}}_{\text{water}}}-{\text{pCO}}_{{\text{2}}_{\text{air}}})\mathrm{\left(}\mathrm{3}\mathrm{\right)}$

where k is the gas transfer velocity parameterized using wind speed (Wanninkhof, 1992), K₀ is the solubility of CO₂ in seawater, pCO_2water is the partial pressure of CO₂ in the water at the time of sampling, and pCO_2air is the atmospheric partial pressure of CO₂, assumed to remain constant at 400 μatm. Wind speed data were obtained from the Australian Bureau of Meteorology at nearby Heron Island. Wind speeds were consistently <10 m s^-1, the speed at which models for calculating CO₂ fluxes across the water–air interface begin to widely diverge (Ho et al., 2006). To compare flux estimates and to assess whether wind speed was a significant driver of CO₂ evasion and DIC dynamics during this study, piston velocities (k) were calculated using the equations presented by both Raymond and Cole (2001) and Wanninkhof (1992).

The relative influence of P_net on changes in carbonate chemistry was calculated to investigate ecosystem functioning as described by Cyronak et al. (2018):

$\%{\text{P}}_{\text{net}}=1-\frac{{\text{m}}_{\text{TA}-\text{DIC}}}{2}\times 100\mathrm{\left(}\mathrm{4}\mathrm{\right)}$

where m_TA-DIC refers to the TA–DIC slope. The P_net: G_net ratio can then be simply calculated by dividing % P_net by % G_net (i.e., 100–%P_net).

Rates of G_net and P_net were calculated using samples procured at the beginning and end of the low tide isolation only. Logistical difficulties in intensive sampling on the reef crest resulted in no half-hourly samples collected at DK-13 at night. Therefore, samples collected half-hourly during the low tide period as described by McMahon et al. (2018) were not included in metabolic calculations. Diel integrations of metabolic rates were calculated as the sum of average hourly G_net and P_net rates. “Daytime” was defined from between 0600–1900. Uncertainties were calculated as the propagation of errors of relevant parameters (Harvard, 2007) as follows:

$S{E}_{\text{final}}=\text{EP}\times \overline{x}\mathrm{\left(}\mathrm{5}\mathrm{\right)}$

where SE_final refers to the standard error for the variable in question (i.e., G_net), $\overline{x}$ refers to the calculated value of the variable in question, and EP refers to the error propagation, or a sum of fractional uncertainties which make up the constituents in prior equations:

$\text{EP}=\surd \left({\left(\frac{\delta a}{a}\right)}^2+{\left(\frac{\delta b}{b}\right)}^2+\cdots {\left(\frac{\delta z}{z}\right)}^2\right)\mathrm{\left(}\mathrm{6}\mathrm{\right)}$

where the letters a... z refer to the mean values of variables used to calculate $\overline{x}$, and δa..δz refer to the standard error of the specific variable. Wind speed, depth, temperature, salinity, and analytical TA and DIC uncertainties were used in error calculations of instantaneous rates. Final hourly and daily metabolic rates also include intra-hourly variability of G_net and P_net in the error propagation, which represents the vast majority of the uncertainties reported.

Results

Live hard coral cover ranged from 31 ± 3.2% to 41 ± 9.0% at the reef flat (Figure 2). Few bleached corals were observed and the majority of dead corals appeared to be deceased for a long period of time as they were colonized by filamentous algae. There was very little calcareous and no large macroalgae observed on the reef flat transects. Nutrient concentrations were low and consistent among sites, demonstrating no obvious influence of groundwater seepage on nutrient dynamics at the SHAW site. Averages of ammonium (NH₄⁺), nitrite and nitrate (NO_x), and orthophosphate (PO₄^3-) ranged from 0.17 to 0.21, 0.86 to 0.99, and 0.51–0.53 μmol L^-1 respectively, indicating an oligotrophic environment at the three sites.

FIGURE 2

Figure 2. Mean benthic cover (±SE) Soft coral made up approximately 1% of benthic cover at all sites and coralline algae was only observed at DK-13 with 1% cover.

