# Does Ocean Acidification Benefit Seagrasses in a Mesohaline Environment? A Mesocosm Experiment in the Northern Gulf of Mexico

L. Guerrero-Meseguer, T. Cox, C. Sanz-Lázaro, S. Schmid, L. Enzor, K.

Major, Frédéric Gazeau, J. Cebrian

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► To cite this version:

L. Guerrero-Meseguer, T. Cox, C. Sanz-Lázaro, S. Schmid, L. Enzor, et al., Does Ocean Acidification Benefit Seagrasses in a Mesohaline Environment? A Mesocosm Experiment in the Northern Gulf of Mexico. Estuaries and Coasts, 2020, 43 (6), pp.1377-1393. 10.1007/s12237-020-00720-5. hal-02991643

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1 **Title:** Does ocean acidification benefit seagrasses in a mesohaline environment? A mesocosm experiment in the northern Gulf of Mexico.

2 **Running page head:** Ocean acidification effects on seagrasses in NGOM

3 **Authors and addresses:** Guerrero-Meseguer, L¹,²*; Cox, TE³,⁴; Sanz-Lazaro, C⁵,⁶; Schmid, S³; Enzor, LA⁷; Major, K⁸; Gazeau, F⁹ & Cebrian, J³,⁸,¹⁰

4 ¹ Departamento de Ecología e Hidrología, Facultad de Biología, Universidad de Murcia.
5 30100 Murcia, Spain

6 ² Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), Terminal de Cruzeiros de Leixões, Avenida Norton de Matos S/N, 4450-208, Matosinhos, Portugal

7 ³ Dauphin Island Sea Lab, Dauphin Island, Alabama, USA

8 ⁴ Department of Biological Sciences, University of New Orleans, Louisiana, USA

9 ⁵ Department of Ecology, University of Alicante, PO Box 99, E-03080 Alicante, Spain.

10 ⁶ Multidisciplinary Institute for Environmental Studies (MIES), Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain.

11 ⁷ Environmental Protection Agency, Gulf Ecology Division, Pensacola, Florida, USA

12 ⁸ University of South Alabama, Mobile, Alabama, USA

13 ⁹ Sorbonne Universités, UPMC Univ Paris, CNRS-INSU, Laboratoire d'Océanographie de Villefranche, Villefranche-sur-mer, France

14 ¹⁰ Stennis Space Center, 1021 Balch Blvd, Northern Gulf Institute, MS 39529 Mississippi State University, Mississippi, USA

21 *Corresponding author: laura.guerrero@um.es

22 **Abstract:**
Ocean acidification is thought to benefit seagrasses because of increased carbon dioxide (CO₂) availability for photosynthesis. However, in order to truly assess ecological responses, effects of ocean acidification need to be investigated in a variety of coastal environments. We tested the hypothesis that ocean acidification would benefit seagrasses in the northern Gulf of Mexico, where the seagrasses *Halodule wrightii* and *Ruppia maritima* coexist in a fluctuating environment. To evaluate if benefits of ocean acidification could alter seagrass bed composition, cores of *H. wrightii* and *R. maritima* were placed alone or in combination into aquaria and maintained in an outdoor mesocosm. Half of the aquaria were exposed to either ambient (mean pH of 8.1 ± 0.04 SD on total scale) or high CO₂ (mean pH 7.7 ± 0.05 SD on total scale) conditions. After 54 days of experimental exposure, the δ¹³C values were significantly lower in seagrass tissue in the high CO₂ condition. This integration of a different carbon source (either: preferential use of CO₂, gas from cylinder, or both) indicates that plants were not solely relying on stored energy reserves for growth. Yet, after 41 to 54 days, seagrass morphology, biomass, photo-physiology, metabolism, and carbon and nitrogen content in the high CO₂ condition did not differ from those at ambient. There was also no indication of differences in traits between the homo- or hetero- specific beds. Findings support two plausible conclusions: 1) these seagrasses rely heavily on bicarbonate use and growth will not be stimulated by near future acidification conditions or 2) the mesohaline environment limited the beneficial impacts of increased CO₂ availability.

**Key words:** Carbon dioxide, pH, productivity, seagrass species interactions

## Introduction
The increase in atmospheric CO₂ since the industrial revolution has altered the equilibrium of inorganic carbon compounds in the ocean, increasing the concentrations of bicarbonate (HCO₃⁻), carbonic acid (H₂CO₃), and hydrogen ions (H⁺) (Elderfield et al. 2005). These changes, referred to as ocean acidification, have caused the average sea surface pH to drop by 0.1 units, and the pH is projected to further decline by 0.06-0.32 units by the end of this century (IPCC, 2013). Ocean acidification is known to impact species physiologies and lead to cascading effects at the ecosystem level (Hall-Spencer et al. 2008).

Seagrass beds are highly productive (Duarte and Cebrián 1996) and they provide refuge for many marine organisms (Hemminga and Duarte 2000). In addition, seagrasses play an important ecological role in coastal waters as carbon sinks (Duarte et al. 2010; Russell et al. 2013). Seagrass are expected to benefit from ocean acidification because they are carbon limited at present dissolved inorganic carbon (DIC) levels (Koch et al. 2013). Indeed, previous reports have shown increases in seagrass productivity (Durako 1993; Zimmerman et al. 1997; Invers et al. 2002), vegetative growth (Jiang et al. 2010; Russell et al. 2013; Martínez-Crego et al. 2014; Campbell and Fourqurean 2018), carbohydrate storage (Campbell and Fourqurean 2013b) and flowering frequency (Palacios and Zimmerman 2007) under lowered pH conditions.

Coastal environments, however, are highly dynamic in terms of fluctuating light, nutrients, and salinity, particularly in mesohaline estuaries. Estuaries commonly receive freshwater inputs that change the chemical and physical properties of the seawater (Aufdenkampe et al. 2011). High biological activity, often fueled by nutrient inputs and hydrodynamic processes in shallow areas, can result in highly variable pH and CO₂ environments. Many estuarine organisms already experience diurnal incremental changes in pH outside of those predicted for
the open ocean within the next century (Duarte et al. 2013). As a result, the decrease in pH by 66
ocean acidification could be similar to which naturally occurs in these estuarine habitats and 67
subsequently it may not alter the usual development of estuarine organisms (Frieder et al. 2014; 68
Pacella et al. 2018). On the other hand, future climate conditions will intensify changes in pH and 69
this may act on organism physiology (Hofmann et al. 2011; Waldbusser and Salisbury 2014). 70

Ocean acidification also has the potential to shift interactions, such as competitive 71
strengths, between species (Connell et al. 2013; Russell et al. 2013; Takeshita et al. 2015). Due to 72
inter-specific differences in HCO₃⁻ utilization efficiency, the response to lowered pH levels varies 73
considerably among seagrass species (Invers et al. 2001; Campbell and Fourqurean 2013a). 74
Species which rely less on CO₂ and have efficient HCO₃⁻ use should be less sensitive to altered 75
future carbonate chemistry and thus benefit less from ocean acidification (Koch et al. 2013). 76
Seagrasses also have different carbon allocation strategies, which further suggests differential 77
growth responses to elevated partial pressure of CO₂ (pCO₂; Ow et al. 2015). Some seagrass 78
species invest more in below ground tissue (i.e. *Enhalus acoroides*; Duarte and Chiscano 1999), 79
other ephemeral seagrasses have short leaf turnover (i.e. *Halodule wrightii* and *Ruppia maritima*; 80
Gallegos et al. 1994; Dunton 1990), while other long-lived species such as *Posidonia oceanica* 81
have longer shoot plastochrone intervals (Duarte and Chiscano 1999; Kilminster et al. 2015). 82
These differences in turnover of carbon could alter their carbon demand (see discussion in Ow et 83
al. 2015). Additionally, in terrestrial communities, the direct positive effects of elevated CO₂ for 84
plant species are at times outweighed by negative effects due to stimulation of the growth of 85
other plant competitors (Poorter and Navas 2003). Indeed, differences in seagrass species 86
composition have been observed near a CO₂ volcanic vent; species with large blade-like leaves 87
dominated and presumably kept the smaller successional species from benefitting (Takeshita et al. 2015). Despite these observations, there have been few investigations on the differential impacts of ocean acidification on co-habiting seagrass species, and how such impacts affect species composition and structure.

*Halodule wrightii* Asch. and *Ruppia maritima* L. are widespread seagrasses that co-exist in heterospecific beds in mesohaline estuaries of the north-central Gulf of Mexico. These species have short growth cycles and different seasonal peaks in biomass. *Halodule wrightii* grows throughout the year and typically reaches maximum biomass in late summer-early fall. *Halodule wrightii* also allocates a larger fraction of total biomass to roots and rhizomes compared to *R. maritima* (Dunton 1990; Anton et al. 2009). *Ruppia maritima* grows during cool temperatures and undergoes senescence after flowering in spring (Pulich 1985; Cho and Poirrier 2005; Anton et al. 2009). Even though *H. wrightii* and *R. maritima* provide similar ecosystem services (Christiaen et al. 2016), elevated pCO₂ conditions may stimulate production to change the services they provide (e.g. refuge ability, production). Furthermore, acidification could act to alter the ability for them to coexist. Under environmental stress, *R. maritima* can outcompete *H. wrightii* (Christiaen et al. 2016). Both seagrasses may increase their productivity under elevated pCO₂, but *R. maritima* production is known to be carbon saturated in some settings (Sand-Jensen and Gordon 1984; Koch et al. 2013; Campbell and Fourqurean 2013a). Due to higher richness of species, mixed seagrass beds are expected to attract more associated fauna, to be more productive and to have a broader range of tolerance to environmental conditions than monospecific beds (Duffy et al. 2006; Gustafsson and Boström 2011, 2013). Despite so, it has been little examined how elevated pCO₂ can alter the biomass of *H. wrightii* and *R. maritima* in heterospecific
110

seagrass beds formed in the Gulf of Mexico. Since these seagrasses can alter their cycle of

111

development with changes in environmental condition (Cho et al. 2008), this knowledge is

112

essential for the persistence of mixed seagrass beds and any ecological benefits heterospecific

113

beds may provide.

