# Effects of ocean acidification on Posidonia oceanica epiphytic community and shoot productivity

T. Erin Cox, Stefano Schenone, Jeremy Delille, Victoria Díaz-Castañeda, Samir Alliouane, Jean-Pierre Gattuso, Frédéric Gazeau

► To cite this version:

T. Erin Cox, Stefano Schenone, Jeremy Delille, Victoria Díaz-Castañeda, Samir Alliouane, et al.  
Effects of ocean acidification on Posidonia oceanica epiphytic community and shoot productivity.  
Journal of Ecology, 2015, 103 (6), pp.1594-1609. 10.1111/1365-2745.12477 . hal-03334124

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# Effects of ocean acidification on *Posidonia oceanica* epiphytic community and shoot productivity

T. Erin Cox¹,²*, Stefano Schenone¹,³, Jeremy Delille¹,², Victoria Díaz-Castañeda⁴, Samir Alliouane¹,², Jean-Pierre Gattuso¹,²,⁵ and Frédéric Gazeau¹,²

¹Laboratoire d'Océanographie de Villefranche (LOV), UMR 7093, UPMC Univ Paris 06, Sorbonne Universités, Observatoire Océanologique, F-06230 Villefranche-sur-mer, France; ²Laboratoire d'Océanographie de Villefranche (LOV), UMR 7093, CNRS, Observatoire Océanologique, F-06230 Villefranche-sur-mer, France; ³Università degli Studi di Genova (DiSTAV), Corso Europa 26, Genova 16132, Italy; ⁴Department of Ecology, Center for Scientific Research and Higher Education of Ensenada, 22 860 Ensenada, Mexico; and ⁵Institute for Sustainable Development and International Relations (IDDRI), Sciences Po, 27 rue Saint Guillaume, F-75007 Paris, France

Summary

1. Biological interactions can alter predictions that are based on single-species physiological response. It is known that leaf segments of the seagrass *Posidonia oceanica* will increase photosynthesis with lowered pH, but it is not clear whether the outcome will be altered when the whole plant and its epiphyte community, with different respiratory and photosynthetic demands, are included. In addition, the effects on the *Posidonia* epiphyte community have rarely been tested under controlled conditions, at near-future pH levels.

2. In order to better evaluate the effects of pH levels as projected for the upcoming decades on sea-grass meadows, shoots of *P. oceanica* with their associated epiphytes were exposed in the labora-tory to three pH levels (ambient: 8.1, 7.7 and 7.3, on the total scale) for 4 weeks. Net productivity, respiration, net calcification and leaf fluorescence were measured on several occasions. At the end of the study, epiphyte community abundance and composition, calcareous mass and crustose coral-line algae growth were determined. Finally, photosynthesis vs. irradiance curves (PE) was produced from segments of secondary leaves cleaned of epiphytes and pigments extracted.

3. *Posidonia* leaf fluorescence and chlorophyll concentrations did not differ between pH treatments. Net productivity of entire shoots and epiphyte-free secondary leaves increased significantly at the lowest pH level yet limited or no stimulation in productivity was observed at the intermediate pH treatment. Under both pH treatments, significant decreases in epiphytic cover were observed, mostly due to the reduction of crustose coralline algae. The loss of the dominant epiphyte producer yet sim-ilar photosynthetic response for epiphyte-free secondary leaves and shoots suggests a minimal con-tribution of epiphytes to shoot productivity under experimental conditions.

4. Synthesis. Observed responses indicate that under future ocean acidification conditions foreseen in the next century an increase in *Posidonia* productivity is not likely despite the partial loss of epi-phytic coralline algae which are competitors for light. A decline in epiphytic cover could, however, reduce the feeding capacity of the meadow for invertebrates. *In situ* long-term experiments that con-sider both acidification and warming scenarios are needed to improve ecosystem-level predictions.

**Key-words:** calcified growth, carbon dioxide, climate change, coralline algae, net calcification, pH, photosynthesis, plant–climate interactions, *Posidonia oceanica*, seagrass–epiphyte interactions

**Introduction**

Seagrasses are aquatic flowering plants that are distributed
worldwide in estuarine and marine environments (Short et al.
2007). In nearshore waters (0–50 m), these producers

engineer ecosystems which provide valuable ecological ser-
vices that are beneficial to humans. Services include the main-
tenance of biodiversity, nursery for commercial pelagic fishes,
water-quality control and shore-line protection (Costanza
et al. 1997). The leaves and matte of seagrasses are colonized
by a high diversity of organisms (Borowitzka, Lavery & van
Keulen 2006), referred to as epiphytes following the

<footnote>*Correspondence author. E-mail: erincox@hawaii.edu</footnote>
definition of Steel & Wilson (2003). Epiphytes can account for over 50% of the canopy biomass (Borowitzka, Lavery & van Keulen 2006) and with the dominant cover of seagrasses play major roles in coastal primary production (e.g. Pergent et al. 1994; Duarte & Chiscano 1999), nutrient cycling (e.g. Cornelisen & Thomas 2002; Romero et al. 2006) and carbon storage (Duarte et al. 2010). The surface cover of meadows has significantly decreased in the last few decades (Marbà & Duarte 1997; Krause-Jensen et al. 2004) due to coastal development or indirectly from anthropogenic activities that alter water quality (Orth et al. 2006; Waycott et al. 2009). Seagrass meadows could be further altered through recent and ongoing changes in ocean carbon chemistry (Koch et al. 2013).

Ocean acidification is the ongoing decrease in the pH of the Earth's ocean as a consequence of the uptake of 26% of anthropogenic CO₂ emissions (2003–2012; Le Quéré et al. 2014). Since the beginning of the industrial era, surface ocean pH has already decreased by 0.1 units, equivalent to an increased acidity of 30% (Ciais et al. 2013). Additional decreases of 0.06–0.32 units are expected by 2100 depending on the considered CO₂ emission scenario (Ciais et al. 2013). With changes in pH, the relative proportions of dissolved inorganic carbon (C<sub>T</sub>) chemical species are concurrently being altered. CO₂ will have the largest proportional increase by 2100, followed by an increase in already widely available bicarbonate ions (HCO₃⁻) and a decrease in carbonate ions (CO₃²⁻; Orr, Fabry & Aumont 2005; Ciais et al. 2013). For many calcified organisms, such as species of epiphytes, the decrease in CO₃²⁻ and lowered pH is problematic for the formation and persistence of calcium carbonate structures (e.g. Kroeker et al. 2013). In contrast, marine producers, such as seagrasses, are thought to benefit from ocean acidification because they are able to use the carbon from CO₂ and HCO₃⁻ for photosynthesis yet tend to have a higher affinity for CO₂ (Invers et al. 2001; Koch et al. 2013).

Similar to single-species studies conducted with terrestrial C3 plants, experiments with seagrasses under elevated partial pressure of CO₂ (pCO₂) have generally supported the notion that marine photosynthesizers will be ‘winners’ with ocean acidification. Presumably due to their terrestrial origins, CO₂ limitation for photosynthesis is a common physiological feature of all seagrasses (Invers et al. 1997; Buapet et al. 2013; Koch et al. 2013). Past experiments under elevated pCO₂ conditions have shown increased seagrasses photosynthesis (Invers et al. 1997; Zimmerman et al. 1997; Jiang, Huang & Zhang 2010), growth (Hall-Spencer et al. 2008; Burnell et al. 2014) and reproduction (Palacios & Zimmerman 2007). However, benefits for seagrasses may largely depend on biological interactions that are difficult to mimic in the laboratory.

Biological interactions, like those between epiphytes and seagrasses, could modulate the responses of seagrass meadows to ocean acidification (Connell, Russell & Irving 2011; Hendriks et al. 2013; Burnell et al. 2014; Garrard et al. 2014; Martínez-Crego, Olivé & Santos 2014). Common

epiphytes on seagrass leaves include crustose coralline red algae, filamentous brown algae, foraminifera, polychaetes, bryozoans and hydrozoans (Piazzi et al. 2004; Mabrouk et al. 2013). The total and relative abundance of the different epiphytic species varies considerably in space and time and like seagrasses are dependent on physico-chemical factors (Harlin 1975), as well as grazing pressure (Van Montfrans, Wetzel & Orth 1984). In terrestrial ecosystems, with a longer history of experimentation, ecosystem-level responses to elevated CO₂ availability appear to be dependent upon the differential response of species and their competitive abilities for not only CO₂, but also for light, water and nutrients (Bazzaz 1990; Korner 2003). Similarly, observed changes in aquatic plant communities in acidifying lakes were not merely the result of physiological response of individuals but were also the result of competition between species for a resource (Farmer 1990). Although epiphytes add to production and nutrient cycling in meadows, they are hypothesized to inhibit seagrass photosynthesis by (i) altering light quality and quantity reaching leaves (Cebrián et al. 1999), (ii) increasing boundary layers needed for efficient carbon and nutrient uptake (Sand-Jensen, Revsbech & Jorgensen 1985; Borowitzka, Lavery & van Keulen 2006) and (iii) increasing photorespiration by elevating O₂ at the surface of the leaf via their photosynthetic activity (Sand-Jensen, Revsbech & Jorgensen 1985). Filamentous algae often respond positively to increased pCO₂ availability (Koch et al. 2013). Thus, under elevated pCO₂ conditions, algae could proliferate and compete for similar resources. Competition between seagrass and epiphytes has occurred in nutrient-enriched areas (Mabrouk et al. 2013) and was observed between epiphytes and the seagrass *Amphibolis antartica* under highlight and elevated CO₂ (Burnell et al. 2014). Among epiphytic groups, coralline algae and foraminifera are calcifying along with several species of polychaetes that form calcified tubes and some bryozoans that have calcified surfaces. Complicating matters further, seagrass production increases pH and may buffer the effect of ocean acidification, particularly in daylight for calcifiers growing at the leaf surface (Hendriks et al. 2013).