A total of 178 seawater chemistry observations were made at the three sites over 35 low tide periods, resulting in 69 calculated rates of both G_net and P_net (Figure 3). Estimates of CO₂ evasion rates between the two methods were within 2 mmol m^-2 hr^-1. The average CO₂ fluxes to the atmosphere were 1.75 mmol m^-2 hr^-1 when using Wanninkhof (1992), and 2.57 mmol m^-2 hr^-1 when using Raymond and Cole (2001). These values are equivalent to 3.7–5.5% of the hourly DIC change observed in the lagoon, implying that uncertainties in CO₂ emissions play a minor role in P_net estimates. Due to the minor contribution of CO₂ fluxes to DIC changes, the fluxes derived using the Raymond and Cole equation will be used for all reported P_net rates. TA at DK-13 in 2017 was very similar to 2009, with a diel average of 2244 ± 80 μmol kg^-1 during this study versus 2248 ± 80 μmol kg^-1 reported by Silverman et al. (2012), with similar daytime and nighttime values. In contrast, DIC was much higher in 2017. Nighttime average DIC was 2417 ± 60 μmol kg^-1 versus 2095 ± 86 μmol kg^-1 in 2009. Daytime mean DIC was also higher by 100 μmol kg^-1, resulting in a higher diel DIC average and lower Ω_ar.

FIGURE 3

Figure 3. Calculated values of P_net and G_net (±SE) for each sampling period at all study sites. Error bars are not visible in many cases as the error is smaller than the symbol. Dotted lines represent net 0 production for G_net and P_net.

Daytime uptake and nighttime release of TA and DIC occurred at each of the sites studied. However, dips in uptake occurred during mid-day periods when calcification and photosynthesis are typically highest, thought to be a result of high cloud cover on 15 November when the 1200–1400 period was sampled. The TA and DIC at the beginning of this low-tide isolation (which would be the most similar to source water) was not depleted compared to other sampling periods. Therefore, a change in source water carbonate chemistry is unlikely to have caused the atypical G_net and P_net observations during this time. DK-13 had a larger diel range and more extreme values of TA, DIC, and production than both the DAVIS and SHAW sites (Table 1). At the DAVIS site, G_net tracked very closely to SHAW but P_net more closely tracked DK-13 (Figure 3). Due to low-tide time constraints, we were unable to sample the DAVIS site from approximately 1200–1400, preventing diel-integrated estimates of metabolic rates at this site.

TABLE 1

Table 1. Daytime (0600–1900), nighttime (1900–0600), and diel averages (±SE) of seawater chemistry observations and metabolic estimates at each site during low tide slack periods from 14 to 26 November 2017.

Calcification tended to increase with light intensity and photosynthesis (Figure 4). Calcification at DK-13 had a negative correlation with pCO₂ and a positive relationship with Ω_ar (Figure 4). Neither the SHAW nor DAVIS sites were significantly correlated with Ω_ar. Slopes of TA–DIC regression equations ranged from 0.32 ± 0.03 at SHAW to 0.59 ± 0.05 at DK-13 (Figure 5 and Table 2).

FIGURE 4

Figure 4. Correlations between calcification and potential controls. Regression lines and equations refer to significant correlations (p < 0.05). Note that G_net and Ω_ar may initially appear to have hysteric loops at the DAVIS and DK-13 sites (see McMahon et al., 2013 for comparison). However, when the data are broken down into morning (lighter colors), afternoon (medium colors), and night (darker colors), the data are scattered and do not present a clear hysteric loop, though DK-13 has an overall positive correlation with Ω_ar regardless of time of day.

FIGURE 5

Figure 5. Paired TA and DIC measurements at all sites. Background colors represent the aragonite saturation state normalized to average site temperature and salinity (McDougall and Barker, 2011). White lines represent offshore concentrations of TA and DIC as reported in Mongin et al. (2016).

TABLE 2

Table 2. Previously reported and calculated slopes (±standard deviation) of TA–DIC regression equations and P_net:G_net ratios.

