Comment on bg-2021-137 Anonymous Referee # 2 Referee comment on " Reviews and Syntheses : Spatial and temporal patterns in metabolic fluxes inform potential for seagrass to locally mitigate ocean acidification

(1) The study has omitted large parts of the literature on seagrass productivity, there are several other, very important studies, many reporting O2 dynamics (especially in the older literature). And why report only oxygen evolution studies? There are also reports from other methods, like e.g. Tokoro et al that measured CO2 directly, or in situ PAM work, like e.g. Gobert et al 2015, and references therein.

have shown that submerged aquatic vegetation, such as seagrass beds, can locally draw down 28 CO2 and raise seawater pH in the water column through photosynthesis, but empirical studies of 29 local OA mitigation are still quite limited. Here, we leverage the extensive body of literature on 30 seagrass community metabolism to highlight key considerations for local OA management 31 through seagrass conservation or restoration. In particular, we synthesize the results from 62 32 studies reporting in situ rates of seagrass gross primary productivity, respiration, and/or net 33 community productivity to highlight spatial and temporal variability in carbon fluxes. We 34 illustrate that daytime net community production is positive overall, and similar across seasons 35 and geographies. Full-day net community production rates, which illustrate the potential 36 cumulative effect of seagrass beds on seawater biogeochemistry integrated over day and night, 37 were also positive overall, but were higher in summer months in both tropical and temperate 38 on seagrass community metabolism on O2 fluxes can provide important spatial and temporal 113 context for managers interested in carbon fluxes and the potential for local OA mitigation. 114 115 Here, we synthesize published studies of seagrass metabolism to characterize the variability in 116 carbon fluxes associated with GPP, R, and NCP across seasons and geographies. In recognition 117 of the substantial temporal diel variability in carbon fluxes associated with daytime NCP and 118 nighttime respiration, as well as the uncertainty in our understanding of how this temporal 119 variability is integrated by vulnerable marine organisms associated with seagrass beds, we focus 120 on both hourly rates of NCP taken during peak daylight hours and full-day NCP. Hourly 121 measurements of NCP collected during peak daylight hours can provide insight into the 122 maximum elevation of seawater pH. Similarly, hourly measurements of respiration provide 123 insight into the potential maximum depression of nighttime pH. In contrast, measurements of 124 NCP taken over longer time periods or that incorporate the full 24 hour cycle (full-day NCP) 125 provide insight into the cumulative effect of seagrass on seawater chemistry. In particular, we 126 tested: (1) If seasonal variability is present in daytime and full-day carbon fluxes, (2) If the 127 temporal variation in carbon fluxes varies among tropical and temperate geographies, and (3) 128 How much of the residual variation in carbon fluxes not accounted for by seasons or geography 129 can be attributed to variation in temperature and aboveground biomass of the seagrass 130 assemblage. To connect the metabolic measurements to seawater chemistry, we model potential 131 changes in bulk seawater pH based on the estimated carbon fluxes given variation in seawater 132 residence time and water depth.  included as a data point in the synthesis when deployments/measurements were repeated across 149 different locations, months, or species. We collected measurements of GPP, R or NCP from each 150 study using data reported in the text, tables, or graphs using software (Graph Click or Data 151 Thief), or provided by the authors by request. In addition, we recorded information on the 152 photosynthetic quotient (PQ) and respiratory quotient (RQ) values used to convert from O2 to 153 carbon, as well as other metadata associated with the study (e.g., species, location, temperature, 154 month the study was conducted, etc.). We classified each study as either tropical or temperate 155 based on the designation in the primary study and then classified the metabolic measurements as 156 either (a) hourly rates or (b) daily rates. This classification was defined by the reporting within reporting was ostensibly due to differences in the length of the deployment used to measure 159 metabolism (e.g., <4 hour deployment = an hourly rate, ~12-24 hour deployment = a daily rate). 160 The shorter "hourly" deployments were usually taken during peak daylight hours, which we used 161 to infer the potential for any daytime local OA mitigation. In contrast, we use the daily rates to 162 infer the cumulative, full-day local OA mitigation potential of seagrass. It is important to note 163 that positive daily NCP (used to infer the full-day local OA mitigation potential) can still 164 encompass marked diel or diurnal variability in carbon fluxes that could prove deleterious to 165 seagrass associated species during transient periods of low pH. To assess differences in estimates based on the methods used to measure metabolism, we plotted 177 the carbon fluxes as a function of study type. Based on these plots (Fig. S1), we decided to 178 perform separate analyses for studies that used the "mass balance" approach (Odum 1956) versus 179 other methods (e.g., incubations, eddy correlations). The studies using the mass balance 180 approach often do not differentiate between water column and benthic productivity, and as such, biomass. To standardize months to seasons across the hemispheres, we used the numerical 193 notation for months in the northern hemisphere (i.e., January = 1, etc.). For the southern 194 hemisphere, we subtracted 6 from the numerical notation and used the absolute value. In 195 addition, we tested for differences between seagrass communities in temperate and tropical 196 geographies based on the hypothesis that seagrass meadows in temperate geographies have 197 greater seasonality in light, temperature, and aboveground biomass, and thus, should have a more 198 pronounced seasonal fluctuation (Fig S3 and S4). 199

