Macroalgal responses to ocean acidification depend on nutrient and light levels

Introduction

Ocean acidification due to increased atmospheric CO₂ levels is altering the concentrations of dissolved inorganic carbon (DIC) in surface waters; CO²⁻₃ levels are falling, which is expected to corrode marine carbonates, whilst CO₂ and HCO⁻₃ levels are rising which can stimulate photosynthesis (Connell et al., 2013; Cornwall et al., 2015). As some primary producers are better able to capitalize on increasing carbon availability than others, this is expected to alter marine communities (Hepburn et al., 2011; Connell et al., 2013; Koch et al., 2013; Gaylord et al., 2015). In the Mediterranean, surveys of coastal CO₂ seeps have repeatedly shown that coralline algae and sea urchins become less common as pH and CO²⁻₃ fall, whereas brown algae, such as Cystoseira spp., Dictyota spp., Sargassum vulgare and Padina pavonica, proliferate as CO₂ and HCO⁻₃ levels rise (Porzio et al., 2011; Baggini et al., 2014). The ways in which ocean acidification affects communities of primary producers are likely to vary regionally, depending on the species present and abiotic factors such as temperature, light and nutrient availability (Giordano et al., 2005; Brodie et al., 2014; Hofmann et al., 2014).

To begin to understand the influence of physicochemical factors on the responses of macroalgae to ocean acidification, we grew common types of brown algae (from the Families Fucales and Dictyotales) at CO₂ seeps in a multifactorial experiment in which we manipulated light (irradiance) and nutrient levels. At low light levels, macroalgae are thought to be more likely to rely on carbon uptake via diffusion than use energetically expensive carbon concentrating mechanisms (Raven and Beardall, 2014; Raven et al., 2014) which has led to the idea that any benefits of ocean acidification on growth would only be seen at lower light levels for the majority of species (Hepburn et al., 2011). However, ocean acidification also has the potential to damage photoprotective mechanisms which kick-in at high light levels (Pierangelini et al., 2014). Algae minimize damage from high irradiance by down-regulating photosystems-they also produce chemicals, such as phenolic compounds in the brown algae, which screen ultraviolet light and dissipate energy (Figueroa et al., 2014a). In oligotrophic waters, such as those of the Mediterranean, nutrient availability generally limits macroalgal growth (Ferreira et al., 2011), photosynthetic capacity (Pérez-Lloréns et al., 1996) and photoprotective mechanisms (Celis-Plá et al., 2014a).

Our study centers upon a highly oligotrophic region (the Tyrrhenian Sea) which is undergoing rapid changes in carbonate chemistry coupled with coastal eutrophication and increased land run-off (Oviedo et al., 2015). In this region, as with elsewhere in the world, canopy-forming brown algae have undergone a decline in abundance due to anthropogenic perturbation (Scherner et al., 2013; Strain et al., 2014; Yesson et al., 2015). Here, we investigate the interactive effects of increasing CO₂ levels and eutrophication on Cystoseira compressa and Padina pavonica using a pH gradient caused by volcanic seeps. These species were chosen because they are abundant around shallow Mediterranean CO₂ seeps (Baggini et al., 2014), because Cystoseira spp. are indicators of high water quality in the Mediterranean (Bermejo et al., 2013) and since Padina spp. tolerate loss of external calcification as CO₂ levels increase (Johnson et al., 2012; Pettit et al., 2015).

Macroalgal responses to ocean acidification may well depend upon their nutrient metabolism, which can vary widely between species (Hofmann et al., 2014; Hurd et al., 2014). Here, we compared interspecific physiological and biochemical responses to ocean acidification under different light and nutrient levels using standard methods for the study of multiple physical stressors in algae (Martínez et al., 2012; Celis-Plá et al., 2014a). Our hypothesis was that both brown algal species would benefit from ocean acidification in shaded conditions when nutrient levels were elevated. We expected that high light levels would inhibit photosystems, and that any benefits from high CO₂ would only occur if sufficient nutrients were available. If this were true we expected to observe increased photosynthetic activity (using electron transport rates and carbon content as a proxy) and increases in phenolic and antioxidant production in shaded nutrient-enriched treatments.