During the day, all sites were net calcifying and net photosynthesizing and at night were net dissolving and net respiratory (Table 1). Daily integrated G_net rates at SHAW were ∼50% lower than DK-13 estimates, demonstrating significant spatial variability within the reef flat. DK-13 was net autotrophic, with daily rates of estimated organic production nearly 200 mmol m^-2 d^-1 greater than the SHAW site, which was net heterotrophic (Table 1 and Figure 6).

FIGURE 6

Figure 6. Estimated coral cover, G_net, and P_net rates (±SE) at the DK-13 and SHAW sites over time. Dotted line represents net 0 production for P_net. K1972 refers to Kinsey (1972); S2012 refers to Silverman et al. (2012); K2016 refers to Kwiatkowski et al. (2016); S2015 refers to Shaw et al. (2015), and D2019 refers to this study.

Discussion

Spatial Variability Within the OTI Reef Flat

It is often assumed that metabolic rate estimates derived from a single sampling site on a coral reef can represent the entire ecosystem, provided that benthic structure and hydrodynamics are consistent (Kinsey, 1980; Hatcher, 1990). Numerous experiments have been undertaken to investigate metabolic parameters provided by different reef habitats (e.g., lagoon versus reef flat, Shamberger et al., 2011, 2014, 2018) and benthic structure (e.g., sand versus coral bottom, Yates and Halley, 2006). However, reef types are primarily defined based on geomorphological features without accounting for biological modification of an ecosystem, which creates a mosaic of different microhabitats within a larger area. Here, we use observations from three locations within a single reef habitat type at the same location to assess intra-ecosystem spatial variability. We observed significant spatial variation in G_net of 50% and P_net of 166% (relative to DK-13 rates) that would prevent straightforward upscaling of results from a specific location to other areas (Figure 3). These differences may be attributed to (1) variation in the benthic community composition, (2) altered metabolism as a result of site-specific abiotic conditions (such as lower light levels, pH, or nutrients), or (3) site-specific alterations to seawater carbonate chemistry (such as the influx of TA and/or DIC via groundwater seepage) masking metabolic signals.

DK-13 and SHAW had benthic assemblages with 31 ± 3.2 and 41 ± 4.2% live hard coral cover, respectively, with larger differences represented by the proportion of coral rubble and sand. We found higher percentages of live coral and lower percentages of dead coral than was observed in 2009 (Figure 2; Silverman et al., 2012). Coral was dominated by Acropora spp., Montipora spp., Isopora spp., Pocillopora damicornis, and Porites lobata at both sites, similar to community composition found by Silverman et al. (2012), although in general, Montipora spp. and Isopora spp. were more prevalent in the present study than in 2009. If ecosystem cover and speciation is similar, then differences in metabolism could be related to individual coral biology. Intraspecific genetic and intraspecific endosymbiont variability may influence the calcification rates of coral individuals (Parkinson et al., 2015; Rocker et al., 2015; Hughes et al., 2019). Site-specific differences in coral individuals may converge to produce a different metabolic pulse. However, this effect and its magnitude is yet to be experimentally verified, and is recommended for future study.

The 3D structural complexity of each site and how that relates to hydrodynamics and organismal biomass will influence ecosystem productivity (Perry et al., 2008; Hamylton et al., 2013; Long et al., 2013). Sites which differ in rugosity will presumably present a different metabolic signature, though this hypothesis has not been experimentally tested in the field. Long et al. (2013) hypothesized that there would be an exponential increase in metabolism with increasing total surface area to planar area factor. It is possible that a differing reactive surface area is contributing to the difference between the two sites observed here. If ecosystem metabolism is solely based on coral cover surface, then the SHAW site should have the highest rates of calcification. However, our results show that SHAW has 50% lower G_net than DK-13 even though the SHAW planar cover is 10% higher (Figure 6).

