Moderate Increase in TCO2 Enhances Photosynthesis of Seagrass Zostera japonica, but Not Zostera marina: Implications for Acidification Mitigation

Introduction

The uptake of CO₂ from anthropogenic fossil fuel emissions by the global oceans is shifting the acid-base balance of the carbonate system in a process known as ocean acidification (OA). Increasing CO₂ concentration outpaces the natural buffering capacity of seawater and increases the total CO₂ (TCO₂), which is the sum of all forms of carbonic acid and its conjugate bases (Doney et al., 2009; Hönisch et al., 2012). The dissolution of anthropogenic CO₂ in nearshore waters interacts with a host of other processes that drive the dynamics of nearshore carbonate chemistry, such as biological metabolism, riverine discharge and associated organic matter composition, tidal pumping, upwelling, nutrient input, and eutrophication (Feely et al., 2008, 2010; Cai, 2011; Duarte et al., 2013; Waldbusser and Salisbury, 2014; Wallace et al., 2014). The synergy of these factors induces high variability to the carbonate system, and results in periodic and episodic decreases in pH and aragonite saturation state (Ω_ar) that are more extreme than the ~0.4 pH and ~1.5 Ω_ar decreases predicted for global ocean averages by the year 2100 (Ciais et al., 2013; Duarte et al., 2013; Waldbusser and Salisbury, 2014). Among these drivers, photosynthesis and respiration are the dominant processes controlling coastal ocean carbonate chemistry (Gattuso et al., 1998; Sunda and Cai, 2012; Waldbusser and Salisbury, 2014). The diurnal variability of biological photosynthesis and respiration can, therefore, either dampen or amplify the magnitude of extreme carbonate chemistry events, which may occur from episodic influxes of fresh water or upwelling. Gaining a better understanding of how these biological signals modify and potentially ameliorate acidification is imperative, particularly when the effects of acidification can impact the economic and social stability of coastal human communities that are dependent on ocean resources (Ekstrom et al., 2015).

Many marine calcifiers and a variety of other marine species will be negatively affected by OA as decreases in pH, calcium carbonate saturation state and the substrate-to-inhibitor ratio—[${\text{HCO}}_3^{-}$]/[H⁺]—have been shown to inhibit calcification, growth, and acid-base regulation (Pörtner, 2008; Kroeker et al., 2013; Thomsen et al., 2015; Waldbusser et al., 2015; Fassbender et al., 2016). Conversely, some autotrophic organisms may directly benefit from the increase in seawater CO₂ associated with acidification, which can stimulate photosynthesis and increase growth (reviewed in Kroeker et al., 2010, 2013; Koch et al., 2013). For example, photosynthetic rates of many seagrass species have been shown to increase with TCO₂ (Beer and Koch, 1996; Thom, 1996; Zimmerman et al., 1997; Invers et al., 2001; Cox et al., 2016; Ow et al., 2016). In addition, some studies have concluded that the carbon uptake by seagrass beds may mitigate acidification on specific spatial and temporal scales when irradiance is high (Manzello et al., 2012; Unsworth et al., 2012; Hendriks et al., 2014).

OA mitigation by seagrass arises from the removal of CO₂ from seawater through photosynthesis, which raises pH and reduces TCO₂ thus minimizing acidification on short timescales (Marbá et al., 2006; Unsworth et al., 2012). This direct action of OA mitigation by seagrass is, however, reversed during times of dark respiration, which transiently increases TCO₂. For most seagrass systems, periods of high photosynthetic activity are robust enough to reduce TCO₂ in spite of the enhanced remineralization of allochthonous organic matter, which accumulates within seagrass beds and fuels the production of TCO₂ via respiration (Koch et al., 2006; Duarte et al., 2013; Hendriks et al., 2014; Baumann et al., 2015). Since most seagrass systems tend to be net autotrophic, or at least seasonally net autotrophic, they can sequester copious amounts of carbon on seasonal timescales that correspond to changes in above-ground biomass, while carbon can be sequestered on longer timescales by below-ground retention in the sediment (Duarte et al., 2005, 2010; Chung et al., 2011; McLeod et al., 2011; Fourqurean et al., 2012; Unsworth et al., 2012; Marbà et al., 2015; Poppe, 2016). Estimated values of organic carbon retained in seagrass sediments to a depth of 1 m range from 9.1 to 625 mg ha⁻¹, with actual measurements extending to 829 mg ha⁻¹ (Fourqurean et al., 2012). Despite the importance of carbon storage, it is the instantaneous photosynthetic carbon uptake that drives OA mitigation potential on short timescales.

Presumably, extreme acidification events in nearshore coastal waters will increase in frequency, duration, and magnitude as a result of anthropogenic OA (Harris et al., 2013; Hauri et al., 2013). For early life stage calcifiers undergoing rapid growth and development, sensitivity to OA is heightened and driven by the duration and intensity of exposure (Kurihara, 2008; Talmage and Gobler, 2009; Hettinger et al., 2012; Waldbusser et al., 2015). Therefore, an increase in acidification severity will likely result in chemical conditions that periodically surpass physiological thresholds of resident organisms already living near their tolerance limits (Grantham et al., 2004; Waldbusser and Salisbury, 2014). Instantaneous carbon uptake rates by seagrasses, however, may be able to expand periods of favorable carbonate chemistry for these sensitive species, or dampen the episodic extremes of acidification on hourly timescales, thus lessening the exposure to extreme acidification.

In the U.S. Pacific Northwest (PNW), two Zostera seagrass species are among the seagrasses commonly found in soft-sediment habitats in the Salish Sea: native Zostera marina L. and non-native Zostera japonica Ascher & Graebner (Harrison and Bigley, 1982). The native seagrass Z. marina has specifically been identified by Washington State as a biological means to ameliorate acidification (Washington State Blue Ribbon Panel on Ocean Acidification Ocean Acidification., 2012). In addition, both Z. marina and Z. japonica have strong habitat-associations with organisms vulnerable to OA, such as bivalves (Ferraro and Cole, 2012; Mach et al., 2014; Dumbauld and McCoy, 2015). The non-native Z. japonica has colonized previously unvegetated mudflats and is found in the mid to upper intertidal zone, whereas Z. marina has a distribution extending from the lower intertidal to shallow subtidal region; species overlap between Z. marina and Z. japonica can occur in the lower intertidal zone on flat shorelines (Harrison, 1982; Thom, 1990; Kaldy, 2006; Ruesink et al., 2010). The increasing presence and distribution of Z. japonica in the PNW warrants an inclusion of this non-native species when examining the potential of seagrass in the Salish Sea to mitigate OA.

Research has shown that seagrass carbon uptake rates are species-specific and vary in response to altered carbonate chemistry (Campbell and Fourqurean, 2013; Koch et al., 2013). Thus far, Z. marina has been the focus of many studies examining its photosynthetic response to increases in TCO₂ (Beer and Koch, 1996; Thom, 1996; Zimmerman et al., 1997; Koch et al., 2013); however, this is not the case for Z. japonica. There have not been any published studies to our knowledge examining Z. japonica response to TCO₂. Only a handful of studies have examined Z. japonica photosynthesis and physiology for populations established in the PNW (see Shafer et al., 2011; Shafer and Kaldy, 2014; Kaldy et al., 2015). Shafer and Kaldy (2014) found that Z. japonica local to the central Oregon coast has a substantially greater photosynthetic rate than Z. marina under the same chemical conditions. While this study provides insight into the relative photosynthetic differences between the two species, a comprehensive understanding of variable TCO₂ and irradiance response is needed in order to determine how this difference drives OA mitigation potential. In addition, it is important to examine if the local adaptations that exist throughout a species' distribution (Backman, 1991 and references therein; Shafer et al., 2011) result in photosynthetic differences that are dissimilar between central Oregon and Salish Sea populations.

In this study, we conducted a series of laboratory experiments to better understand how Z. marina and Z. japonica may alter carbonate chemistry under conditions of elevated TCO₂ and variation in light intensity, as would occur over a diurnal period. We aimed to determine how the photosynthetic carbon uptake of Z. marina and Z. japonica shift the carbonate system on short timescales potentially counteracting acidification. Specifically, we conducted one experiment per species to (1) examine the differences in species' photosynthetic rates (i.e., carbon uptake rates), (2) quantify the response of photosynthetic rates to differing levels of TCO₂, and (3) use those results to estimate how the photosynthetic carbon uptake could induce hourly changes on the carbonate system over a diurnal cycle at various biomass to water volume ratios.

Materials and Methods

Sample Site and Collection

Padilla Bay, Washington, is a tidally dominated estuary in the Salish Sea, and is a part of the National Estuarine Research Reserve System (48°31′14.1″N, 122°35′24.4″W). The Z. marina and Z. japonica meadows in Padilla Bay constitute a submerged and emergent total area of ~4,000 ha, where Z. marina accounts for ~3,000 of the total area (Bulthuis, 2013). The higher intertidal region is dominated by Z. japonica, which is morphologically different than Z. marina and has a leaf surface area and mass that is ~5x less than that of Z. marina. Z. japonica constitutes ~22% of the seagrass biomass in Padilla Bay, and the spread of the non-native species has increased the total areal extent of seagrass acreage. In the intertidal where Z. japonica overlaps with Z. marina, a 250 m transect was marked for shoot collection, which occurred every 25 m (48°29′36.6″N, 122°29′8.5″W). Two weeks prior to shoot collection, two HOBO Pendant Temperature/Light 64k data loggers were attached to the transect marking poles ~1 m above the sediment (above the height of the canopy) to capture in situ irradiance at the sample site which was used to generate a realistic but idealized light field to drive model visualizations. HOBO data (in units of lux) were converted to daylight photon flux density (PFD) as described by Thimijan and Heins (1983). The measurements from the two sensors were averaged and smoothed, and this curve was scaled to the maximum value recorded by land-based PAR measurements at the Padilla Bay farm station. It is important to note that the HOBO light sensors only measure in the planar flux and not the spherical or scalar flux, which is a more comprehensive irradiance flux; therefore, the sensor data are used to define the shape of the curve, while the scaling should bring the overall range of irradiance values within realistic bounds.