Materials and Methods

Experimental Design

Macroalgal incubations took place from 19 to 22 March 2013, along a CO₂ gradient near Vulcano, Italy (Figure 1; Boatta et al., 2013). Cystoseira compressa and Padina pavonica were collected at 0.5 m depth from a reference zone. Thalli (5 g fresh weight) were held in individual mesh cylinders (15 cm long × 5 cm in diameter) set 1 m apart and suspended at 0.2 m depth off a floating line that was anchored to the seabed perpendicular to the coast. This array was replicated at an ambient CO₂ site (ca 500 μatm CO₂), a medium CO₂ site (ca 700–800 μatm CO₂) and a high CO₂ site (ca 1200 μatm CO₂) (Table 1).

FIGURE 1

Figure 1. Sample sites and location of experiments on Cystoseira compressa and Padina pavonica off Vulcano, Italy; (1) Ambient CO₂ site (ca 500 μatm CO₂), (2) Medium CO₂ site (ca 700–800 μatm CO₂) and (3) High CO₂ (ca 1200 μatm CO₂).

TABLE 1

Table 1. Seawater carbonate chemistry at three sites off Vulcano Island.

Each CO₂ zone had 12 replicates per treatment per species (nutrient enriched + ambient light or 100%_PAB, i.e., 100% of surface irradiance defined as PAB irradiance (PAR + UVR), nutrient enriched + shaded light or 70%_PAB, i.e., 70% of surface irradiance defined as PAB irradiance (PAR + UVR), non-enriched + ambient light or 100%_PAB, non-enriched shaded light or 70%_PAB). Light levels were manipulated using a 1 mm² size pore mesh that reduced light levels to 70% of that of the unshaded treatments. The filter we used does not modify the light spectra (Aphalo et al., 2013). Mesh bags containing 100 g of a slow-release fertilizer comprising 17% N (NH⁺₄ and NO⁻₃), 17% P (P₂O₅) and 17% K (Multicote^®, Haifa Chemicals, USA) were attached below nutrient enriched cylinders. For the non-enriched treatments, a bag with 100 g of sand was used as a control. The nutrient treatments were set 20 m apart from each other so that non-enriched treatments were unaffected.

Environmental Conditions

The seawater carbonate system was monitored at each site (Table 1). A 556 MPS YSI (Yellow Springs, USA) probe was used to measure salinity, pH and temperature (°C). The pH sensor was calibrated using NBS scale standard buffers. On 20th March 2013, water samples for total alkalinity (TA) were strained through 0.2 μm filters, poisoned with 0.05 ml of 50% HgCl₂, and then stored in the dark at 4°C. Three replicates were analyzed at 25°C using a titrator (Mettler Toledo, Inc.). The pH was measured at 0.02 ml increments of 0.1 N HCl.

Total alkalinity was calculated from the Gran function applied to pH variations from 4.2 to 3.0, from the slope of the curve HCl volume vs. pH. The pCO₂ and the saturation state of aragonite were calculated from pH_NBS, TA, temperature and salinity using the CO₂ SYS package (Pierrot and Wallace, 2006), using the constants of Roy et al. (1993) and Dickson (1990). Saturation state (Ω) is the ion product of calcium and carbonate ion concentrations as:

$\begin{array}{cc}\Omega =[C{a}^{2+}][C{O}_3^{2-}]/K’sp& (1)\end{array}$

The apparent solubility product K'sp depends on temperature, salinity, pressure, and the particular mineral phase (e.g. calcite and aragonite in this case).

Irradiance was monitored at the sea surface at two wavelength bands using PAR (QSO-SUN 2.5V) and UV-A (USB-SU 100, Onset Computer Corporation, Massachusetts, USA) sensors sealed in a water proof box (OtterBox3000). Water temperature was monitored using a HOBO logger (Onset Computer Corporation, Massachusetts, USA). The nutrient enrichment caused by the release of the fertilizer was assessed taking triplicate seawater samples at both enriched and non-enriched sites. Seawater was strained using portable GF/F filters (Whatman International. Ltd., Maidstone, UK) then transported to the laboratory inside an isotherm bag (4°C, in darkness), and kept at −20°C. Nitrate (NO⁻₃) was determined using an automated analyzer (SanPlus⁺⁺ System, SKALAR, Breda, Netherlands) applying standard colorimetric procedures (Koroleff, 1983).

Physiological and Biochemical Variables

Several physiological variables were obtained from the algae within each cylinder at the end of the experiment. These variables were also measured in C. compressa and P. pavonica from ambient CO₂ site (500 μatm) populations at 0.5 m depth. Carbon and nitrogen contents were determined using an element analyzer CNHS-932 model (LECO Corporation, Michigan, USA).