114

The objectives of this study are to (1) evaluate the effects of ocean acidification on the
productivity and vegetative growth of seagrasses in the mesohaline waters of the northern Gulf of

116

Mexico and to (2) test for potential shifts in composition of *H. wrightii* and *R. maritima* resulting

117

from an increase in CO₂ availability. To do this, cores of *H. wrightii* and *R. maritima* were placed

118

alone (homospecific beds) or side by side, in combination (heterospecific beds), into aquaria and

119

maintained in an outdoor mesocosm under ambient and elevated pCO₂ (low pH) conditions for

120

up to five weeks. Afterwards, the morphology and biomass, photo-physiology, chemical

121

composition, and metabolism of the seagrasses were measured. We hypothesized that enhanced

122

CO₂ availability would stimulate photosynthesis and benefit growth and production. We also

123

hypothesized that the stimulation of seagrass productivity would alter the composition of *H.*

124

*wrightii* and *R. maritima* beds. It is important to note that we were not directly testing

125

competition between seagrass species per se, albeit competition may be happening at the fringing

126

interface between patches, but rather we are testing whether any differences in CO₂ stimulated

127

growth cause densities or biomass to shift through stimulating the productivity of one species

128

more than the other, or through differences in their carbon allocation. Additionally, *Halodule*

129

*wrightii* and *R. maritima* were not replanted to form a mixed interspersed bed, with presumably

130

more interspecific interactions, because this distribution pattern would not represent the ecology
observed in the area. Seagrasses were observed growing in discrete bordering patches in the
natural setting.

## Methods

### Seagrass bed collection

Sixty rectangular cores of seagrass beds (10 x 4 cm; 4 cm deep) were collected from single species patches of *H. wrightii* and *R. maritima* from approximately 1 m depth in Point-aux-Pins, Bayou la Batre (30°23'4.26"N, 88°18'42.73"W northern Gulf of Mexico, Alabama, USA) on 27th February 2017. In the field, cores were introduced into 30 aquaria (21 x 13 x 13 cm) in pairs, such that there were 10 aquaria with two cores of *H. wrightii*, 10 aquaria with two cores of *R. maritima*, and 10 aquaria with a core of *H. wrightii* and a core of *R. maritima*. We butted the cores against each other to simulate homospecific beds of either species as well as the fringing area between adjacent beds of *H. wrightii* and *R. maritima*. The aquaria filled with cores were immediately brought back to Dauphin Island Sea Lab and kept in an outdoor experimental setup for seventy days (16 days of acclimation, 54 days of experimental manipulation, with final measures taken after at least 4.9 weeks of different CO₂ exposure, Fig. 1). The experiment was concluded on May 8, 2017, after 54 days of CO₂ exposure. This period of time, from Feb. 27 May 8, was selected because these seagrass species have short shoot turnovers (few months) and increase their growth in spring (Pulich 1985; Dunton 1990; Hemminga and Duarte 2000; Kilminster et al. 2015).

### Experimental setup

Two aquaria of each seagrass bed type (*Halodule-Halodule*, HH; *Ruppia-Ruppia*, RR; and heterospecific, *Halodule-Ruppia*, HR) were randomly assigned to five experimental blocks in
an outdoor flow through system (Fig. 1). Then, one of the two aquaria for each type within the block was assigned to the ambient CO₂ treatment (natural pCO₂/ pH), and the other to the high CO₂ treatment (high pCO₂/ low pH). Aquaria were arranged randomly within each block and covered with screen to prevent excess light stress (Fig.1; Cebrian et al. 2013). Seawater was pumped from the bay (1 m depth) into header tanks, from where it was channeled into the aquaria to overflow into surrounding water bath and released back into the bay. There were two header tanks per block, one for the ambient CO₂ aquaria and another for the high CO₂ aquaria, for 10 headers tanks in total and each tank feeding three aquaria (Fig. 1). The residence time of the seawater in each aquarium was approximately 30 minutes. The experiment had six treatments resulting from the crossing between seagrass beds types and CO₂ levels (i.e. HH/ambient; HH/high; RR/ambient; RR/high; HR/ambient; and HR/high), with five replicates per treatment. However, due to system failure and human error, replicate aquaria were reduced for some treatments.

A pH stat system (IKS Aquastar Germany) was used to control bubbling of CO₂ from a gas cylinder into the header tanks for the high CO₂ aquaria. For each block, the header tank bubbled with CO₂ was chosen at random from the two.

Environmental conditions in the aquaria were constantly monitored. Water temperature was logged by HOBO pendants using 1 logger per block (HOBO Onset Computer Corporation, Bourne, MA, USA). Surface photosynthetic active radiation (PAR) was downloaded from an environmental station maintained by the Dauphin Island Sea Lab (30°15.075'N, 88°04.670'W Dauphin Island, Alabama, USA; http://cf.disl.org/mondata/mainmenu.cfm) located within 0.1 miles from the outdoor flow-through system. Point measurements of salinity were obtained
throughout the study duration using a hand-held YSI-85 conductivity probe (YSI, Yellow Springs, Ohio, USA).

pH was monitored in aquarium and header tanks with an inLab Routine Pro calibrated glass electrode (Mettler Toledo, Ohio, USA). The pH was measured on the total scale (pH<sub>T</sub>) using certified reference material provided by A. Dickson (Batch 30). Using this method, pH<sub>T</sub> was measured in aquaria approximately every three days. In addition to measuring pH<sub>T</sub> and total alkalinity (A<sub>T</sub>) in header tanks, water samples (120 ml) were collected approximately once per week and at the same hour of the morning. These water samples were collected from one of the ambient and high CO<sub>2</sub> treatment header tanks chosen at random. pH was also ‘spot’ checked (data not reported) with loggers and discrete measures at different hours in header tanks and aquaria to make certain that the offsets between experimental treatments were maintained.

Samples for A<sub>T</sub> were filtered on combusted glass microfiber filters membranes and immediately inoculated with 72 µl of 33% saturated mercuric chloride solution (HgCl) and stored until analyzed. A standard provided by A. Dickson (Batch 157) was used to check precision and accuracy (A<sub>T</sub>, 3.9 and 0.1 µmol kg<sup>-1</sup>, respectively; n = 7). The carbonate chemistry was assessed using pH<sub>T</sub>, A<sub>T</sub>, salinity and temperature using the R package “seacarb” (Gattuso et al. 2018).

The pCO₂ in the ambient treatment was ~350 µatm, which corresponded to the value found in local coastal waters. In the "high CO₂" treatment was applied a pCO₂ of ~1244 µatm or a pH offset of approximately -0.3 to -0.4 to mimic the maximum pH decrease expected by the end of this century based on IPCC scenario for 2100 (IPCC, 2013).

## 195 Morphology and biomass
Shoot density was determined during the acclimation period (day 2), and after 54 days of exposure to experimental conditions. Shoot density was measured for each core, and the two cores representing the same species were averaged for homospecific aquaria. On day 2 of the acclimation period and after 54 days of CO₂ perturbation, we haphazardly selected five shoots from each core in each aquarium. We counted the leaves on the shoots and measured the length of each leaf on the shoot. With these measurements, we calculated shoot height (average leaf length per shoot), leaf number per shoot, and summed the length of the leaf material per shoot.

Then we calculated the average for the ten shoots in homospecific aquaria or the average of five shoots of each species in heterospecific aquaria. In combination, these measurements allowed us to infer whether, as a response to enhanced CO₂, shoots grew existing leaves longer, produced shorter and younger leaves, or a combination of both. For instance, the average number of leaves per shoot may not change, but shoots may show longer leaves (increased shoot height) and larger total leaf material, indicating shoots elongate their existing leaves, but do not produce more new leaves under enhanced CO₂. In contrast, higher number of leaves per shoot in combination with shorter shoot height and larger total leaf material per shoot would indicate a response to enhanced CO₂ centered in the production of new leaves.

Plant biomass was only measured at the end of the study (54 days of CO₂ exposure) due to destructive sampling. Sediment was carefully rinsed off above-ground (leaves and vertical rhizomes) and below-ground materials (roots and horizontal rhizomes) in distilled water and epiphytes were carefully scraped off their surfaces. Above- and below-ground materials were separated, dried at 60°C, and the dry weight (DW) determined. Above-ground biomass contained
parts of the plant exposed to light and the below ground biomass contained parts of the plant that
were buried in the sediment.

219 **Photo-physiology**

220 Photo-physiological measurements (dark- and light- adapted yield and rapid light curves)
221 were done with a diving-pulse amplitude modulated fluorometer (diving-PAM, Waltz, Germany)
222 11 days into the acclimation period and after 43 days of exposure to experimental conditions. To
223 take the measurements, the leaves were placed side by side on the Waltz dark-adapted fiberoptic
224 clip, so that the initial *F*' value would read above 400. For dark-adapted yield measurements,
225 leaves were placed in the dark for five minutes prior to exposure to a saturating light pulse. The
226 same leaf location was used for light-adapted measures which were collected after allowing the
227 leaves to acclimate to light conditions for 10 minutes. We used the same leaf location for both
228 measures to minimize stress or damage to leaves. All measures were collected in one day, from
229 mid-morning to late afternoon. To account for the changing environmental conditions over this
230 time period, all fluorescence measures were collected randomly within a block (1 replicate of
231 each condition in a block) before proceeding to the next block. Fluorescence measures for each
232 block were completed within a 1.5 – 2 h window. Because all replicates in both experimental
233 conditions were handled similarly and given the same period of relaxation and excitation, we
234 were able to make direct comparisons of results.

235 The intensity and width of the saturation pulse was adjusted to ensure a distinct plateau of
236 maximum quantum yield at a set distance from the blade. Namely, for all samples a saturation
237 intensity setting of 1 with a width of 0.8 was used in the initial measurements, and an intensity of
238 2 and a width of 0.8 in the final measurements (Genty et al. 1989).
The irradiances for rapid light curves (RLCs) were each applied for 10 s followed by a saturating pulse of 0.8. Irradiances ranged between 0 to 1700 µmol m⁻² s⁻¹ and were corrected for battery decline using the standard function in the WinControl software. Thus, irradiances at each increasing light step from 0 were as follows: 11–14, 49–67, 134–178, 255–332, 411–539, 593–786, 924–1227 and 855–1563 µmol m⁻² s⁻¹. The absorption factor needed to calculate RLCs parameters was determined using the methods described in Beer and Björk (2000) and averaged to 0.84. The *r*ETR values were plotted against the light irradiances to produce a curve fitting the exponential model proposed by Platt et al. (1980). Derived parameters of RLCs include photosynthetic efficiency (α), dynamic photoinhibition parameter (β), relative electron transport rate maximum (*r*ETR<sub>max</sub>) and the minimum saturation irradiance (*E*<sub>K</sub>), which were all calculated following Ralph and Gademann (2005).

To better interpret the photo-physiological experiments, we also measured leaf chlorophyll *a* (Chl *a*) content, but only at the end of the experiment (54 days of exposure to experimental conditions) due to the destructive nature of this sampling. To do this, we haphazardly selected one shoot from each core (two shoots of the same species in the homospecific aquaria, and one shoot of each species in the heterospecific aquaria) and clipped the upper 5 cm section of the middle leaf on the shoot. Chlorophyll was extracted from that section in the dark in 90% acetone for 24 h, and the extract measured in a fluorometer (Model TD-700 Turner Designs, California, USA, Welschmeyer 1994). The two values of Chl *a* content from the same species in homospecific aquaria were averaged to avoid pseudo-replication.