The effect of ocean acidification on seagrass and epiphytes has received some attention in the past few years, particularly in the Mediterranean Sea. It is here where the endemic *Posidonia oceanica* forms dense monospecific meadows that cover 23% of shallow waters (Agostini, Desjobert & Pergent 1998). *Posidonia oceanica* has a C3 photosynthetic pathway, can be carbon limited in the field and carbonic anhydrase activity has been documented to supply ~50% of the total dissolved inorganic carbon needed for photosynthetic saturation (Invers et al. 1997, 2001; Koch et al. 2013). Invers et al. (1997, 2001, 2002) have documented increased photosynthesis over a range of decreasing pH. These results were produced from short-term incubations (hours) of leaf segments. However, *P. oceanica* is one of the most structurally complex seagrasses (Gobert et al. 2006). Because of its leaf replacing form, leaves within a single shoot are different ages, have different epiphyte abundances and have correspondingly different photosynthetic activity (Alcoverro, Manzanera & Romero
1998). In addition, *P. oceanica* has an extensive below-
ground system which stores and mobilizes carbon and adds to
respiration (Alcoverro, Manzanera & Romero 2001; Marbà
et al. 2002). Epiphytes on *P. oceanica* have been studied in
the laboratory by Martin *et al.* (2008). They reported net dis-
solution of epiphytic coralline algae after 2 weeks at a very
low pH<sub>T</sub> (total scale) of 7.0, corresponding to a strong under-
saturation of magnesium calcite, aragonite and calcite.

*Posidonia oceanica* also occurs along CO₂ vents where pH varies along a gradient from a volcanic source. Vent studies can incorporate seagrass–epiphyte dynamics and account for any long-term acclimation (Hall-Spencer *et al.* 2008; Apostolaki *et al.* 2014). In Ischia (Italy), Martin *et al.* (2008) showed a complete disappearance of epiphytic coralline algae on *P. oceanica* at a station with an average pH<sub>T</sub> of 7.7, but with very large temporal variations (< 7.0–> 8.1). There, *P. oceanica* had greater biomass only at the station nearest to the CO₂ source (Hall-Spencer *et al.* 2008). In contrast, in Vulcano Island (Italy), in the Mediterranean Sea, Apostolaki *et al.* (2014) observed no significant decrease in the epiphyte load and carbonate content for epiphytes on *Cymodocea nodosa* at pH<sub>T</sub> 7.65 compared to control (pH<sub>T</sub> 8.01). They also observed relatively increased photosynthesis yet lower biomass for *C. nodosa* at the pH<sub>T</sub> 7.65 station. Vent systems are not perfect predictors of future ocean ecology owing to large variability of pH in space and time (Hall-Spencer *et al.* 2008; Kerrison *et al.* 2011), which makes it difficult to determine a reliable dose–response relationship, and to identify thresholds or tipping points. Based on available information, it is difficult to predict how ocean acidification will impact *P. oceanica* meadows over the next century.

Any change in seagrass and epiphyte abundance could have
repercussions throughout the meadow, affecting its ability to
store carbon, provide food for fishes and invertebrate herbi-
vores and maintain biogeochemical processes (e.g. nutrient
cycling, oxygen levels, carbon cycle). Our objective with this
study was, for the first time, to perform a highly controlled
laboratory experiment using the whole structure of *P. ocean-
ica* including epiphytes to better evaluate the effects of pH
levels projected for the upcoming decades on meadows. More
specifically, we maintained shoots, including portions of the
root and rhizomes and epiphytes for 4 weeks at three pH<sub>T</sub>
levels (ambient, 7.7 and 7.3). Net calcification rates, biomass,
community composition and abundance of epiphytes as well
the growth of the dominant epiphytic producer were measured
to answer whether ocean acidification will impact (i) the abil-
ity of certain epiphytes to form and maintain their calcified
structures and (ii) epiphyte community composition and abun-
dance. Furthermore, whole shoot net productivity and fluores-
cence and productivity of epiphyte-free secondary leaves were
measured to answer the following questions: (i) Will
*P. oceanica* shoot productivity be increased with ocean acidi-
fication when the physiological demands of different struc-
tures and epiphytes are included? (ii) Will the change in
epiphyte abundances alter the photosynthetic response to
increased pCO₂? The results evaluate whether a biological

interaction (seagrass–epiphyte) under an altered environmental
condition (pH) affects ecologically relevant processes in a
meadow.

**Materials and methods**

SPECIMEN COLLECTION AND PREPARATION

Sixty shoots (33 cm maximum leaf length) of *P. oceanica* were collected on 3 February 2014, by SCUBA divers, at 12 m depth in the Bay of Villefranche (NW Mediterranean Sea, France; 43°40.73′ N, 07°19.39′ E). Shoots were placed in black plastic bags to prevent photograph-damage, transported to the Laboratoire d'Océanographie de Villefranche and immersed for 48 h in a tank containing 50 mg L⁻¹ of calcite, under ca. 150 µmol photons m⁻² s⁻¹ in a room maintained close to *in situ* seawater temperature conditions (14–15 °C). During the staining period, epiphytes were fed *ad libitum* with the microalga *Isochrysis galbana* to ensure optimal growth rates and calcification. After marking with calcite, plants were haphazardly sorted into 12 sets of 5 shoots. Using plastic ties, each set of shoots was attached by their rhizomes to a weighted plastic grid to create a single transplant unit. Units were placed into 12, 8.4 L perspex, cylindrical aquaria with an open top. Dried and sieved sediment collected onsite and from a nearby beach was placed at bottom of aquaria to stabilize roots and improve water clarity.

EXPERIMENTAL SET-UP

For the duration of the experiment, between the 4 February and 21
March 2014, plants were maintained in the same chilled laboratory
under conditions designed to mimic the ambient environment for win-
ter months in the north-western Mediterranean Sea at 12 m depth.
The experimental design is shown in Fig. S1 (Supporting Informa-
tion). A light bank was constructed using six 39 W Solar Nature
Ultra light (JBL) tubes set to a 9.5:14.5 light: dark photoperiod.
Plants received a maximum of 85 µmol photon m⁻² s⁻¹ at the sur-
face of the cylinders above the canopy, 50–60 µmol photon m⁻² s⁻¹
at the middle of the shoots and 20–25 µmol photon m⁻² s⁻¹ near the
base of the shoot. Irradiance was measured with fibre quantum sensor
attached to a diving-pulse amplitude modulated fluorometer (diving-
PAM, Walz, Germany).

The experimental set-up consisted of three treatments [pH 8.1 (con-
trol), pH 7.7 and pH 7.3]. Four cylinders were randomly assigned to
each treatment (Fig. S1). Three 200-L header tanks supplied seawater,
by gravity, to the 12 cylinders (four cylinders per header tank). The
flow to the cylinders was adjusted to reach complete renewal of sea-
water in about 15 min. Header tank seawater was pumped from a
depth of 8 m at 100 m from the coast at a minimal rate of 50 L h⁻¹.
A pump was located in each header tank to ensure mixing.

Plants were acclimated at ambient pH levels (~8.1) for about
1 week to ensure stable photo-physiology (see section PAM fluores-
cence below). Thereafter, in the perturbed header tanks, pH was
decreased by ~0.4 and ~0.7 pH units in pH 7.7 and pH 7.3, respec-
tively, and plants were maintained at these conditions for 4 weeks.
The pH offset was controlled by bubbling pure-CO₂ in the header
tanks using a continuous pH-stat system (IKS, Karlsbad, Germany).
The pH electrodes of the system were intercalibrated every 2 days
using a glass combination electrode (Metrohm Ecotrode plus, Cour-
taboeuf, France) calibrated on the total scale using TRIS buffer with
a salinity of 38 corresponding to the salinity in the Bay of
Villefranche.
CARBONATE CHEMISTRY AND NUTRIENT
MEASUREMENTS

Every 2 or 3 days, samples were taken for dissolved inorganic carbon ($C_T$, 60 mL) in each header tank and for total alkalinity ($A_T$, 300 mL) only in the ambient header tank. Samples for $A_T$ were first filtered on GF/F membranes, and both types of samples were immediately inoculated with 100 µL of a saturated solution mercuric chloride (HgCl₂) and analysed within 2 days. $C_T$ was analysed using an AIRICA (Automated Infra Red Inorganic Carbon Analyzer; Marianda, Kiel, Germany) coupled to a LI-COR infrared gas analyser (LI-6262), on triplicate 1.2 mL subsamples at 22 °C. The instrument was calibrated before every set of measurements using certified reference material from A. Dickson (Scripps University, San Diego, USA; Batch 132). $A_T$ was determined at 22 °C on triplicate 50 mL subsamples by potentiometric titration on a Metrohm Titrando 888 titrator coupled to a glass electrode (Metrohm Ecotrode plus) calibrated daily on the total scale using TRIS buffers of salinity 38 and a thermometer (pt 1000). $A_T$ was calculated as described by Dickson, Sabine & Christian (2007). During the experiment, standards provided by A. Dickson (batch 132) were used to check precision and accuracy (n = 11; 1.3 and 1.3 µmol kg⁻¹, respectively). pH_T levels were also measured every 2 days in each cylinder using a glass combination electrode (Metrohm Ecotrode plus) calibrated as described above. The carbonate chemistry was assessed using $C_T$ and $A_T$ and the R package seacarb (Lavigne, Epitalon & Gattuso 2014). Water was collected from the header tanks once a week for nutrient analysis and stored frozen (−20 °C). Nutrients were analysed in triplicate using an autoanalyser (SEAL Analytical GmbH, Norderstedt Germany; AA3 HR).