In vivo chlorophyll a fluorescence associated with Photosystem II was determined by using a portable pulse amplitude modulated (PAM) fluorometer (Diving-PAM, Walz GmbH, Germany). Macroalgal thalli were collected from natural populations (initial time) and after 4 days of incubation in the experiment (for each treatment or cylinder), and were put in 10 mL incubation chambers to obtain rapid light curves for each treatment. F_o and F_m were measured after 15 min in darkness to obtain the maximum quantum yield (F_v/F_m) being F_v = F_m–F_o, F_o the basal fluorescence of 15 min dark adapted thalli and F_m maximal fluorescence after a saturation light pulse of >4000 μmol m⁻² s⁻¹ (Schreiber et al., 1995). The electron transport rate (ETR) was determined after 20 s exposure in eight increasing irradiances of white light (halogen lamp provided by the Diving-PAM). The ETR was calculated according to Schreiber et al. (1995) as follows:

$\begin{array}{cc}ETR(\mu molelectrons{m}^{-2}{s}^{-1})=\Delta F/{F^{\prime }}_m\times E\times A\times F_{II}& (2)\end{array}$

where ΔF/F′_m is the effective quantum yield, being ΔF = Fm′−Ft (Ft is the intrinsic fluorescence of alga incubated in light and Fm′ is the maximal fluorescence reached after a saturation pulse of algae incubated in light), E is the incident PAR irradiance expressed in μmol photons m⁻² s⁻¹, A is the thallus absorptance as the fraction of incident irradiance that is absorbed by the algae (see Figueroa et al., 2003) and F_II is the fraction of chlorophyll related to PSII (400–700 nm) being 0.8 in brown macroalgae (Figueroa et al., 2014a). ETR parameters as maximum electron transport rate (ETR_max) and the initial slope of ETR vs. irradiance function (α_ETR) as estimator of photosynthetic efficiency were obtained from the tangential function reported by Eilers and Peeters (1988). Finally, the saturation irradiance for ETR (Ek_ETR) was calculated from the intercept between ETR_max and α_ETR. Non-photochemical quenching (NPQ) was calculated according to Schreiber et al. (1995) as:

$\begin{array}{cc}NPQ=(F_m-{F^{\prime }}_m)/{F^{\prime }}_m& (3)\end{array}$

Maximal NPQ (NPQ_max) and the initial slope of NPQ vs. irradiance function (α_NPQ) were obtained from the tangential function of NPQ vs. irradiance function according to Eilers and Peeters (1988).

Pigments were extracted from 20 mg fresh weight of thalli using 2 mL of 100% acetone and analyzed using an ultra-high-performance liquid chromatographer (Shimadzu Corp., Kyoto, Japan) equipped with a photodiode array detector to measure peaks in the range 350–800 nm. After extraction samples were centrifuged at 16200 g for 5 min (Sorvall Legend Micro 17, Thermo Scientific, Langenselbold, Germany) and then the extracts were filtered (0.22 μm nylon filters). The separation, was achieved with one column C-18 reversed phase (Shim-pack XR-ODS column; 3.0 × 75 mm i. d.; 2.2 μm particle size; Shimadzu, Kyoto, Japan) protected by a guard column TR-C-160 K1 (Teknokroma, Barcelona, Spain). The carotenoid composition was determined according to García-Plazaola and Becerril (1999) with some modifications (García-Plazaola et al., 2012), using commercial standards (DHI LAB Products). The mobile phase consisted of two components: Solvent A, acetonitrile: methanol: Tris buffer (0.1 M, pH 8) (84:2:14); and solvent B, methanol: ethyl acetate (68:32). The pigments were eluted using a linear gradient from 100% A to 100% B for the first 7 min, followed by an isocratic elution with 100% B for the next 4 min. This was followed by a 50 s linear gradient from 100% B to 100% A and an isocratic elution with 100% B for the next 3 min to allow the column to re-equilibrate with solvent A, prior to the next injection.