259 **Metabolism**
Net community productivity (NCP), and respiration rates were determined from the change in dissolved oxygen content during two-hour incubations using clear (for NCP) or dark (for respiration) chambers (10.2 x 5.7 x 5 cm) placed onto both cores in each aquarium. Measurements were done 7 days after collection and after 48 days of experimental exposure. At each sampling time, one clear and one dark chamber were placed at the exact same location on the core (i.e. the location of the chambers was marked in the first deployment and repeated for the second). Incubations were performed on clear days (mean PAR of 880 µmol photons m⁻² s⁻¹ in the first incubation and 1150 µmol photons m⁻² s⁻¹ in the final incubation). Dissolved oxygen content was measured with a Portable Meter Hach connected to probe with an optical sensor (HQ30d, Hach, Loveland, Colorado, USA; accuracy of 0.1 mg/L over a range of 0 to 8 mg/L and precision ± 0.5% of accuracy range). Rates of NCP and respiration were derived, and rates of gross primary productivity (GCP) from those rates, as explained in Cebrian et al. 2009. The two values of GCP were averaged in the homospecific aquaria to avoid pseudo-replication.

## 273 Chemical composition

At the end of the experiment (after 54 days of experimental conditions), δ¹³C and δ¹⁵N values and carbon (C) and nitrogen (N) content were analyzed in the below- and above-ground tissue. Dried plant tissue (previously prepared for biomass determination) was ground, weighed, and subsequently measured at the stable isotope facility at the University of California, Davis using an elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany) interfaced to a continuous flow isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope values are reported in standard d-notation relative to an international standard (V-PDB and air for
carbon and N, respectively). Glycine reference compounds with well-characterized isotopic compositions were used to ensure accuracy of all isotope measurements.

## 283 Data analysis

Two-way ANOVAs were used to test for differences in environmental variables in the header tanks (T, S, pH<sub>T</sub>, A<sub>T</sub>, pCO<sub>2</sub>, C<sub>T</sub>, Ω<sub>A</sub> and Ω<sub>C</sub>); “ph treatment” and “time” were used as fixed factors. The parameters measured on seagrasses were also analyzed with two-way ANOVA separately for each species with seagrass bed type and pH treatment as fixed factors for data obtained at the end of the experiment to test for CO<sub>2</sub> effects. Tukey’s multiple comparison tests were used to examine pairwise differences. Comparisons were additionally done for data obtained during the acclimation period to ensure homogeneous conditions among treatments before starting the CO<sub>2</sub> application (Supplementary Table 1). Prior to analyses, data were tested for normality using the Shapiro test and for homogeneity of variance using the Bartlett’s test, and transformed when necessary to comply with the assumptions of ANOVA. The statistical α was adjusted to < 0.01 in order to account for the many comparisons and avoid false positives (Benjamini and Hochberg 1995). For the same reason, the statistical α was adjusted to < 0.005 for four parameters which could not be transformed to meet parametric requirements (Underwood 1997). All results are expressed as mean ± standard error (SE) throughout this manuscript unless otherwise stated.

## 299 Results

### 300 Environmental conditions

pH<sub>T</sub>, pCO<sub>2</sub>, and total DIC (C<sub>T</sub>) significantly differed between the ambient and high CO<sub>2</sub> header tanks (Supplementary Table 3). The pH<sub>T</sub> in the header tanks during the experimental
period varied from 8 – 8.4 in the ambient treatment and of 7.2 – 8.0 in the high CO₂ treatment (Table 1). In ambient header tanks, pCO₂ and total DIC (C_T) ranged from 118.8 to 426.6 µatm and from 1268 to 1686 µmol kg⁻¹, respectively; while in the high CO₂ header tanks values ranged from 342.4 to 2910.4 µatm and from 1504 to 2001 µmol kg⁻¹. Levels of A_T in the header tanks did not differ between treatments but, they significantly fluctuated during the experimental period (Table 1, Supplementary Table 3). In the ambient treatment header tanks, A_T ranged from 1443.7 to 1835.9 µmol kg⁻¹ and from 1543.7 to 2069.9 µmol kg⁻¹ in the high CO₂ treatment header tanks (Table 1). The fluctuation was related to changes in salinity. As salinity decreased the levels of A_T also decreased in a linear manner; perhaps this relationship is due to the dilution of weathering products. Salinity and temperature in the header tanks significantly varied through time, but not between treatments (Supplementary Table 3). The seawater in the ambient treatment was saturated with respect to both aragonite and calcite. In the high CO₂ treatment calcite and aragonite were under saturation most of the time, except after the storms on the 20th of March and 28th of April (Table 1). Furthermore, levels of seawater saturation also differed between treatments.

The environment variables in the aquaria reflected those of the header tanks (Fig. 2). The mean (± SD) temperature logged by HOBO pendants was 23.0 ± 0.6°C, ranging from 13.6 to 31.8°C (Supplementary Table 2). Salinity in aquaria over the duration of the study ranged from 4.3 to 30.7 (Fig. 2, Supplementary Table 1). During daylight hours of the study, mean PAR (± SD) was 774.3 ± 3.4 µmol photons m⁻²s⁻¹ and ranged from 10.0 as a minimum in morning and in twilight hours to a maximum of 2123.3 µmol photons m⁻²s⁻¹ at the peak of a sunny day.
The pH<sub>T</sub> in aquaria was variable in both, ambient and high CO<sub>2</sub> treatment, but the range of pH<sub>T</sub> difference between the treatments was maintained between -0.29 to -0.44 along the experimental period (Fig. 2). Under the ambient treatment the pH<sub>T</sub> in aquaria averaged (± SD) 8.09 ± 0.04, while in the high CO<sub>2</sub> treatment it was 7.70 ± 0.05 (Fig. 2). The pH<sub>T</sub> offset from ambient was similar between the three seagrass habitat types (HH, HR and RR), showing an average pH<sub>T</sub> offset of -0.39 ± 0.08 (Fig. 2).

## Morphology and biomass

After 54 days of pH manipulation, shoot and leaf development of *H. wrightii* and *R. maritima* did not appear to be affected by elevated pCO₂ and plants also did not differ in morphology when grown in homo- or hetero-specific beds (Table 2, Figs. 3 and 4). Over the course of the experiment, in *H. wrightii* cores means (± SE) of: shoot density per core (from 27.6 ± 2.0 to 35.1 ± 2.), leaf number per shoot (from 2.4 ± 0.1 to 2.8 ± 0.1), and total leaf material (from 13.0 ± 0.7 to 20.9 ± 1.3 cm) increased. The mean (± SE) shoot density of *R. maritima* per core was 34.9 ± 3.1 at the initial assessment and was 31.3 ± 3.6 at the final assessment. Over the course of the experiment, the means (± SE) of: leaves per shoot (from 2.8 ± 0.1 to 3.3 ± 0.1), total leaf material (from 12.4 ± 0.5 to 22.2 ± 1.0 cm) and average shoot height (from 4.6 ± 0.2 to 6.5 ± 0.22 cm) increased.

The above-ground biomass was not significantly affected by pCO₂ and nor by co-occurrence of other seagrass species (Table 2). Above-ground biomass was 0.38 ± 0.04 g DW in *H. wrightii* and 0.21± 0.04 g DW in *R. maritima*. The allocation of biomass to below-ground also did not differ for seagrasses grown in homo- or hetero-specific beds and for seagrasses at the two
345 pH treatments (Table 2). The below-ground biomass for *H. wrightii* and *R. maritima* at the end of
346 the experiment was 0.34 ± 0.08 and 0.16 ± 0.07 g DW, respectively (Table 2, Figs. 3 and 4).

347 **Photo-physiology**

348 The parameters derived from the rapid light curves of *H. wrightii* did not differ between
349 ambient and elevated pCO₂ exposure and did not differ with bed type (Table 2, Fig. 5). For
350 example, the derived α for *H. wrightii* was 0.29 ± 0.01 and 0.30 ± 0.01 electrons/photons in
351 homo-specific aquaria and 0.32 ± 0.01 and 0.32 ± 0.01 electrons/photons in hetero-specific
352 aquaria after exposure to ambient and elevated pCO₂ conditions, respectively. Furthermore, mean
353 *r*ETRmax, *E*<sub>K</sub>, and *β* values did not significantly differ (Table 2) among bed type and pCO₂
354 condition for *H. wrightii* (mean ± SD: *r*ETRmax from 99.1 ± 10.9 to 108.3± 21.6 µmol electrons
355 m⁻² s⁻¹, *E*<sub>K</sub> from 308.8 ± 34.5 to 356.4 ± 54.5 µmol photon m⁻²s⁻¹, and *β* from 98.5± 8.4 to 105.9
356 ± 5.6 electrons/photons).

357 After 43 days of pH manipulation, the parameters derived from the rapid light curves of
358 *R. maritima* also did not differ between ambient and elevated pCO₂ exposure and did not differ
359 with bed type (Table 2, Fig. 5). This result is evident in the curves (Fig. 5) with the similar range
360 of derived values of α, *r*ETRmax, and *E*<sub>K</sub> regardless of growing condition (mean ± SD: α from 0.29
361 ± 0.02 to 0.32 ± 0.02 electrons/photons, *r*ETRmax from 103.8 ± 23.4 to 111.9 ± 11.1µmol
362 electrons m⁻² s⁻¹, *E*<sub>K</sub> from 325.2 ± 89.6 to 377.5 ± 84.7 µmol photon m⁻²s⁻¹). Similar to
363 observations for *H. wrightii*, there was a trend of greater photoinhibition for *R. maritima* plants
364 within the ambient CO₂, heterospecific bed condition when compared to the other treatments.
365 This trend also occurred in the initial period (Supplementary Figure 2d), but it was not
statistically significant at α<0.05 nor when the statistical α was adjusted to 0.01 for the many
comparisons (Table 2, β ranged from 95.9 ± 11.9 to 103.7 ± 14.0 electrons/photons).

For both species, dark- and light-adapted yields did not differ with bed type nor pCO₂
condition. *H. wrightii* plants yielded 0.74 ± 0.01 after the dark acclimation and 0.70 ± 0.03 in the
light. *R. maritima* plants yielded 0.76 ± 0.02 and 0.69 ± 0.02 after dark and light acclimation,
respectively.

Leaf Chl *a* content was not affected by pCO₂ nor by seagrass bed type (Table 2). The average of leaf Chl *a* content was 0.011 ± 0.002 and 0.010 ± 0.002 mg cm⁻² per leaf for *H. wrightii* and *R. maritima*, respectively.