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We then tested for effects of temperature and aboveground biomass on the residual variation of 201 the monthly models. Specifically, we first fit mixed-effects models of both hourly and daily rates 202 of GPP, R, and NCP using maximum likelihood with geography as a categorical factor (tropical 203 vs. temperate) and a linear and quadratic term for month as fixed effects, as well as the two-way 204 interactions between geography and each term for month. We also included replicate nested in 205 study as a random effect to account for non-independence arising from the inclusion of repeated 206 measures from the same sites over time and measurements from multiple sites within a single 207 study: 208 metabolism ~ month + month ! + geography + month × geography 209 We then used backwards model selection to determine the significance of fixed effects based on 212 likelihood ratio tests. Final models were fit using restricted maximum likelihood to calculate 213 model estimates. We then performed two separate analyses using (1) environmental temperature 214 and (2) aboveground biomass to assess any remaining variability in the residuals from the 215 seasonal models. First, using just the subset of studies that either reported temperature or 216 biomass, we fit the final seasonal models again using restricted maximum likelihood to obtain 217 the conditioned residuals. Then, using these residuals, we fit linear models with geography as a 218 categorical factor, plus a linear term for either temperature or aboveground biomass as well as 219 the interaction between geography and temperature/biomass as fixed effects. We used 220 backwards model selection, comparing nested models with a series of ANOVAs. Finally, we 221 tested for net autotrophy (NCP>0) using a one-tailed t-test. All analyses were performed in R 222 (version 3.6.2) (R Core Team 2019) with the following packages as needed: nlme (version  To illustrate how water depth and residence time may mediate the effects of the carbon fluxes 227 associated with seagrass communities on bulk seawater pH for potential local OA mitigation, we 228 applied the range of hourly net carbon fluxes (NCP) covered in our synthesis to a simplified, 229 steady state box model developed by Koweek et al. (2018). We use the hourly rate rather than 230 the full-day rate because we recognize that the effects of seagrass on seawater carbonate 231 chemistry will be intermittent and fluctuate over the daylight hours. We modeled the change in 232 dissolved inorganic carbon as a function of NCP as 233 where L = the box length (m), D = mean water velocity (m s -1 ), = is the seawater density (kg 236 m 3 ), and ℎ = water depth (m),. Because of the familiarity among managers and decision makers 237 with seawater pH, we then converted the delta DIC to pH, assuming a relevant, temperate coastal 238 ocean condition (e.g., total alkalinity = 2300 mol/kg, temperature = 15°C, and salinity = 35 239 ppm). We then plotted the change in pH as a function of hourly daytime carbon fluxes (i.e., 240 hourly NCP) for two different water depths (0.5 and 2m) and three different water residence 241 times ( / = 15 minutes, 60 minutes, and 4 hours) at each water depth. We selected four hours 242 as the maximum duration for the model for two reasons: seagrass beds are rarely extensive 243 enough for water to remain over seagrass for more than a few hours, and longer residence times 244 would tend to overlap with lower-light conditions when the hourly NCP does not apply. months, while fewer studies measured the metabolism in fall and winter conditions (Fig. 1). 257 Environmental temperature was highest during late summer/early fall months and was higher 258 overall in tropical biomes (Fig S3). Aboveground biomass was highest during summer months 259 and higher in the temperate geographies ( Fig S4).