Approximately 200 shoots of each species were collected from Padilla Bay along the entire 250 m transect. Healthy-looking adult Z. marina shoots with intact rhizomes were collected by hand during low tide (0.8 m MLLW) on August 16, 2015 from Padilla Bay, placed in a cooler, and transported to Shannon Point Marine Center in Anacortes, Washington, within 1 h of collection. Ten days later on August 26, 2015, adult Z. japonica shoots with intact rhizomes were collected in the same manner at low tide (~0.0 MLLW) for a second experiment. One hundred of the most healthy-looking shoots (i.e., shoots without visible damage and well-preserved rhizomes) were then haphazardly selected, rinsed with seawater, and dispersed among four separate 40-l acrylic flow through tanks under low PFD (~50 μmol photons m⁻² s⁻¹) on a 12:12 L:D cycle for ~48 h before experimentation.

Experimental Design

We used a 5 × 5 factorial design that targeted 25 treatment CO₂ and light combinations: estimated PFD levels 0, 40, 200, 500, and 750 μmol photons m⁻² s⁻¹, and pCO₂ of 140, 250, 400, 650, and 900 μatm, where the 400 pCO₂ treatment is the approximate ambient condition for the open ocean. Treatment pCO₂ values were selected because they correspond to an observed range of diurnal in situ variability of the carbonate system in Padilla Bay (Love et al., 2016). Even though pCO₂ was the variable used to prepare treatments by gas equilibration, TCO₂ is a more appropriate metric for our experiment given that CO₂ and ${\text{HCO}}_3^{-}$ are presumably both utilized for photosynthesis; therefore, we identify treatment levels by initial TCO₂ rather than pCO₂ henceforth. In order to achieve the full factorial design, we used closed experimental incubation vials housing leaf segments rather than whole shoots in large open aquaria. Each treatment combination had quadruplicate replication and duplicate blanks for a total of 100 leaf-segment and 50 blank vials. Blanks had the same PFD exposure and initial TCO₂ but lacked leaf segments, and were used to account for any changes in seawater chemistry induced by microbial activity.

During each experiment, 150 incubation vials (20 ml borosilicate scintillation vials with a polyethylene cone-shaped liner) were placed in five clear acrylic water bath trays (56.5 × 7.62 × 3.81 cm) fitted with flow-through seawater for temperature control. Each water bath tray was mounted to a single 1.5 cm clear acrylic sheet, and placed directly above an individual bulb in a light fixture housing five T5 high output 54W 6,500 K Spectralux bulbs. Incubation vials were nearly fully submerged (water line stopped at cap) when placed in water bath trays. Vinyl mesh wraps were constructed and fitted to incubation vials to attenuate light. Mesh covers either had one, three, or six layers, which provided a PFD range from ~40 to 500 μmol photons m⁻² s⁻¹.

Treatment Water and Leaf Segment Preparation

To obtain a spectrum of TCO₂ concentrations, seawater pumped from Guemes Channel was collected, 0.2 μm filtered, sterilized via autoclave, and distributed into four 20 L polycarbonate carboys. Four mixtures of pure CO₂ (research grade-5) and compressed ambient air stripped of CO₂ using a regenerative molecular sieve adsorber (Twin Tower Engineering, CAS2-11) were generated using four sets of Sierra SmartTrak mass flow controllers. Treatment water was bubbled for at least 72 h with gas mixtures that were first passed through humidifiers (one liter Nalgene bottles filled halfway with tap water). Carboys were held in an incubator, which maintained water temperature at approximately 12.5°C. Further details are described in the equilibration system portions of Love et al. (2017). A fifth CO₂ treatment was produced by mixing, equilibrated, identical volumes of the two lowest CO₂ mixtures.

Preparation of seagrass tissue used for incubations began approximately 20 h before each experiment. This began by selecting the middle section of the second youngest leaf in a shoot, and wiping the entire leaf clean of epiphytes. To test similar leaf surface area for both species, a 2 cm leaf segment for Z. marina and 4 cm leaf segment for Z. japonica was excised. While the middle section of the leaf was selected for, variation among shoot length of the second youngest leaf existed. Each leaf segment was then cut in two, and one half randomly frozen for chlorophyll extraction and the other placed into the incubation vial filled with 5 ml of filtered and sterilized seawater. Leaf segment stocked vials were then held in a low light incubator at ~12°C for 16–20 h until experimentation.

Immediately before each experiment, stocked vials were emptied of filtered seawater, rinsed and filled with treatment seawater. Vials were overflowed to eliminate headspace, and a 0.5-mm glass bead was inserted as a stirring mechanism. Mesh coverings (no mesh, 1 layer, 3 layer, 6 layer, and black electrical tape for opaque) were placed around vials to achieve varied levels of PFD. Vials were haphazardly placed into water bath trays, and incubated for 90 min until termination of the experiment. Vials were physically inverted by hand, three times, every 5 min to stir water inside and minimize the development of a TCO₂ poor boundary layer around leaf segments. Temperature in water bath trays was continuously monitored and recorded every 20 min with a Fluke 1523 reference thermometer from the beginning of the experiment until all vials were terminated. PFD was measured at each vial location with a QSL-101 PAR irradiance sensor (Biospherical Instruments Inc.) by setting vials with mesh coverings in their respective locations after the conclusion of the experiment. Due to the discontinuity of PFD along a bulb (i.e., vials positioned at the center of a bulb received more light than those at either end), light measurements were treated as continuous data for all statistical analyses.

Leaf Segment and Carbonate Chemistry Analysis

Photosynthetic rates were determined from leaf segment incubations of Z. marina and Z. japonica by measuring treatment TCO₂ concentration before and after incubation. While photosynthetic rates are normalized to TCO₂, the O₂ convention is followed. That is, positive photosynthetic rates are a negative flux of TCO₂ from the medium, while respiration is a positive flux of TCO₂ to the medium: this is opposite when O₂ normalized. Initial TCO₂ samples were collected in triplicate 20 ml scintillation vials, poisoned with 10 μl of saturated HgCl₂, capped, wrapped with parafilm to minimize any potential gas leakage, and refrigerated at 2°C until analysis. Initial total alkalinity (TA) samples were collected in triplicate 350 ml amber glass bottles with polyurethane-lined crimp-sealed metal caps and poisoned with 30 μl of saturated HgCl₂. Salinity was measured from each TA sample with a refractometer before poisoning occurred, and again for each treatment vial when samples were processed.

Incubations were terminated by removing vials, one at a time haphazardly across treatments, but ordinally by replicate assignment—this allowed for an equal experimental termination time across treatments. Leaf segments were removed from the vial, marked for incubation time, and stored in an empty vial for dry-weight measurement. The experimental vial was immediately poisoned and stored in the same manner as initial condition samples. TCO₂ samples were analyzed within 5 days of each experiment using an Apollo SciTech AS-C3 dissolved inorganic carbon analyzer. TA samples were titrated within 30 days of the experiment using the open-cell method as in Dickson et al. (2007) with a Metrohm 888 Titrando. Certified reference material was used to construct a five-point standardization curve for TCO₂ and to verify accuracy of TA open-cell titration (Batch 144, A.G., Dickson, Scripps Institute of Oceanography). All other carbonate chemistry parameters were calculated using CO₂SYS (Pierrot et al., 2006) with K₁ and K₂ equilibrium constants from Mehrbach et al. (1973) and refit by Dickson and Millero (1987). Dry weight was recorded after rinsing leaf segments 3x with deionized water and drying at 55°C for at least 24 h. Frozen leaf segments were prepped for chlorophyll extraction by sonicating for 30 s in a 10 ml 90% acetone solution. Segments were then refrozen at −20°C for 24 h and centrifuged for 5 min directly before chlorophyll measurement. Extract was measured with a Trilogy fluorometer (Turner designs), acidified with 0.1 N HCl, and measured again. Chlorophyll and phaeopigment concentrations were calculated following the methods described by Lorenzen (1966).

Statistical Methods and Photosynthetic Response

Predicted photosynthetic rates for both species were determined by modeling the empirical data separately for each experiment as a function of continuous PFD, and a combined effect of continuous PFD and TCO₂ in one integrated, iterative (600 iterations) model, using the non-linear and linear curving fitting functions and curve fitting tools in the MathWorks software Matlab (V. 2015b). Species comparison was determined from the robustness of the model fit and whether or not 95% confidence intervals overlapped for each predicted photosynthetic parameter. Photosynthetic rates were normalized to chlorophyll (a/b) rather than dry-weight in order to account for variability of pigment concentration that occurs between and along leaves (Enríquez et al., 2002). Following the methods described in Jassby and Platt (1976), the net photosynthetic rate (P_net) was calculated as:

$\begin{array}{cc}{P}_{net}= P_{max}\tanh \left(\frac{\alpha E}{P_{max}}\right)+R_{d }& \left(1\right)\end{array}$

where P_max is the maximum photosynthetic rate (μmol TCO₂ mg chl⁻¹ h⁻¹), E is the PFD (μmol photons m⁻² s⁻¹), R_d is the dark respiration rate (μmol TCO₂ mg chl⁻¹ h⁻¹), and α (photosynthetic efficiency) is the initial slope of the photosynthetic-irradiance curve (μmol TCO₂ mg chl⁻¹ h⁻¹ (μmol photons m⁻² s⁻¹)⁻¹). In order to integrate the empirical instantaneous photosynthetic rates (i.e., carbon uptake) to hourly changes in the carbonate system, TCO₂ was integrated into the standard photosynthesis-irradiance curve (Equation 1) as a linear effect on P_max. A vector of initial TCO₂ concentration was applied to the P_max term, thus allowing P_max to vary with TCO₂:

$\begin{array}{cc}{P}_{max}^{TC}= P_{base }+\beta \ast TC{O}_{2i}& \left(2\right)\end{array}$

where $P_{\text{max}}^{\text{TC}}$ is the TCO₂ dependent P_max, β is the slope of the P_max response to TCO₂, P_base is the intercept, and TCO_2i is the initial TCO₂ concentration. The integrated TCO₂-irradiance model was, thus, a combination of the above equations:

$\begin{array}{cc}{P}_{net}= P_{max}^{TC}\tanh \left(\frac{\alpha E}{P_{max}^{TC}}\right)+R_{d }.& \left(3\right)\end{array}$

where P_net is now a function of irradiance and TCO₂. The estimated mean P_max and photosynthetic efficiency (α) from the TCO₂-irradiance model (Equation 3) output were used to calculate the saturation irradiance:

$\begin{array}{cc}{E}_k= \frac{P_{max}^{TC}}{\alpha }& \left(4\right)\end{array}$

where E_k is the saturation irradiance and $P_{\text{max}}^{\text{TC}}$ and α the outputs from the TCO₂-irradiance model (Equation 3). Since all TCO₂-irradiance model predictions of P_max are TCO₂ dependent ($P_{\text{max}}^{\text{TC}}$), for simplicity, we will simply refer to them as P_max rather than $P_{\text{max}}^{\text{TC}}$. P_max-values for both species at every initial TCO₂ treatment were statistically compared based on the predicted standard error and confidence bounds from the model output. In addition, P_max-values as a function of initial CO₂ and ${\text{HCO}}_3^{-}$ were observed as both carbonate species are generally utilized for photosynthesis.