Total phenolic compounds were determined using 0.25 g fresh weight samples pulverized with a mortar and pestle with sand and 2.5 mL of 80% methanol. After keeping the samples overnight at 4°C, the mixture was centrifuged at 2253 g for 30 min at 4°C, and then the supernatant was collected. Total phenolic compounds were determined colorimetrically using Folin-Ciocalteu reagent and phloroglucinol (1,3,5-trihydroxybenzene, Sigma P-3502) as standard. Finally the absorbance was determined at 760 nm using a spectrophotometer (UV Mini-1240, Shimadzu) (Celis-Plá et al., 2014b). Total phenolic content was expressed as mg g⁻¹ DW after determining the fresh to dry weight ratio in the tissue (5.2 for C. compressa and 4.5 P. pavonica, respectively). The results are expressed as average ± SE from three replicates of each treatment. Antioxidant activity was measured on polyphenol extracts according to Blois (1958); 150 μL of DPPH (2,2-diphenyl-1-picrylhydrazyil) prepared in 90% methanol were added to each extract. The reaction was complete after 30 min in darkness at ambient temperature (~20°), and the absorbance was read at 517 nm in a spectrophotometer (UVmini-1240, Shimadzu). The calibration curve made from DPPH was used to calculate the remaining concentration of DPPH in the reaction mixture after incubation. Values of DPPH concentration (mM) were plotted against plant extract concentration expressed as the EC₅₀ value (oxidation index, mg DW mL⁻¹) required to scavenge 50% of the DPPH in the reaction mixture. Ascorbic acid was used as a control (Celis-Plá et al., 2014b).

Statistical Analysis

The effects of the in situ treatments on the physiological responses of C. compressa and P. pavonica were assessed using analysis of variance. Three fixed factors were considered: Site with three levels: ambient CO₂ site, medium CO₂ and high CO₂, Irradiance with two levels: 70 and 100% of surface irradiance (PAR + UVR irradiance), and two nutrient levels; enriched (N+) and non-enriched (N). This design allowed us to test interactive and additive effects of the variables on physiological responses after the 4 day experimental period. Student Newman Keuls tests (SNK) were performed on significant ANOVA interactions. Homogeneity of variance was tested using Cochran tests and by visual inspection of the residuals. All data conformed to homogeneity of variance. Analyses were performed by using SPSS v.21 (IBM, USA).

Results

Environmental Conditions

Cystoseira compressa and Padina pavonica were abundant at all three stations; P. pavonica was visibly less calcified at the site with the highest levels of CO₂. The seawater temperature was about15°C and the salinity was 38 at all stations; at the Ambient site, mean pH was 8.11, at the Medium CO₂ site (700–800 μatm), mean pH was 7.97 and at the High CO₂ site (1200 μatm), it was 7.86 (Table 1).

The average daily irradiance for the experimental period was 5360 kJ m⁻² for PAR and 666 kJ m⁻² for UVA. The nutrient enriched treatments had approximately 100 times the nitrate concentration of the ambient seawater; ambient vs. enriched ratios were 0.16 ± 0.04 vs. 106.17 ± 9.37 μM for the ambient site, 0.13 ± 0.01 vs. 106.33 ± 9.37 μM at the medium CO₂ site and 0.25 μM ± 0.01 vs. 106.42 ± 9.37 μM at the high CO₂ site (mean ± SE, n = 3).

Physiological and Biochemical Responses

The carbon content of C. compressa increased with increasing CO₂, whereas in P. pavonica showed interactive effects between all factors. Carbon, in P. pavonica showed maximal values 279.9 ± 6.5 with increased CO₂, in non-enrichment enriched treatments and minimal values 225.3 ± 2.4 mg g⁻¹ DW with decreased CO₂, non-nutrient enriched and 70%_PAB conditions (Table 2, Table S1). The nitrogen content of C. compressa was greatest in the high CO₂, nutrient enriched and 70%_PAB treatment (Figure 2A, Figure S1); conversely, in P. pavonica the nitrogen content was highest at the reference site, ambient CO₂ treatment (Figure 2B, Figure S1). The ratio C:N of C. compressa did not show significant differences between treatments (Figure 3A, Figure S1), whereas the ratio in P. pavonica showed significant effects for CO₂ levels and nutrient enrichment showed maximal values (19.5 ± 5.8) with increased CO₂, non-nutrient enriched in 100%_PAB conditions and minimal values 15.9 ± 0.5 in medium CO₂, nutrient enrichment and 70%_PAB conditions (Figure 3B, Figure S1).

TABLE 2

Table 2. Carbon content, photosynthetic efficiency (α_ETR), maximal electron transport rate (ETR_max), expressed in μmol m⁻² s⁻¹), irradiance of saturation of ETR (Ek_ETR), maximal non-photochemical quenching (NPQ_max) (mean values ± SE, n = 3) of Cystoseira compressa and Padina pavonica in relation to Irradiance (70%_PAB: low irradiance and 100%_PAB: ambient irradiance), Nutrients (Nutrient+ and Ambient Nutrient) and CO₂ (ambient CO₂ site: 500 μatm, Medium CO₂ site: 700–800 μatm and High CO₂: 1200 μatm) treatments.