Methodological analyses 269
Our results illustrate greater variability in the ranges of response observed using the "mass 270 balance" method, which extend in magnitude beyond those observed using other methods for 271 measuring both GPP and R (Fig. S1 a-f). This greater variability does not appear to be driven by 272 timing of the measurements as the "mass balance" method produced metabolic measurements of Measurements of hourly carbon fluxes (N=83 for NCP), typically obtained from shorter duration 281 deployments conducted during peak sunlight hours, reveal differences in seasonal patterns of 282 GPP and respiration. Both GPP and R peak during summer months across both ecosystems (Fig  283   2a-b). Despite higher biomass in temperate systems during summer months (Fig S4), we do not 284 detect a statistical difference in the seasonal patterns among GPP in temperate and tropical 285 ecosystems (Table 1). This result is highly influenced by two studies in tropical geographies 286 (Morgan and Kitting 1984, Herbert and Fourqurean 2008); when these studies are not included, 287 summertime GPP is higher in temperate geographies than in tropical geographies (Fig S5). 288 Similarly, R peaks in summer months in both temperate and tropical ecosystems, and we detect a 289 sharper increase and a higher seasonal peak in R in temperate ecosystems (Fig 2b; Table 1). The 290 seasonal peaks in GPP and R effectively cancel each other out, resulting in no statistically 291 detectable difference in hourly NCP rates across seasons (Fig 2c). Although the net hourly

Full-day carbon fluxes 325
We found 164 measurements/deployments that reported full-day NCP using methods that span a 326 wider range of photoperiods and environmental conditions, and thus provide insight into the 327 potential for full-day local OA mitigation. Based on the accompanying daily rates of GPP and R, 328 there is evidence of a seasonal cycle in carbon fluxes to and from the water column associated 329 with seagrass metabolism (Fig. 3a-b). The seasonal fluctuation differed statistically between 330 temperate and tropical geographies, with a sharper slope in the seasonal fluctuation among the 331 tropical studies (Fig 2a). We did not detect a difference in R between geographies. In general, the 332 seasonal fluctuation in GPP exceeds the seasonal fluctuation in respiration in both geographies, 333 resulting in higher daily net carbon flux from the seawater to the seagrass associated with NCP in 334 summer months (Fig. 3c). The seasonal fluctuation in NCP was greater among the tropical 335 studies than the temperate studies ( Table 2). The mean NCP for tropical geographies was 62.5 336 (+/-62.4 SD) mmol C/m 2 /day, with 84% of the 77 total reported measurements being 337 autotrophic. The mean NCP for temperate geographies was 28.8 (+/-79.0) mmol C/m 2 /day, with 338 68% of the 187 total reported measurements being autotrophic. Overall, the seagrass meadows in