Model Evaluation: Photosynthesis and Carbon Uptake

The TCO₂-irradiance model was conceived by applying realistic PFD values representing a diurnal cycle and the experimental TCO₂ treatment values as model input parameters (Equation 3), producing hourly photosynthesis estimates and the concomitant uptake of TCO₂. A Gaussian function was fit to field collected PFD measurements resulting in a generalized light cycle. The TCO₂ values for the ambient treatment from both experiments were averaged (1,964 μmol kg⁻¹) and utilized as a baseline concentration from which hourly photosynthetic carbon uptake could be used to calculate changes in TCO₂ at each hourly step over a diurnal period. Assuming TA constant (also averaged from both experiments—2,134 μmol kg⁻¹), the relative changes in pH and Ω_ar could then be calculated based on the change in TCO₂ as driven by the estimated photosynthetic carbon uptake of each species. The calculated differences were always determined from the baseline TCO₂ for each time step. This is in accordance with the initial condition of each of our experimental TCO₂ and light conditions, and is analogous to the situation when residence time in a seagrass bed is small. In the scenario where reduction in TCO₂ is compounded, concentrations in the conceived model would be reduced to minimal values rapidly upon initiation of photosynthesis and the effects of both species-specific rates and initial TCO₂ would disappear under the general carbon limitation.

Estimated Changes in Carbonate Chemistry

The experimental results were determined at a fixed ratio of biomass to water volume, constraining the ability to compare the laboratory results to other studies. Therefore, the model predicted results were examined at different biomass to water volume ratios, which would be similar to variation in seagrass density and water depth in the field. Predicted P_max results were applied to a simple box model consisting of a 1 m area, with seagrass at a moderate density, and carbonate chemistry predictions driven by saturating irradiance, TCO₂ and water depth. The specific changes induced on the carbonate system by both species' maximum photosynthetic potential were extrapolated from vial volume (20 ml) to different volumes of static water corresponding to various depths over a 1 m² patch of seagrass with a biomass of 100 gDW m⁻². It should be noted that we don't take into account light attenuation at these depths, but at mid-day, irradiance should still be saturating. Estimates for both species from various locations in Padilla Bay reported in Bulthuis (2013), show a range from ~60–200 gDW m⁻² with a mean of ~100 for both species. The change in TCO₂ was calculated assuming a well-mixed water column, the average mg chl gDW⁻¹ (1.88 ± 0.89 and 0.67 ± 0.33 for Z. marina and Z. japonica, respectively) for each species, and using projected P_max values, as:

$\begin{array}{cc}\Delta TC{O}_2= TC{O}_{2i}-\left(C_b\ast \left(\frac{b_i}{a}\right)\ast \left(\frac{1}{d}\right)\ast f\ast r\right)& \left(5\right)\end{array}$

where TCO_2i is the initial TCO₂, C_b is mg chl gDW⁻¹, b_i is the gDW over a m² (a), d is depth (m), r is P_max (μmol TCO₂ mg chl⁻¹ h⁻¹), and f is the unit conversion m³ 1,000 L⁻¹. Changes in TCO₂ from photosynthetic carbon uptake by each species were then used to determine the changes in carbonate chemistry parameters while assuming TA constant (averaged from both experiments—2,134 μmol kg⁻¹).

Results

Incubation Conditions

Experimental conditions for the leaf segment incubations were similar between experiments (Table 1), which were run at nearly the exact same time of day in the mid-morning (<2 min apart). Temperature averaged 13.2 ± 0.4°C, for the Z. marina experiment, and 13.4 ± 0.1°C for the Z. japonica experiment. Photon flux density (PFD) had a greater variance for the high light treatments than for the lower, more heavily shaded treatments. Salinity ranged from 30.5 to 32 ppt for the Z. japonica experiment (Table 1). This variation was likely a result of unequal effectiveness by the humidifiers during bubbling, or from differential evaporation in the autoclaved bottles used to fill carboys. The pCO₂ of the seawater treatments did not indicate complete equilibration with the gas mixtures, which ranged from 100 to 1,200 μatm whereas seawater treatment conditions only ranged from 138 to 918 μatm (Table 1). However, the treatment values did produce a large range in TCO₂ and were similar across both experiments. The high TCO₂ treatment had the greatest difference between experiments, which was 55 μmol kg⁻¹ higher for the Z. marina experiment than for the Z. japonica experiment (Table 1). It should be noted that sample size by treatment TCO₂ varied from 20 to 17 due to either the malfunction of the dissolved inorganic carbon analyzer or loss of treatment seawater stored in the incubation vial.

TABLE 1

Table 1. Initial conditions for both Z. marina and Z. japonica experiments.

Species-Specific Photosynthetic Response to Irradiance and TCO₂

All photosynthetic parameters for Z. japonica were best predicted by the TCO₂-irradiance model (Equation 3), which had a more robust fit (RMSE = 150 μmol TCO₂ mg chl⁻¹ h⁻¹) than the standard photosynthesis-irradiance model (Equation 1), RMSE = 161 μmol TCO₂ mg chl⁻¹ h⁻¹ (Table 2). There was no difference in the predictive power between the TCO₂-irradiance and the standard photosynthesis-irradiance models for Z. marina, both had an RMSE of 43 μmol TCO₂ mg chl⁻¹ h⁻¹. Based on the TCO₂-irradiance model relating the photosynthesis-irradiance curve to TCO₂, the mean maximum photosynthetic rate (P_max) for Z. japonica was 3x greater than Z. marina, and photosynthetic efficiency (α) was 4.5x greater on a per chlorophyll basis (Table 2, Figure 1). The non-linear fit for both seagrasses in Figure 1 is the mean fit over all initial TCO₂, which displays higher variability for Z. japonica compared to Z. marina indicating a greater response by Z. japonica to TCO₂. The model produced a good fit for Z. japonica and for Z. marina for two out of the three photosynthetic parameters, however, the mix of positive and negative TCO₂ fluxes in the respiration vials resulted in a positive value rather than a negative value predicted by the model for Z. marina and Z. japonica respiration (intercept) (Figure 1, Table 2). Across all treatments, the mean leaf segment mg chl:gDW⁻¹ ratio—used to normalize the model results—was ~3x greater for Z. marina (1.88 ± 0.89) than for Z. japonica (0.67 ± 0.33). Model predicted mean P_max for Z. japonica was 360 ± 33 μmol TCO₂ mg chl⁻¹ h⁻¹ with an α of 3.70 ± 1.1 μmol TCO₂ mg chl⁻¹ h⁻¹ (μmol photons m⁻² s⁻¹)⁻¹, and where considered significant with standard error (SE) (Table 2). Model prediction for Z. marina P_max was 113 ± 10 μmol TCO₂ mg chl⁻¹ h⁻¹ with an α of 0.819 ± 0.21 μmol TCO₂ mg chl⁻¹ h⁻¹ (μmol photons m⁻² s⁻¹)⁻¹, and were also predicted as significant (Table 2). In addition, the point at which each species experiences a saturating irradiance (E_k) was also different. Z. japonica displayed a lower E_k at 97 (propagated SE = 31) μmol photons m⁻² s⁻¹, which was ~70% of Z. marina's E_k of 138 (propagated SE = 37) μmol photons m⁻² s⁻¹. The maximum photosynthetic rates—which were used to fit the P_max values—for Z. japonica appeared to respond positively with increasing TCO₂ (Figure 2). Z. marina, however, did not display any positive increase in photosynthetic rate with increasing TCO₂. It is important to note that Figure 2A strictly shows one half of the TCO₂-irradiance model (photosynthetic rate to TCO₂) and does not directly reflect the interaction between TCO₂ and light, as is really described by the model.

TABLE 2

Table 2. Predicted photosynthetic parameters from the TCO₂-irradiance (TCO₂-IRR) model (Equation 3) and the standard photosynthesis-irradiance (P vs. E) model (Equation 1).

FIGURE 1

Figure 1. Photosynthetic-irradiance curves for Z. marina (closed circles) and Z. japonica (closed squares). Net photosynthetic rates (μmol TCO₂ mg chl⁻¹ h⁻¹) collated for all initial TCO₂ treatments from experimental 90 min incubations. Fitted lines are the estimated curve from the TCO₂-irradiance model (Equation 3). All negative data points indicate respiration (μmol TCO₂ mg chl⁻¹ h⁻¹).

FIGURE 2

Figure 2. (A) Experimental results of net photosynthetic rates (P_net μmol TCO₂ mg chl⁻¹ h⁻¹) for Z. marina (closed circles) and Z. japonica (closed squares) plotted against initial TCO₂ concentration. All negative data points indicate respiration (μmol TCO₂ mg chl⁻¹ h⁻¹). The surface plots of the TCO₂-irradiance model for Z. marina (B) and Z. japonica (C) highlight full model response.

The TCO₂-irradiance model predicted a positive increase in P_max to increasing carbon availability by Z. japonica over the entire range of TCO₂ tested (Figure 3). The TCO₂-irradiance model predicted a significant and positive β term for Z. japonica, whereas β for Z. marina was non-significant (where β is the slope of the P_max to TCO₂ relationship)_. Due to the variance of maximum photosynthetic rates and the resulting non-significant effect of TCO₂ on Z. marina, we are unable to properly analyze any trends in Z. marina P_max with TCO₂, ${\text{HCO}}_3^{-}$, and CO₂. According to the model, Z. japonica P_max increased by ~50% from the lowest to highest initial TCO₂ treatment (Figure 3). Across all initial TCO₂ treatments, Z. japonica had a significantly higher P_max than Z. marina (i.e., non-overlapping 95% CI) (Table 3).