PAM FLUORESCENCE

A diving-PAM, equipped with a red light emitting diode and an internal halogen lamp, was used to measure fluorescence. During the accumulation period, the quantum yield of dark-adapted leaves was monitored over 5 days from two to five haphazardly selected cylinders between 18:00 and 18:30 (local time). During the manipulation, dark-adapted yields and rapid light curves (RLCs) were measured on separate leaves within the cylinders between 07:30 and 08:30 (local time), just prior to shoot incubations (see Shoot-Epiphyte Metabolism).

We followed the procedures described in Ralph & Gademann (2005). The PAM settings were adjusted to optimize signal (~200 units) and the fibre-optic cable held 8 cm above the sheath at 3 mm distance and 90° angle from the haphazardly selected secondary leaf. The irradiances for RLCs ranged between 0 and 80 µmol photons m⁻² s⁻¹ were applied for 10 s followed by a saturating pulse (0.8 s). The absorption factor used to calculate parameters was determined, following the methods described in Beer & Björk (2000), on 25 March 2014 and averaged 0.78 ± 0.05 SD (n = 2). The rETR values were plotted against the measured irradiances to produce a curve fitted with the exponential model proposed by Platt, Gallegos & Harrison (1980). Parameters derived from the curves include (i) α the photosynthetic efficiency measured by the initial slope of the RLC before the onset of saturation, (ii) the relative maximum electron transport rate, rETRmax, and (iii) E_k, optimal irradiance for maximal electron transport, which is determined by the equation:

$$E_k = \frac{r\text{ETR}_{\text{max}}}{\alpha}.$$

For dark-adapted quantum yield, leaves were placed in the dark for 15 min. Then, leaves were exposed to a white saturating light pulse

(0.8 s) after leaves had been pre-illuminated with the weak red modulated light. The maximum PSII quantum yield was calculated using the equation of Genty, Briantais & Baker (1989) when dark-adapted.

SHOOT-EPHYTE METABOLISM

Immediately before, after 2 weeks and after 4 weeks of exposure to experimental pH levels, net primary productivity (NPP), respiration (R) and net calcification rates (G) were determined from incubations of shoots with their epiphytes. To perform an incubation, all shoots from a cylinder were transferred into smaller transparent chambers (4.7 L) without sediment. Water (14.6 ± 0.3 °C) was mixed by a magnetic stirrer and replaced for each incubation with water from the corresponding header tank.

Shoot-epiphyte NPP and R measurements were determined from incubations that lasted 1 h and were conducted between 9:00 and 10:00 (local time) at 60–65 µmol photons m⁻² s⁻¹ at the middle of the chamber. Incubations for determination of R were conducted in the dark between 19:00 and 20:00 (local time). Incubation chambers were sealed on top and pH was allowed to fluctuate. In each chamber, dissolved oxygen (O₂) was continuously monitored by PreSens© OXY-4 oxygen meter with PSt3 fibre-optic minisensors that were calibrated prior to use (0% and 100% saturation). Changes in O₂ production per min was used to determine NPP and R. NPP rates were standardized to the dry weight of the leaves while R rates were standardized to the dry weight of rhizomes, roots and leaves. Dry weights were determined at the end of the experiment.

Shoot G measurements were determined from incubations that lasted 7 h and were conducted in daylight between 11:00 and 18:00 (local time) at the same light level as NPP determinations (see above). Chambers were left open, air from outside the building was bubbled to maintain constant oxygen levels and, in the pH 7.7 and pH 7.3 cylinders, pH was further controlled by bubbling pure-CO₂ using a continuous pH-stat system following the method described above. Net rates of calcification were estimated using the alkalinity anomaly technique (Smith & Key 1975). Seawater was sampled at the beginning and end of the incubations for measurements of $A_T$ as described above. Net calcification rates were calculated based on the change in $A_T$ during the incubation following the equation:

$$G = - \frac{\Delta A_T}{2t},$$

where G is net calcification rate (in µmol CaCO₃ kg⁻¹ h⁻¹) and t is the incubation time (in h). G rates were further standardized to the dry weight of rhizomes, roots and leaves.

LEAF PHOTOSYNTHESIS VS. IRRADIANCE CURVES

At the end of the experiment, a series of incubations with *Posidonia* leaf segments were performed to produce photosynthesis vs. irradiance (PE) curves. A shoot was haphazardly selected from each cylinder and a 10-cm segment was cut from the upper portion of a secondary leaf blade. Epiphytes and sessile animals were gently scraped away. Half of this segment (5 cm) was placed onto a holder put inside a sealed 60-mL biochemical oxygen demand (BOD) bottle filled with seawater from the respective header tank (pH 8.1, pH 7.7 and pH 7.3) with a magnetic stirrer below. BOD bottles were then submerged into a 50 L aquarium filled with seawater maintained at 14.8 ± 0.4 °C (SD). Light was provided at 90° angle to the segment surface by a 250 W metal-halide lamp and adjusted to nine increasing irradiances (5–150 µmol photons m⁻² s⁻¹) using neutral density
ultraviolet filters. Irradiance was measured using a diving-PAM fibre quantum sensor. Leaf segments were also placed into darkness prior to light exposure for determination of R. R was assumed to be constant in light and dark. Plants were maintained at each irradiance level or in darkness for 15–30 min while the concentration of O₂ was continuously monitored using the oxygen meter described previously. For each incubation, seawater in the BOD bottle was replaced with water from the respective header tank. There was a technical error in measures for one cylinder from pH 7.7 and this sample was excluded from analyses. Thus, the sample size is n = 3 for pH 7.7 and n = 4 for pH 8.1 and pH 7.3.

The other half of the leaf segment was dried in a 60 °C oven until constant weight and used to normalize O₂ rates (µmol O₂ g dw⁻¹ min⁻¹). Normalized O₂ rates were plotted against irradiance and curve parameters estimated through a nonlinear regression, using the hyperbolic tangent model equation of Jassby & Platt (1976).

$$P = P_{\text{gmax}} \times \tanh(\alpha \times E/P_{\text{max}}) + R,$$

where Pmax (µmol O₂ g dw⁻¹ min⁻¹) is the maximum photosynthetic rate and represents the light-saturated or carbon-limited region of photosynthesis, E is the photon flux density (µmol photons m⁻² s⁻¹), and α is the slope or the efficiency with which a plant harvests light (µmol O₂ g dw⁻¹ min⁻¹/µmol photons m⁻² s⁻¹). The irradiance at which photosynthesis is saturated (Iₖ) was estimated from the intersection of the initial slope with Pmax (Iₖ = Pmax/α). Iₖ or the irradiance at which photosynthesis equals respiration (i.e. above which plants exhibit net photosynthesis) was determined using the ratio R/α.

## LEAF PIGMENT ANALYSIS

The remaining 5 cm leaf segment was cut into pieces and ground in chilled glass homogenizer with 90% acetone for extraction of pigments. The glass tube was wrapped in aluminium foil and left for 24 h. Extract was centrifuged for 15 min and the absorbance of the supernatant measured in quartz glass cuvette with a UV/VIS spectrophotometer (Lambda 2; Perkin Elmer, Waltham, MA, USA). Total chlorophyll concentration was calculated following the equations of Jeffrey & Humphrey (1975).

## EPIPHYTE COMMUNITY COMPOSITION AND COVER

At the end of the experiment, epiphyte community abundance and composition in each cylinder was estimated by using two approaches. First, direct counts on a standard number of leaves were performed. This allows the identification of impact to rare, small or solitary epiphytes. Second, a point-contact method was used to determine the per cent cover of each taxon. This method quantifies the abundance of organisms that occupy large surface area, are colonial, or for species where it is difficult to distinguish individuals (e.g. algae). Lastly, presence data from these two approaches were combined to compare the overall species richness. The organisms were identified to the lowest possible taxonomic or functional unit (LPT/FU). The details of groupings can be found with Table S1.

We counted and identified epiphytic individuals on an external and intermediary leaf randomly collected from each cylinder. The leaves from different cylinders were intact, appeared healthy and of similar length (~20 cm). Leaves were visually searched on internal and external faces, in entirety, under a dissecting microscope at 10× magnification and organisms encountered identified. Algae, which have morphologies not conducive to counts, were excluded from this analysis.

For the point-contact approach, a ZooScan (Hydroptic, France; Gorsky et al. 2010) was used to produce high-resolution colour images of leaves and epiphytes. Leaves were cut from their rhizomes, just above the sheath, and placed in their entirety, without overlapping onto the scanner bed. Then, one side of all the leaves was scanned at 2400 dpi with illumination from below.

The high-resolution scans of leaves from two randomly selected shoots per cylinder were used to determine the per cent cover of epiphytes, using the software IMAGEJ® (National Institute of Health, Washington, DC, USA). The cell counter plug-in for IMAGEJ®, superimposed a grid (0.1 × 0.1 cm) on the image of the leaf. Organisms that occurred directly underneath each intersection of the grid were identified to the LPT/FU and counted. Per cent cover by taxon was determined for each shoot by dividing the LPT/FU intersections by the total number of intersections and multiplying by 100. Shoot values were averaged so that each organism had one per cent cover value by cylinder.

## GROWTH OF CRUSTOSE CORALLINE ALGAE

Randomly collected *Posidonia* leaves from the different cylinders were used to take pictures of encrusting algae under an epifluorescence binocular. Using the software IMAGEJ®, the colour images were split into three channels. This produced a blue channel that was completely dark representing the leaf, a green channel which pictured the green of the calcine-stained portion of the epiphyte and a red channel that showed clearly the unstained epiphyte area. For the red and green channels, the threshold was adjusted so that only the green or red area of the epiphyte was completely highlighted (see Fig. S2). Growth after the calcine mark was estimated as the ratio of red to green pixels.