Average PAR, temperature, pCO₂, and Ω_ar were similar among sites (Table 1). A significant (p < 0.05) correlation between NH₄⁺ and G_net was observed at DK-13 only, though the low sample size (n = 13), low ammonium concentrations (mean ± SE = 0.21 ± 0.06 μmol L^-1), and scatter in data (r² = 0.40) shed doubt on this result. There were no other observed trends to indicate that nutrient concentrations or benthic uptake affected calcification or organic production. As coral cover, species composition, and environmental conditions at DK-13 and SHAW were similar (see Figure 2 and Table 1), it seems most plausible that site-specific processes are occurring which alter seawater carbonate chemistry. External sources of TA and/or DIC to the site would invalidate assumptions in the slack-water approach (i.e., that TA and DIC changes are due only to local biological processes) (Lowe and Falter, 2015), leading to altered calcification and organic production estimates at the SHAW site, and incorrect relationships between Ω_ar and ecosystem productivity. Furthermore, the SHAW site exhibited irregular trends in pH and DO during data collection compared to DK-13 and the atypical in situ production observations were confirmed by laboratory TA and DIC observations.

Since the SHAW site is situated very close to the island, a groundwater input effect was suspected and the DAVIS site was added for comparison. The DAVIS site exhibited similar, though less prominent, trends to SHAW (Figure 3). Groundwater exchange driven by tidal pumping releases nutrients (McMahon and Santos, 2017) and carbon (Cyronak et al., 2013b) into water near coral cays. Analyses of shallow seeping groundwater near the SHAW site revealed TA and DIC concentrations 16 and 48 μmol kg^-1 greater than surface seawater, respectively, likely due to dissolution of carbonate coral rubble which dominates OTI land matter. Shallow seeps had a higher salinity than surface water by 0.8, suggesting that groundwater seepage is in the form of recirculated seawater rather than fresh groundwater as also observed in a nearby coral cay (McMahon and Santos, 2017). The SHAW site was <5 m from the shoreline at low tide (Figure 1). An influx of water with elevated DIC directly adjacent to the site may explain how P_net appeared to shift from net productive to net respiratory since 2013 (Figure 6). However, more detailed sampling of groundwater composition and influence is necessary to quantify its effect on nearshore water chemistry. Significant groundwater exchange was demonstrated for Rarotonga, Cook Islands with an associated groundwater-derived TA influx of ∼45 mmol m^-2 hr^-1 to the coral reef (Cyronak et al., 2013a). The fluxes observed at Rarotonga would be large enough to modify our G_net estimates and invalidate the assumption that changes in alkalinity and DIC are driven primarily by benthic metabolism. The effects of groundwater discharge to nearshore coral reef waters are maximized at absolute low tide (Santos et al., 2010; McMahon and Santos, 2017), thereby elevating surface water DIC and TA during slack water periods, potentially masking metabolic rate estimates (Suzuki and Kawahata, 2003; Crook et al., 2013; Cyronak et al., 2014a; Richardson et al., 2017). As such, estimates of metabolism at certain nearshore sites may not accurately represent the rates of calcification and/or photosynthesis occurring on other parts of the reef.

Prior to the present study, coral calcification rates have been estimated at a third site at OTI. Albright et al. (2016, 2018) used a Eulerian approach over the reef flat and wall separating the first and third lagoons to investigate the in situ effects of reduced Ω_ar on G_net, revealing daytime calcification rates of 13.9 ± 1.4 mmol m^-2 hr^-1 in their control group. The daytime rate of 14.7 ± 1.45 mmol m^-2 hr^-1 at DK-13 presented in this study is remarkably similar to estimates by Albright et al. (2018). The Albright site is also unlikely to be influenced by groundwater inputs due to its distance from shore, supporting our suggestion that the nearshore SHAW site was influenced by proximity to the island.