FIGURE 3

Figure 3. Model predicted maximum photosynthetic rates (P_max) for Z. japonica (closed squares) and Z. marina (closed circles) as a function of TCO₂, ${\text{HCO}}_3^{-}$, and CO₂ with SE. Discrete ${\text{HCO}}_3^{-}$ and CO₂ concentrations are the initial values for each treatment corresponding to pCO₂- and TCO₂-values (Table 1). Dashed lines are the linear fits of the predicted P_max for Z. marina (green) and Z. japonica (yellow), which is the β term from the TCO₂-irradiance model (Table 2).

TABLE 3

Table 3. TCO₂-irradiance model (Equation 3) predicted P_max (μmol TCO₂ mg chl⁻¹ h⁻¹) values for both Z. japonica and Z. marina at each initial TCO₂ concentration.

Model Predictions for Diurnal Variation of the Carbonate System

Under realistic natural light conditions, both species are at saturating irradiances for about 6 h during a typical summer day. The resulting maximum predicted photosynthetic rates for Z. japonica, are 535 μmol TCO₂ mg chl⁻¹ h⁻¹ when initial TCO₂ is set to 2051 μmol kg⁻¹ (highest TCO₂ treatment), 387 μmol TCO₂ mg chl⁻¹ h⁻¹ when TCO₂ is set at 1964 μmol kg⁻¹ (ambient treatment) and only 145 μmol TCO₂ mg chl⁻¹ h⁻¹ when TCO₂ is set at 1770 μmol kg⁻¹ (lowest TCO₂ treatment) (Figure 4). The predicted maximum photosynthetic rate for Z. marina is 116 μmol TCO₂ mg chl⁻¹ h⁻¹ under these saturating light conditions, and no sensitivity to TCO₂ was detected. Due to the poor respiration data, estimates of respiration were excluded during times of zero irradiance. When light was saturating, model estimates predict that the carbon drawdown based on a P_max of 387 TCO₂ mg chl⁻¹ h⁻¹ for Z. japonica would result in a maximum increased change in pH and omega of 0.65 and 3.8 units mg chl⁻¹ h⁻¹ compared to the 0.25 and 1.1 unit increase by Z. marina. The estimated changes are, however, based on the total carbon draw down by the leaf segment in a 20 ml vial and in isolation from in situ bed conditions, which would be driven by additional considerations.

FIGURE 4

Figure 4. (A) Predicted TCO₂-irradiance model results of net photosynthetic rates for Z. japonica (yellow line) and Z. marina (green line) under a generalized light cycle from Padilla Bay and mean initial TCO₂ concentration of 1964 μmol kg⁻¹ corresponding to the median 400 pCO₂ treatment. Shaded region is the 95% CI. Upper (gray) and lower (black) bounds are the predicted net photosynthetic rate of Z. japonica at initial TCO₂ concentrations of 2051 and 1770 μmol kg⁻¹, respectively. (B) Average lux readings from two HOBO light data loggers deployed at field sample site. (C) Gaussian model fit of the Padilla bay irradiance converted to photon flux density: i.e., a generalized light cycle.

Model (Equation 5) estimated hourly changes in pH, Ω_ar, and the substrate-to-inhibitor ratio ([${\text{HCO}}_3^{-}$]/[H⁺]) for biomass to water volume ratios similar to a typical seagrass bed under 1 m of water were greater when mediated by Z. japonica than for Z. marina, with identical initial TCO₂ (Figure 5). Under these conditions, Z. japonica induced an hourly rate of change for pH and Ω_ar that was approximately 20% greater than Z. marina (Figure 5). Both seagrass species induced positive changes in carbonate chemistry parameters. The greatest change in pH (0.49 h⁻¹) and Ω_ar (1.61 h⁻¹) occurred at a shallow depth of 0.2 m with high TCO₂ (2051 μmol kg⁻¹) and was mediated by Z. japonica (Figure 5). The greatest change in the substrate-to-inhibitor ratio was not determined by high carbon uptake rates via photosynthesis, but rather by the low concentration of H⁺ at low TCO₂.

FIGURE 5

Figure 5. Yellow and green lines are the predicted change in (A) pH, (B) aragonite saturation state (Ω_ar), and (C) substrate-to-inhibitor (mol μmol⁻¹) ratio based on the predicted P_max (μmol TCO₂ mg chl⁻¹ h⁻¹) of Z. japonica (closed squares) and Z. marina (open circles) at an initial [TCO₂] of 1964 μmol kg⁻¹. Gray and black lines are the upper and lower bound of the predicted change in pH, aragonite saturation state, and substrate-to-inhibitor ratio of Z. japonica at an initial TCO₂ of 2,051 and 1,770 μmol kg⁻¹, respectively. Predicted change is over a 1 m² area and given depth assuming a biomass of 100 gDW m⁻² (Equation 5). TCO₂ concentrations correspond to the 140, 400, and 900 pCO₂ treatments for Z. japonica and 400 pCO₂ treatment for Z. marina (TCO₂ corresponding to 400 pCO₂ treatments was averaged from both experiments). TA (i.e., average TA of both 400 pCO₂ treatments), temperature, and salinity were utilized from each respective treatment (Table 1) to constrain the carbonate system to calculate predictions.

Discussion

Species-specific difference in TCO₂-dependent photosynthetic carbon uptake is a critical component that determines the capacity of seagrass to remove dissolved CO₂ from seawater and potentially create OA refugia on short timescales. Our results indicate that, on a per chlorophyll basis, Z. japonica is able to take up more TCO₂ and more efficiently utilize available irradiance compared to Z. marina occurring in the same intertidal zone under similar TCO₂ and irradiance conditions (Figures 1, 2, Table 2). Because of this, Z. japonica may be more effective at mitigating OA (increasing pH and Ω_ar) for more hours of the day than Z. marina on a per chlorophyll basis (Figure 4). The maximum potential drawdown of TCO₂ over the 6 h of saturating irradiance induces a positive change in pH (from 8.03 to 8.33 and 8.40) and Ω_ar (from 1.94 to 3.1 and 3.4) by Z. marina and Z. japonica, respectively, in our model. Even though these estimates of carbonate chemistry modification by Z. marina and Z. japonica are specific to our experimental conditions, we are able to detect the differential response of TCO₂-dependent carbon uptake between the two species, and provide insight for potential OA mitigation by two PNW seagrasses.

Extrapolated Changes in Carbonate Chemistry

One focus of this study was to better understand the OA mitigation potential of seagrass clippings in isolation, and extend those results to various biomass to water volume ratios in an attempt to conceptualize how these attributes of seagrass beds drive carbonate chemistry. While these results are not representative of in situ conditions, by incorporating realistic biomass values, the box model results provide a point of comparison to field based studies and information about photosynthetic effects measured under controlled conditions to those quantifying potential seagrass contribution to a dynamic and complex system. The estimates presented here are considered the maximum potential possible by each species, as photosynthetic rates calculated from leaf segments are likely an overestimate of whole plant photosynthetic rates (Herzka and Dunton, 1997); however, previous studies have shown that empirically derived photosynthetic rates can be similar between leaf segments and whole plants (see Table 2 in Lee et al., 2007; Table 6 in Shafer and Kaldy, 2014). It is important to note that the predicted changes in carbonate chemistry are derived from our measured mg chl:gDW⁻¹ ratio (Equation 5), however, we do not use gDW normalized photosynthetic rates as the variance was extremely high within treatments and resulted in non-significant differences (see Miller, 2016).

When extending our laboratory results of seagrass photosynthetic carbon uptake to changes in carbonate chemistry given specific biomass to water volume ratios, the expected potential change in pH, Ω_ar, and the substrate-to-inhibitor ratio—[${\text{HCO}}_3^{-}$]/[H⁺]—would be positively affected by seagrass carbon uptake (Figure 5). Estimated increases in pH per hour from this model at a volume corresponding to 1 m depth over 6 h result in maximum pH changes from 0.09 to 0.73 under low and high TCO₂ conditions for Z. japonica (Figure 5). Since Z. japonica photosynthesis is carbon sensitive, the more realistic pH change is likely somewhere in the middle and more accurately predicted by the mean TCO₂ treatment, which would result in a pH change of 0.37 over 6 h. This would not be a concern for Z. marina, however, as we did not find sensitivity to TCO₂ over the range tested in this study. These rates are based on maximum photosynthetic rates, but also on conservative assumptions about short residence time in seagrass beds. Where residence time is longer, chemical changes can be compounded and result in large swings, or the modified water can be exported to other locales. Observed diurnal change of pH in whole seagrass systems ranged from 0.2 to 0.7 units over several hours measured within, or directly above, shallow seagrass beds at a depth no greater than 12 m (Frankignoulle and Bouquegneau, 1990; Invers et al., 1997; Unsworth et al., 2012; Hendriks et al., 2014). We estimated diurnal increases in Ω_ar from 0.59 to 1.6 at a 1 m depth over 6 h under low and high TCO₂ conditions for Z. japonica (Figure 5). Model predictions from an extensive data set of Indo-Pacific seagrass community metabolism estimated maximum diurnal changes in Ω_ar to be 2.9 (Unsworth et al., 2012), whereas field based calculations of diurnal changes ranged from 1.38 to 1.67 (Hendriks et al., 2014). At shallower water depths, the higher Indo-Pacific change in Ω_ar of 2.9 is easily reached under our box model conditions (Figure 5). The congruence of these estimates illustrates how the laboratory studies presented here corroborate the idea that seagrass meadows can be drivers of locally important changes in carbonate chemistry. We note that the results of our box model should be viewed as context for field results and not as estimates of in situ processes. In order to make accurate predictions of carbonate chemistry variability in seagrass beds, higher resolution in situ studies and whole plant laboratory studies need to be performed.