## EPIPHYTE CALCAREOUS MASS

At the conclusion of the experiment, all leaves were stripped of epiphytes by scraping them with a dull razor blade. The percentage of CaCO₃ contained in the epiphytes was assessed using the weight loss after acidification method (Bosence 1989; Perry & Beavington-Penney 2005). Epiphytes were dried at 40 °C for 48 h, weighted, acidified with 4 mL of 5% HCl, rinsed twice with deionised water, dried at 40 °C for 48 h and reweighted.

## DATA TREATMENT AND STATISTICS

During the acclimation period, quantum yields were averaged by day and the variability between days for each cylinder of plants calculated. No statistical testing was performed on these data.

Parameters derived from RLCs, quantum yields, NPP, R and G collected were screened for normality and homogeneity of variance. Data were transformed if they did not meet the requirements for parametric testing. Then, a series of two-way repeated-measures analyses of variance (ANOVA) were used to test for statistical differences among treatments and sampling intervals. An interaction of sampling interval and treatment was included. Tukey's pairwise comparisons were performed when two-way ANOVA revealed significant differences.

Parameters derived from PE curves, total chlorophyll concentrations, amounts of CaCO₃ and percentage cover of epiphytes were compared among different treatments using one-way ANOVA followed, if significant (P < 0.05), by Tukey's multiple comparison tests.

Multivariate analyses were used to compare the epiphyte community. First, data were transformed to account for the patchy
nature of epiphytes. A square root transformation was applied on the per cent cover data, while a more severe fourth root transformation was applied on the count data. For each data set, the Bray-Curtis Index was used to create resemblance matrix between cylinders. Then, statistical differences were tested for each data set using a one-way analysis of similarity (ANOSIM) with 999 permutations among the *a priori* defined treatments. These analyses included pairwise comparisons among treatments. When significant differences were found, a SIMPER analysis was used to identify the amount each LPT/FU contributes to the overall dissimilarity.

For growth based on calcine staining, quality usable images were pooled by treatment. Thus, our sample size is the number of images analysed per treatment and not the replicate number of cylinders. As data did not meet parametric requirements, growth (red/green ratio) was compared among the different treatments using a Kruskal-Wallis test followed, if significant (P < 0.05), by a Dunn's pairwise multiple comparisons.

# Results

## ACCLIMATION PERIOD

Over the first few days of the acclimation period (7–9 February), the photo-physiology of plants was not stable (Fig. S3). Initial variation in quantum yields for a single cylinder of plants from day-to-day was as much as 0.13 and the average quantum yield from five cylinders of plants was 0.74 with a standard deviation of 0.07. After plants were held in the laboratory for a week, the within-sample variability between days (10–11 February) was much reduced, with a maximum difference of 0.04. The yield averaged from five cylinders of plants by the 4th day of acclimation was slightly lower, 0.736, and the deviation reduced to 0.02. It was clear by the 7th day of the acclimation period that the photo-physiology of plants was stable. The mean quantum yield had not changed over 4 days (mean 0.73 ± 0.01 SD) and the day-to-day (11–12 February) variation within a cylinder was reduced to 0–0.007.

## ENVIRONMENTAL PARAMETERS AND CARBONATE CHEMISTRY

Environmental parameters measured in the header tanks every 2–3 days are shown in Table 1. Temperature and $A_T$ were relatively constant during the experiment within a range of 14.3–15.3 °C and 2547–2563 µmol kg$^{-1}$, respectively. Under ambient conditions, $pH_T$ averaged 8.11 ± 0.02 (±1 SD), while in the two low pH treatments, pH 7.7 and pH 7.3, $pH_T$ was decreased to, on average, 7.72 ± 0.05 and 7.34 ± 0.04, respectively. Shoots maintained under pH 8.1 and pH 7.7 conditions were exposed to seawater always supersaturated with respect to both calcite and aragonite. In contrast, for pH 7.3, seawater was under-saturated regarding aragonite ($\Omega_a$ = 0.5–0.8) and close to saturation for calcite ($\Omega_c$ = 0.8–1.2). $pH_T$ levels measured directly in the cylinders are shown in Fig. 1 and were close to $pH_T$ levels measured in the respective header tanks.

## PAM FLUORESCENCE

*Posidonia oceanica* leaves produced RLCs with low $r$ETR$_{max}$, $E_k$, and alpha parameters (Fig. 2a–f). Overall (n = 36 measures from 12 cylinders of plants), the mean (±SD) alpha was 0.289 ± 0.068 (Fig. 2d). The $r$ETR$_{max}$ values ranged from 1.3 to 8.0 µmol e⁻ m⁻² s⁻¹ (Fig. 2e); while $E_k$ ranged from 10 to 26 µmol photons m⁻² s⁻¹ (Fig. 2f).

At initial measures, RLCs were similar in $r$ETR at each irradiance for all treatments (Fig. 2a). Then, at two- and 4-week intervals, there is more differentiation in the RLCs among treatments, but from analyses on the derived parameters, there was no statistical indication of a pH effect. However, there was a non-significant trend of elevated RLCs with lowered pH at week two and then this trend was absent at week four (Fig. 2b, c). The trend is reflected in the derived parameters (Fig. 2d–f). $E_k$ was the only parameter found to statistically differ among treatments (Fig. 2f, two-way repeated-measures ANOVA, for treatments: d.f. = 2, MS = 0.0373, *F* = 5.43, *P* = 0.03). The pH 7.7 plants were significantly lower in $E_k$ from pH 7.3 plants (*P* < 0.02), but there was no difference between pH 7.3 and pH8.1.

Dark-adapted quantum yield values were variable. No statistical differences among treatments or within sampling intervals were found (Fig. 2g; two-way repeated-measures ANOVA, treatment, d.f. = 2, MS = 0.004, *F* = 0.21, *P* = 0.8; sampling interval, d.f. = 2, MS = 0.001, *P* = 0.4; treatment x sampling interval, d.f. = 4, MS = 0.001, *F* = 0.64, *P* = 0.64). Values of yield from all plants over the three sampling intervals (n = 36 measures from the 12 cylinders of plants) ranged from 0.689 to 0.879.

## SHOOT-EPIPHYTE METABOLISM

NPP of *P. oceanica* shoots differed among treatments and sampling intervals (Fig. 3; treatments, d.f. = 2, MS = 0.176, *F* = 6.79, *P* = 0.007; treatment x sampling interval, d.f. = 4, MS = 0.082, *F* = 3.15, *P* = 0.04). Plants from pH 7.3 greatly increased rates from initial rates at week two (Tukey's pairwise comparison test, *P* = 0.002), and rates remained elevated from initial at week four (Tukey's pairwise comparison test, *P* = 0.006). Furthermore, NPP rates for plants from pH 7.3 were statistically greater than pH8.1 plants at the conclusion of the study (Tukey's pairwise comparison, *P* = 0.02).

Respiration rates were consistent among plants exposed to different pH treatments over the duration of the experiment (Fig. 3). Over the 36 measures from 12 cylinders of plants, rates ranged from −0.07 to −0.01 µmol O₂ g dw⁻¹ min⁻¹.

