A direct CO₂ control system for ocean acidification experiments: testing effects on the coralline red algae Phymatolithon lusitanicum

Introduction

Mesocosm systems are widely used in ocean acidification (OA) research, allowing the simulation of predicted ocean conditions for the near future. While important information has been obtained regarding the response of different kinds of marine organisms to increased CO₂ and lower pH levels, inconsistencies and uncertainties in results are common, even among similar experiments with related taxa (Gattuso, Mach & Morgan, 2013). Some of these discrepancies may be related to the control mechanisms used to maintain the desired experimental levels (typically pH levels). A greater understanding of the complex responses of marine organisms and ecosystems to high CO₂ requires accurate carbonate system manipulations and well-controlled experimental setups (Schulz et al., 2009).

Four parameters are often used to describe the seawater carbonate system in OA experiments: pH, CO₂ partial pressure (pCO₂), dissolved inorganic carbon (DIC) and total alkalinity (TA). From any two of these parameters it is possible to calculate the other two as well as a few other parameters of the system. However, such calculations rely on empirically derived apparent dissociation constants, which are functions of temperature and salinity (Dore et al., 2009). Because there is no consensus on which two parameters should preferentially be measured, carbonate system calculations in different OA studies are often based on different input parameters. The direct measurement of pCO₂ in seawater is considered to be difficult in small volumes, and so this variable is usually calculated from pH, TA or DIC data. Therefore, real uncertainties in both measured and calculated values persist and are often unknown, especially under high pCO₂ (Hoppe et al., 2012). For example, in the few datasets of OA studies where all parameters were measured, Hoppe et al. (2012) found that pCO₂ values calculated from TA and DIC were about 30% lower than those calculated from TA and pH or from DIC and pH. Because of this, the calculated parameters of the carbonate system (e.g. pCO₂, calcite, saturation state) are not always comparable between CO₂ perturbation studies (see Hoppe et al., 2012).

The most used control variable in OA experiments is pH. After a long debate on the use of different calibration scales and protocols (Dickson, 1993; Rérolle et al., 2012) the total scale has been established as the most appropriate and recommended scale for pH determinations in seawater (Rérolle et al., 2012). The determination of seawater pH is intrinsically difficult, it is temperature and salinity dependant and only for solutions whose pH values closely match that of a standard can the pH be regarded as an approximate measure of the hydrogen ion activity of the solution (Gieskes, 1969). Most chemical sensors have inherent drifts and unpredictable behaviors, requiring frequent recalibrations to improve accuracy. In addition, accuracy is also affected by the type of electrode, calibration type, electrode filling solution, measurement conditions and susceptibility to electromagnetic interferences (Seiter & DeGrandpre, 2001; Dickson, 1993).

The spectrophotometric pH measurement is more accurate and precise than the potentiometric method and is being established as the reference method (see Dickson, 1993; Hoppe et al., 2010; Rérolle et al., 2012). There are already semi-automated systems that can be used to implement the standard spectrophotometric approach (Carter et al., 2013; McGraw et al., 2010). However, their accuracy is still dependent on the inherent uncertainties of spectrophotometric pH measurements such as dye impurities (see Carter et al., 2013; McGraw et al., 2010). The accuracy of the pH measurements could be improved by purifying the dye and determining the molar absorptivity ratios for the dye and spectrometer (McGraw et al., 2010), or adequately addressing the dye impurities and the indicator’s temperature and salinity (Carter et al., 2013). Even if the spectrophotometric measurement is the ideal technique to measure seawater pH, there are still difficulties to apply it to real-time pH determinations or monitoring/control systems, partially because this technique cannot be applied for high frequency (e.g. one min interval) underway measurements (Frankignoulle & Borges, 2001).

In addition, the calculation of pCO₂ from pH is influenced by other variables such as salinity and, even more critically, temperature. Particularly in systems with temperature fluctuations, the necessary pH to reach a certain target pCO₂ will be different for every temperature, which is naturally incompatible with the conventional pH-stats control systems. Hence, even considering that the pH readings can be accurate, these systems will always have variations of pCO₂ as a function of temperature. Because the ocean’s pH is expected to decrease as a consequence of the increase in atmospheric CO₂, it is logical that OA experiments should target the future projections of pCO₂ concentrations in seawater.