Photosynthetic Response to TCO₂

Based on our results from the TCO₂-irradiance model (Equation 3), Z. japonica increased its P_max proportionally across TCO₂ treatments—where the slope (β) of the linear TCO₂ response was highly significant (p = < 0.001). Conversely, we were unable to detect any positive response of Z. marina P_max with increasing TCO₂. That is according to the TCO₂-irradiance model, the slope of the linear TCO₂ response was non-significant and slightly negative (p = 0.327, β = −0.054). Due to the lack of any significant trend and high variance within treatments, we suggest that Z. marina P_max was not positively affected by increasing TCO₂ under our study conditions. Our findings here are somewhat contradictory as most studies have reported Z. marina to have enhanced photosynthetic rates with increasing TCO₂ (Beer and Koch, 1996; Thom, 1996; Zimmerman et al., 1997), however, a recent study also found there to be no positive effect of TCO₂ on Z. marina photosynthesis up to pCO₂ levels of 2000 μatm (Pajusalu et al., 2016).

One reason for the different result in this study regarding Z. marina response to increasing TCO₂ may be due to the range of concentrations tested. The lowest enriched TCO₂ treatment in previous studies was ~25% higher than our highest TCO₂ treatment (Thom, 1996 does not give exact numbers, but minimum CO₂ increase was 25% higher than ambient), ranging up to 230% times greater than our highest treatment (Beer and Koch, 1996). The first reason for the relatively low TCO₂ tested in this study is that ambient seawater from the Salish Sea has lower salinity and, therefore, a lower TCO₂ than open ocean values. In addition, the range of TCO₂ tested in this study was representative of conditions currently observed in Padilla Bay, not the much greater range in other studies, which were designed to highlight potential physiological responses, not necessarily in situ conditions. The result showing that Z. marina P_max does not respond to TCO₂ in this study may indicate that northern Salish Sea populations are not sensitive to TCO₂ at these lower concentrations.

Additionally, the high degree of variability within our treatments may have obscured a positive response. Zimmerman et al. (1997) showed that Z. marina increased its P_max 225% with an exposure to TCO₂ 77% higher than their ambient treatment (from 2,074 to 3,673 μmol kg⁻¹). Over this range of TCO₂, a linear relationship has been observed between P_max and TCO₂ (Beer and Koch, 1996). The linear equation derived from Zimmerman et al. (1997) can be extrapolated down to the range in this study (1,770–2,051 μmol kg⁻¹), which would therefore predict that the 18% increase in TCO₂ between our lowest and highest TCO₂ treatments would induce a 55% increase in Z. marina P_max. Given that the coefficient of variation for Z. marina P_net at, or above, E_k ranged from 30 to 60% over all initial TCO₂ treatments, any small positive responses to TCO₂ by Z. marina may likely be concealed by the variance—assuming an extrapolation of the Zimmerman et al. (1997) data is accurate given this is outside of the bounds tested. Physiological and methodological factors contributing to the large variance within treatments may be a result of, insufficient incubation period, variance in illumination, shoot to shoot variability of the photosynthetic apparatus, inefficiencies in chlorophyll extraction from leaf segments, or propagation of a diffusive boundary layer due to periodic rather than continuous stirring over the 90+ min incubation time, which would increase the variability of the relative TCO₂ conditions experienced by the leaf segment compared to the entire vial.

Interestingly, the increase in Z. japonica P_max was ~50% over an 18% increase in TCO₂, which coincidently is very close to the predicted increase from the linear equation derived from Zimmerman et al. (1997). This robust response by Z. japonica was observed despite a coefficient of variation in P_net at, or above, E_k identical to that of Z. marina (30–60%). This is the first time that a response in the photosynthetic rate of Z. japonica to TCO₂ has been documented, to our knowledge. Previous research has shown that Z. japonica photosynthesis is more sensitive to light and salinity (Shafer et al., 2011; Shafer and Kaldy, 2014) than is Z. marina photosynthesis (Hellblom and Björk, 1999; Shafer and Kaldy, 2014). Given that the two species appear to express different physiological response to environmental factors, it is likely that the utilization of TCO₂ would also be inherently different. The differential response of P_max to TCO₂ between species may be due to the mechanism by which TCO₂ is utilized for photosynthesis. Previous research suggests that Z. marina ${\text{HCO}}_3^{-}$ utilization is saturated at pH ranging from 7.5 to 8.5 (Invers et al., 2001), which was the range of pH in our study (Table 1). Although we did not specifically measure independent HCO₃- and CO₂-uptake, an increase in ${\text{HCO}}_3^{-}$ in addition to CO₂ may be one mechanism that explains such a robust response by Z. japonica to TCO₂; however, further studies are needed to examine Z. japonica's photosynthetic response to HCO₃- and CO₂ independently.

Species-Specific Photosynthetic Rates

The relative difference between Z. marina and Z. japonica photosynthetic rates reported in our study are similar to previous findings, but differ in magnitude (Figure 1). The large degree of variability around the mean estimated model fit for Z. japonica photosynthesis can be partially explained by the increasing response of photosynthesis to TCO₂ (Figure 2). We reiterate, however, that the variability in our respiration measurements may somewhat lessen the predictive power of model estimates, particularly the photosynthetic efficiency, as the initial slope of the photosynthetic-irradiance curve is dependent on robust respiration values. For this reason, we have focused specifically on only our P_max results when interpreting photosynthetic carbon uptake induced changes on carbonate chemistry. A likely reason for the mix of positive and negative TCO₂ fluxes by our leaf segments in dark vials may be due to a short incubation time, volume of media to leaf segment ratio, or internal seagrass biorhythms of photosynthetic and respiration cycles. Similar to our findings, Shafer and Kaldy (2014) found that central Oregon populations of Z. japonica have a P_max that is ~3x greater than Z. marina when exposed to the same light conditions; however, the chlorophyll standardized rates reported in that study were considerably lower than the rates found in this study. The differing magnitude of rates suggest that other factors such as organization of the photosynthetic package within seagrass tissue may have a significant impact on photosynthetic rates rather than chlorophyll pigment concentration alone. A multitude of environmental factors such as intertidal location, canopy density, age of shoot and leaf, time of year, and acclimatization to epiphytic growth can affect chlorophyll pigment concentration and the photoacclimation amongst shoots and along leaves, thus leading to drastically different mg chl:gDW⁻¹ ratios (Dennison and Alberte, 1986; Durako and Kunzelman, 2002; Enríquez et al., 2002; Major and Dunton, 2002; Cummings and Zimmerman, 2003; Drake et al., 2003; Larkum et al., 2006 and references therein). Any of these factors may be responsible for the lower mg chl:gDW⁻¹ ratio and dissimilar photosynthetic rates in our study. In addition, the Z. marina samples were collected at the upper limit of their distribution, potentially affecting the physiology and phenotypic expression of Z. marina at this location. Local acclimatization to in situ temperature and salinity can also impact photosynthetic rates via osmotic stress and changes in the photosynthesis-respiration ratio (Kenneth and Short, 2006 and references therein). In addition, our study examined photosynthetic rates by measuring the change in TCO₂, which to our knowledge, has not been done before in the lab and rarely done in the field despite the robustness of the method (Silva et al., 2009). The photosynthetic quotient (O₂/CO₂), while assumed to be unity or close to in many studies, is based on community metabolism in seagrass beds or meadows (Oviatt et al., 1986; Leuschner and Rees, 1993; Mateo et al., 2001; Martin et al., 2005). Measuring TCO₂ rather than O₂ may result in photosynthetic rates that are dissimilar if the photosynthetic quotient is not close to unity under these conditions. That is, given the higher photosynthetic rates found in this study, the change in CO₂ per O₂ would have to be much lower in seagrass communities than what was measured in our vials. Alternatively, differences in chlorophyll extraction methods, or inefficient extraction may have resulted in the higher photosynthetic rates found in our study. Additionally, recycling and movement of gases within the lacunal system are not well-understood (Mateo et al., 2001), further convoluting the comparison of instantaneous O₂ production and CO₂ uptake. It may be that deriving photosynthesis by measuring TCO₂ is not as robust as the O₂ normative, but due to the lack of studies measuring seagrass photosynthesis with TCO₂, this remains uncertain. Studies which capture O₂ production and CO₂ uptake would be best suited to determine the relative advantage of each in determining the interactions between carbonate chemistry and seagrass photosynthesis.

Variations of Carbonate Chemistry in Seagrass Beds

While this study provides a point of reference for how seagrass beds can modify carbonate chemistry, determining the effects of macrophytes on acidification is challenging due to the extreme spatial and temporal variability of carbonate chemistry in these zones (Hendriks et al., 2014, 2015; Krause-Jensen et al., 2015; Challener et al., 2016). A multitude of factors such as seagrass epiphyte communities, heterotrophic respiration, tidal exchange, groundwater flux, and riverine input all contribute in modifying carbonate chemistry on various spatial and temporal scales. For example, differences in seagrass shoot density over a given area result in different mixing rates and flow regimes, which will further shift the carbonate chemistry of water parcels that have been modified by photosynthetic carbon uptake (Peterson et al., 2004; Koch et al., 2006; Marbá et al., 2006; Hendriks et al., 2014). In addition, variations in chlorophyll content along the leaf and amongst shoots results in differential photosynthetic rates and carbon uptake within the seagrass canopy, thereby creating disparate water parcels with respect to TCO₂ concentration on various spatial scales. These various interactions between water flow within a seagrass bed, irradiance, and TCO₂ are some of the determining factors of carbon assimilation and uptake (McPherson et al., 2015) and, therefore, directly affect the scale of OA mitigation by seagrasses via the formation of microzones with differential carbonate chemistry. Because organisms vulnerable to OA are more sensitive at particular life-stages and over hourly durations, the scale of carbonate chemistry variability (i.e., magnitude and duration of low saturation state and pH) is critical to understand when determining organismal resilience to OA (Kurihara, 2008; Barton et al., 2012; Hettinger et al., 2012; Onitsuka et al., 2014; Waldbusser et al., 2015; Miller and Waldbusser, 2016). Therefore, in order to elucidate a more nuanced amelioration of acidification by seagrasses, it will be necessary to conduct in situ studies that can effectively capture all the drivers of the carbonate system in spatiotemporal context.