Temporal Changes Within the OTI Reef Flat

The decline in calcification reported by Silverman et al. (2012), Shaw et al. (2015), and Kwiatkowski et al. (2016) were not observed at DK-13 in our study (Figure 6), even though sampling was performed in the same month as previous studies with seawater temperatures within 1°C. The daily G_net rates of 146 ± 20.24 mmol m^-2 d^-1 DK-13 are comparable to the original estimate of 125 ± 12.5 mmol m^-2 d^-1 (Kinsey, 1972) and are four times greater than the preceding estimate of 36.3 ± 9.5 mmol m^-2 d^-1 (Kwiatkowski et al., 2016). Organic production increased at DK-13 by 271% relative to the 2014 estimation of 41.7 ± 21.6 mmol m^-2 d^-1. At the SHAW site, daily G_net rates were calculated to be 72.9 ± 15.20 mmol m^-2 d^-1, over double the rate presented by Shaw et al. (2015). Also contrasting previous results, the SHAW site was found to be net respiratory with P_net rates of - 74.6 ± 48.14 mmol m^-2 d^-1 compared with the net autotrophic estimate of +35 mmol m^-2 d^-1 from the 2013 study (Figure 6). However, the comparison of results from the SHAW site should be interpreted with caution since groundwater inputs may have affected the estimates of ecosystem metabolism.

Our study calculated metabolic rates using paired samples procured at the beginning and end of the low tide isolation rather than samples collected hourly during the low tide as done by prior ecosystem metabolism studies at OTI. Intensive sampling is useful to investigate small-scale processes within a site and provides a larger dataset to determine diel rates. However, the shorter time interval corresponds to a smaller change to seawater chemistry, becoming more subject to analytical uncertainties and small-scale, site-specific environmental perturbations. In this study, calcification estimates using consecutive pairs of TA and DIC observations within a low tide and estimates using samples from the beginning and end of low tide were within 15% of each other at all three sites, with the greatest differences occurring at SHAW (13.4%) and the smallest differences occurring at DK-13 (3.5%). Therefore, we believe the difference in analytical procedures still allow for appropriate comparison to previous studies.

Alterations in TA–DIC slopes over time indicate the influences of inorganic and organic production on reef chemistry. Observations during periods of community degradation and following recovery provide different TA–DIC slopes (Courtney et al., 2017). Therefore, these TA–DIC slopes may be useful to assess the magnitude of impact an event has on the overall seawater carbonate system and ecosystem functioning (Cyronak et al., 2018). TA–DIC analyses indicate that the relative influence of calcification on the seawater carbonate chemistry was higher in 2017 at DK-13, but remained relatively stable at SHAW (Figure 5 and Table 2). The increase in live coral cover at both DK-13 and SHAW may be a likely driver of the elevated G_net rates and TA–DIC slopes presented in this study. Concerns have been raised about the apparent lack of relationship between calcifier cover and G_net estimates (DeCarlo et al., 2017; Page et al., 2017). Our results imply that calcification rates track coral cover at One Tree Island even though the small sample size prevents confident conclusions (Figures 6, 7).

FIGURE 7

Figure 7. Estimated percent live coral cover versus G_net at DK-13 (±SE). In this case, the regression line does not correspond with a significant correlation (p > 0.05). Dotted lines represent a 95% confidence interval. As in Figure 6, K1972 refers to Kinsey (1972); S2012 refers to Silverman et al. (2012); K2016 refers to Kwiatkowski et al. (2016); S2015 refers to Shaw et al. (2015), and D2019 refers to this study.

Coral cover determined by metabolism studies at the OTI reef flat was approximately 35–40 % in the 1970s, but decreased to 13–17% at the DK-13 site from 2009 to 2014 and 25% at the SHAW site in 2013, a likely result of the damaging effects of Cyclone Hamish in 2009 (Kinsey, 1978; Kinsey and Davies, 1979; Silverman et al., 2012; Woolsey et al., 2012; Shaw et al., 2015). The greater coral cover in 2017 compared to previous years is supported by long-term monitoring of coral cover at OTI. Coral cover fell to 10–20% in 2013 on the reef perimeter and recovered to 30–40% in 2017 on the fore reef approximately 200 and 500 m from the DK-13 and SHAW sites, respectively (AIMS, 2018a,b). Tropical cyclone Hamish developed and struck the OTI reef on its southeasterly rim with wind speeds of up to 95 km hr^-1, causing a reduction in coral cover of 20% and increase in filamentous algae of 30% on the reef flat that encompasses the DK-13 and SHAW sites. Prior to the cyclone, DK-13 was reported to have little to no filamentous algae (Kinsey and Davies, 1979). By 2012, the coral community had not recovered with high algal cover persisting (Woolsey et al., 2012).