## Metabolism

NCP, GCP, and respiration (in units of mg O₂ m² h⁻¹) did not statistically differ between ambient and elevated pCO₂ condition for either species, and rates did not differ when plants were grown in homo- or hetero- specific beds (Table 2, Fig. 6). It was noted that there was a lot of variation in some metabolic measures at the end of the study, particularly for *H. wrightii* beds in hetero-specific aquaria maintained under elevated pCO₂ conditions. In *H. wrightii* beds, the NCP was 1.15 ± 0.24, respiration was -0.86 ± 0.14, and GCP was 2.01 ± 0.21. In *R. maritima* beds the NCP was 0.76 ± 0.19, respiration was -1.08 ± 0.12, and GCP was 1.84 ± 0.19.

## Chemical composition

The δ¹³C values in above- and below- ground biomass of *H. wrightii* differed between the high and ambient CO₂ treatment. The δ¹³C values were significantly decreased in the plants grown in the high CO₂ condition (-4.02 ± 0.07 ‰ in leaf and -2.59 ± 0.04 ‰ in root) than in the plants which developed in the ambient treatment (-3.29 ± 0.07 ‰ in leaf and -2.29 ± 0.03 ‰ in
root; Table 2, Fig. 6). The δ¹³C value of *R. maritima* above- and below-ground biomass were also significantly decreased in the CO₂ than in the ambient treatment, showing -3.87 ± 0.06 ‰ (above-ground) and -2.77 ± 0.04 ‰ (below-ground) in the CO₂ treatment and -3.32 ± 0.05 ‰ (above-ground) and -2.35 ± 0.03 ‰ (below-ground) in the ambient treatment (Table 2, Fig. 6).

The δ¹⁵N value and C and N contents in above- and below-ground biomass did not differ between treatments, indicating a similar carbon and nitrogen investment regardless of pH treatment and seagrass bed type (Table 2, Fig. 6).

## Discussion

Seagrasses did not benefit from ocean acidification conditions and there were no observed changes in seagrass bed composition during this study. This experimental duration (54 days in March to May) captured a large portion of the peak growth period. The lower δ¹³C values in above and below ground tissues within the high CO₂ condition indicates plants were integrating a different carbon source into their tissues and thus, they not solely relying on stored energy reserves for growth. Nevertheless, we did not observe a difference in seagrass traits for plants grown under high CO₂ conditions. Furthermore, there was no evidence of increased production (using oxygen evolution, fluorescence, carbon content) needed for long-term carbon gains. This outcome indicates that there is some complexity in seagrass response to increased CO₂ predicted in the coming decades (Fig. 7).

## Response of seagrass morphology and biomass

The absence of a response in seagrass morphology and biomass to ocean acidification conditions are in contrast with those obtained in others studies where stimulation has resulted in seagrass gains in productivity, above-ground development, root biomass, and non-structural
carbohydrates (Beer et al. 1977; Durako 1993; Hall-Spencer et al. 2008; Jiang et al. 2010; Campbell and Fourqurean 2013b; Cox et al. 2015; Zimmerman et al. 2017). In contrast, other studies support our findings and have found a neutral effect of ocean acidification on productivity and/or biomass of some seagrass species (Burnell et al. 2014; Apostolaki et al. 2014; Cox et al. 2016; Campbell and Fourqurean 2018). This 'lack of effect' is often attributed to other limitations or stressors in the seagrass environment. For example, the increased $pCO_2$ availability for seagrass species did not counteract negative impacts of warming temperatures (Collier et al. 2018), limiting light (Hendriks et al. 2017), or heavy metals (Olivé et al. 2017). Other researchers have underscored $CO_2$ availability as one abiotic factor of several limiting seagrass physiology (Burnell et al. 2014; Cox et al. 2016; Schneider et al. 2017; Pajusalu et al. 2018). Furthermore, outcomes may differ when the producer is held under constant or fluctuating pH (Britton et al. 2016).

## 422 Efficient users of bicarbonate

Another highly plausible reason for the lack of ocean acidification stimulation could be related to the physiologies of *Halodule wrightii* and *Ruppia maritima*. Both species have physiologies that rely heavily on bicarbonate use. For example, seagrass species of the genus *Halodule* sp. was shown to be less sensitive to the increases of DIC than other tropical species such as *Cymodocea serrulata* under high light conditions (Ow et al. 2015). Campbell and Fourqurean (2013b) additionally showed that *Thalassia testudinum* increased photosynthesis by 100% from a pH of 8.2 to 7.4 while *H. wrightii* relied more on bicarbonate use with an increase of 20% over the same pH range. In addition, the internal inorganic carbon concentrations of *R. maritima* was close to saturation under natural conditions when the ratio of DIC to oxygen was
low and photorespiration occurred (Buapet et al. 2013; Koch et al. 2013). Lastly, in culture, *R. maritima* had adequate growth on a bicarbonate media (Bird et al. 2016). Therefore, it appears that these two species are not as sensitive to pH changes as some other seagrass species.

## Duration of study

Discounting acclimation and adaptation, it is unlikely that there is a long-term benefit from the high CO₂ condition on vegetative growth for these species that was not captured by our experimental duration. *H. wrightii* and *R. maritima* have relatively short shoot turnover rates where growth can be 2 to 4 mm per day (Dunton 1990). For instance, *Halodule wrightii* is able translocate 14% of carbon from the leaves to the rhizome and roots in few hours (Moriarty et al. 1986). The short turnovers of these species appear to be specially marked in the estuarine waters of the Gulf of Mexico where *R. maritima*, completes its growth cycle in four months after flowering (Pulich 1985; Cho and Poirrier 2005). The experimental duration was during the period of peak biomass for *R. maritima* and a portion of the growth period for *H. wrightii*. Initiation of flowering by *R. maritima* and early flower stages were noted in homospecific and heterospecific beds under ambient and high CO₂ conditions but, unlike effects reported for *Zostera marina* (Palacios and Zimmerman 2007), the onset of flowering was not more frequent at either pCO₂ condition. *Halodule wrightii*, on the other hand, allocates more carbon in below ground tissue (Anton et al. 2011), yet we did not find any statistically significant differences in biomass allocation and we did not detect changes in nitrogen storage in the leaves or roots which could indicate an early positive response to the high CO₂ levels.

The analysis of the stable carbon isotope composition of plants is a useful tool in understanding physiological processes and the response of plants to varying environmental
conditions (Hemminga and Mateo, 1996). In our study, there was low δ¹³C values measured in above and below ground tissues for plants grown in the high CO₂ condition. Seagrasses preferentially use CO₂ over HCO₃⁻ and atmospheric CO₂ is more deplete in ¹³C (-9 ‰, Kroopnick et al. 1985). Therefore, under ocean acidification conditions (higher pCO₂), we would expect seagrasses to have lower δ¹³C values. However, the isotope value of the gas from the cylinders (-4.9 ‰ median measured from cylinders by Campbell and Fourqurean 2011) is also deplete in ¹³C and background measures of DIC were not measured. Therefore, we cannot rule out the influence of the gas from the cylinder on δ¹³C values. Nevertheless, the integration of a different carbon source in tissues (i.e. different δ¹³C from ambient) and the observed increase in mean biomass in both conditions over the study duration allows us to conclude that the absence of positive affects in the high pCO₂ condition are not likely due to reliance and growth resulting from stored reserves.

## Other limitations and potential stressors

Other limitations or stressors in the environment could be a factor contributing to our results. The seagrass beds of *H. wrightii* and *R. maritima* were grown under highly variable environmental conditions (See Fig. 2), which are typical of mesohaline estuarine habitats (Pulich 1985; Cho and Poirrier 2005; Anton et al. 2009). The northern central Gulf of Mexico has six rivers that drain into it, thus it could be less suited for seagrass growth than in other estuarine waters in determinate moments, especially after periods of heavy storms. For instance, during the second month of the experiment, heavy rainfall in the study area resulted in seagrasses experiencing a mean salinity of 16 with low salinity events persisting for several days. These storms also increased water turbidity and caused the average salinity in the Bay to decrease from
17 to 7 psu, reaching a minimum of 3.8 (see Fig. 2). *Ruppia maritima* and *H. wrightii* are eury- to mixo-haline species and thus, low salinity water outside their preferred range can slow productivity and seawater below 6 can be lethal (Adair et al. 1994; Doering et al. 2002). These seagrasses also seem to be negatively affected by high turbidity (Kantrud 1991; Dunton 1996; Cho and Poirrier 2005). Therefore, the environmental changes in salinity and turbidity during our experiment could limit the productivity and development of *H. wrightii* and *R. maritima*, counter acting any positive effects of ocean acidification.

## 483 Conclusions

The outcome of this study (Fig. 7), in context with literature, leads to the speculation that acidification in the next decade will not stimulate the vegetative growth of *H. wrightii* and *R. maritima* to alter seagrass bed structure. The absence of positive effects on physiology and growth may be related to the variable environmental conditions and, albeit not measured by this study, the efficiency of these seagrasses to use HCO₃⁻.

Although we did not find the increase in pCO₂ to stimulate vegetative growth for seagrasses in the northern Gulf of Mexico, ocean acidification is known to positively affect the physiology or growth of other seagrass species. Therefore, the responses of seagrass meadows to ocean acidification appear to vary with seagrass species and their capacity to tolerate changes in the environment. As climate change continues, it is necessary to integrate the influence of environmental variability, as well as species interactions, for seagrass ecosystems to determine their susceptibility to anthropogenic perturbations.

## 496 Acknowledgements
We would like to acknowledge the construction assistance from Dauphin Island Technical Support. We also are grateful to Josh Goff, Laura West, Adam Chastan, Emory Lan, and Andrew Moorehead for their help collecting seagrass cores and assisting with data collection. Lastly, we kindly thank Tom Gouba and Jonathan Whittman for sharing dock space and removal of the system. We also thank Samir Alliouane and Dr. Jean-Pierre Gattuso for providing us technical advice and equipment. In addition, we also want to thank the useful comments from two anonymous reviewers greatly improved the quality of this manuscript.