FIGURE 2

Figure 2. Nitrogen content (mg g⁻¹ DW) (mean ± SE, n = 3) in (A) Cystoseira compressa and (B) Padina pavonica at Ambient CO₂ (ca 500 μatm, left boxes), Medium CO₂ (ca 700–800 μatm, middle boxes) and High CO₂ (ca 1200 μatm, right boxes), in Irradiance and Nutrient treatments. Dark bars indicate 70%_PAB (low irradiance), gray bars indicate 100%_PAB (ambient irradiance). N+ and N indicate nutrient enriched and non-enriched treatments respectively. Upper values in right box indicate initial time values (It: mean ± SE, n = 3). Lower-case letters denote significant differences after an SNK test.

FIGURE 3

Figure 3. C:N ratio (mean ± SE, n = 3) in (A) Cystoseira compressa and (B) Padina pavonica at Ambient CO₂ (ca 500 μatm, left boxes), Medium CO₂ (ca 700–800 μatm, middle boxes) and High CO₂ (ca 1200 μatm, right boxes), in Irradiance and Nutrient treatments. Dark bars indicate 70%_PAB (low irradiance), gray bars indicate 100%_PAB (ambient irradiance). N+ and N indicate nutrient enriched and non-enriched treatments respectively. Upper values in right box indicate initial time values (It: mean ± SE, n = 3).

The maximal quantum yield (F_v/F_m) was significantly different between CO₂ treatments, nutrient and irradiance in both macroalgae (Figure 4, Figure S2). In C. compressa, the F_v/F_m was greatest in 70%_PAB treatments with high CO₂, and non-enriched enrichment (Figure 4A, Figure S2), but in P. pavonica this was greatest in the nutrient enriched treatments (Figure 4B, Figure S2). The α_ETR values also varied significantly between treatments in both species (Table 2, Table S2). In C. compressa, α_ETR was greatest in 70%_PAB treatments at high CO₂ with non-enriched enrichment; in P. pavonica α_ETR was greatest in the high CO₂ conditions (Table 2, Table S2). ETR_max in C. compressa was highest in high CO₂, 70%_PAB and non-nutrient enrichment, also in 100%_PAB and nutrient enrichment, and also this was higher with decreased CO₂, 100%_PAB, in non-nutrient enrichment. In P. pavonica, ETR_max varied significantly depending on nutrient and irradiance, without interactions (Table 2, Table S2). In contrast, the Ek_ETR in C. compressa had one significant interaction among nutrient x irradiance. P. pavonica had significant interactions between CO₂ level, nutrient and irradiance. The Ek_ETR, in C. compressa, was greatest in the 100%_PAB treatments that had no CO₂ or nutrient enrichment, but in P. pavonica Ek_ETR was greatest in 70%_PAB conditions (Table 2, Table S2). In both species, the maximal non-photochemical quenching (NPQ_max) was affected by the interaction of all factors. In C. compressa, NPQ_max increased significantly with increasing CO₂ conditions, under nutrient enriched and 100%_PAB, also increased in ca 700–800 μatm but in 70%_PAB. As well as, NPQ_max increased under ambient CO₂ conditions in 100%_PAB in nutrient non-enriched. Finally, in P. pavonica, the NPQ_max was significantly higher in 70%_PAB at 700 μatm CO₂ treatment with nutrient enrichment (Table 2, Table S2).

FIGURE 4

Figure 4. Maximal quantum yield or F_v/F_m (mean ± SE, n = 3) in (A) Cystoseira compressa and (B) Padina pavonica at Ambient CO₂ (ca 500 μatm, left boxes), Medium CO₂ (ca 700–800 μatm, middle boxes) and High CO₂ (ca 1200 μatm, right boxes), in Irradiance and Nutrient treatments. Dark bars indicate 70%_PAB (low irradiance), and gray bars indicate 100%_PAB (ambient irradiance). N+ and N indicate nutrient enriched and non-enriched treatments respectively. Upper values in right box indicate initial time values (It: mean ± SE, n = 3). Lower-case letters denote significant differences after an SNK test.