Coral reefs may take over a decade to recover from significant disturbances, especially when additional concurrent stressors are involved (Gouezo et al., 2019). For example, significant recovery of coral communities after a devastating El Niño event was demonstrated 20 years after the initial disturbance, with an increase in coral cover of 20–50% at Cocos Island reefs, Brazil (Guzman and Cortés, 2007). Our current live coral cover estimates of 30–40% at the OTI reef flat indicate that there has been coral growth in the area over the past few years (Figure 6), though filamentous algae (using dead coral as substrata) has held its cover since the cyclone.

Calcification and Reef Acidification

Metrics of ocean acidification such as the partial pressure of CO₂ in seawater (pCO₂) and the aragonite saturation state (Ω_ar) have been extensively used to explain coral calcification and predict future reef states. Laboratory and field experiments suggest that coral reefs will begin net dissolving when atmospheric pCO₂ rises to 600–1000 ppm (Yates and Halley, 2006; Silverman et al., 2007; Shamberger et al., 2011). Diel average surface water pCO₂ at the OTI reef flat was above 600 ppm, with net dissolution present at nighttime only (Figure 3 and Table 1). The DK-13 site had the highest pCO₂, with a maximum value of 1534 ppm versus a maximum of 1000 ppm at the SHAW site (Figure 4). The CO₂ concentrations observed here are higher than most other coral reefs studied (Cyronak et al., 2014b), but have been consistent over the past decade, with Silverman et al. (2012) reporting a diel average of 591 ± 288 ppm and a maximum pCO₂ of 1697 ppm, which occurred during the day. There is the commonly observed, albeit noisy, negative trend between G_net and pCO₂ at the DK-13 site only (Figure 4), though this correlation may not be representative of the processes driving G_net at One Tree Island. Several investigations suggest that the effects of CO₂ and/or Ω_ar on calcification is not necessarily causal (Anthony et al., 2011; Comeau et al., 2018; McMahon et al., 2018).

In contrast, the relatively high CO₂ concentrations and low Ω_ar here may be a product of calcification itself (Gattuso et al., 1996; Jokiel et al., 2014). Cyronak et al. (2014b) developed a model using metabolic data from nearby Heron Island to examine how coral ecosystems modify the pCO₂ of their surrounding waters. Increasing calcification by 10% from a “baseline” rate will increase in situ CO₂ by 12 μatm, which could explain the increase in diel pCO₂ at DK-13 from 591 ± 288 in 2009 to 698 ± 49.6 in 2017 concurrent with an increase in calcification of 84% since 2009 (Table 1). Longhini et al. (2015) also observed high pCO₂ in coral reef flat waters in Brazil even though the ecosystem is net productive and no external sources were located. The authors concluded that the carbonate chemistry of reef waters during daytime low tide is related to (1) super-saturation of CO₂ at high tide during the previous night, and (2) net calcification occurring in nearby waters, which may also be the mechanisms driving high CO₂ observed in this study. We highlight that our CO₂ values are averaged over low tide periods only, and total diel CO₂ may differ as offshore water flowing over the reef flat during high tide is likely to lower local CO₂ concentrations.

Previously, G_net at the OTI reef flat has been significantly correlated with Ω_ar (Shaw et al., 2015). Our results indicate only weak associations between G_net and Ω_ar at one site, with no clear influence presented at either of the other sites (Figure 4). Net dissolution was predicted to occur at the OTI reef flat when Ω_ar fell to ∼2.5 (Shaw et al., 2015). The diel Ω_ar average during this study of 2.28 and 2.41 at DK-13 and SHAW, respectively, correspond with the highest observed G_net rates ever estimated at these sites (Figure 6). Among all sites, Ω_ar only elevated above the reported dissolution threshold during the daytime at SHAW with an average of 2.57 (Table 1). However, we found no significant correlation between Ω_ar and G_net at the SHAW site (Figure 4). Hysteresis in the relationship between the effect on calcification of co-varied light, temperature, P_net and Ω_ar has been observed at nearby Heron Island (McMahon et al., 2013; Albright et al., 2015). Here, G_net closely tracked P_net throughout the entire diel cycle (Figure 3), and hysteresis was not observed at SHAW or DK-13 when calcification and Ω_ar were split into morning, afternoon, and nightly time frames. Net dissolution only occurred when PAR was negligible and when P_net was <0, as observed elsewhere (McMahon et al., 2013; Muehllehner et al., 2016). Therefore, changes in Ω_ar as a result of P_net shifting carbonate equilibria were not the driver of calcification at these sites as previously demonstrated in mesocosm experiments (Jokiel et al., 2014).