Net calcification statistically differed among sampling intervals and pH treatments (Fig. 3, two-way repeated-measures ANOVA, treatment, d.f. = 2, MS = 0.0001, *F* = 17.4, *P* < 0.001; sampling interval, d.f. = 2, MS = 0.0002, *F* = 42.6, *P* < 0.001; treatment x sampling interval, d.f. = 4, MS = 0.00002, *F* = 5.16, *P* = 0.006). Before the start of the pH regulation, net calcification rates of all 12 cylinders were positive and similar. After 2 weeks of pH regulation, G decreased in all treatments. Under ambient conditions,
<table><caption>**Table 1.** Carbonate chemistry in the three header tanks (pH 8.1, pH 7.7 and pH 7.3) during the experimental period</caption><thead><tr><th rowspan="2">Day</th><th rowspan="2">Temp (°C)</th><th rowspan="2">A<sub>T</sub> (µmol kg<sup>-1</sup>)</th><th colspan="3">pH 8.1</th><th colspan="3">pH 7.7</th><th colspan="3">pH 7.3</th></tr><tr><th>pH<sub>T</sub></th><th>pCO<sub>2</sub> (µatm)</th><th>C<sub>T</sub> (µmol kg<sup>-1</sup>)</th><th>Ω<sub>a</sub></th><th>pH<sub>T</sub></th><th>pCO<sub>2</sub> (µatm)</th><th>C<sub>T</sub> (µmol kg<sup>-1</sup>)</th><th>Ω<sub>a</sub></th><th>pH<sub>T</sub></th><th>pCO<sub>2</sub> (µatm)</th><th>C<sub>T</sub> (µmol kg<sup>-1</sup>)</th><th>Ω<sub>a</sub></th></tr></thead><tbody><tr><td>0</td><td>14.5</td><td>2556</td><td>8.09</td><td>392</td><td>2280</td><td>3.0</td><td>4.6</td><td>7.71</td><td>1053</td><td>2459</td><td>1.4</td><td>2.2</td><td>7.33</td><td>2661</td><td>2595</td><td>0.6</td><td>1.0</td></tr><tr><td>2</td><td>14.5</td><td>2567</td><td>8.13</td><td>349</td><td>2264</td><td>3.2</td><td>5.0</td><td>7.72</td><td>1024</td><td>2464</td><td>1.4</td><td>2.2</td><td>7.37</td><td>2402</td><td>2590</td><td>0.7</td><td>1.1</td></tr><tr><td>5</td><td>14.7</td><td>2562</td><td>8.12</td><td>362</td><td>2266</td><td>3.1</td><td>4.9</td><td>7.75</td><td>951</td><td>2447</td><td>1.5</td><td>2.4</td><td>7.38</td><td>2350</td><td>2581</td><td>0.7</td><td>1.1</td></tr><tr><td>7</td><td>14.8</td><td>2557</td><td>8.12</td><td>361</td><td>2261</td><td>3.2</td><td>4.9</td><td>7.77</td><td>914</td><td>2436</td><td>1.6</td><td>2.5</td><td>7.29</td><td>2924</td><td>2610</td><td>0.6</td><td>0.9</td></tr><tr><td>9</td><td>14.7</td><td>2560</td><td>8.09</td><td>388</td><td>2279</td><td>3.0</td><td>4.7</td><td>7.78</td><td>883</td><td>2433</td><td>1.6</td><td>2.5</td><td>7.38</td><td>2352</td><td>2579</td><td>0.7</td><td>1.1</td></tr><tr><td>12</td><td>14.5</td><td>2562</td><td>8.10</td><td>376</td><td>2276</td><td>3.1</td><td>4.7</td><td>7.74</td><td>973</td><td>2451</td><td>1.5</td><td>2.3</td><td>7.26</td><td>3127</td><td>2626</td><td>0.5</td><td>0.8</td></tr><tr><td>14</td><td>14.6</td><td>2561</td><td>8.12</td><td>355</td><td>2263</td><td>3.2</td><td>4.9</td><td>7.71</td><td>1061</td><td>2464</td><td>1.4</td><td>2.2</td><td>7.44</td><td>2047</td><td>2560</td><td>0.8</td><td>1.2</td></tr><tr><td>16</td><td>14.4</td><td>2553</td><td>8.11</td><td>364</td><td>2263</td><td>3.1</td><td>4.8</td><td>7.67</td><td>1170</td><td>2473</td><td>1.3</td><td>2.0</td><td>7.33</td><td>2674</td><td>2594</td><td>0.6</td><td>0.9</td></tr><tr><td>19</td><td>14.5</td><td>2547</td><td>8.07</td><td>405</td><td>2280</td><td>2.9</td><td>4.5</td><td>7.62</td><td>1318</td><td>2484</td><td>1.1</td><td>1.8</td><td>7.34</td><td>2581</td><td>2582</td><td>0.6</td><td>1.0</td></tr><tr><td>21</td><td>14.3</td><td>2549</td><td>8.09</td><td>390</td><td>2275</td><td>2.9</td><td>4.6</td><td>7.66</td><td>1198</td><td>2473</td><td>1.2</td><td>1.9</td><td>7.31</td><td>2760</td><td>2595</td><td>0.6</td><td>0.9</td></tr><tr><td>23</td><td>14.7</td><td>2549</td><td>8.13</td><td>350</td><td>2248</td><td>3.2</td><td>5.0</td><td>7.70</td><td>1061</td><td>2452</td><td>1.4</td><td>2.2</td><td>7.31</td><td>2794</td><td>2595</td><td>0.6</td><td>0.9</td></tr><tr><td>26</td><td>15</td><td>2563</td><td>8.14</td><td>340</td><td>2250</td><td>3.3</td><td>5.1</td><td>7.73</td><td>994</td><td>2453</td><td>1.5</td><td>2.3</td><td>7.36</td><td>2511</td><td>2590</td><td>0.7</td><td>1.0</td></tr><tr><td>28</td><td>14.7</td><td>2560</td><td>8.09</td><td>384</td><td>2277</td><td>3.0</td><td>4.7</td><td>7.72</td><td>1024</td><td>2456</td><td>1.4</td><td>2.2</td><td>7.31</td><td>2813</td><td>2606</td><td>0.6</td><td>0.9</td></tr><tr><td>30</td><td>15</td><td>2554</td><td>8.12</td><td>353</td><td>2251</td><td>3.2</td><td>5.0</td><td>7.76</td><td>937</td><td>2436</td><td>1.6</td><td>2.4</td><td>7.32</td><td>2709</td><td>2593</td><td>0.6</td><td>1.0</td></tr><tr><td>33</td><td>15.3</td><td>2556</td><td>8.15</td><td>330</td><td>2235</td><td>3.4</td><td>5.3</td><td>7.80</td><td>828</td><td>2415</td><td>1.7</td><td>2.7</td><td>7.37</td><td>2449</td><td>2578</td><td>0.7</td><td>1.1</td></tr><tr><td>Mean</td><td>14.7</td><td>2557</td><td>8.11</td><td>367</td><td>2265</td><td>3.1</td><td>4.8</td><td>7.72</td><td>1026</td><td>2453</td><td>1.4</td><td>2.2</td><td>7.34</td><td>2610</td><td>2592</td><td>0.6</td><td>1.0</td></tr><tr><td>SD</td><td>0.3</td><td>6</td><td>0.02</td><td>22</td><td>14</td><td>0.1</td><td>0.2</td><td>0.05</td><td>128</td><td>18</td><td>0.2</td><td>0.2</td><td>0.04</td><td>269</td><td>15</td><td>0.1</td><td>0.1</td></tr></tbody></table>

Temperature (Temp), total alkalinity (A<sub>T</sub>), pH on the total scale (pH<sub>T</sub>), partial pressure of CO<sub>2</sub> (pCO<sub>2</sub>), dissolved inorganic carbon (C<sub>T</sub>) and saturation states with respect to aragonite and calcite (Ω<sub>a</sub> and Ω<sub>c</sub>, respectively) are presented.
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** pH (on the total scale; pH~T~) levels measured every 2–3 days in the cylinders assigned to the three different treatments (pH 8.1, pH 7.7 and pH 7.3) and containing the shoots used for the experiment. Error bars correspond to standard deviations between the four replicate cylinders per treatment (they are usually smaller than the symbols).</figcaption></figure>

although remaining positive, a decrease of 53 (±1 SD) % was observed at week two with rates remaining constant at week four (decrease of 49 ± 17%). In pH 7.7, net calcification after 2 weeks was more variable among the replicate communities and dropped to an average level close to 0 (decrease of 92 ± 28%). These rates were statistically differed from initial rates but did not differ from the rates at ambient (pH8.1) in week two. Although very large variations between

replicates were found, rates were maintained at a similar level at week four (decrease of 91 ± 61%). For pH 7.3, net calcification rates were reduced substantially (and statistically) to reach negative values at week two and week four (–209 ± 53% and –205 ± 40%, respectively). Statistical tests showed that pH had an effect on net calcification rates although, due to large variations especially under pH 7.7, this effect was only significantly different among both week two and week four intervals at pH 7.3.

## LEAF PHOTOSYNTHESIS VS. IRRADIANCE CURVES

The PE curves produced from the leaves held in pH 7.3 clearly differed from the ones obtained on leaves in pH8.1 and pH 7.7 (Fig. 4). The series of one-way ANOVAS support a treatment effect for α (Fig. 4b, d.f. = 2, MS = 0.035, *F* = 11.1, *P* = 0.005), *P*<sub>max</sub> (Fig. 4d, d.f. = 2, MS = 6.5, *F* = 6.4, *P* = 0.02), *I*<sub>c</sub> (Fig. 4f, d.f. = 2, MS = 0.397, *F* = 11.8, *P* = 0.004) and *R* (Fig. 4e, d.f. = 2, MS = 0.018, *F* = 11.1, *P* = 0.005). The α and the *P*<sub>max</sub> of leaves were greater at each level of pH treatment (pH 8.1 < pH 7.7 < pH 7.3) yet these parameters only statistically differed between pH8.1 and pH 7.3 (Tukey's pairwise comparisons: α, pH8.1 to pH 7.3, *P* = 0.004; *P*<sub>max</sub>, pH8.1 to pH 7.3, *P* = 0.02). Leaves from pH 7.7 and pH 7.3 shared a similar *I*<sub>k</sub> (mean ± SE, pH 7.7 = 17.6 ± 2.2; pH 7.3 = 17.6 ± 0.8 µmol photon m<sup>-2</sup> s<sup>-1</sup>) and *I*<sub>c</sub> (mean ± SE, pH 7.7 = 0.78 ± 0.12; pH

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Changes in fluorescence values for *Posidonia oceanica* leaves by treatment and sampling interval. Panels (a–c) show relative electron transport rates (*r*ETR) mean (±SE) at increasing irradiances initially (a), at week two (b) and at week four (c). Panels (d–g) show mean (±SE) of the derived RLC parameter [(d–f): photosynthetic efficiency α, (d) relative maximum electron transport rate *r*ETR<sub>max</sub>, (e) optimal irradiance for maximal relative electron transport *E*<sub>k</sub>, (f) and the dark-adapted quantum yield (g) measured initially, at week 2 and at week 4.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Metabolic rates (mean ± SE) of *Posidonia oceanica* shoots + epiphytes maintained under different pH treatments (pH 8.1, pH 7.7 and pH 7.3), as measured repeatedly at different sampling intervals, held under constant irradiance for measures in light. Respiration (a), net primary productivity (b) and net calcification (c) are represented initially and at two and 4 weeks for the different pH treatments. Lower case letters above bars represent similar statistical groupings.</figcaption></figure>

7.3 = 0.94 ± 0.04 µmol photons m⁻² s⁻¹) and leaves from these treatments tended to saturate or compensate rates at lower irradiances than the leaves from pH8.1 (mean ± SE, Iₖ = 21.9 ± 1.9, I_c = 1.4 ± 0.11 µmol photons m⁻² s⁻¹). However, only I_c values differed more than what would be predicted by chance. Pairwise significant differences were found for I_c between pH8.1 and pH 7.7 (P = 0.005) and between pH 8.1 and pH 7.3 leaves (P = 0.02). In contrast, R rates from leaves measured during PE incubations were largest at the lowest pH treatment (pH 7.3), but pH 7.3 leaves were only statistically lower than the rates determined from pH 7.7 leaves (Tukey's pairwise comparison, P = 0.004).