Nutrient enrichment increased Chla significantly in C. compressa. In contrast, in P. pavonica significant differences were found for the following interactions: CO₂ level × nutrient, CO₂ level × irradiance and nutrient × irradiance (Table 3, Table S3). The same occurred for Chlc in P. pavonica; but there was no significant difference in C. compressa (Table 3, Table S3). The carotenoids, fucoxanthin and violaxanthin in C. compressa did not differ among factors (Table 3, Table S3) but in P. pavonica the fucoxanthin and violaxanthin contents were affected by the interaction of all factors. Fucoxanthin increased in 70%_PAB, non-enriched treatments in ambient CO₂ whereas violoxanthin levels were highest in 70%_PAB, ca 700–800 μatm CO₂, nutrient enriched treatment (Table 3, Table S3).

TABLE 3

Table 3. Pigment contents; Chlorophyll a (Chla), chlorophyll c (Chlc), Fuxocanthin and Violaxanthin contents are expressed in mg g⁻¹ DW and Antioxidant activity expressed as EC₅₀ in mg DW mL⁻¹ (mean values ± SE, n = 3) of Cystoseira compressa and Padina pavonica in relation to Irradiance (70%_PAB: low irradiance and 100%_PAB: ambient irradiance), Nutrient (Nutrient+ and Ambient Nutrient) and CO₂ (ambient CO₂ site: 500 μatm, Medium CO₂ site:700-800 μatm and High CO_2: 1200 μatm) treatments.

Phenolic content (PC) was affected by the interaction of all factors in both species (Figure 5, Figure S4). In C. compressa, PC was highest in CO₂ and nutrient enriched conditions (Figure 5A, Figure S4). In P. pavonica at 1200 μatm CO₂, PC was high in 100%_PAB and nutrient enriched treatments and in 70%_PAB treatments non-nutrient enrichment (Figure 5B, Figure S4). Antioxidant activity (EC₅₀) showed a significant interaction between CO₂ level × nutrient and CO₂ level × irradiance in C. compressa; however in P. pavonica the only significant difference found in antioxidant activity was between CO₂ level and irradiance. In C. compressa and P. pavonica, EC₅₀ was lowest (i.e., it had higher antioxidant activity) in the high CO₂, 70%_PAB light conditions and nutrient enriched treatments (Table 3, Table S4).

FIGURE 5

Figure 5. Total phenolic compounds (mg g⁻¹ DW) (mean ± SE, n = 3) in (A) Cystoseira compressa and (B) Padina pavonica from Ambient CO₂ (ca 500 μatm, left boxes), Medium CO₂ (ca 700–800 μatm, middle boxes) and High CO₂ (ca 1200 μatm, right boxes), in Irradiance and Nutrient treatments. Dark bars indicate 70%_PAB (low irradiance), gray bars indicate 100%_PAB (ambient irradiance). N+ and N indicate nutrient enriched and non-enriched treatments respectively. Upper values in right box indicate initial time values (It: mean ± SE, n = 3). Lower-case letters denote significant differences after an SNK test.

Discussion

Recent reviews surmise that ocean acidification is likely to increase macroalgal productivity due to beneficial effects of increased dissolved inorganic carbon (DIC) levels which can stimulate the growth of algae and allows them to divert more resources into anti-herbivore and photo-protective compounds (Harley et al., 2012; Brodie et al., 2014). Here we show that calcified and non-calcified macroalgae can indeed benefit physiologically from increases in DIC, but that the benefits, and the extent of the algal response, depend upon nutrient and light availability. Figure 6 summarizes our projections that brown macroalgal stands will both proliferate in the shallows (because of up-regulation of anti-herbivore and photo-protective compounds) and extend deeper due to a combination of ocean acidification and anthropogenic nutrient input, whereas other work on Mediterranean CO₂ seeps has established that sea urchins and coralline algae are adversely affected by acidification (Baggini et al., 2014). In vivo chlorophyll a fluorescence parameters (maximal quantum yield or F_v/F_m and maximal electron transport rate or ETR_max) and algal biochemical composition (Chla, total phenolic compounds and antioxidant activity, %C) helps explain the dominance of phaeophytes at a variety of coastal Mediterranean CO₂ seeps. Increases in brown macroalgal cover at CO₂ seep sites are probably due to a combination of the direct stimulus of increased DIC for photosynthesis for species with inefficient carbon concentrating mechanisms (CCMs), and decreased grazing since sea urchins for example are excluded by hypercapnia (Calosi et al., 2013).

FIGURE 6

Figure 6. The effects of ocean acidification on rocky shore communities will depend on nutrient levels. (A) A typical brown algal canopy with coralline algae and sea urchins at present-day levels of CO₂, nutrients and light. (B) Increased CO₂ in oligotrophic conditions has adverse impacts on most calcified organisms, such as coralline algae and sea urchins, yet dense brown algal stands persist at shallow depths despite corrosion of facultatively calcified algae, such as Padina. (C) Ocean acidification remains detrimental to most calcified organisms, but when this is combined with nutrient enrichment fleshy algae can flourish since they are able to take full advantage of increases in CO₂, extending deeper into the photic zone despite increased turbidity.