The partial pressure of CO₂ (pCO₂) is a parameter commonly used in oceanography, mostly for underway measurements on board research vessels. In these systems, water is continuously pumped through a water-gas equilibrator (Frankignoulle, Borges & Biondo, 2001), where the partial pressure of CO₂ in the water phase is equilibrated with the air and CO₂ is then read in the gas phase by an infrared gas analyser (IRGA). These systems provide very accurate and robust measurements of seawater pCO₂, which makes them particularly adequate for long-term or continuous data-acquisition setups.

In the context of high-CO₂ research, either on terrestrial, freshwater or marine (OA) environments, pCO₂ is the obvious variable used to describe present conditions and future scenarios. Abril et al. (2015) recently pointed out that regional and global estimates of CO₂ outgassing from freshwater based on pH and TA are most likely overestimated and direct pCO₂ measurements are recommended in inland waters. Therefore, it appears only logical that CO₂ could be used not only as a simple descriptor but also as the control variable in experimental systems. If such is already happening in terrestrial high-CO₂ research (e.g. Lin et al., 1999) and a recommendation for its use in freshwater has recently been published (see Abril et al., 2015), such is not the case yet for OA experimental systems, where pH-stats remain the most common control method.

Here we describe a technical approach to set and control pCO₂ levels in an open-circuit mesocosm system using CO₂ as the control variable. Seawater in header tanks is equilibrated with air, and CO₂ is analyzed with a non-dispersive IRGA. This information is then transmitted to a Proportional-Integral-Derivative (PID) digital controller that in turn regulates pure CO₂ injection in the header tanks through an algorithm-regulated process, thus avoiding fluctuations of the target pCO₂ that are characteristic of all on-off processes. This semi-automated system can maintain continuous and stable pCO₂ set points and does not require frequent calibrations. It is more autonomous than previous CO₂ control systems and is suitable for running long-term experiments. The system was developed with the objective of assessing the long-term effects of OA on the coralline algae Phymatolithon lusitanicum, recently described as a new species for science and the most common in the mäerl beds from Southern Portugal (see Peña et al., 2015). Here we evaluated the long-term effects (11 months) of high CO₂ in the photosynthetic rates of the algae. As well, the effects of temperature on the respiration rates of P. lusitanicum, at different CO₂ levels, was assessed.

Several authors have highlighted the need for more long-term experiments to evaluate the response of coralline algae to OA (e.g. Martin et al., 2013; Ragazzola et al., 2013; McCoy & Kamenos, 2015). Long-term studies can reveal very different results with respect to short-term experiments (McCoy & Kamenos, 2015) and give important information on the potential for physiological acclimation (see Hurd et al., 2009; Martin et al., 2013; Ragazzola et al., 2013). Long-term experiments, where the synergistic effect of light, temperature and other factors is also investigated, are essential to understand how ecosystems will respond to global change.

Methods

Mesocosm layout

The whole circuit was assembled indoors, in a temperature-controlled room. Following decantation, sand- and cartridge-filtering (10–20 and 5 μm), running seawater enters a preliminary 2,000 L tank, where it is strongly bubbled with compressed air, to insure full pCO₂ equilibration with the atmosphere (Fig. 1). It is then UV-filtered (16 and 8 W) and pumped to the three 200 L header tanks of the circuit, one per pCO₂ level, where CO₂ is injected. From each header tank, water is pumped to six 25 L individual experimental aquaria, in a continuous 0.05 L min⁻¹ flow. This is a flow-through open circuit. The temperature is largely maintained by the room’s air-conditioning system, with the assistance of dedicated water-chillers, when deemed necessary. While pCO₂ is continuously measured in the header tanks, temperature, pH, dissolved oxygen and salinity are monitored regularly in the experimental aquaria.