Conclusion

A comparison of the photosynthetic potential between Z. marina and Z. japonica has implications for elucidating the contribution each species has on the carbonate system. Z. japonica exhibits a strong increase in photosynthetic rate in response to increasing TCO₂, while a similar response from Z. marina could not be identified. In the intertidal zone where species overlap occurs, our results indicate that Z. japonica also has a 3-fold greater photosynthetic potential than Z. marina when normalized to chlorophyll. By measuring photosynthetic potential as a change in TCO₂ and utilizing measured mg chl:gDW⁻¹ in addition to estimated m² biomass of each species, our results were extended to conceptualize how these additional attributes of biomass to depth ratios in seagrass beds affect hourly changes in carbonate chemistry. Based on our findings, Z. japonica appears to be better suited to mitigate OA on a per chlorophyll basis given its higher photosynthetic rate and efficiency and enhanced response to increases in TCO₂. Our study illuminates the potential of PNW populations of Z. marina and Z. japonica to modify the carbonate system, and provides a direct comparison of photosynthetic potential when exposed to varying levels of TCO₂. This is an initial step in attempting to determine the OA mitigation potential of seagrass systems in the PNW, where quantitative estimates can aid management practices and provide a better understanding for the local initiatives that aim to protect Washington state's aquaculture resources (Blue ribbon panel on OA 2012).

Author Contributions

CM conceptualized and designed the experiment with modifications directed by BL and SY. CM led the writing of the paper with contributions by BL and SY. CM performed all data analysis, with statistical analysis supported by SY. BL provided substantial support for utilization of model projections. All authors reviewed and contributed to the writing of the final manuscript.

Funding

This work was supported by the Padilla Bay National Estuarine Research Reserve assistantship. Funding granted by the Padilla Bay foundation, Jude Apple and Sharon Riggs: Contract/Grant # Borman-01-2015.

Conflict of Interest Statement

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

Acknowledgments

The authors would like to thank David Shull for thoughtful interpretation of our results. CM would like to thank Katherina L. Schoo for assistance with the ocean acidification experimental system. CM would also like to thank Rachel Blyth, Melissa Ciesielski, Taylor Clement, Rosie Gradoville, and Anne Harmann who were integral for carrying out the experiments.

References

Backman, T. (1991). Genotypic and phenotypic variability of Zostera marina on the west coast. Can. J. Bot. Rev. Can. Bot. 69, 1361–1371. doi: 10.1139/b91-176

CrossRef Full Text | Google Scholar

Barton, A., Hales, B., Waldbusser, G. G., Langdon, C., and Feely, R. A. (2012). The Pacific oyster, Crassostrea gigas, shows negative correlation to naturally elevated carbon dioxide levels: implications for near-term ocean acidification effects. Limnol. Oceanogr. 57, 698–710. doi: 10.4319/lo.2012.57.3.0698

CrossRef Full Text | Google Scholar

Baumann, H., Wallace, R. B., Tagliaferri, T., and Gobler, C. J. (2015). Large natural pH, CO₂ and O₂ fluctuations in a temperate tidal salt marsh on diel, seasonal, and interannual time scales. Estuar. Coasts 38, 220–231. doi: 10.1007/s12237-014-9800-y

CrossRef Full Text | Google Scholar

Beer, S., and Koch, E. (1996). Photosynthesis of marine macroalgae and seagrasses in globally changing CO₂ environments. Mar. Ecol. Prog. Ser. 141, 199–204. doi: 10.3354/meps141199

CrossRef Full Text | Google Scholar

Bulthuis, D. A. (2013). The Ecology of Padilla Bay, Washington: an Estuarine Profile of a National Estuarine Research Reserve. Mount Vernon, WA: Washington State Department of Ecology, Padilla Bay National Estuarine Research Reserve. Available online at: www.padillabay.gov/researchrep/profile.asp

Cai, W. J. (2011). “Estuarine and coastal ocean carbon paradox: CO₂ sinks or sites of terrestrial carbon incineration?,” in Annual Review of Marine Science, Vol. 3, eds C. A. Carlson and S. J. Giovannoni (Palo Alto, CA: Annual Reviews), 123–145.

Google Scholar

Campbell, J. E., and Fourqurean, J. W. (2013). Mechanisms of bicarbonate use influence the photosynthetic carbon dioxide sensitivity of tropical seagrasses. Limnol. Oceanogr. 58, 839–848. doi: 10.4319/lo.2013.58.3.0839

CrossRef Full Text | Google Scholar

Challener, R. C., Robbins, L. L., and McClintock, J. B. (2016). Variability of the carbonate chemistry in a shallow, seagrass-dominated ecosystem: implications for ocean acidification experiments. Mar. Freshw. Res. 67, 163–172. doi: 10.1071/MF14219

CrossRef Full Text | Google Scholar

Chung, I. K., Beardall, J., Mehta, S., Sahoo, D., and Stojkovic, S. (2011). Using marine macroalgae for carbon sequestration: a critical appraisal. J. Appl. Phycol. 23, 877–886. doi: 10.1007/s10811-010-9604-9

CrossRef Full Text | Google Scholar

Ciais, P. C., Sabine, G., Bala, L., Bopp, V., Brovkin, J., Canadell, A., et al. (2013). “Carbon and other biogeochemical cycles,” in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, eds Stocker, T. F. D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley (Cambridge; New York, NY: Cambridge University Press), 465–570.

Google Scholar

Cox, T. E., Gazeau, F., Alliouane, S., Hendriks, I. E., Mahacek, P., Le Fur, A., et al. (2016). Effects of in situ CO₂ enrichment on structural characteristics, photosynthesis, and growth of the Mediterranean seagrass Posidonia oceanica. Biogeosciences 13, 2179–2194. doi: 10.5194/bg-13-2179-2016

CrossRef Full Text | Google Scholar

Cummings, M. E., and Zimmerman, R. C. (2003). Light harvesting and the package effect in the seagrasses Thalassia testudinum Banks ex König and Zostera marina L.: optical constraints on photoacclimation. Aquat. Bot. 75, 261–274. doi: 10.1016/S0304-3770(02)00180-8

CrossRef Full Text | Google Scholar

Dennison, W., and Alberte, R. (1986). Photoadaptation and growth of Zostera marina L. (eelgrass) transplants along a depth gradient. J. Exp. Mar. Biol. Ecol. 98, 265–282. doi: 10.1016/0022-0981(86)90217-0

CrossRef Full Text | Google Scholar

Dickson, A., and Millero, F. (1987). A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater Media. Deep Sea Res. Oceanogr. Res. Pap. 34, 1733–1743. doi: 10.1016/0198-0149(87)90021-5

CrossRef Full Text | Google Scholar

Dickson, A. G., Sabine, C. L., and Christian, J. R. (2007). Guide to Best Practices for Ocean CO₂ Measurements, Vol. 3. Sidney, BC: PICES Special Publication.

Google Scholar

Doney, S. C., Fabry, V. J., Feely, R. A., and Kleypas, J. A. (2009). Ocean acidification: the other CO₂ problem. Annu. Rev. Mar. Sci. 1, 169–192. doi: 10.1146/annurev.marine.010908.163834

PubMed Abstract | CrossRef Full Text | Google Scholar

Drake, L. A., Dobbs, F. C., and Zimmerman, R. C. (2003). Effects of epiphyte load on optical properties and photosynthetic potential of the seagrasses Thalassia testudinum Banks ex König and Zostera marina L. Limnol. Oceanogr. 48, 456–463. doi: 10.4319/lo.2003.48.1_part_2.0456

CrossRef Full Text | Google Scholar

Duarte, C. M., Hendriks, I. E., Moore, T. S., Olsen, Y. S., Steckbauer, A., Ramajo, L., et al. (2013). Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH. Estuar. Coasts 36, 221–236. doi: 10.1007/s12237-013-9594-3

CrossRef Full Text | Google Scholar

Duarte, C. M., Marbà, N., Gacia, E., Fourqurean, J. W., Beggins, J., Barron, C., et al. (2010). Seagrass community metabolism: assessing the carbon sink capacity of seagrass meadows. Glob. Biogeochem. Cycles 24:GB4032. doi: 10.1029/2010gb003793

CrossRef Full Text | Google Scholar

Duarte, C. M., Middelburg, J. J., and Caraco, N. (2005). Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8. doi: 10.5194/bg-2-1-2005

CrossRef Full Text | Google Scholar

Dumbauld, B. R., and McCoy, L. M. (2015). Effect of oyster aquaculture on seagrass Zostera marina at the estuarine landscape scale in Willapa Bay, Washington (USA). Aquac. Environ. Interact. 7, 29–47. doi: 10.3354/aei00131

CrossRef Full Text | Google Scholar

Durako, M. J., and Kunzelman, J. I. (2002). Photosynthetic characteristics of Thalassia testudinum measured in situ by pulse-amplitude modulated (PAM) fluorometry: methodological and scale-based considerations. Aquat. Bot. 73, 173–185. doi: 10.1016/S0304-3770(02)00020-7

CrossRef Full Text | Google Scholar

Ekstrom, J. A., Suatoni, L., Cooley, S. R., Pendleton, L. H., Waldbusser, G. G., Cinner, J. E., et al. (2015). Vulnerability and adaptation of US shellfisheries to ocean acidification. Nat. Clim. Change 5, 207–214. doi: 10.1038/nclimate2508

CrossRef Full Text | Google Scholar

Enríquez, S., Merino, M., and Iglesias-Prieto, R. (2002). Variations in the photosynthetic performance along the leaves of the tropical seagrass Thalassia testudinum. Mar. Biol. 140, 891–900. doi: 10.1007/s00227-001-0760-y

CrossRef Full Text | Google Scholar

Fassbender, A. J., Sabine, C. L., and Feifel, K. M. (2016). Consideration of coastal carbonate chemistry in understanding biological calcification. Geophys. Res. Lett. 43:2016GL068860. doi: 10.1002/2016GL068860

CrossRef Full Text | Google Scholar

Feely, R. A., Alin, S. R., Newton, J., Sabine, C. L., Warner, M., Devol, A., et al. (2010). The combined effects of ocean acidification, mixing, and respiration on pH and carbonate saturation in an urbanized estuary. Estuar. Coast. Shelf Sci. 88, 442–449. doi: 10.1016/j.ecss.2010.05.004