## LEAF PIGMENT ANALYSIS

The total chlorophyll content of leaves did not differ with pH levels (one-way ANOVA, d.f. = 2, F = 0.08, P = 0.9). The

mean ± SE total chlorophyll content by treatment was 7.4 ± 1.5, 7.6 ± 0.6, 8.0 ± 1.1 mg Chl L⁻¹ g dw⁻¹ for pH8.1, pH 7.7 and pH 7.3, respectively. In addition, absorbance spectra were similar (Fig. S4).

## EPIPHYTE COMMUNITY COMPOSITION AND COVER

Overall, 43 different LPT/FU were identified as epiphytes among which 12 produce calcified structures. A total of 36, 32 and 27 LPT/FU were found on leaves exposed to pH 8.1, pH 7.7 and pH 7.3, respectively (see Table S1).

The multivariate analyses (ANOSIM) conducted on epiphyte density data, produced from the direct count methodology, failed to find significant differences among treatments (global *R* of 0.77, *P* = 0.11).

The per cent cover and composition of epiphytes, as determined from the images of the scanned seagrass leaves, was different between treatments. The total percentage of epiphyte cover decreased with decreasing pH levels (Fig. 5) and was significantly different between the three treatments (one-way ANOVA; d.f. = 2, *F* = 35.4, *P* < 0.001). Replicates from each treatment reveal a greater similarity in structure within treatments than among treatments. The one-way ANOSIM confirmed a significant difference due to treatment and a global *R* of 0.77 (*P* = 0.001). All statistical pairwise comparisons between treatments were significant with a high global *R* value (0.45–1.0).

The SIMPER analyses (Table 2) identified 83% similarity in the cover of organisms observed from replicate ambient cylinders. The coverage of crustose coralline algae was responsible for 29% of this similarity. On average, crustose coralline algae covered ~10% of the leaf material analysed and comprised 46% of the epiphyte cover. Also, the percentage cover of foraminifera was very similar among replicate ambient cylinders and contributed 12% to the overall ambient (pH 8.1) cylinder similarity. The epiphyte community structure was found to be 83% similar among pH 7.7 cylinders. A proportion of the 41% similarity was due to the percentage of pigmented crustose coralline algae which occupied on average ~4% of the leaf surface and a mean of 33% of the epiphyte cover. Bleached crustose coralline algae had a mean cover of ~3% and comprised 24% of the epiphyte coverage. There was 80% community similarity among pH 7.3 cylinders. In pH 7.3, the epiphyte cover was dominated by the low coverage (mean < 1.4% cover) of biofilm, Bryozoa and other Rhodophyta.

The greatest dissimilarity (38%) in epiphyte community structure based on coverage data occurred between pH 8.1 and pH 7.3, with less dissimilarity between pH 7.7 and pH 7.3 (34%), and even less dissimilarity between pH 8.1 and pH 7.7 (20%; Table 2). Dissimilarities were driven largely by the bleaching or loss of crustose coralline algae. Pigmented crustose coralline algae coverage was ~45% greater on ambient leaves than the cover determined for pH 7.7 leaves, and it was absent from pH 7.3 leaves. In contrast, bleached crustose coralline algae coverage was greatest in pH 7.7 and lowest in pH 7.3.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Photosynthesis vs. irradiance (PE) curves (a) and derived parameters (b–f, mean ± SE) for *Posidonia oceanica* leaves, cleaned of epiphytes, maintained under different pH treatments. In panel (a), lines represent modelled curves from mean values for each of the treatments. In panels (b–f), lower case letters above and below vertical bars represent similar statistical groupings among pH treatments.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Per cent cover of epiphytes and percentage of calcium carbonate (% CaCO₃) at the end of the experiment in each of the three treatments. Error bars correspond to standard deviations between the four replicate cylinders per treatment. Different letters above bars indicate similar statistical groupings.</figcaption></figure>

## CORALLINE ALGAE GROWTH

The comparison of growth based on calcein staining (red to green pixels ratio) showed that growth was maximal under ambient (pH8.1) conditions (0.87 ± 0.03; *n* = 7), minimal for pH 7.3 (0.29 ± 0.05; *n* = 4) and intermediate for pH 7.7

(0.63 ± 0.08; *n* = 16). Statistical tests showed that only two treatments, pH 7.3 and pH 8.1, differed.

## CALCIUM CARBONATE MASS

Percentage of CaCO₃ (±SD) was highest under ambient (pH8.1) conditions (36 ± 11%) and lowest at pH 7.3 (18 ± 11%; Fig. 5). Results of the one-way ANOVA and of the Tukey's multiple comparison tests showed that only these two treatments were significantly different.

## Discussion

This study confirmed that *P. oceanica* productivity increases with decreasing pH and that the epiphyte community is sensitive to decreases in pH. The loss in the epiphytic crustose coralline algae did not change the effect of pH on shoot productivity. Increases in seagrass productivity with lowered pH is still apparent when plant structures and epiphytes with different photosynthetic and respiratory demands are directly included in investigations. The strongest effects on epiphytes and seagrass productivity were observed at the lowest pH~T~ level of 7.32, coinciding with a clear under-saturation of seawater aragonite and magnesian calcite (0–25% MgCO₃) and with an increase in CO₂ availability (Koch et al. 2013). At a
<table><caption>**Table 2.** Results from SIMPER analyses showing the different groups and their respective percentage cover that contribute to the % dissimilarity in community structure among treatments (pH 8.1, pH 7.7 and pH 7.3)</caption><thead><tr><th>Dissimilarity = 20.4%</th><th>pH 8.1<br>% cover<br>Mean ± SE</th><th>pH 7.7<br>% cover<br>Mean ± SE</th><th>Dissimilarity ± SD</th><th>Contribution (%)</th><th>Cumulative contribution (%)</th></tr></thead><tbody><tr><td>Crustose coralline algae (C)</td><td>9.8 ± 1.8</td><td>4.4 ± 0.5</td><td>4.8 ± 2.0</td><td>23.6</td><td>23.6</td></tr><tr><td>Biofilm</td><td>3.6 ± 0.5</td><td>1.8 ± 0.8</td><td>3.3 ± 1.1</td><td>16.1</td><td>39.7</td></tr><tr><td>Other Rhodophyta</td><td>1.8 ± 0.5</td><td>0.9 ± 0.4</td><td>2.6 ± 1.4</td><td>12.5</td><td>52.2</td></tr><tr><td>Bleached crustose coralline algae (C)</td><td>2.0 ± 0.5</td><td>3.2 ± 1.3</td><td>2.2 ± 1.1</td><td>10.7</td><td>62.9</td></tr><tr><td>Foraminifera (C)</td><td>1.8 ± 0.3</td><td>1.2 ± 0.5</td><td>1.9 ± 1.7</td><td>9.2</td><td>72.2</td></tr><tr><td>Phaeophyceae</td><td>0.3 ± 0.1</td><td>0.1 ± 0.0</td><td>1.8 ± 1.4</td><td>8.6</td><td>80.8</td></tr><tr><td>Bryozoa (C)</td><td>1.4 ± 0.3</td><td>1.2 ± 0.4</td><td>1.6 ± 1.2</td><td>7.7</td><td>88.5</td></tr><tr><td>Porifera</td><td>0.8 ± 0.2</td><td>0.5 ± 0.2</td><td>1.1 ± 1.3</td><td>5.5</td><td>94.0</td></tr><tr><td>Hydrozoa</td><td>0.1 ± 0.0</td><td>0.1 ± 0.1</td><td>0.8 ± 1.4</td><td>4.0</td><td>98.0</td></tr><tr><td>Polychaeta (C)</td><td>0.0 ± 0.0</td><td>0.0 ± 0.0</td><td>0.4 ± 0.9</td><td>2.1</td><td>100.0</td></tr></tbody><thead><tr><th>Dissimilarity = 38.1%</th><th>pH 8.1<br>% cover<br>Mean ± SE</th><th>pH 7.3<br>% cover<br>Mean ± SE</th><th>Dissimilarity ± SD</th><th>Contribution (%)</th><th>Cumulative contribution (%)</th></tr></thead><tbody><tr><td>Crustose coralline algae (C)</td><td>9.8 ± 1.8</td><td>—</td><td>17.1 ± 6.6</td><td>44.9</td><td>44.9</td></tr><tr><td>Biofilm</td><td>3.6 ± 0.5</td><td>1.4 ± 0.6</td><td>4.4 ± 1.1</td><td>11.5</td><td>56.4</td></tr><tr><td>Bleached crustose coralline algae (C)</td><td>2.0 ± 0.5</td><td>0.6 ± 0.4</td><td>3.9 ± 1.4</td><td>10.2</td><td>66.6</td></tr><tr><td>Hydrozoa</td><td>0.1 ± 0.0</td><td>0.6 ± 0.3</td><td>2.9 ± 1.6</td><td>7.5</td><td>74.1</td></tr><tr><td>Foraminifera (C)</td><td>1.8 ± 0.3</td><td>0.7 ± 0.1</td><td>2.7 ± 1.9</td><td>7.2</td><td>81.3</td></tr><tr><td>Other Rhodophyta</td><td>1.8 ± 0.5</td><td>1.2 ± 0.0</td><td>2.3 ± 1.4</td><td>6.0</td><td>87.3</td></tr><tr><td>Phaeophyceae</td><td>0.3 ± 0.1</td><td>0.1 ± 0.0</td><td>2.1 ± 1.5</td><td>5.5</td><td>92.7</td></tr><tr><td>Bryozoa (C)</td><td>1.4 ± 0.3</td><td>1.3 ± 0.3</td><td>1.3 ± 1.6</td><td>3.5</td><td>96.2</td></tr><tr><td>Porifera</td><td>0.8 ± 0.2</td><td>0.7 ± 0.1</td><td>1.0 ± 1.3</td><td>2.5</td><td>98.7</td></tr><tr><td>Polychaeta (C)</td><td>0.0 ± 0.0</td><td>0.0 ± 0.0</td><td>0.5 ± 0.9</td><td>1.3</td><td>100.0</td></tr></tbody><thead><tr><th>Dissimilarity = 34.2%</th><th>pH 7.7<br>% cover<br>Mean ± SE</th><th>pH 7.3<br>% cover<br>Mean ± SE</th><th>Dissimilarity ± SD</th><th>Contribution (%)</th><th>Cumulative contribution (%)</th></tr></thead><tbody><tr><td>Crustose coralline algae (C)</td><td>4.4 ± 0.5</td><td>—</td><td>13.3 ± 7.6</td><td>38.9</td><td>38.9</td></tr><tr><td>Bleached crustose coralline algae (C)</td><td>3.2 ± 1.3</td><td>0.6 ± 0.4</td><td>6.4 ± 1.7</td><td>18.7</td><td>57.6</td></tr><tr><td>Hydrozoa</td><td>0.1 ± 0.0</td><td>0.6 ± 0.3</td><td>2.9 ± 1.6</td><td>8.3</td><td>66.0</td></tr><tr><td>Other Rhodophyta</td><td>0.9 ± 0.4</td><td>1.2 ± 0.4</td><td>2.7 ± 1.2</td><td>7.8</td><td>74.8</td></tr><tr><td>Biofilm</td><td>1.8 ± 0.8</td><td>1.4 ± 0.6</td><td>2.6 ± 1.8</td><td>7.5</td><td>81.3</td></tr><tr><td>Foraminifera (C)</td><td>1.2 ± 0.5</td><td>0.7 ± 0.1</td><td>2.1 ± 1.0</td><td>6.1</td><td>87.5</td></tr><tr><td>Bryozoa (C)</td><td>1.2 ± 0.4</td><td>1.3 ± 0.3</td><td>2.0 ± 1.2</td><td>5.8</td><td>93.2</td></tr><tr><td>Porifera</td><td>0.5 ± 0.2</td><td>0.7 ± 0.1</td><td>1.3 ± 1.5</td><td>3.7</td><td>97.0</td></tr><tr><td>Phaeophyceae</td><td>0.1 ± 0.0</td><td>0.1 ± 0.0</td><td>1.1 ± 1.2</td><td>3.1</td><td>100.0</td></tr></tbody></table>