pCO₂ control system

The CO₂ control system is depicted in Fig. 2. At the 200 L header tank (1) water is flushed with industry-grade commercial CO₂. From the header tanks water is pumped (2) through an 8 W UV filter (3) and is distributed to the experimental aquaria (4) Part of the water is directly channeled to a gas flushing equilibrator (5). The air-flushing equilibrator is used to equilibrate gas partial pressures between water and air, and was built according to Frankignoulle, Borges & Biondo (2001). It consists of a vertical Plexiglas tube (height: 80 cm, diameter: 10 cm), sealed at both ends and filled with glass marbles to increase the exchange surface and reduce air volume. Seawater enters the equilibrator from the top (at 3 L min⁻¹) and percolates downwards, being returned to the header tank. The air coming from the IRGA (7) is injected at the bottom and aspired at the top of the equilibrator, returning to the IRGA for analysis, after passing through a Drierite® column (6) for humidity scrubbing. The air that circulates between the IRGA and the equilibrator is therefore a closed circuit of low volume (∼785 ml). The CO₂ dissolved in the water equilibrates with the air (wet air) and is directly read by the IRGA (WMA-4, PP Systems, MA, USA). All air-carrying tubing is made out Tygon® to minimize CO₂ losses.

The PID controller (8) (TEMPATRON PID330; Tempatron, Farnell, UK) is an advanced controller, originally designed for the control of processes in a wide range of industrial applications. In this system, the PID controller receives the CO₂ readings from the IRGA and controls the operation of an in-line solenoid valve (9) that opens and closes CO₂ injection (10). The controller determines the difference between the measured CO₂ and the programmed set point and attempts to minimize it by adjusting the process control input (CO₂ injection). The PID controller also comprises an auto-tune feature that improves accuracy and stability. It is applied in cases when the control result is unsatisfactory or for an initial set up of a new process. This function allows the controller to “learn” the process characteristics and automatically set the necessary control coefficients to minimize deviations of the measured values relative to the set point. In practical terms, this means that CO₂ is injected in very short bursts and the controller waits for the system’s response (the evolution pattern of the measured CO₂) before injecting more CO₂. The CO₂ is mixed with air (11) and injected into the header tank with an air pump (12) through air-stones (13) that create fine bubbles and allow a faster equilibration.

Results

CO₂ control system assessment

The response time and performance of the system was evaluated at three different pCO₂ levels. Each level took between two to five hours to reach steady-state (Fig. 3). At the higher pCO₂ levels the system took longer to stabilize and experienced higher amplitude oscillations, requiring a frequent use of the auto-tune mode.

The mean pCO₂ values under control conditions ranged from 397 to 470 μatm of CO₂. The mean values for the intermediate CO₂ treatment went from 539 to 577 μatm of CO₂, and the mean values for the high CO₂ treatment went from 740 to 804 μatm of CO₂. The control and the two enriched pCO₂ levels in the system were consistent throughout the experimental period and reflected the daily variations observed under natural conditions (Fig. 4). TA ranged from 2,491.5 to 2,520.4 μmol. kgSW⁻¹ and there were no significant differences between the sampled points of the experimental system and CO₂ treatments. Table 1 shows the carbonate system after 11 months at the three different CO₂ concentrations.

Table 1:

Carbonate chemistry for each pCO₂ level (400, 550 and 750 μatm) after 11 months of experimental treatment.

Total alkalinity (TA), salinity, temperature and pH were measured while dissolved inorganic carbon (DIC), aragonite saturation state (Ωarag) and pCO₂ were calculated using the CO₂ SYS software. Values expressed as means ± SE (n = 11).