Drivers of chemical variability 365
Within seasons, there is still marked variation in GPP and respiration (Fig. 2-3). Using the subset 366 of studies that report environmental temperature (N = 28), we found that temperature did not 367 explain the residual variability in any metric besides hourly GPP (Fig. 4; Table 3), suggesting the 368 seasonal models may generally account for hypothesized temperature effects. As noted, 369 temperature explained some of the residual variability from the seasonal models of hourly GPP, 370 with the effect differing among tropical and temperate geographies (Fig S6; Hourly GPP 371 Geography x Temperature: F42 = 10.83, P = 0.001). Among studies reporting aboveground 372 biomass (N=23), biomass explains some of the residual variability in daily NCP, although the 373 effect depends on geography as well (Fig 5; Table 3). Aboveground biomass also explains some 374 of the residual variability in the seasonal models of hourly GPP, respiration, and NCP, and the 375 effect of biomass on hourly GPP also depended on the geography (Fig. S7; Table 3). 376

Potential OA amelioration 402
The steady state box model illustrates that the largest potential change in seawater pH occurs 403 when NCP is highest and the water depth and residence time are lowest (Fig. 6). Using the mean 404 hourly NCP from our analysis (~5.32 mmol C/m 2 /hour; Fig. 2C), the potential change in 405 seawater pH in a seagrass meadow that meets the assumptions of the box model (e.g., ∆O2:∆DIC 406 = 1) in a low flow environment at low tide (i.e., 0.5m water depth) ranges from 0.006 -0.085 pH 407 units for a residence time from 15 minutes to 4 hours. At the modeled higher tide (i.e., 2m water 408 depth), the potential changes in seawater pH for the same NCP range from 0.001-0.022 pH units 409 for the same residence times (15 minutes to 4 hours).

Spatial and temporal variability in seagrass metabolism 427
Here, we report that the NCP of seagrass beds during daylight hours is positive and similar 428 across seasons and geographies (Fig 2C). This is ostensibly due to GPP generally exceeding R 429 during daylight and the seasonal fluctuations in hourly rates of GPP and R balancing each other 430 out. If O2 fluxes translate proportionally to community drawdowns in CO2, the assumption 431 underlying our box model, our results suggest that the maximum potential local OA mitigation 432 due to seagrass metabolism during daylight hours is similar across time and ecosystems, but 433 small in magnitude. 434

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We also demonstrate that seagrass beds are generally net autotrophic over the length of the day 436 (based on daily NCP), and the magnitude of this full-day NCP is more pronounced during 437 summer months and in tropical geographies (Fig 3C). However, underlying the summertime 438 peak in full-day NCP is the potential for marked diurnal variability in pH. In particular, the 439 demonstrated summertime peak in hourly respiration rates could drive more pronounced 440 nighttime lows in pH/saturation state during the most pronounced windows for net autotrophy 441 (i.e., summer months with the highest daily NCP). However, diurnal fluctuations in seawater pH 442 associated with seagrass metabolism will also be influenced by hydrodynamics that are not 443 generally what would be expected (higher biomass = higher metabolic rates than expected based 474 on the seasonal model), the negative relationship between aboveground biomass and the residual 475 variation in hourly NCP is somewhat surprising. This relationship suggests that 476 deployments/measurements in seagrass beds with higher aboveground biomass generally had 477 lower hourly NCP than what is predicted by the seasonal model. This negative relationship may 478 be explained by self-shading in dense meadows, or it could be due to other organisms that 479 contribute to daytime respiration (e.g., heterotrophs) that are associated with the higher biomass 480 meadows due to its structural complexity or other habitat features, but are not accounted for in 481 the aboveground biomass measurements. Dedicated experiments may be able to determine the 482 mechanism for these findings; however, the positive relationships between aboveground biomass 483 and the residual variation in daily NCP suggests that, overall, higher aboveground biomass 484 generally increases production relative to respiration. 485 486