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738
<table><caption>**Table 1.** Environmental data and carbonate chemistry (as calculated from pH<sub>T</sub>, A<sub>T</sub>) in the header tanks of the ambient and high CO<sub>2</sub> treatments during the experimental period. Temperature (T, °C); salinity (S); pH on the total scale (pH<sub>T</sub>); total alkalinity (A<sub>T</sub>, µmolkg<sup>-1</sup>); partial pressure of CO<sub>2</sub> (pCO<sub>2</sub>, µatm); dissolved inorganic carbon (C<sub>T</sub>, µmol kg<sup>-1</sup>) and saturation states with respect to aragonite (Ω<sub>A</sub>) and calcite (Ω<sub>C</sub>). Error estimates can be found in Supplementary Table 4 and were generated using the precision around the standard (for A<sub>T</sub> precision was 3.9 µmol kg<sup>-1</sup>, n = 7) together with the error for probes (pH = 0.01, T = 0.1, and S = 0.01) in seacarb.</caption><thead><tr><th rowspan="2">Day</th><th rowspan="2">Date</th><th rowspan="2">T</th><th rowspan="2">S</th><th rowspan="2">A<sub>T</sub></th><th colspan="3">Ambient</th><th colspan="8">High CO<sub>2</sub></th></tr><tr><th>pH<sub>T</sub></th><th>pCO<sub>2</sub></th><th>C<sub>T</sub></th><th>Ω<sub>A</sub></th><th>Ω<sub>C</sub></th><th>T</th><th>S</th><th>A<sub>T</sub></th><th>pH<sub>T</sub></th><th>pCO<sub>2</sub></th><th>C<sub>T</sub></th><th>Ω<sub>A</sub></th><th>Ω<sub>C</sub></th></tr></thead><tbody><tr><td>1</td><td>17 March 2017</td><td>15.6</td><td>15.2</td><td>1443.7</td><td>8.4</td><td>118.8</td><td>1268</td><td>2.1</td><td>3.5</td><td>15.6</td><td>15.3</td><td>1586.4</td><td>8.0</td><td>342.4</td><td>1504</td><td>1.1</td><td>1.9</td></tr><tr><td>3</td><td>20 March 2017</td><td>19.0</td><td>12.9</td><td>1525.3</td><td>8.4</td><td>131.3</td><td>1342</td><td>2.3</td><td>4.0</td><td>18.9</td><td>12.7</td><td>1543.7</td><td>7.4</td><td>1824.8</td><td>1591</td><td>0.3</td><td>0.5</td></tr><tr><td>11</td><td>28 March 2017</td><td>23.3</td><td>20.5</td><td>1835.9</td><td>8.1</td><td>344.9</td><td>1670</td><td>2.1</td><td>3.4</td><td>23.8</td><td>20.3</td><td>1835.5</td><td>7.7</td><td>825.7</td><td>1771</td><td>1.1</td><td>1.7</td></tr><tr><td>15</td><td>1 April 2017</td><td>21.2</td><td>17.2</td><td>1829.2</td><td>8.1</td><td>337.5</td><td>1686</td><td>1.9</td><td>3.2</td><td>21.2</td><td>17.4</td><td>1905.5</td><td>7.7</td><td>954.4</td><td>1867</td><td>0.9</td><td>1.5</td></tr><tr><td>33</td><td>19 April 2017</td><td>24.6</td><td>21.4</td><td>1801.6</td><td>8.1</td><td>315.7</td><td>1617</td><td>2.3</td><td>3.7</td><td>24.5</td><td>22.3</td><td>2069.9</td><td>7.7</td><td>1010.7</td><td>2001</td><td>1.2</td><td>1.9</td></tr><tr><td>42</td><td>28 April 2017</td><td>26.0</td><td>16.9</td><td>1692.7</td><td>8.0</td><td>409.5</td><td>1568</td><td>1.7</td><td>2.8</td><td>26.1</td><td>16.9</td><td>1751.9</td><td>7.2</td><td>2910.4</td><td>1813</td><td>0.3</td><td>0.6</td></tr><tr><td>45</td><td>5 May 2017</td><td>25.2</td><td>17.2</td><td>1642.0</td><td>8.0</td><td>426.6</td><td>1530</td><td>1.5</td><td>2.5</td><td>25.5</td><td>17.3</td><td>1672.0</td><td>7.7</td><td>839.8</td><td>1623</td><td>0.9</td><td>1.5</td></tr><tr><td>Mean</td><td></td><td>22.1</td><td>17.3</td><td>1681.5</td><td>8.2</td><td>297.8</td><td>1526</td><td>2.0</td><td>3.3</td><td>22.2</td><td>17.5</td><td>1766.4</td><td>7.6</td><td>1244.0</td><td>1739</td><td>0.8</td><td>1.4</td></tr><tr><td>SD</td><td></td><td>3.8</td><td>2.9</td><td>154.2</td><td>0.2</td><td>124.5</td><td>162</td><td>0.3</td><td>0.5</td><td>3.9</td><td>3.1</td><td>185.9</td><td>0.3</td><td>856.7</td><td>174</td><td>0.4</td><td>0.6</td></tr></tbody></table>
<table><caption>**Table 2.** Summary of two-way ANOVA results testing for the effects of ambient and elevated pCO₂ on the morphology, photo-physiology, and metabolism of *H. wrightii* and *R. maritima* in homospecific and heterospecific aquaria (n= 4 to 5). Degrees freedoms were 1 for all analyses. Significant effects are marked in bold. Asterisks above variables indicate that data did not meet parametric assumptions and a statistical α < 0.005 was used.</caption><thead><tr><th>Response variable</th><th>Species</th><th>Factors</th><th>F-stat</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="6"><strong>Morphology and biomass</strong><br>Shoot density</td><td rowspan="3"><i>H. wrightii</i></td><td>pH</td><td>0.997</td><td>0.335</td></tr><tr><td>Bed type</td><td>0.589</td><td>0.456</td></tr><tr><td>pH x Bed type</td><td>0.913</td><td>0.355</td></tr><tr><td rowspan="3"><i>R. maritima</i></td><td>pH</td><td>0.126</td><td>0.728</td></tr><tr><td>Bed type</td><td>0.129</td><td>0.725</td></tr><tr><td>pH x Bed type</td><td>6.005</td><td>0.028</td></tr><tr><td rowspan="6">Number of leaves per shoot</td><td rowspan="3"><i>H. wrightii</i></td><td>pH</td><td>0.037</td><td>0.850</td></tr><tr><td>Bed type</td><td>1.771</td><td>0.206</td></tr><tr><td>pH x Bed type</td><td>0.359</td><td>0.559</td></tr><tr><td rowspan="3"><i>R. maritima</i></td><td>pH</td><td>1.213</td><td>0.291</td></tr><tr><td>Bed type</td><td>0.153</td><td>0.702</td></tr><tr><td>pH x Bed type</td><td>0.032</td><td>0.862</td></tr><tr><td rowspan="6">Total leaf material</td><td rowspan="3"><i>H. wrightii</i></td><td>pH</td><td>0.184</td><td>0.675</td></tr><tr><td>Bed type</td><td>8.418</td><td>0.012</td></tr><tr><td>pH x Bed type</td><td>0.173</td><td>0.685</td></tr><tr><td rowspan="3"><i>R. maritima</i></td><td>pH</td><td>1.466</td><td>0.248</td></tr><tr><td>Bed type</td><td>2.656</td><td>0.127</td></tr><tr><td>pH x Bed type</td><td>3.163</td><td>0.099</td></tr><tr><td rowspan="6">Average shoot height</td><td rowspan="3"><i>H. wrightii</i></td><td>pH</td><td>0.380</td><td>0.548</td></tr><tr><td>Bed type</td><td>6.349</td><td>0.026</td></tr><tr><td>pH x Bed type</td><td>0.154</td><td>0.701</td></tr><tr><td rowspan="3"><i>R. maritima</i></td><td>pH</td><td>0.392</td><td>0.542</td></tr><tr><td>Bed type</td><td>6.349</td><td>0.026</td></tr><tr><td>pH x Bed type</td><td>0.154</td><td>0.701</td></tr></tbody></table>
<table><tbody><tr><td rowspan="10">Above-ground biomass</td><td rowspan="3">H. wrightii</td><td>Bed type</td><td>1.146</td><td>0.304</td></tr><tr><td>pH x Bed type</td><td>3.689</td><td>0.077</td></tr><tr><td>pH</td><td>0.445</td><td>0.516</td></tr><tr><td rowspan="3">R. maritima (Ln(x) transformed)</td><td>Bed type</td><td>0.546</td><td>0.472</td></tr><tr><td>pH x Bed type</td><td>0.456</td><td>0.511</td></tr><tr><td>pH</td><td>0.440</td><td>0.518</td></tr><tr><td rowspan="3">H. wrightii*</td><td>Bed type</td><td>0.006</td><td>0.937</td></tr><tr><td>pH x Bed type</td><td>1.086</td><td>0.315</td></tr><tr><td>pH</td><td>4.065</td><td>0.063</td></tr><tr><td rowspan="3">R. maritima (Ln(x) transformed)</td><td>Bed type</td><td>0.483</td><td>0.498</td></tr><tr><td>pH x Bed type</td><td>0.496</td><td>0.493</td></tr><tr><td>pH</td><td>0.001</td><td>0.976</td></tr><tr><td rowspan="3">H. wrightii*</td><td>Bed type</td><td>0.544</td><td>0.473</td></tr><tr><td>pH x Bed type</td><td>0.815</td><td>0.382</td></tr></tbody></table>