CO2 treatments	TA	S	T	pH	DIC	Ωarag	indirect pCO2
(μatm)	(μmol/kgSW)	(psu)	(°C)		(μmol/kgSW)		(μatm)
400	2,461.76 ± 4.08	34.87 ± 0.03	15.65 ± 0.19	8.18 ± 0.01	2,183.93 ± 7.48	3.09 ± 0.05	345.62 ± 7.31
550	2,449.17 ± 2.67	34.85 ± 0.02	15.93 ± 0.12	8.10 ± 0.00	2,215.27 ± 2.84	2.65 ± 0.03	430.60 ± 4.69
750	2,468.32 ± 2.84	34.63 ± 0.16	15.75 ± 0.14	7.90 ± 0.03	2,326.21 ± 11.02	1.81 ± 0.11	755.75 ± 56.41

DOI: 10.7717/peerj.2503/table-1

The pH at the acidified treatments decreased gradually allowing for a proper acclimation of the algae. Temperatures in the aquaria during the experiment ranged from 13 to 19 °C, salinity ranged from 34 to 38 psu and dissolved oxygen was always close to 100% of saturation (∼6.7 mgO₂/L).

Long term effect of OA on coralline algae

After 11 months of high pCO₂ exposure, net photosynthesis was positively affected by elevated pCO₂ and light (P ≤ 0.001). Results suggest that these algae have an irradiance threshold of 200 μmol photons·m⁻²·s⁻¹ (PAR), above which photosynthetic rates saturate (Fig. 5). The maximum photosynthetic rates (P_max) increased with pCO₂ (P = 0.003) reaching the maximum values at 750 μatm of CO₂ (0.29 μmol O₂·m⁻²·s⁻¹) with respect to 550 μatm (0.24 μmol O₂·m⁻²·s⁻¹) and control conditions (400 μatm) (0.14 μmol O₂·m⁻²·s⁻¹) (Fig. 6). Respiration increased with temperature (P ≤ 0.001) but it was unaffected by CO₂ (P = 0.965). The highest respiration rates (μmol O₂·m⁻²·s⁻¹) were observed at 26 °C and the lowest at 12 °C (Fig. 7).

Discussion and Conclusions

This mesocosm is equipped with a simple, accurate and reliable control system that operates almost unattended apart from the regular maintenance and data download routines. Instead of using pH, the system uses CO₂ as a control variable, measured with an accurate CO₂ analyzer coupled to a PID controller. This combination circumvents the problems and uncertainties associated with pH control and allows the maintenance of long-term stability in the set pCO₂ values. This allows a proper acclimation of the organisms to different CO₂ levels and a more realistic response of their metabolic rates. It is also possible to test other relevant variables simultaneously with CO₂, such as temperature, irradiance, nutrients etc.

The combination of the gas-flushing equilibrator and the air pump results in an improved gas mixing, taking the system only 2–5 h to reach a steady state. The gas-flushing equilibrator is a simple structure, easy to build and with a short response time, that can be used in highly turbid waters (Frankignoulle, Borges & Biondo, 2001) and CO₂ oversaturated water (Frankignoulle & Borges, 2001). Even if an air pump to bubble the experimental water is not commonly used in OA studies, it has been suggested by Gattuso & Lavigne (2009) as a simple alternative to inject the CO₂-air mix. In this experiment, this combination has proven to be efficient.

In addition, this gas bubbling system is more economical than previous designs that use pre-mixed gasses, which are expensive in long-term experiments and only suitable for small volumes (Gattuso et al., 2010). Once the system is stabilized, the consumption of food-grade 100% CO₂ is largely minimized by the reduction of gas waste obtained with the PID control, making the system not only suitable but also affordable for long-term experiments (months to years).

This control system can also be adapted to other types of organisms, namely phytoplankton, since there is no direct bubbling into the experimental tanks. Seawater aeration by bubbling might lead to difficulties in phytoplankton cultures because it may enhance the coagulation of organic matter (Gattuso & Lavigne, 2009) or damage the calcareous structures.

It is now widely accepted that OA experiments should incorporate the natural patterns of daily fluctuations in pH. This is particularly relevant when studying species that live in environments with considerable variability, such as shallow marine habitats or coastal upwelling systems (see Eriander, Wrange & Havenhand, 2016; Reum et al., 2015). With this control system, the habitat-specific pCO₂ variability can be easily incorporated in the control routine, in a similar way than with pH stats (see Eriander, Wrange & Havenhand, 2016). The PID controller can be programmed to adjust the CO₂ injection along the day, as a function of both the target value and its desired degree of daily fluctuation.