CrossRef Full Text | Google Scholar

Feely, R. A., Sabine, C. L., Hernandez-Ayon, J. M., Ianson, D., and Hales, B. (2008). Evidence for upwelling of corrosive acidified water onto the continental shelf. Science 320, 1490–1492. doi: 10.1126/science.1155676

PubMed Abstract | CrossRef Full Text | Google Scholar

Ferraro, S. P., and Cole, F. A. (2012). Ecological periodic tables for benthic macrofaunal usage of estuarine habitats: Insights from a case study in Tillamook Bay, Oregon, U.S.A. Estuar. Coast. Shelf Sci. 102, 70–83. doi: 10.1016/j.ecss.2012.03.009

CrossRef Full Text | Google Scholar

Fourqurean, J. W., Duarte, C. M., Kennedy, H., Marbà, N., Holmer, M., Angel Mateo, M., et al. (2012). Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509. doi: 10.1038/ngeo1477

CrossRef Full Text | Google Scholar

Frankignoulle, M., and Bouquegneau, J. (1990). Daily and yearly variations of total inorganic carbon in a productive coastal Area. Estuar. Coast. Shelf Sci. 30, 79–89. doi: 10.1016/0272-7714(90)90078-6

CrossRef Full Text | Google Scholar

Gattuso, J. P., Frankignoulle, M., and Wollast, R. (1998). Carbon and carbonate metabolism in coastal aquatic ecosystems. Annu. Rev. Ecol. Syst. 29, 405–434. doi: 10.1146/annurev.ecolsys.29.1.405

CrossRef Full Text | Google Scholar

Grantham, B. A., Chan, F., Nielsen, K. J., Fox, D. S., Barth, J. A., Huyer, A., et al. (2004). Upwelling-driven nearshore hypoxia signals ecosystem and oceanographic changes in the northeast Pacific. Nature 429, 749–754. doi: 10.1038/nature02605

PubMed Abstract | CrossRef Full Text | Google Scholar

Harris, K. E., DeGrandpre, M. D., and Hales, B. (2013). Aragonite saturation state dynamics in a coastal upwelling zone. Geophys. Res. Lett. 40, 2720–2725. doi: 10.1002/grl.50460

CrossRef Full Text | Google Scholar

Harrison, P., and Bigley, R. (1982). The recent introduction of the seagrass Zostera japonica Aschers. and Graebn. to the Pacific coast of North America. Can. J. Fish. Aquat. Sci. 39, 1642–1648. doi: 10.1139/f82-221

CrossRef Full Text | Google Scholar

Harrison, P. G. (1982). Spatial and temporal patterns in abundance of two intertidal seagrasses, Zostera americana den hartog and Zostera marina L. Aquat. Bot. 12, 305–320. doi: 10.1016/0304-3770(82)90024-9

CrossRef Full Text | Google Scholar

Hauri, C., Gruber, N., McDonnell, A. M. P., and Vogt, M. (2013). The intensity, duration, and severity of low aragonite saturation state events on the California continental shelf. Geophys. Res. Lett. 40, 3424–3428. doi: 10.1002/grl.50618

CrossRef Full Text | Google Scholar

Hellblom, F., and Björk, M. (1999). Photosynthetic responses in Z. ostera marina to decreasing salinity, inorganic carbon content and osmolality. Aquat. Bot. 65, 97–104. doi: 10.1016/S0304-3770(99)00034-0

CrossRef Full Text | Google Scholar

Hendriks, I. E., Duarte, C. M., Olsen, Y. S., Steckbauer, A., Ramajo, L., Moore, T. S., et al. (2015). Biological mechanisms supporting adaptation to ocean acidification in coastal ecosystems. Estuar. Coast. Shelf Sci. 152, A1–A8. doi: 10.1016/j.ecss.2014.07.019

CrossRef Full Text | Google Scholar

Hendriks, I. E., Olsen, Y. S., Ramajo, L., Basso, L., Steckbauer, A., Moore, T. S., et al. (2014). Photosynthetic activity buffers ocean acidification in seagrass meadows. Biogeosciences 11, 333–346. doi: 10.5194/bg-11-333-2014

CrossRef Full Text | Google Scholar

Herzka, S. Z., and Dunton, K. H. (1997). Seasonal photosynthetic patterns of the seagrass Thalassia testudinum in the western Gulf of Mexico. Mar. Ecol. Prog. Ser. 152, 103–117. doi: 10.3354/meps152103

CrossRef Full Text | Google Scholar

Hettinger, A., Sanford, E., Hill, T. M., Russell, A. D., Sato, K. N. S., Hoey, J., et al. (2012). Persistent carry-over effects of planktonic exposure to ocean acidification in the Olympia oyster. Ecology 93, 2758–2768. doi: 10.1890/12-0567.1

PubMed Abstract | CrossRef Full Text | Google Scholar

Hönisch, B., Ridgwell, A., Schmidt, D. N., Thomas, E., Gibbs, S. J., Sluijs, A., et al. (2012). The Geological record of ocean acidification. Science 335, 1058–1063. doi: 10.1126/science.1208277

PubMed Abstract | CrossRef Full Text | Google Scholar

Invers, O., Romero, J., and Pérez, M. (1997). Effects of pH on seagrass photosynthesis: a laboratory and field assessment. Aquat. Bot. 59, 185–194.

Google Scholar

Invers, O., Zimmerman, R. C., Alberte, R. S., Perez, M., and Romero, J. (2001). Inorganic carbon sources for seagrass photosynthesis: an experimental evaluation of bicarbonate use in species inhabiting temperate waters. J. Exp. Mar. Biol. Ecol. 265, 203–217. doi: 10.1016/S0022-0981(01)00332-X

CrossRef Full Text | Google Scholar

Jassby, A. D., and Platt, T. (1976). Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547. doi: 10.4319/lo.1976.21.4.0540

CrossRef Full Text | Google Scholar

Kaldy, J. E. (2006). Production ecology of the non-indigenous seagrass, dwarf eelgrass (Zostera japonica Ascher. & Graeb.), in a Pacific Northwest estuary, USA. Hydrobiologia 553, 201–217. doi: 10.1007/s10750-005-5764-z

CrossRef Full Text | Google Scholar

Kaldy, J. E., Shafer, D. J., and Magoun, A. D. (2015). Duration of temperature exposure controls growth of Zostera japonica: implications for zonation and colonization. J. Exp. Mar. Biol. Ecol. 464, 68–74. doi: 10.1016/j.jembe.2014.12.015

CrossRef Full Text | Google Scholar

Kenneth, A., and Short, F. T. (2006). “Zostera: biology, ecology, and management,” in Seagrasses: Biology, Ecology and Conservation, eds A. W. DLarkum, R. J. Orth, and C. M. Duarte (Dordrecht: Springer), 361–386.

Google Scholar

Koch, E. W., Ackerman, J., Verduin, J., and van Keulen, M. (2006). “Fluid dynamics in seagrass ecology—from molecules to ecosystems,” in Seagrasses: Biology, Ecology and Conservation, eds A. W. D. Larkum, R. J. Orth, and C. M. Duarte (Dordrecht: Springer), 193–225.

Google Scholar

Koch, M., Bowes, G., Ross, C., and Zhang, X.-H. (2013). Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132. doi: 10.1111/j.1365-2486.2012.02791.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Krause-Jensen, D., Duarte, C. M., Hendriks, I. E., Meire, L., Blicher, M. E., Marbà, N., et al. (2015). Macroalgae contribute to nested mosaics of pH variability in a sub-Arctic fjord. Biogeosciences Discuss 12, 4907–4945. doi: 10.5194/bgd-12-4907-2015

CrossRef Full Text | Google Scholar

Kroeker, K. J., Kordas, R. L., Crim, R., Hendriks, I. E., Ramajo, L., Singh, G. S., et al. (2013). Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896. doi: 10.1111/gcb.12179

PubMed Abstract | CrossRef Full Text | Google Scholar

Kroeker, K. J., Kordas, R. L., Crim, R. N., and Singh, G. G. (2010). Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434. doi: 10.1111/j.1461-0248.2010.01518.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kurihara, H. (2008). Effects of CO₂ driven ocean acidification on the early developmental stages of invertebrates. Mar. Ecol. Prog. Ser. 373, 275–284. doi: 10.3354/meps07802

CrossRef Full Text | Google Scholar

Larkum, A. W. D., Drew, E. A., and Ralph, P. J. (2006). “Photosynthesis and metabolism,” in Seagrasses: Biology, Ecology and Conservation, eds A. W. D. Larkum, R. J. Orth, and C. M. Duarte (Dordrecht: Springer), 323–345.

Google Scholar

Lee, K. S., Park, S. R., and Kim, Y. K. (2007). Effects of irradiance, temperature, and nutrients on growth dynamics of seagrasses: a review. J. Exp. Mar. Biol. Ecol. 350, 144–175. doi: 10.1016/j.jembe.2007.06.016

CrossRef Full Text | Google Scholar

Leuschner, C., and Rees, U. (1993). CO₂ gas exchange of two intertidal seagrass species, Zostera marina L. and Zostera noltii Hornem., during emersion. Aquat. Bot. 45, 53–62.

Google Scholar

Lorenzen, C. J. (1966). A method for the continuous measurement of in vivo chlorophyll concentration. Deep Sea Res. Oceanogr. Abstr. 13, 223–227.

Google Scholar

Love, B. A., O'Brien, C., and Bohlmann, H. (2016). “Photosynthetically driven cycles produce extreme pCO₂ variability in a large eelgrass meadow and readily measured proxies can be used to estimate these changes,” in (EC54A–1311) 2016 Ocean Sciences Meeting, AGU/ASLO/TOS (New Orleans, LA).