Other Mediterranean seep locations show similar trends to Vulcano, with increases in Cystoseira and Padina species at elevated CO₂ locations compared to reference locations (Johnson et al., 2012; Baggini et al., 2014). Work in other regions has also shown that ocean acidification can directly benefit some macroalgae, such as Gracilaria lemaneiformis in China (Zou and Gao, 2009) and mat-forming Feldmannia spp. in Australia (Russell et al., 2011), as well as canopy-forming phaeophytes such as Nereocystis luetkeana and Macrocystis pyrifera (Swanson and Fox, 2007; Roleda et al., 2012). We found that the benefits of increased DIC were even more pronounced when combined with increased nutrients. This is what we expected, given that macroalgae tend to be nutrient-limited in oligotrophic waters such as those of the Mediterranean Sea (Ferreira et al., 2011). Both our study species increased electron transport rates and the accumulation of photoprotectors when exposed to a Nitrogen Phosphorus Potassium fertilizer, but these were short-term experiments with macroalgae grown in isolation. We suspect that chronic eutrophication combined with ocean acidification may benefit more opportunistic algal groups, to the detriment of brown macroalgae based on research by Russell et al. (2009) and Falkenberg et al. (2013). In our study, C. compressa and P. pavonica had increased carbon content at elevated CO₂, which was augmented by increases in a range of other physiological parameters when nutrient levels were also increased. The F_v/F_m ratio was highest at increased CO₂ concentrations with no nutrient enrichment in C. compressa, but highest at increased CO₂ with nutrient enrichment for P. pavonica (Figure 4). The maximal photosynthetic activity (ETR_max) in C. compressa was reduced at high nutrient levels in shaded conditions but in fully lit conditions nutrients did not have significant effects under high DIC conditions. In other Cystoseira species, such as C. tamariscifolia, both F_v/F_m and ETR_max also decrease in nutrient enriched treatments in field experiments at various depths (Celis-Plá et al., 2014a). In another experiment, Celis-Plá et al. (2014b) found the highest ETR_max in C. tamariscifolia in thalli with the lowest internal nitrogen stores i.e., winter compared to summer grown algae. On the other hand, Ek_NPQ increased in all cases with the increased CO₂ as an acclimation to high light levels. On this basis, it is clear that the responses of coastal macroalgal communities to ocean acidification will depend on nutrient availability, and will be species-specific. Given these results we expect that in temperate waters, brown algae will benefit from increases in CO₂ if sufficient nutrients are available (Johnson et al., 2012). However, as with all ecology, we can expect that there will be a region-specific balancing act. We show here that in oligotrophic conditions brown macroalgae were unable to take full advantage of increased inorganic carbon availability. There is added complexity when we consider that many regions have experienced a die-back of canopy-forming brown algae due to excess nutrients or sedimentation (Strain et al., 2014); ocean acidification may exacerbate this problem since increased DIC may further benefit those algae that presently compete with fucoids and kelps in eutrophic conditions (Connell et al., 2013).

Light quantity and quality drive physiological processes in macroalgae (Hanelt and López-Figueroa, 2012), so we were not surprised to find that shading affected their responses to ocean acidification. We anticipated two outcomes of the effects of light: we expected ETR rates to be higher as the most obvious response to light, but we also expected low-light macroalgae to increase ETR rates and %C when transplanted to higher CO₂ concentrations. Our first expectations were met, as maximum quantum yield, photosynthetic efficiency, irradiance of saturation and non-photochemical quenching for chlorophyll fluorescence all increased at higher light levels and were, at times, amplified by increasing CO₂ and nutrient levels. The only instance where our second expectation was met was for P. pavonica under ambient nutrients, which had significantly higher %C (and non-significantly higher ETR_max) when transplanted to elevated CO₂ sites. Previous studies at the same sites found elevated ETR_max when comparing P. pavonica at an elevated CO₂ site compared to an ambient CO₂ site (Johnson et al., 2012). If the duration of our experiment had been longer, our transplanted P. pavonica may also have significantly increased their ETR_max. Our results emphasize the likelihood that ocean acidification will act upon primary production differently at different latitudes and depths, not always according to our expectations. This is important since increases in land nutrient run-off, due to changes in land use and/or rainfall, are altering light levels in coastal waters (Scherner et al., 2013).