(C) denotes groups that are composed of calcifying taxa. Groups that were not found in a treatment are noted with a dash (-).

$pH_T$ of 7.75, a level projected for the end of the century following the business-as-usual representative concentration pathway RCP8.5 (Ciais et al. 2013), results are less clear. *Posidonia oceanica* showed limited stimulation of photosynthesis and we did not always detect significant changes from ambient conditions for calcification rates, epiphytic carbonate content, epiphyte density and growth.

The result of increasing productivity with decreasing pH and the subsequent greater availability in $pCO_2$ is in agreement with results previously shown by Invers et al. (1997, 2001, 2002) from incubations of leaf segments. Results are also in agreement with the response of other seagrass species to lowered pH (Zostera marina, Zimmerman et al. 1997; C. nodosa, Phyllospadix torreyi, Invers et al. 2001; Thalassia hemprichii, Jiang, Huang & Zhang 2010; and Thalassia testudinum, Halod-

ule wrightii, Syrigodium filiforme, Campbell & Fourqurean 2013). PAM fluorometry has identified photosynthetic changes that accompany elevated $pCO_2$ for several seagrasses (Jiang, Huang & Zhang 2010; Apostolaki et al. 2014; Burnell et al. 2014), but was not effective in identifying pH effects for *P. oceanica* (this study; Hall-Spencer et al. 2008). In the investigations, with the seagrasses *A. antartica* and *T. hemprichii* either irradiance was altered or chlorophyll pigments increased (Jiang, Huang & Zhang 2010; Burnell et al. 2014), which was not the case in our study. The increased productivity, as measured through oxygen evolution, was attributed to the increased $CO_2$ availability combined with the carbon limitation for marine seagrass photosynthesis (Koch et al. 2013).

However, the extent of benefit for *P. oceanica* productivity over the next century may be minimal. This statement is
supported by the lack of statistical differences between both ambient shoot and leaf photosynthetic rates when compared to rates from the pH 7.7 treatment. The relationship between decreasing pH and increasing *Posidonia* photosynthesis has been established over a wide range of pH from 9.2 to 7.9 (unknown pH scale; Invers *et al.* 1997), or with more extreme low levels (pH~T~ 7.3 this study; pH 7.5 (unknown pH scale), Invers *et al.* (2002); pH~T~ 7.0, Hall-Spencer *et al.* 2008). Within the pH 9.2–7.9 range (Invers *et al.* 1997), the slope of the pH–photosynthesis relationship for *Posidonia* is significant but slight. This limited reaction to increased CO~2~ availability reflects the plants ability as *P. oceanica* utilizes HCO~3~⁻ more efficiently than three other seagrass species (Invers *et al.* 2001). *Posidonia oceanica* is thought to rely upon external carbonic anhydrase for HCO~3~⁻ utilization (Invers *et al.* 2001). It would appear from multiple studies with *P. oceanica* under different carbonate chemistry conditions that at a pH~T~ of ~7.5 the bicarbonate-mediated pathway is saturated and overall photosynthesis can then be stimulated by further increases in CO~2~ availability and passive diffusion through membranes. HCO~3~⁻ utilization is also hypothesized to be plastic trait perhaps mediated through the induction of carbonic anhydrase to confer an advantage in environments with large fluctuations in carbonate chemistry (Uku, Beer & Björk 2005).

The epiphyte community considered in this study was typical of a winter season (e.g. Lepoint *et al.* 1999; Prado, Alcoverro & Romero 2008) with a low percentage cover of epiphytic species (~22 ± 2% of leaf cover in the ambient treatment) and a clear dominance of encrusted coralline algae (~46 ± 15% of epiphytic cover in the ambient treatment). Epiphytic leaf coverage reported in our study is slightly lower than those usually found in the literature at this depth (minimum of ~30%; Prado, Alcoverro & Romero 2008). Our methods may have lead to reduced cover estimates. We quantified cover on all aged leaves, whereas other studies randomly choose a subset of leaves for determination (e.g. Martin *et al.* 2008) or they focus on older leaves where the epiphytes are more abundant (Piazzı *et al.* 2004; Prado, Alcoverro & Romero 2008; Mabrouk *et al.* 2013). However, we must also stress that these estimates were done at the end of the experiment after 4 weeks of incubation and manipulations. It is possible that sessile organisms (e.g. small gastropods, amphipods, isopods) were progressively lost, which could partly explain the decrease in net calcification observed after 2 weeks of incubation.

Our epiphyte results at lowered pH levels are congruent with those obtained by Martin *et al.* (2008) following a 2-week incubation in the laboratory at pH~T~ 7.0 and to those observed *in situ* by Martin *et al.* (2008) and Fabricius *et al.* (2011) near natural CO~2~ vents, respectively, in Ischia (Italy) and Papua-New Guinea. Rates of net calcification were measured, to the best of our knowledge, for the first time on these epiphytic organisms. Results confirm the tendency of calcareous epiphytes to dissolve under corrosive conditions, leading to a decrease of the carbonate content in epiphytes from 36 ± 11% under ambient conditions to 18 ± 4% at pH~T~ 7.32. Similar to what was observed by Saderne & Wahl (2013) on epiphytes of *Fucus serratus*, a clear decrease in

growth of calcified structures, estimated by the calcine staining technique, was observed at pH~T~ 7.32. Interestingly, at a pH~T~ of 7.75, only the community assemblage analysis based on the estimation of percentage cover of epiphytes on scanned pictures allowed a sufficient statistical power to highlight clear, consistent differences. This novel technique should be considered for future studies on epiphytes because of the low analytical time, low manpower requirements and the associated ease of replication.

Differences in terms of epiphytic cover were mostly due to the decrease in crustose coralline algae, the most abundant organisms, that explained almost 25% of the dissimilarity between ambient and pH~T~ 7.75. There is a dense literature on the effects of ocean acidification on growth, survival and mineralogy of calcifying macroalgae that has been reviewed by Nelson (2009), Koch *et al.* (2013), Hofmann & Bischof (2014) and McCoy & Kamenos (2015). The present study confirms the vulnerability of these organisms to this disturbance. It also highlights the fact that, among all epiphytic organisms, calcareous algae are the most sensitive since, in contrast to crustose algae, other calcifying epiphytes contributed very little to the observed dissimilarities in terms of community assemblage between ambient and low pH levels. This can be explained by the fact that crustose coralline algae precipitate high magnesium calcite (10–20 mol% MgCO~3~) well-known to be more soluble than aragonite and calcite (Morse & Mackenzie 1990).

Photosynthesis and calcification are both important processes for coralline algae that are linked (Koch *et al.* 2013). The uptake of CO~2~ for photosynthesis increases pH and favours the precipitation of CaCO~3~. Changes in the carbonate chemistry predicted to occur with ocean acidification can uncouple this relationship. Because of the mixed results at a pH~T~ of 7.75, it is difficult to attribute a pH at which the physiological response tips to negative impact for epiphytic coralline algae. We did identify stress, as bleaching, at pH~T~ of 7.75. Perhaps this is just below the uncoupling point, under these conditions, for these epiphytic calcifiers. We caution that the degree of ocean acidification impact on calcifiers (and for seagrass) can depend on other environmental conditions. For example, flow rates can control the diffusion boundary layer thickness and thereby alter the surrounding pH and calcium carbonate saturation (Hurd 2015).