Google Scholar

Love, B. A., Olson, M. B., and Wuori, T. (2017). Technical Note: a minimally-invasive experimental system for pCO2 manipulation in plankton cultures using passive gas exchange (Atmospheric Carbon Control Simulator). Biogeosciences Discuss. 2016, 1–19. doi: 10.5194/bg-2016-502

CrossRef Full Text

Mach, M. E., Wyllie-Echeverria, S., and Chan, K. M. A. (2014). Ecological effect of a nonnative seagrass spreading in the Northeast Pacific: a review of Zostera japonica. Ocean Coast. Manag. 102, 375–382. doi: 10.1016/j.ocecoaman.2014.10.002

CrossRef Full Text | Google Scholar

Major, K. M., and Dunton, K. H. (2002). Variations in light-harvesting characteristics of the seagrass, Thalassia testudinum: evidence for photoacclimation. J. Exp. Mar. Biol. Ecol. 275, 173–189. doi: 10.1016/S0022-0981(02)00212-5

CrossRef Full Text | Google Scholar

Manzello, D. P., Enochs, I. C., Melo, N., Gledhill, D. K., and Johns, E. M. (2012). Ocean acidification refugia of the Florida reef tract. PLoS ONE 7:e41715. doi: 10.1371/journal.pone.0041715

PubMed Abstract | CrossRef Full Text | Google Scholar

Marbà, N., Arias-Ortiz, A., Masque, P., Kendrick, G. A., Mazarrasa, I., Bastyan, G. R., et al. (2015). Impact of seagrass loss and subsequent revegetation on carbon sequestration and stocks. J. Ecol. 103, 296–302. doi: 10.1371/journal.pone.0041715

CrossRef Full Text | Google Scholar

Marbá, N., Holmer, M., Gacia, E., and Barrón, C. (2006). “Seagrass beds and coastal biogeochemistry,” in Seagrasses: Biology, Ecology and Conservation, eds A. W. D. Larkum, R. J. Orth, and C. M. Duarte (Dordrecht: Springer), 135–157.

Martin, S., Clavier, J., Guarini, J. M., Chauvaud, L., Hily, C., Grall, J., et al. (2005). Comparison of Zostera marina and maerl community metabolism. Aquat. Bot. 83, 161–174. doi: 10.1016/j.aquabot.2005.06.00

CrossRef Full Text | Google Scholar

Mateo, M. A., Renom, P., Hemminga, M. A., and Peene, J. (2001). Measurement of seagrass production using the ¹³C stable isotope compared with classical O₂ and ¹⁴C methods. Mar. Ecol. Prog. Ser. 223, 157–165. doi: 10.3354/meps223157

CrossRef Full Text | Google Scholar

McLeod, E., Chmura, G. L., Bouillon, S., Salm, R., Bjork, M., Duarte, C. M., et al. (2011). A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO₂. Front. Ecol. Environ. 9, 552–560. doi: 10.1890/110004

CrossRef Full Text | Google Scholar

McPherson, M. L., Zimmerman, R. C., and Hill, V. J. (2015). Predicting carbon isotope discrimination in eelgrass (Zostera marina L.) from the environmental parameters—light, flow, and [DIC]. Limnol. Oceanogr. 60, 1875–1889. doi: 10.1002/lno.10142

CrossRef Full Text | Google Scholar

Mehrbach, C., Culberso, C. H., Hawley, J., and Pytkowic, R. M. (1973). Measurement of apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907.

Google Scholar

Miller, C. A. (2016). Seagrasses (Zostera marina) and (Zostera japonica) Display a Differential Photosynthetic Response to TCO2: Implications for Acidification Mitigation. Master's thesis, Western Washington University.

Miller, C. A., and Waldbusser, G. G. (2016). A post-larval stage-based model of hard clam Mercenaria mercenaria development in response to multiple stressors: temperature and acidification severity. Mar. Ecol. Prog. Ser. 558, 35–49. doi: 10.3354/meps11882

CrossRef Full Text | Google Scholar

Onitsuka, T., Kimura, R., Ono, T., Takami, H., and Nojiri, Y. (2014). Effects of ocean acidification on the early developmental stages of the horned turban, Turbo cornutus. Mar. Biol. 161, 1127–1138. doi: 10.1007/s00227-014-2405-y

CrossRef Full Text | Google Scholar

Oviatt, C., Rudnick, D., Keller, A., Sampou, P., and Almquist, G. (1986). A comparison of system (O₂ and CO₂) and ¹⁴C measurements of metabolism. Mar. Ecol. Prog. Ser. 28, 57–67.

Google Scholar

Ow, Y. X., Uthicke, S., and Collier, C. J. (2016). Light levels affect carbon utilisation in tropical seagrass under ocean acidification. PLoS ONE 11:e0150352. doi: 10.1371/journal.pone.0150352

PubMed Abstract | CrossRef Full Text | Google Scholar

Pajusalu, L., Martin, G., Põllumäe, A., and Paalme, T. (2016). The influence of CO₂ enrichment on net photosynthesis of seagrass Zostera marina in a brackish water environment. Front. Mar. Sci. 3:239. doi: 10.3389/fmars.2016.00239

CrossRef Full Text | Google Scholar

Peterson, C. H., Luettich, R. A., Micheli, F., and Skilleter, G. A. (2004). Attenuation of water flow inside seagrass canopies of differing structure. Mar. Ecol. Prog. Ser. 268, 81–92. doi: 10.3354/meps268081

CrossRef Full Text | Google Scholar

Pierrot, D. E., Lewis, E., and Wallace, D. W. R. (2006). MS Excel Program Developed for CO₂ System Calculations. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory (ORNL).

Poppe, K. (2016). An Ecogeomorphic Model to Assess the Response of Padilla Bay's Eelgrass Habitat to Sea Level Rise. Master's thesis, Western Washington University.

Pörtner, H. O. (2008). Ecosystem effects of ocean acidification in times of ocean warming: a physiologist's view. Mar. Ecol. Prog. Ser. 373, 203–217. doi: 10.3354/meps07768

CrossRef Full Text | Google Scholar

Ruesink, J. L., Hong, J. S., Wisehart, L., Hacker, S. D., Dumbauld, B. R., Hessing-Lewis, M., et al. (2010). Congener comparison of native (Zostera marina) and introduced (Z. japonica) eelgrass at multiple scales within a Pacific Northwest estuary. Biol. Invasions 12, 1773–1789. doi: 10.1007/s10530-009-9588-z

CrossRef Full Text | Google Scholar

Shafer, D. J., and Kaldy, J. E. (2014). Comparison of photosynthetic characteristics of the seagrass congeners Zostera marina L. and Zostera japonica Ascher. & Graeb. Aquat. Bot. 112, 91–97. doi: 10.1016/j.aquabot.2013.09.002

CrossRef Full Text | Google Scholar

Shafer, D. J., Kaldy, J. E., Sherman, T. D., and Marko, K. M. (2011). Effects of salinity on photosynthesis and respiration of the seagrass Zostera japonica: a comparison of two established populations in North America. Aquat. Bot. 95, 214–220. doi: 10.1016/j.aquabot.2011.06.003

CrossRef Full Text | Google Scholar

Silva, J., Sharon, Y., Santos, R., and Beer, S. (2009). Measuring seagrass photosynthesis: methods and applications. Aquat. Biol. 7, 127–141. doi: 10.3354/ab00173

CrossRef Full Text | Google Scholar

Sunda, W. G., and Cai, W. J. (2012). Eutrophication induced CO₂-acidification of subsurface coastal waters: interactive effects of temperature, salinity, and atmospheric Pco₂. Environ. Sci. Technol. 46, 10651–10659. doi: 10.1021/es300626f

CrossRef Full Text | Google Scholar

Talmage, S. C., and Gobler, C. J. (2009). The effects of elevated carbon dioxide concentrations on the metamorphosis, size, and survival of larval hard clams (Mercenaria mercenaria), bay scallops (Argopecten irradians), and Eastern oysters (Crassostrea virginica). Limnol. Oceanogr. 54, 2072–2080. doi: 10.4319/lo.2009.54.6.2072

CrossRef Full Text | Google Scholar

Thimijan, R., and Heins, R. (1983). Photometric, radiometric, and quantum light units of measure - a review. HortScience 18, 818–822.

Google Scholar

Thom, R. (1990). Spatial and temporal patterns in plant standing stock and primary production in a temperate seagrass system. Bot. Mar. 33, 497–510.

Google Scholar

Thom, R. M. (1996). CO₂-enrichment effects on eelgrass (Zostera marina L.) and bull kelp (Nereocystis luetkeana (mert.) P & R.). Water Air Soil Pollut. 88, 383–391.

Google Scholar

Thomsen, J., Haynert, K., Wegner, K. M., and Melzner, F. (2015). Impact of seawater carbonate chemistry on the calcification of marine bivalves. Biogeosciences 12, 4209–4220. doi: 10.5194/bg-12-4209-2015

CrossRef Full Text | Google Scholar

Unsworth, R. K. F., Collier, C. J., Henderson, G. M., and McKenzie, L. J. (2012). Tropical seagrass meadows modify seawater carbon chemistry: implications for coral reefs impacted by ocean acidification. Environ. Res. Lett. 7:024026. doi: 10.1088/1748-9326/7/2/024026

CrossRef Full Text | Google Scholar

Waldbusser, G. G., Hales, B., Langdon, C. J., Haley, B. A., Schrader, P., Brunner, E. L., et al. (2015). Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nat. Clim. Change 5, 273–280. doi: 10.1088/1748-9326/7/2/024026

CrossRef Full Text | Google Scholar

Waldbusser, G. G., and Salisbury, J. E. (2014). “Ocean acidification in the coastal zone from an organism's perspective: multiple system parameters, frequency domains, and habitats,” in Annual Review of Marine Science, Vol. 6, eds C. A. Carlson and S. J. Giovannoni (Palo Alto, CA: Annual Reviews), 221–247.

Google Scholar

Wallace, R. B., Baumann, H., Grear, J. S., Aller, R. C., and Gobler, C. J. (2014). Coastal ocean acidification: the other eutrophication problem. Estuar. Coast. Shelf Sci. 148, 1–13. doi: 10.1016/j.ecss.2014.05.027

CrossRef Full Text | Google Scholar

Washington State Blue Ribbon Panel on Ocean Acidification and Ocean Acidification. (2012). From Knowledge to Action, Washington State's Strategic Response. Washington State Department of Ecology. Available online at: https://fortress.wa.gov/ecy/publications/publications/1201015.pdf

Zimmerman, R. C., Kohrs, D. G., Steller, D. L., and Alberte, R. S. (1997). Impacts of CO₂ enrichment on productivity and light requirements of eelgrass. Plant Physiol. 115, 599–607.

PubMed Abstract | Google Scholar