Implications for local OA mitigation and management 487
The results of our steady state box model analyses illustrate the potential scope for seagrass NCP 488 to influence seawater pH on an hourly basis (Fig. 6), with the change in pH being proportional to 489 NCP during daylight hours and R during nighttime hours. While the box model is useful in 490 making coarse estimates on what particular NCP values might correspond to in seawater pH, it is 491 important to note that it only represents a first step in translating the seagrass community 492 metabolism estimates to seawater biogeochemistry. This is in part because the ratio of NCP 493 based on carbon fixed and oxygen evolved in seagrass communities is likely to be quite variable. 494 Although the ratio between O2 produced and carbon fixed by an individual seagrass is generally 495 assumed to be balanced (i.e., 1:1), the other processes that occur in a seagrass meadow, including 496 respiration from organisms living within the seagrass and carbonate production and dissolution, 497 also influence the dissolved inorganic carbon (DIC) concentration in the seawater. Current 498 empirical measurements of NCPDIC:NCPO2 in seagrass meadows range from 0.3 to 6.8 (Ziegler 499 and Benner 1998, Barrón et al. 2006), suggesting the effect of seagrass NCP on seawater pH 500 could be substantially more or less pronounced than illustrated here. Because of this variability 501 in the relationship between O2 and DIC, care must be taken when interpreting the results from 502 the box model. A better understanding of the NCPDIC:NCPO2 in particular meadows will better 503 inform their potential for local OA mitigation. Finally, the utility of seagrass as a climate 504 mitigation tool will depend on the goal of the management, and in most cases, will require more 505 research. For example, if the goal of management is to prevent negative effects of ocean 506 acidification on oyster growth, then studies that quantify the sensitivity of oyster growth to the 507 variability in pH observed here are still required. 508 509

Conclusions 510
Few conservation or restoration efforts currently take into account the potential chemical 511 ecosystem services of seagrasses and other submerged aquatic vegetation. Here, we demonstrate 512 that daytime carbon fluxes associated with seagrass metabolism are likely to be similar across 513 seasons and geography, while the full-day carbon fluxes peak during summer months in both 514 tropical and temperate geographies. Integrating across seasons, seagrass meadows are net 515 autotrophic. However, our simplified model results suggest the daytime carbon fluxes reported 516 across the global ocean may translate to small changes in seawater pH. These seasonal patterns 517 largely capture the present-day effects of variability in temperature and aboveground biomass on 518 seagrass metabolism, but likely do not adequately model the effects of future warming as it 519 becomes physiologically stressful. 520

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These results highlight challenges, as well as gaps in our understanding, that may impede the use 522 of seagrasses for sustained local OA mitigation. In particular, we demonstrate that while peak 523 daytime carbon fluxes are similar across seasons and geographies, nighttime respiration is 524 highest during summer months. Thus, although seagrass beds are generally net autotrophic, 525 nighttime respiration could reduce seawater pH during periods of greatest autotrophy. We 526 provide examples of how water depth and residence time can influence the effect of seagrass on 527 seawater pH, and we demonstrate that the overall magnitude of the effect is likely quite small. 528 529 This work has elucidated several gaps that need to be addressed by the scientific community. For 530 example, certain geographies, such as the North Pacific, are currently underrepresented in our 531 dataset. Thus, continued study of seagrass metabolism and its effects on seawater carbonate 532 chemistry are needed to expand our area of inference. In addition, studies are needed to constrain 533 the relationship between dissolved oxygen fluxes and DIC, and this relationship may be 534 important to elucidate at local scales to truly understand the potential for OA mitigation at a 535 given location. Perhaps most importantly, more information is needed to understand how 536 vulnerable organisms respond to the chemical variability highlighted in our study (Gimenez et al. 537 the considerations of geographic and temporal variability in carbon fluxes illustrated here, we 539 recognize that seagrass conservation and restoration may be important strategies for climate 540 adaptation for numerous other reasons, including carbon sequestration and habitat provisioning. We would like to thank K. J. Nickols and Y. Takeshita for their helpful comments and 559 suggestions, which greatly improved the manuscript. This work was initiated by a working group 560 of seagrass and biogeochemistry experts, convened at Bodega Marine Laboratory with support 561