If we use the regression equation describing the relationship between calcification and Ω_ar at DK-13 (Figure 4), a net dissolution threshold value of 1.20–1.62 would be estimated. This is lower than previous estimates for the southern GBR and similar to predictions made for Hawaiian coral reefs (see McMahon et al., 2013; Table 1, and Shamberger et al., 2011, respectively). Since the 2009 study at OTI, diel Ω_ar has fallen by an average of ∼29%, with a disproportionate influence on daytime Ω_ar (37.6% lower versus 12.8% lower at nighttime in 2017), yet daytime calcification has increased and nighttime dissolution has decreased in relation to Silverman et al. (2014). Results from the 2014 study at DK-13 indicate that Ω_ar only weakly influences short-term variabilities in calcification estimates at DK-13 (Kwiatkowski et al., 2016), supporting our new results obtained in 2017.

Andersson and Gledhill (2013), McMahon et al. (2013), and Cyronak et al. (2015) caution against using linear Ω_ar–G_net relationships solely to predict bottom-up ecosystem collapse via coral dissolution, as many factors contribute to influence metabolic rates. For example, water residence times and light availability may more accurately and consistently predict calcification rates in coral reefs (Demicco and Hardie, 2002; Falter et al., 2013). The variability in reported impacts of ocean acidification and warming highlights the importance of taking the magnitude of stress and interactive factors into consideration when predicting future reef calcification (Kornder et al., 2018). Multiple factors, such as increased coral cover and/or external TA sources, may have driven the increase in calcification at OTI, which offset the proposed negative effects of declining aragonite saturation states.

The increase in coral cover is the most likely cause of the elevated calcification rates during the day (Page et al., 2017; Figure 7), yet the cause of the de-coupled relationship between G_net and Ω_ar and the underlying mechanism of coral growth remains unclear. Recovery of the benthic community after severe storms in the last two decades would be expected, but reduced Ω_ar should have inhibited this recovery. However, examples of coral reefs thriving and even recovering in low Ω_ar are not uncommon (Guzman and Cortés, 2007; Shamberger et al., 2011). Coral reefs in areas of Palau sustain a high coral cover regardless of waters having average pCO₂ of >1400 ppm and Ω_ar < 3 (Camp et al., 2017). Since the methodologies, interpretation, and geochemical conditions were similar between our study and the previous studies at OTI, predictions of long term G_net based on short term G_net and Ω_ar correlations are likely invalid for this reef flat in line with the early suggestion by Andersson and Gledhill (2013). Long-term estimates of calcification and a clearer understanding about the role of local processes would be required to make predictions on when coral reefs may become net dissolving.

Comparisons to Other Reefs

G_net at DK-13 was within the range of estimates produced from nearby islands in the southern GBR. Calcification rates at Lady Elliot Island ranged from 73 mmol m^-2 d^-1 in summer to 123 mmol m^-2 d^-1 during winter, with ∼ 40% coral cover (Shaw et al., 2012, 2016). G_net around Heron Island is estimated to be ∼160 mmol m^-2 d^-1 during autumn. Both of these studies collected data using low-tide slack water sampling on reef flats (Shaw et al., 2012; McMahon et al., 2018). P_net at the Heron Island reef flat in 2014 was estimated to be ∼585 mmol m^-2 d^-1, much higher than our estimates for OTI. However, the Heron Island lagoon in autumn of 2012 was net respiratory, with P_net estimates of -6 mmol m^-2 d^-1 (Albright et al., 2015), indicating high variability between reef types. The differences in benthic metabolism around OTI and Heron Island are apparent in metrics often used to determine the relative control that organic and inorganic productivity have on the carbonate system. The organic to inorganic production ratio (P_net: G_net ) around Heron Island of 3.65–4.0 (McMahon et al., 2013; Albright et al., 2015, respectively) compared with 2.38 at DK-13 is reflected in the relatively lower TA–DIC slope (0.33 versus 0.59, respectively), indicating that these two ecosystems differ in their benthic functioning, despite being less than 20 km apart. Our results show that organic production is responsible for 70.5% of the changes in DIC at OTI (Table 2). This value falls well within the range of 32–88% reported for all coral reefs that have been studied using similar approaches, though it is on the low end for reefs in the Great Barrier Reef Marine Park and the larger Indo-Pacific region (Cyronak et al., 2018).