Did these changes in epiphyte abundance and community alter shoot productivity under elevated pCO~2~? Algal epiphytes add to overall productivity and per gram tissue their productivity can even be greater than *P. oceanica*. In a Bay of Port Cros, France, epiphytes had the same productivity vs. irradiance patterns and were influenced by the same environmental characteristics as *P. oceanica* (Libes 1986). Otherwise, epiphytes are thought to compete with seagrass photosynthesis. For example, epiphytes decreased light quantity by 30% for 250-day-old *Posidonia* leaves (Cebrián *et al.* 1999). The loss of the coralline algal producer at lowered pH most certainly altered oxygen evolution, but the extent of any alteration seems limited. Shoot productivity at pH8.1 (ambient) did not statistically differ from the productivity at pH 7.7. Similar oxygen production rates per gram tissue were observed
between shoot and leaf incubations, with similar outcome (higher rates of photosynthesis at pH 7.3). These results are contrary to expected if the epiphyte community significantly increased or inhibited productivity.

In contrast, indirect interactions overwhelmed the direct physiological response to increased carbon availability in several other ecosystems. In an experiment with tropical forest plants, the physiology of C3 plants was stimulated by increased pCO₂, but when the community was combined, the canopy plants eventually dominated because they were better competitors for light (Bazzaz 1990). In aquatic lakes with acidifying conditions, the loss of the formerly abundant wetland plants (isoetids) was attributed to an increase in epiphytes and the subsequent reduction in available light to the leaves (Farmer 1990). Many ephemeral or mat-forming species on coral reefs and in kelp forests are able to rapidly adjust their physiology to take advantage of increased resource availability when normally they are subordinate (Connell et al. 2013). These examples are in contrast to this study where the photosynthetic response to increased carbon availability was directly related to the species physiological abilities. The small impact on productivity from epiphytes in our experiment is not surprising given the high biomass of *P. oceanica* and the low percentage cover of epiphytes. However, in other seagrass systems, the epiphytes altered the response. For the seagrass *A. antartica*, high irradiances and elevated pCO₂ resulted in an overgrowth of filamentous algal species and decreased growth (Burnell et al. 2014) and for *Zostera noltii* CO₂ addition increased epiphyte proliferation and suppressed leaf production (Martínez-Crego, Olivé & Santos 2014). Campbell & Fourqurean (2014) performed a 11-month field perturbation experiment on a tropical seagrass meadow, leading to the same conclusion, that is a significant reduction of coralline algae and an increase in filamentous algae at elevated CO₂ (ca. 800 µatm, pH offset of -0.3).

During our experiment, filamentous algal species occurred in low abundances, the concentration of nitrate + nitrite ranged between 0.5 and 2.0 µmol L⁻¹ (data not shown), while the concentration of phosphate varied from 0.04 to 0.09 µmol L⁻¹ (data not shown). It is likely that such nutrient limiting conditions, combined with relative low temperature and light levels, prevented the overgrowth of filamentous brown algae as observed in systems with much higher nutrient availability. In the Bay of Villefranche, nutrient concentrations are relatively low with nitrate + nitrite and phosphate concentrations rarely exceeding 2 and 0.2 µmol L⁻¹ (http://somlit-db.epoc.u-bordeaux1.fr/bdd.php). However, it would be interesting to repeat this experiment in summer or at a different depth. In winter (and for our experiment), there are low irradiances and shoots have relatively young leaves with less epiphytic mass from recent leaf turnover (see Alcov erro, Duarte & Romero 1995; Cebrián et al. 1999). Filamentous phaeophyceans are at greatest abundances in the shallow waters, while calcified rhodophytes are more common with depth (Borowitzka, Lavery & van Keulen 2006).

Within the limitations of our study, results suggest two important implications to meadow dynamics: (i) herbivore

interactions could be modified with changes in the epiphyte community and (ii) the hypothesis that ocean acidification conditions in the next decade will increase productivity of *P. oceanica* and hence elevate the buffering capacity of meadows is unsupported.

In the Mediterranean Sea, most species living in *Posidonia* meadows rely on the epiphyte community as a food source (Lepoint *et al.* 2000) and few species directly graze on *Posidonia* leaves because of their increased C/N ratio (Duarte 1990), relatively greater cellulose content (Lawrence 1975), and their production of chemical deterrents (e.g. phenols; Agostini, Desjobert & Pergent 1998). As up to 40% of epiphytic algal production is lost to grazers (Peduzzi 1987), there is a tight coupling between epiphytic growth and dynamics of herbivores (Tomas, Turon & Romero 2005; Heck & Valentine 2006). Although our experimental set-up did not test directly the potential modification of plant-herbivore interactions with decreasing pH levels, it appears likely that a reduction, or even the complete disappearance, of crustose coralline algae, will have important implications to the nutritive value of seagrass beds in the coming decades. Additionally, Arnold *et al.* (2012) have reported significant decreases in the production of chemical defences (phenolics) of *P. oceanica* at proximity of a CO₂ vent, leading to increased rates of grazing on the plant itself. However, many grazers of *P. oceanica* leaves and epiphytes produce calcareous structures (e.g. gastropods, sea urchins) and will likely be impacted by ocean acidification (Kroeker *et al.* 2013). To date, very few studies have focused on plant-herbivore interactions under elevated pCO₂ levels (Asnaghi *et al.* 2013; Poore *et al.* 2013; Campbell & Fourqurean 2014) and more investigations, conducted under realistic environmental conditions are necessary.

In natural conditions, there are diel fluctuations in pH which depend upon the production of the meadow and water flow (Invers *et al.* 1997). Diel pH fluctuations have been documented to be as much as 0.24 to 0.3 in shallow meadows (Invers *et al.* 1997; Hendriks *et al.* 2013). Under the pH 7.7 scenario proposed here, this fluctuation is still within the range of minimal photosynthetic benefit for *P. oceanica*. It has also been suggested that productivity of *Posidonia* may buffer the impacts on calcifiers and provide a daily window of maximum aragonite saturation where calcification may be more efficient. The degree of buffering is largely dependent upon the density and leaf length of shoots which is greater in shallow meadows in summer (Hendriks *et al.* 2013). In this study, any buffering from productivity was eliminated by the controlled pH levels. However, the hypothesis that ocean acidification will stimulate seagrass productivity and thus increase the buffering capacity of meadow (Hendriks *et al.* 2013, 2014) is not supported for monospecific meadows of *P. oceanica* at pH level expected in the next decade.

Speculated implications of these results to meadows are tentative because laboratory experiments with one-factor manipulations overlook the complexity of meadows and the many direct and indirect interactions between species and the environment. For instance, *P. oceanica* can assimilate
ammonium, nitrate and phosphate through the leaves from the water column and through the roots from the sediment. Its nutrient acquiring strategies are well suited for a nutrient poor environment (Gobert, Laumont & Bouquegneau 2002). Nitrogen-fixing bacteria occur on seagrass leaves and aide in nutrient cycling (Goering & Parker 1972). Ocean acidification could facilitate the production of these bacteria (Hutchins, Mulholland & Fu 2009). Nutrient increases have been shown to favour algal producers at the expense of seagrass (Lee, Park & Kim 2007). Therefore, long-term in situ experiments appear essential to our future understanding of meadows. The use of free ocean carbon enrichment systems (Gattuso et al. 2014) over a full annual cycle would greatly improve risk assessment for *P. oceanica* and its epiphyte community in the Mediterranean Sea. Several studies already showed the high vulnerability of *P. oceanica* to warming (e.g. Marbà & Duarte 2010) and eutrophication (Waycott et al. 2009) and the potential synergistic negative effects of warming and acidification on coralline algae calcification (e.g. Diaz-Pulido et al. 2009; Martin & Gattuso 2009). It is therefore of the utmost importance to perform experiments, preferably in situ, combining all these anthropogenic stressors in order to improve future projections.

## Acknowledgements

This work was funded by the EC FP7 projects 'European Project on Ocean Acidification' (EPOCA; grant agreement 211384) and 'Mediterranean Sea Acidification in a changing climate' (MedSeA; grant agreement 265103), and by the 'European Free Ocean Carbon Enrichment' project (eFOCE; BNP-Paribas Foundation). VDC expresses gratitude to CONACYT and CICESE in Mexico for the sabbatical research scholarship granted. We are grateful to A. Elineau for help with the ZooScan and C. Rouvière for help with the epifluorescence binocular. We thank Carlos Duarte and Iris Hendriks for use of their PAM instrumentation. We also thank Lidia Urbini for her assistance, Jean-Jacques Pangrazi and Paul Mahachek for assistance with the construction of the cylindrical aquaria, and Karen Tait and her engineering colleagues at Plymouth Marine Laboratory for building the basic components of the incubation chambers. All data provided by this study are available within the manuscript or can be found in supplementary material.

## Data accessibility

All data are included within the manuscript or available as supplementary material.

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Received 2 July 2015; accepted 1 September 2015

Handling Editor: Brian Silliman

## Supporting Information

Additional Supporting Information may be found in the online version of this article:

<caption>**Table S1.** Counts of invertebrate epiphytic taxa and group identification</caption>

**Figure S1.** Experimental set-up.

**Figure S2.** Example of measuring crustose coralline algal growth.

**Figure S3.** Quantum yield of *Posidonia oceanica* during the acclimation period.

**Figure S4.** Absorbance spectra.