Photo-physiology

<table><tbody><tr><td rowspan="10">Chlorophyll a</td><td rowspan="3">H. wrightii</td><td>pH</td><td>0.226</td><td>0.642</td></tr><tr><td>Bed type</td><td>0.558</td><td>0.467</td></tr><tr><td>pH x Bed type</td><td>0.186</td><td>0.673</td></tr><tr><td rowspan="3">R. maritima</td><td>pH</td><td>0.379</td><td>0.548</td></tr><tr><td>Bed type</td><td>0.776</td><td>0.393</td></tr><tr><td>pH x Bed type</td><td>0.493</td><td>0.494</td></tr><tr><td rowspan="3">H. wrightii</td><td>pH</td><td>0.135</td><td>0.721</td></tr><tr><td>Bed type</td><td>2.600</td><td>0.135</td></tr><tr><td>pH x Bed type</td><td>0.575</td><td>0.464</td></tr><tr><td rowspan="3">R. maritima</td><td>pH</td><td>1.942</td><td>0.189</td></tr><tr><td>Bed type</td><td>0.282</td><td>0.605</td></tr><tr><td>pH x Bed type</td><td>0.250</td><td>0.626</td></tr><tr><td rowspan="3">H. wrightii</td><td>pH</td><td>0.091</td><td>0.768</td></tr><tr><td>Bed type</td><td>3.666</td><td>0.082</td></tr><tr><td>pH x Bed type</td><td>0.734</td><td>0.410</td></tr><tr><td rowspan="3">R. maritima</td><td>pH</td><td>0.377</td><td>0.551</td></tr><tr><td>pH x Bed type</td><td>0.734</td><td>0.410</td></tr><tr><td>pH</td><td>0.377</td><td>0.551</td></tr></tbody></table>
<table><tbody><tr><td rowspan="4">rETR<sub>max</sub></td><td rowspan="2">H. wrightii</td><td>Bed type</td><td>0.173</td><td>0.685</td></tr><tr><td>pH x Bed type</td><td>0.648</td><td>0.436</td></tr><tr><td rowspan="2">R. maritima</td><td>pH</td><td>0.031</td><td>0.864</td></tr><tr><td>Bed type</td><td>1.478</td><td>0.249</td></tr><tr><td rowspan="4">E<sub>K</sub></td><td rowspan="2">H. wrightii</td><td>pH x Bed type</td><td>0.276</td><td>0.610</td></tr><tr><td>pH</td><td>0.001</td><td>0.986</td></tr><tr><td rowspan="2">R. maritima</td><td>Bed type</td><td>0.068</td><td>0.798</td></tr><tr><td>pH x Bed type</td><td>0.518</td><td>0.485</td></tr><tr><td rowspan="4">Light-adapted yield</td><td rowspan="2">H. wrightii</td><td>pH</td><td>0.026</td><td>0.874</td></tr><tr><td>Bed type</td><td>3.528</td><td>0.087</td></tr><tr><td rowspan="2">R. maritima</td><td>pH x Bed type</td><td>0.739</td><td>0.408</td></tr><tr><td>pH</td><td>0.425</td><td>0.527</td></tr><tr><td rowspan="4">Dark-adapted yield</td><td rowspan="2">R. maritima*</td><td>Bed type</td><td>0.140</td><td>0.714</td></tr><tr><td>pH x Bed type</td><td>0.510</td><td>0.489</td></tr><tr><td rowspan="2">H. wrightii*</td><td>pH</td><td>0.484</td><td>0.498</td></tr><tr><td>Bed type</td><td>2.190</td><td>0.161</td></tr><tr><td rowspan="4">Metabolism</td><td rowspan="2">R. maritima*</td><td>pH x Bed type</td><td>0.073</td><td>0.791</td></tr><tr><td>pH</td><td>0.001</td><td>0.980</td></tr><tr><td rowspan="2">H. wrightii</td><td>Bed type</td><td>0.143</td><td>0.711</td></tr><tr><td>pH x Bed type</td><td>0.216</td><td>0.649</td></tr><tr><td rowspan="4">GCP</td><td rowspan="2">H. wrightii</td><td>pH</td><td>0.226</td><td>0.642</td></tr><tr><td>Bed type</td><td>0.558</td><td>0.467</td></tr><tr><td rowspan="2">R. maritima*</td><td>pH x Bed type</td><td>0.186</td><td>0.673</td></tr><tr><td>pH</td><td>0.379</td><td>0.548</td></tr><tr><td rowspan="4">GCP</td><td rowspan="2">R. maritima*</td><td>Bed type</td><td>0.776</td><td>0.393</td></tr><tr><td>pH x Bed type</td><td>0.493</td><td>0.494</td></tr><tr><td rowspan="2">H. wrightii<br>(Square root<br>transformed)</td><td>pH</td><td>0.001</td><td>0.972</td></tr><tr><td>Bed type</td><td>0.001</td><td>0.981</td></tr><tr><td rowspan="2">R. maritima</td><td>pH x Bed type</td><td>0.484</td><td>0.498</td></tr><tr><td>pH</td><td>0.798</td><td>0.388</td></tr></tbody></table>
<table><tbody><tr><td rowspan="6">NCP</td><td rowspan="3">H. wrightii<br/>(Square root<br/>transformed)</td><td>Bed type</td><td>0.308</td><td>0.588</td></tr><tr><td>pH x Bed<br/>type</td><td>1.662</td><td>0.220</td></tr><tr><td>pH</td><td>0.034</td><td>0.856</td></tr><tr><td rowspan="3">R. maritima</td><td>Bed type</td><td>0.973</td><td>0.341</td></tr><tr><td>pH x Bed<br/>type</td><td>3.509</td><td>0.082</td></tr><tr><td>pH</td><td>2.643</td><td>0.128</td></tr><tr><td rowspan="6">Respiration</td><td rowspan="3">H. wrightii</td><td>Bed type</td><td>0.005</td><td>0.943</td></tr><tr><td>pH x Bed<br/>type</td><td>0.408</td><td>0.534</td></tr><tr><td>pH</td><td>0.141</td><td>0.713</td></tr><tr><td rowspan="3">R. maritima</td><td>Bed type</td><td>0.823</td><td>0.380</td></tr><tr><td>pH x Bed<br/>type</td><td>3.155</td><td>0.097</td></tr><tr><td>pH</td><td>1.537</td><td>0.237</td></tr><tr><td rowspan="6">δ<sup>13</sup>C</td><td rowspan="3">H. wrightii</td><td>Bed type</td><td>0.614</td><td>0.447</td></tr><tr><td>pH x Bed<br/>type</td><td>1.088</td><td>0.316</td></tr><tr><td>pH</td><td>64.40<br/>0</td><td><strong>&lt;0.001</strong></td></tr><tr><td rowspan="3">R. maritima</td><td>Bed type</td><td>0.390</td><td>0.543</td></tr><tr><td>pH x Bed<br/>type</td><td>4.599</td><td>0.051</td></tr><tr><td>pH</td><td>59.89<br/>0</td><td><strong>&lt;0.001</strong></td></tr><tr><td rowspan="3">R. maritima</td><td>Bed type</td><td>0.336</td><td>0.572</td></tr><tr><td>pH x Bed<br/>type</td><td>6.709</td><td>0.022</td></tr><tr><td>pH</td><td>36.68<br/>0</td><td><strong>&lt;0.001</strong></td></tr><tr><td rowspan="6">δ<sup>15</sup>N</td><td rowspan="3">H. wrightii</td><td>Bed type</td><td>0.006</td><td>0.940</td></tr><tr><td>pH x Bed<br/>type</td><td>0.828</td><td>0.384</td></tr><tr><td>pH</td><td>51.88<br/>0</td><td><strong>&lt;0.001</strong></td></tr><tr><td rowspan="3">R. maritima</td><td>Bed type</td><td>0.014</td><td>0.908</td></tr><tr><td>pH x Bed<br/>type</td><td>0.159</td><td>0.699</td></tr><tr><td>pH</td><td>0.431</td><td>0.523</td></tr><tr><td rowspan="3">R. maritima</td><td rowspan="3">H. wrightii</td><td>Bed type</td><td>0.080</td><td>0.782</td></tr><tr><td>pH x Bed<br/>type</td><td>0.004</td><td>0.948</td></tr><tr><td>pH</td><td>0.431</td><td>0.523</td></tr></tbody></table>
<table><tbody><tr><td rowspan="10">C content</td><td rowspan="10">R. maritima</td><td rowspan="3">Root</td><td>pH</td><td>0.287</td><td>0.601</td></tr><tr><td>Bed type</td><td>0.701</td><td>0.418</td></tr><tr><td>pH x Bed type</td><td>0.140</td><td>0.714</td></tr><tr><td rowspan="3">Leaf</td><td>pH</td><td>0.863</td><td>0.377</td></tr><tr><td>Bed type</td><td>0.090</td><td>0.770</td></tr><tr><td>pH x Bed type</td><td>3.118</td><td>0.111</td></tr><tr><td rowspan="3">Root</td><td>pH</td><td>2.293</td><td>0.161</td></tr><tr><td>Bed type</td><td>1.123</td><td>0.314</td></tr><tr><td>pH x Bed type</td><td>0.003</td><td>0.954</td></tr><tr><td rowspan="3">H. wrightii</td><td rowspan="3">Leaf</td><td>pH</td><td>0.428</td><td>0.428</td></tr><tr><td>Bed type</td><td>0.069</td><td>0.069</td></tr><tr><td>pH x Bed type</td><td>0.371</td><td>0.371</td></tr><tr><td rowspan="10">N content</td><td rowspan="10">R. maritima</td><td rowspan="3">Root</td><td>pH</td><td>0.003</td><td>0.956</td></tr><tr><td>Bed type</td><td>0.177</td><td>0.681</td></tr><tr><td>pH x Bed type</td><td>0.835</td><td>0.378</td></tr><tr><td rowspan="3">Leaf</td><td>pH</td><td>0.509</td><td>0.492</td></tr><tr><td>Bed type</td><td>0.303</td><td>0.594</td></tr><tr><td>pH x Bed type</td><td>0.680</td><td>0.429</td></tr><tr><td rowspan="3">Root</td><td>pH</td><td>0.868</td><td>0.373</td></tr><tr><td>Bed type</td><td>0.001</td><td>0.972</td></tr><tr><td>pH x Bed type</td><td>2.730</td><td>0.129</td></tr><tr><td rowspan="3">H. wrightii</td><td rowspan="3">Leaf</td><td>pH</td><td>0.668</td><td>0.429</td></tr><tr><td>Bed type</td><td>0.031</td><td>0.864</td></tr><tr><td>pH x Bed type</td><td>0.143</td><td>0.712</td></tr><tr><td rowspan="3">Root</td><td rowspan="3">Leaf</td><td>pH</td><td>0.002</td><td>0.968</td></tr><tr><td>Bed type</td><td>0.006</td><td>0.938</td></tr><tr><td>pH x Bed type</td><td>0.227</td><td>0.642</td></tr><tr><td rowspan="3">R. maritima</td><td rowspan="3">Leaf</td><td>pH</td><td>2.918</td><td>0.118</td></tr><tr><td>Bed type</td><td>0.303</td><td>0.594</td></tr><tr><td>pH x Bed type</td><td>0.981</td><td>0.345</td></tr></tbody></table>
741

742

<table><tr><td rowspan="3">Root</td><td>pH</td><td>5.991</td><td>0.034</td></tr><tr><td>Bed type</td><td>0.422</td><td>0.531</td></tr><tr><td>pH x Bed type</td><td>0.002</td><td>0.964</td></tr></table>
<figure><img src="image_3.png" /></figure>
A. Boxplots of pH data for each CO₂ treatment - bed type combination

<figure><img src="image_4.png" /></figure>

B. pH, Salinity, and Temperature through time

<figure><img src="image_5.png" /></figure>
Fig. 2 The pH<sub>T</sub>, salinity and temperature in the ambient and high CO<sub>2</sub> aquaria. Panel A is a boxplot of all the discrete measures of pH<sub>T</sub> presented by bed type (homo- or heterospecific and CO<sub>2</sub> treatment (ambient or high)). The dotted white line within the bar is the mean and the whiskers from the bars capture the 5th and 95th percentiles. Panel B top, shows the evolution of pH<sub>T</sub> (mean ± SE, n = 27 aquaria) throughout the experiment as a function of (bottom) probed temperature and salinity (n = 5) used to calculate the carbonate chemistry. The dotted lines indicate the beginning of the perturbation.