One of the most important photoprotective mechanisms available to algae is an ability to dissipate excess thermal energy (Adams et al., 2006). Thermal dissipation measured as non-photochemical PSII fluorescence quenching (NPQ) is triggered by the trans-thylakoidal proton gradient (ΔpH) and zeaxanthin (ZEA) synthesis through the xanthophyll cycle (Gilmore et al., 1994) and is recognized as the most important photoprotective mechanisms in higher plants and several algal divisions (Rodrigues et al., 2002). Fucoxanthin and violaxanthin levels were not affected in C. compressa whereas in P. pavonica fucoxanthin and violaxanthin increased under 70%_PAB conditions, nutrient enrichment and medium CO₂ levels. We used NPQ_max as an indicator of photoprotective energy dissipation efficiency (Celis-Plá et al., 2014b), and we also measured phenolic content and antioxidant activity (EC₅₀), both of which can be used as photoprotectors (Celis-Plá et al., 2014a). In C. compressa and P. pavonica NPQ_max was higher in all shaded treatments with nutrient enrichment, but not in the fully lit treatments, indicating higher photoprotection when nutrients were elevated and light was reduced. Phenols usually accumulated under higher irradiance and (for C. compressa) higher CO₂ treatments, as per past studies on kelp grown at high CO₂ (Swanson and Fox, 2007), or measured under higher irradiance (Connan et al., 2004). However, the effects of CO₂ on autotroph phenol production are not straight forward, as previous work has shown that both seagrass (Arnold et al., 2012) and the macroalga Cystoseira tamariscifolia (Figueroa et al., 2014b) decrease phenol production when CO₂ increased. In C. compressa and P. pavonica, antioxidant activity and EC₅₀ were affected by the interactions between light levels and CO₂. EC₅₀ tended to be higher in shaded, high CO₂ treatments with and without nutrient addition, suggesting a positive correlation with phenolic compounds and their use as antioxidants to prevent photodamage. Together, NPQ_max, phenol production and EC₅₀ indicate that in elevated CO₂ conditions some species will have a higher capacity for photoprotection.

Macroalgae regulate their biochemical composition to changes in solar radiation (Bischof et al., 2006; Figueroa et al., 2014a,b). Whilst light obviously affects photosynthesis, other variables such pH, nutrients and the availability of different DIC species all have the potential to affect photosynthetic rates (Raven and Beardall, 2014). As interactions among such factors will determine the success of algal species and the amount of primary productivity in any time and place, it is crucial to know how the effects of ocean acidification are modified by other key drivers of photosynthesis. Research similar to our study, but with more species, in more locations and for longer durations, is clearly required before solid conclusions can be made with respect to the effects of ocean acidification on macroalgal productivity.

In conclusion, our study shows that ongoing ocean acidification can be expected to increase photosynthetic efficiency and algal productivity. The magnitude of these effects, and the species that benefit, will depend on light and nutrient levels. We show that C. compressa and P. pavonica are able to benefit from an increase in CO₂ levels, rapidly changing their physiology and biochemical composition over 3 day alterations in DIC, irradiance and nutrients. These factors had interactive effects on photosynthetic and photoprotective systems in both species and help explain why brown algae proliferate at CO₂ seeps. Longer-term growth studies involving algal interactions would be useful: we remain concerned that chronic eutrophication combined with ocean acidification may benefit more opportunistic algal groups to the detriment of canopy-forming brown macroalgae. As ocean acidification is not happening in isolation, but alongside a plethora of other anthropogenic changes, an understanding of the interactive effects of multiple stressors is critical to plan for global ocean change. We have shown that elevated CO₂ levels can enhance brown algal productivity, and may boost the kelp and fucoid forests of the planet, but the effects will depend upon interactions with other physicochemical parameters such as light and nutrient availability.

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

This work was supported by the Junta de Andalucía (Project RNM-5750) and by the research group RNM-295. The NERC/Defra/DECC UK Ocean Acidification research programme and Save Our Seas Foundation contributed to fieldwork expenses (this video https://vimeo.com/101795615 shows the field site). PC gratefully acknowledges financial support from “Becas-Chile” (CONICYT) fellowship of doctorate of the Ministry of Education of the Republic of Chile. P. A. Horta is also grateful to CNPq grants, CAPES (PNADB/visitant professor fellowship) and INCT-MC to additional financial support.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fmars.2015.00026/abstract

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