Following the recent recommendations of Cornwall & Hurd (2016), we have in the meantime adapted the experimental design depicted in Fig. 2. Instead of using the header tanks to mix the CO₂ directly with seawater, CO₂ is injected into a large air tank (4,000 L), where it is mixed with air to obtain the target pCO₂ value. This target value is set and controlled by a PID controller coupled to a gas analyser (IRGA). The pre-prepared mixture is then pumped by an air compressor and injected in the header tanks, one per experimental tank. The CO₂-enriched air is equilibrated with seawater in the header tanks using air stones. In this way, all the experimental tanks are true replicates. This experimental design has already been used effectively in an experiment with the seagrass species Cymodocea nodosa (M. Ruocco et al., 2016, unpublished data).

The photosynthetic and respiration results after 11 months of high CO₂ exposure showed that this system is adequate to run long-term experiments with coralline algae and can be easily adapted to be used with other organisms. The increased rates of maximum photosynthesis (P_max) under elevated pCO₂ indicate higher carbon uptake via photosynthesis with the increase of CO₂ concentration. After 11 months of high pCO₂ exposure, P. lusitanicum revealed physiological capacity to acclimate to high pCO₂. This agrees with the results from Noisette et al. (2013a) and Noisette et al. (2013b), in which the photosynthetic rates of the mäerl species Lithothamnium corallioides increased with CO₂. However, Martin et al. (2013) and Martin & Gattuso (2009) found decreasing or unaffected photosynthetic rates of the crustose coralline algae Lithophyllum cabiochae after one year of exposure to high CO₂. Time of acclimation and exposure is an important factor which could partially explain the differences found among OA experimental studies where coralline algae increase, decrease or have a parabolic photosynthetic response to OA (reviewed in Martin et al., 2013). The results from this study suggest that more long-term studies with coralline algae are needed. It appears important to gradually acclimate the algae to acidified conditions and compare the results after at least two different time periods. However, it is also important to consider that the responses in long-term experiments might not be necessarily indicative of those that will occur in the future. Martin & Gattuso (2009) found that after one year in the laboratory the calcification rates of the crustose coralline alga Lithophyllum cabiochae decreased by several orders of magnitude. The authors attributed this decrease to the stress caused by changing environmental conditions from natural to artificial ones. The interaction with natural seasonal fluctuations of environmental parameters such as irradiance and temperature are determinant factors in OA experiments. To properly mimic the natural conditions, we suggest that experimental studies should be accompanied by monitoring programs in the field.

Respiration rates responded to temperature but not to CO₂, suggesting that in a future ocean, the respiration rates of these coralline algae from southern Portugal will increase because of ocean warming, independently of OA. This agrees with previous OA studies where respiration in coralline algae was unaffected by CO₂ (Noisette et al., 2013a) but positively affected by temperature (Noisette et al., 2013b; Martin et al., 2013).

Photosynthesis and respiration results suggest that temperature and irradiance have an important role on the metabolism of coralline algae (see Vásquez-Elizondo & Enríquez, 2016; Martin et al., 2013) and should be carefully controlled and considered in OA experiments. Moreover, the effect of temperature on respiration is key to assess the effects of climate change in these organisms, as it will be reflected on the global carbon budgets of mäerl beds. The direct control of pCO₂ is probably the best way to standardize comparisons among high-CO₂ experiments (see Reum et al., 2015). Since similar control systems are already being used in terrestrial and freshwater ecosystems, the use of a unique control system not only facilitate the inter-comparisons but would also allow more reliable estimates of the impacts of high pCO₂ in the global carbon fluxes.

Many questions remain unanswered in OA research and the inconsistencies and differences in results due to technical problems must be minimized namely through the improvement of experimental setups and standard protocols. We demonstrated that a direct control of CO₂ can be used in long term OA experiments and propose this simple and affordable control system that is also flexible enough to be adapted and customized according to specific requirements.

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