<figure><img src="image_6.png" /></figure>

Fig. 3 *Halodule wrightii* (mean ± SE; n= 4 to 5) morphology (shoot density, a; shoot height, b; leaves per shoot, c; total leaf material, d) and above- and below ground biomass (e, f) after maintained for 34, 41, or 54 days at ambient (blue) and high CO₂ (red) treatments. *Halodule wrightii* was grown in homospecific (*H. wrightii* with *H. wrightii*, HH) and heterospecific (*H. wrightii* with *R. maritima*, HR) beds. Data did not show significant differences between treatments.

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<figure><img src="image_7.png" /><figcaption>**Fig. 4** *Ruppia maritima* (mean ± SE; n= 4 to 5) morphology (shoot density, a; shoot height, b; leaves per shoot, c; total leaf material, d) and above- and below ground biomass (e, f) after maintained for 34, 41, or 54 days at ambient (blue) and high CO₂ (red) treatments. *Ruppia maritima* was grown in homospecific (*R. maritima* with *R. maritima*, RR) and heterospecific (*R. maritima* with *H. wrightii*, HR) beds. Data did not show significant differences between treatments.</figcaption></figure>

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<figure><img src="image_8.png" /><figcaption>**Fig. 5** Rapid light curves from *H. wrightii* (top, a) and *R. maritima* (bottom, b) placed within homospecific (left) and heterospecific (right) beds (*H. wrightii* with *H. wrightii*, HH; *R. maritima* with *R. maritima*, RR and *H. wrightii* with *R. maritima*, HR) after maintained for 43 days under ambient and high CO₂ treatments (continuous modeled lines). Modeled lines and *r*ETR (mean ±SE) values are based upon an average from 4 to 5 aquaria. PAR units were µmol photons m⁻²s⁻¹.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 6** Gross community productivity (GCP; a), δ¹³C (b) and carbon (c) and nitrogen (d) content in leaves obtained in *H. wrightii* (left) and *R. maritima* (right) placed within homospecific and heterospecific beds (*H. wrightii* with *H. wrightii*, HH; *R. maritima* with *R. maritima*, RR and *H. wrightii* with *R. maritima*, HR; mean ± SE; n= 4 to 5) after 779 maintained for 48 days under ambient (blue) and high CO₂ treatments (red). Asterisks (*) 780 indicates significant differences between treatment. 781
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<figure><img src="image_10.png" /></figure>

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**Fig. 7** Graphical summary of the effects of ocean acidification (OA). In a heterospecific bed (represented by aquaria with two species seen in the blue boxes) the increased pCO₂ was predicted to increase seagrass growth and production with either little change to bed composition (alternative prediction 1) or with a shifted interaction where one species comes to dominate in abundance (alternative prediction 2). Yet, we observed no effect of increased pCO₂ on seagrass growth and production. Therefore, these species must either be efficient users of bicarbonate and/or other stressors and limitations outweighed any stimulation from increased pCO₂.
<table><caption>**Supplementary Table 1.** Summary of two-way ANOVA results obtained in the initial measurements of morphology and biomass, photo-physiology, and metabolic parameters for *H. wrightii* and *R. maritima* in homospecific and heterospecific aquaria (n=5). Degrees freedoms were 1 for all analyses.</caption><thead><tr><th>Response variable</th><th>Species</th><th>Factors</th><th>F-stat</th><th>p-value</th></tr></thead><tbody><tr><td colspan="5"><strong>Morphology and biomass</strong></td></tr><tr><td rowspan="3">Shoot density</td><td rowspan="3">H. wrightii</td><td>pH</td><td>0.357</td><td>0.560</td></tr><tr><td>Bed type</td><td>0.654</td><td>0.432</td></tr><tr><td>pH x Bed type</td><td>0.226</td><td>0.642</td></tr><tr><td rowspan="3">Number of leaves per shoot</td><td rowspan="3">R. maritima</td><td>pH</td><td>0.005</td><td>0.946</td></tr><tr><td>Bed type</td><td>0.003</td><td>0.960</td></tr><tr><td>pH x Bed type</td><td>1.282</td><td>0.277</td></tr><tr><td rowspan="3">Total leaf material</td><td rowspan="3">H. wrightii</td><td>pH</td><td>8.401</td><td>0.012</td></tr><tr><td>Bed type</td><td>0.535</td><td>0.477</td></tr><tr><td>pH x Bed type</td><td>0.245</td><td>0.628</td></tr><tr><td rowspan="3">Shoot height</td><td rowspan="3">H. wrightii</td><td>pH</td><td>4.826</td><td>0.045</td></tr><tr><td>Bed type</td><td>0.180</td><td>0.678</td></tr><tr><td>pH x Bed type</td><td>0.356</td><td>0.560</td></tr><tr><td rowspan="3">α</td><td rowspan="3">H. wrightii</td><td>pH</td><td>5.643</td><td>0.032</td></tr><tr><td>Bed type</td><td>0.892</td><td>0.361</td></tr><tr><td>pH x Bed type</td><td>0.014</td><td>0.908</td></tr><tr><td rowspan="3">α</td><td rowspan="3">R. maritima</td><td>pH</td><td>0.011</td><td>0.916</td></tr><tr><td>Bed type</td><td>0.270</td><td>0.611</td></tr><tr><td>pH x Bed type</td><td>0.096</td><td>0.761</td></tr><tr><td rowspan="3">α</td><td rowspan="3">R. maritima</td><td>pH</td><td>0.500</td><td>0.491</td></tr><tr><td>Bed type</td><td>0.568</td><td>0.464</td></tr><tr><td>pH x Bed type</td><td>0.907</td><td>0.357</td></tr><tr><td rowspan="3">α</td><td rowspan="3">R. maritima</td><td>pH</td><td>3.405</td><td>0.086</td></tr><tr><td>Bed type</td><td>0.669</td><td>0.427</td></tr><tr><td>pH x Bed type</td><td>0.064</td><td>0.804</td></tr><tr><td colspan="5"><strong>Photo-physiology</strong></td></tr><tr><td rowspan="3">α</td><td rowspan="3">H. wrightii</td><td>pH</td><td>0.162</td><td>0.694</td></tr><tr><td>Bed type</td><td>3.078</td><td>0.103</td></tr><tr><td>pH x Bed type</td><td>0.088</td><td>0.772</td></tr><tr><td rowspan="3">α</td><td rowspan="3">R. maritima</td><td>pH</td><td>0.388</td><td>0.545</td></tr><tr><td>Bed type</td><td>0.129</td><td>0.726</td></tr></tbody></table>
<table><tbody><tr><td rowspan="4">β</td><td rowspan="3">H. wrightii</td><td>pH x Bed type</td><td>0.388</td><td>0.545</td></tr><tr><td>pH</td><td>0.179</td><td>0.679</td></tr><tr><td>Bed type</td><td>0.254</td><td>0.622</td></tr><tr><td>pH x Bed type</td><td>0.410</td><td>0.533</td></tr><tr><td rowspan="3">R. maritima</td><td rowspan="2">pH</td><td>pH x Bed type</td><td>0.057</td><td>0.816</td></tr><tr><td>Bed type</td><td>0.038</td><td>0.848</td></tr><tr><td>pH x Bed type</td><td>0.766</td><td>0.399</td></tr><tr><td rowspan="3">rETR<sub>max</sub></td><td rowspan="2">H. wrightii</td><td>pH</td><td>0.010</td><td>0.923</td></tr><tr><td>Bed type</td><td>3.175</td><td>0.098</td></tr><tr><td rowspan="3">R. maritima</td><td>pH x Bed type</td><td>0.238</td><td>0.634</td></tr><tr><td>pH</td><td>0.844</td><td>0.376</td></tr><tr><td>Bed type</td><td>0.302</td><td>0.593</td></tr><tr><td rowspan="3">E<sub>K</sub></td><td rowspan="2">H. wrightii</td><td>pH x Bed type</td><td>1.834</td><td>0.201</td></tr><tr><td>pH</td><td>0.091</td><td>0.767</td></tr><tr><td rowspan="3">Dark-adapted yield</td><td rowspan="2">H. wrightii</td><td>Bed type</td><td>3.507</td><td>0.838</td></tr><tr><td>pH x Bed type</td><td>0.388</td><td>0.544</td></tr><tr><td rowspan="3">R. maritima</td><td>pH</td><td>0.120</td><td>0.735</td></tr><tr><td>Bed type</td><td>0.058</td><td>0.813</td></tr><tr><td>pH x Bed type</td><td>0.377</td><td>0.551</td></tr><tr><td rowspan="3">Light-adapted yield</td><td rowspan="2">H. wrightii</td><td>pH</td><td>2.568</td><td>0.129</td></tr><tr><td>Bed type</td><td>2.893</td><td>0.108</td></tr><tr><td rowspan="3">R. maritima</td><td>pH x Bed type</td><td>0.003</td><td>0.955</td></tr><tr><td>pH</td><td>0.021</td><td>0.888</td></tr><tr><td>Bed type</td><td>1.505</td><td>0.238</td></tr><tr><td rowspan="3">GCP</td><td rowspan="2">H. wrightii</td><td>pH x Bed type</td><td>0.736</td><td>0.403</td></tr><tr><td>pH</td><td>1.622</td><td>0.224</td></tr><tr><td rowspan="3">R. maritima</td><td>pH x Bed type</td><td>1.987</td><td>0.180</td></tr><tr><td>pH</td><td>0.227</td><td>0.642</td></tr><tr><td>Bed type</td><td>0.058</td><td>0.813</td></tr><tr><td rowspan="3">Metabolism</td><td rowspan="3">R. maritima</td><td>pH x Bed type</td><td>0.025</td><td>0.876</td></tr><tr><td>pH</td><td>0.155</td><td>0.701</td></tr><tr><td>Bed type</td><td>4.713</td><td>0.051</td></tr><tr><td rowspan="2">GCP</td><td rowspan="2">H. wrightii<br>(squared root<br>transformed)</td><td>pH x Bed type</td><td>0.998</td><td>0.338</td></tr><tr><td>pH</td><td>0.001</td><td>0.988</td></tr></tbody></table>
<table><caption>**Supplementary Table 2.** Temperature, salinity and pH (mean ± SD) measured per block during the experiment in the header tanks. Means of pH were separate between treatments (Ambient and high pCO₂).</caption><tbody><tr><td></td><td>(squared root<br>transformed)</td><td>Bed type</td><td>2.609</td><td>0.132</td></tr><tr><td rowspan="6">NCP</td><td rowspan="3">H. wrightii</td><td>pH x Bed type</td><td>1.589</td><td>0.231</td></tr><tr><td>pH</td><td>0.281</td><td>0.606</td></tr><tr><td>Bed type</td><td>1.074</td><td>0.320</td></tr><tr><td rowspan="3">R. maritima</td><td>pH x Bed type</td><td>0.326</td><td>0.579</td></tr><tr><td>pH</td><td>0.684</td><td>0.424</td></tr><tr><td>Bed type</td><td>0.259</td><td>0.620</td></tr><tr><td rowspan="6">Respiration</td><td rowspan="3">H. wrightii</td><td>pH x Bed type</td><td>2.066</td><td>0.176</td></tr><tr><td>pH</td><td>1.471</td><td>0.249</td></tr><tr><td>Bed type</td><td>0.964</td><td>0.346</td></tr><tr><td rowspan="3">R. maritima</td><td>pH x Bed type</td><td>0.100</td><td>0.757</td></tr><tr><td>pH</td><td>2.199</td><td>0.164</td></tr><tr><td>Bed type</td><td>4.299</td><td>0.060</td></tr><tr><td></td><td></td><td>pH x Bed type</td><td>0.058</td><td>0.814</td></tr></tbody></table>