Calcification estimates have declined since the 1970s in the northern GBR, Hawai’i, and southern GBR (prior to this study). G_net has dropped 46% around Lizard Island and 40% at Kāne′ohe Bay from 1977 to 2009 (Kinsey, 1980; Shamberger et al., 2011; Silverman et al., 2014), and calcification at OTI dropped 70% from 1975 to 2014 (Kinsey, 1977; Kwiatkowski et al., 2016; Figure 6). Field results support ocean acidification and bleaching predictions for ecosystem metabolism in a changing ocean (Watanabe et al., 2006; Albright et al., 2016, 2018). However, caution should be used when employing short-term datasets to predict long-term variability in benthic structure and productivity, especially if those datasets used different methodologies over different seasons or sites (Falter et al., 2001; Courtney et al., 2016). Observations made by Kinsey in the 1970s provide seasonal and annual estimates at each of the three locations mentioned above. However, the more recent studies around Lizard Island and in Kāne′ohe Bay were performed over a period of a few days in a season. Estimates at OTI in the 1970s included 36 estimates from November–January (Kinsey, 1980), the 2009 study included 59 estimates (Silverman et al., 2012), in 2014 the use of autosamplers provided 273 estimates (Kwiatkowski et al., 2016), and our study provides 69 estimates. The more recent studies use longer collection periods over consecutive days to allow for overlaps in sample times and more certainty in estimated metabolic rates. The major short-term variability and the lack of continous, long-term datasets prevent the separation of overlapping drivers over time scales of hours, days, seasons, and decades, supporting claims by Falter et al. (2001). Though we have confidence that the rates reported here and in previous studies are representative of the specific time period being sampled, short-term datasets may not provide the information necessary to predict long-term changes in reef ecosystem functioning.

Conclusion

The OTI reef has had sporadic ecosystem metabolism studies in the last 50 years. We report relatively high calcification at the OTI reef flat in 2017 compared to earlier observations. We also highlight the natural spatial variability within estimated rates of ecosystem metabolism using seawater carbonate chemistry analyses and that care should be taken when comparing study results across sites within an ecosystem. The understanding we have of coral reef calcification rates is often based on an assumption that investigations capture the main drivers of short- and long-term variability within the benthic community, and that linkages between G_net, Ω_ar, and atmospheric CO₂ are well established. Nonetheless, our data compared to earlier work implies that sporadic short-term datasets may not necessarily be used to represent long-term trends in coral reef calcification. Our capacity to understand the underlying mechanisms of ecosystem metabolism and predict future reef states will require us to continue building detailed and uninterrupted longer-term datasets that can separate drivers and rates occurring on time scales of hours to decades.

Author Contributions

KD, BK, and IS designed and planned the study. All authors contributed to data collection and fieldwork. KD and AM processed samples in the laboratory. KD and ES assisted with data calculations and comparisons to prior studies. KD and IS were the primary writers of the manuscript with additions and edits offered by the remainder of the authors.

Funding

Field work and analytical instrumentation were funded by the Australian Academy of Science and the Australian Research Council (FT170100327, LE170100007, and LE120100156).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank the staff at One Tree Island Research Station, James Archibald and Tom Glaze, for support during field investigations, and Kai Schulz for support with DIC analyses.

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