<table><caption>**Supplementary Table 3.** Summary of the results obtained in the two-way ANOVAs performed in the environmental variables of header tanks for the factors “pH treatment” (ambient vs. high pCO₂) and “Time” (day of sample collection). Temperature (T, °C); salinity (S); pH on the total scale (pH<sub>T</sub>); total alkalinity (A<sub>T</sub>,µmolkg<sup>-1</sup>); partial pressure of CO₂ (pCO₂,µatm); dissolved inorganic carbon (C<sub>T</sub>,µmol kg<sup>-1</sup>) and saturation states with respect to aragonite (Ω<sub>a</sub>) and calcite (Ω<sub>c</sub>). Significant effects are marked in bold. The bolded text and values in parenthesis next to carbonate chemistry parameters are the range of estimated errors calculated in R with seacarb using the precision around the</caption><thead><tr><th rowspan="2"></th><th colspan="2">Temperature</th><th colspan="2">Salinity</th><th colspan="4">pH</th></tr><tr><th>MEAN</th><th>±SD</th><th>MEAN</th><th>±SD</th><th colspan="2">Ambient</th><th colspan="2">High CO₂</th></tr><tr><th></th><th></th><th></th><th></th><th></th><th>MEAN</th><th>±SD</th><th>MEAN</th><th>±SD</th></tr></thead><tbody><tr><td>Tank 1</td><td>23.31</td><td>2.290</td><td>15.48</td><td>6.201</td><td>8.074</td><td>0.199</td><td>7.754</td><td>0.289</td></tr><tr><td>Tank 2</td><td>23.22</td><td>2.248</td><td>16.25</td><td>5.829</td><td>8.108</td><td>0.212</td><td>7.628</td><td>0.376</td></tr><tr><td>Tank 3</td><td>23.13</td><td>2.577</td><td>16.43</td><td>5.993</td><td>8.056</td><td>0.228</td><td>7.722</td><td>0.298</td></tr><tr><td>Tank 4</td><td>23.28</td><td>2.353</td><td>16.37</td><td>5.892</td><td>8.159</td><td>0.158</td><td>7.668</td><td>0.320</td></tr><tr><td>Tank 5</td><td>21.90</td><td>3.936</td><td>16.36</td><td>5.842</td><td>8.080</td><td>0.228</td><td>7.742</td><td>0.275</td></tr><tr><td>Total</td><td>22.97</td><td>0.602</td><td>16.18</td><td>0.395</td><td>8.095</td><td>0.040</td><td>7.703</td><td>0.053</td></tr></tbody></table>
809 standard (for A<sub>T</sub> precision was 3.9 µmol kg<sup>-1</sup>, n = 7) together with the error for probes

810 (pH = 0.01, T = 0.1, and S = 0.01)

811

<table><thead><tr><th>Source</th><th>d.f.</th><th>MS</th><th>F-stat</th><th>p-value</th></tr></thead><tbody><tr><td><strong>T</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>pH treatment (P)</td><td>1</td><td>0.035</td><td>1.4</td><td>0.281</td></tr><tr><td>Time (T)</td><td>6</td><td>29.07</td><td>1162.79</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Residual</td><td>6</td><td>0.025</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>13.43</td><td></td><td></td></tr><tr><td><strong>S</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>pH treatment (P)</td><td>1</td><td>0.067</td><td>1.124</td><td>0.330</td></tr><tr><td>Time (T)</td><td>6</td><td>18.41</td><td>307.818</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Residual</td><td>6</td><td>0.060</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>8.527</td><td></td><td></td></tr><tr><td><strong>A<sub>T</sub></strong></td><td></td><td></td><td></td><td></td></tr><tr><td>pH treatment (P)</td><td>1</td><td>25241.5</td><td>5.78</td><td>0.053</td></tr><tr><td>Time (T)</td><td>6</td><td>53967.5</td><td>12.358</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Residual</td><td>6</td><td>4367.0</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>28865.2</td><td></td><td></td></tr><tr><td><strong>pH<sub>T</sub></strong></td><td></td><td></td><td></td><td></td></tr><tr><td>pH treatment (P)</td><td>1</td><td>0.913</td><td>22.036</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Time (T)</td><td>6</td><td>0.064</td><td>1.555</td><td>0.303</td></tr><tr><td>Residual</td><td>6</td><td>0.041</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>0.119</td><td></td><td></td></tr><tr><td><strong>pCO<sub>2</sub> (ambient 4.0 - 13.9, high CO2 10.6 -88.3)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>pH treatment (P)</td><td>1</td><td>3133930.0</td><td>9.018</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Time (T)</td><td>6</td><td>401951.2</td><td>1.157</td><td>0.432</td></tr><tr><td>Residual</td><td>6</td><td>347500.4</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>586972.3</td><td></td><td></td></tr><tr><td><strong>C<sub>T</sub> (all error &lt; 0.00)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>pH treatment (P)</td><td>1</td><td>0.001</td><td>31.546</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Time (T)</td><td>6</td><td>0.001</td><td>10.243</td><td><strong>0.006</strong></td></tr><tr><td>Residual</td><td>6</td><td>0.001</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>0.001</td><td></td><td></td></tr></tbody></table>
ΩA (ambient: 0.09 – 0.13, high CO2: 10.6 -88.3)

<table><tr><td>pH treatment (P)</td><td>1</td><td>4.750</td><td>45.142</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Time (T)</td><td>6</td><td>0.125</td><td>1.184</td><td>0.421</td></tr><tr><td>Residual</td><td>6</td><td>0.105</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>0.471</td><td></td><td></td></tr></table>

ΩC (ambient: 0.18 – 0.22, high CO2: 0.03 -0.11)

<table><tr><td>pH treatment (P)</td><td>1</td><td>13.08</td><td>40.954</td><td><strong>&lt;0.01</strong></td></tr><tr><td>Time (T)</td><td>6</td><td>0.313</td><td>0.98</td><td>0.509</td></tr><tr><td>Residual</td><td>6</td><td>0.319</td><td></td><td></td></tr><tr><td>Total</td><td>13</td><td>1.298</td><td></td><td></td></tr></table>

812

813 **Supplementary Table 4.** Error estimates for the variables *pCO₂*, *C*<sub>T</sub>, Ω<sub>A</sub> and Ω<sub>C</sub> using

814 the function “errors” in the “seacarb” package in R software (A<sub>T</sub> precision of 3.9 µmol kg<sup>-1</sup>, n = 7; error for probes of pH = 0.01, T = 0.1, and S = 0.01).

816

<table><thead><tr><th rowspan="2">Day</th><th rowspan="2">Date</th><th rowspan="2">pCO<sub>2</sub></th><th colspan="3">Ambient</th><th colspan="4">High CO<sub>2</sub></th></tr><tr><th>C<sub>T</sub></th><th>Ω<sub>A</sub></th><th>Ω<sub>C</sub></th><th>pCO<sub>2</sub></th><th>C<sub>T</sub></th><th>Ω<sub>A</sub></th><th>Ω<sub>C</sub></th></tr></thead><tbody><tr><td>1</td><td>17 March 2017</td><td>4.02</td><td>6.28</td><td>0.12</td><td>0.20</td><td>10.55</td><td>5.06</td><td>0.07</td><td>0.11</td></tr><tr><td>3</td><td>20 March 2017</td><td>4.58</td><td>6.50</td><td>0.13</td><td>0.22</td><td>55.55</td><td>4.77</td><td>0.02</td><td>0.03</td></tr><tr><td>11</td><td>28 March 2017</td><td>11.29</td><td>6.58</td><td>0.12</td><td>0.20</td><td>25.55</td><td>5.15</td><td>0.07</td><td>0.10</td></tr><tr><td>15</td><td>1 April 2017</td><td>11.13</td><td>6.14</td><td>0.11</td><td>0.18</td><td>29.63</td><td>4.91</td><td>0.05</td><td>0.09</td></tr><tr><td>33</td><td>19 April 2017</td><td>10.48</td><td>6.86</td><td>0.13</td><td>0.21</td><td>31.18</td><td>5.44</td><td>0.07</td><td>0.12</td></tr><tr><td>42</td><td>28 April 2017</td><td>13.46</td><td>5.77</td><td>0.10</td><td>0.16</td><td>88.31</td><td>5.18</td><td>0.02</td><td>0.03</td></tr><tr><td>45</td><td>5 May 2017</td><td>13.92</td><td>5.52</td><td>0.09</td><td>0.15</td><td>26.39</td><td>4.84</td><td>0.05</td><td>0.09</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>818 **Supplementary Figure 1.** Initial morphology of *H. wrightii* and *R. maritima* (shoot density, a; shoot height, b; leaves per shoot, c; total leaf material, d; mean ± SE) before maintained for 34, 41, and 54days at ambient (blue) and high CO₂ (red) treatments in *homospecific* and *heterospecific* beds(*H. wrightii* with *H. wrightii*, HH; *R. maritima* with *R. maritima*, RR and *H. wrightii* with *R. maritima*, HR).</figcaption></figure>
a.

Halodule wrightii

<figure><img src="image_12.png" /></figure>

Ruppia maritima

<figure><img src="image_13.png" /></figure>

b.

<figure><img src="image_14.png" /></figure>

c.

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

d.

Halodule wrightii

<figure><img src="image_18.png" /></figure>

Homospecific (HH)

<figure><img src="image_19.png" /></figure>

Heterospecific (HR)

Ruppia maritima

<figure><img src="image_20.png" /></figure>

Homospecific (RR)

<figure><img src="image_21.png" /></figure>

Heterospecific (HR)