Validation of carbon isotope fractionation in algal lipids as a p CO 2 proxy using a natural CO 2 seep (Shikine Island, Japan)

. Carbon dioxide concentrations in the atmosphere play an integral role in many Earth system dynamics, including its inﬂuence on global temperature. The past can provide insights into these dynamics, but unfortunately reconstructing long-term trends of atmospheric carbon dioxide (expressed in partial pressure; p CO 2 ) remains a challenge in paleoclimatology. One promising approach for reconstructing past p CO 2 utilizes the isotopic fractionation associated with CO 2 ﬁxation during photosynthesis into organic matter ( ε p ). Previous studies have focused primarily on testing estimates of ε p derived from the δ 13 C of species-speciﬁc alkenone compounds in laboratory cultures and mesocosm experiments. Here, we analyze ε p derived from the δ 13 C of more general algal biomarkers, i.e., compounds derived from a multitude of species from sites near a CO 2 seep off the coast of Shikine Island (Japan), a natural environment with CO 2 concentrations ranging from ambient (ca. 310 µatm) to elevated (ca. 770 µatm) p CO 2 . We observed strong, consistent δ 13 C shifts in several algal biomarkers from a variety of sample matrices over the steep CO 2 gradient. Of the three general algal biomarkers explored here, namely loliolide, phytol, and cholesterol, ε p positively correlates with p CO 2 , in agree-ment with ε p theory and previous culture studies. p CO 2 reconstructed from the ε p of general algal biomarkers show the same trends throughout, as well as the correct control values, but with lower absolute reconstructed values than the measured values at the elevated p CO 2 sites. Our results show that naturally occurring CO 2 seeps may provide useful testing grounds for p CO 2 proxies and that general algal biomarkers show promise for reconstructing past p CO 2 .


Introduction
The current increase in the atmospheric concentration of carbon dioxide (expressed in partial pressure, pCO 2 ) plays a leading role in climate change (Forster et al., 2007). pCO 2 is significantly higher now than it has been in the past 800 ka (Lüthi et al., 2008), and although long-term changes in pCO 2 are not uncommon over millions of years (Foster et al., 2017), this current spike in pCO 2 has occurred within only the past 2 centuries (IPCC, 2013). Uncertainties remain on the exact magnitude to which pCO 2 influences climate, as well as the exact response of the environment to these climate changes. Long-term pCO 2 trends help us better understand the context for these changes and are reconstructed via indirect means, i.e., environmental proxies. Two proxies can span timescales over 100 Ma (Foster et al., 2017), e.g., the terrestrial paleosol proxies and leaf stomata. The paleosol proxy has large uncertainties due to the difficulties in constraining soil respiration (Breecker et al., 2010;Cotton and Sheldon, 2012) due to carbon isotopic fractionation during microbial decomposition (Bowen and Beerling, 2004), carbonate diagenesis (Quast et al., 2006), and other local and regional influences on carbon cycles in these terrestrial settings. Although the leaf stomata proxy is often better constrained than paleosols, some experiments do not show the expected trends (Ellsworth et al., 2011;Ward et al., 2013;DaMatta et al., 2016), suggesting that factors other than pCO 2 , e.g., ecological systems, species, and development stage, also impact the leaf stomata proxy (Xu et al., 2016). The development of new proxies for pCO 2 may help us better constrain past long-term trends, particularly marine-based proxies, which tend to have more homogenized signals but are currently relatively limited in time.
A proxy that has been explored with mixed success over the past several decades is the stable carbon isotopic fractionation associated with photosynthetic inorganic carbon fixations (ε p ), which has been shown to positively correlate with pCO 2 (Bidigare et al., 1997;Jasper and Hayes, 1990;Zhang et al., 2013). ε p occurs as the CO 2 -fixing enzyme in photoautotrophs, rubisco (ribulose 1,5-biphosphate carboxylase oxygenase), favors 12 C, which consequently results in photosynthates isotopically more depleted in 13 C than the original carbon source. A greater abundance of CO 2 increases rubiscobased isotopic discrimination, resulting in an even lower 13 C/ 12 C ratio (δ 13 C) in photoautotroph biomass (Farquhar et al., 1982(Farquhar et al., , 1989Francois et al., 1993;Popp et al., 1989). When this phototrophic biomass is preserved in the geologic record, the δ 13 C of sedimentary organic matter can be used to reconstruct pCO 2 (Hayes et al., 1999). The largely mixed contributions and diagenetic processes on bulk organic matter can, however, mask this signal (Hayes, 1993;Hayes et al., 1999). Thus, isotope analysis of specific biomarker lipids is preferred in order to better define the source of the δ 13 C signal (Jasper and Hayes, 1990;Pagani, 2002).
The most studied biomarkers for calculating ε p are alkenones, i.e., long-chain unsaturated methyl and ethyl ketones produced by select haptophytes (Volkman et al., 1980;de Leeuw et al., 1980). The stable carbon isotopic fractionation of alkenones has been studied using cultures and mesocosms with controlled environments (Laws et al., 1995;Benthien et al., 2007), but conditions do not always mimic natural environments and the natural variation in carbonate chemistry that occurs on a daily to seasonal timescales. Furthermore, these experiments are also time-consuming given that they must have delicately balanced water chemistry, including CO 2[aq] concentrations, pH, and alkalinity, as well as nutrients such as nitrate and phosphate (Popp et al., 1998;Laws et al., 1995;Bidigare et al., 1997), along with the additional challenge of maintaining a constant δ 13 C of the CO 2[aq] , while photoautotrophs continually enrich the growth water as they fix CO 2 . Water column studies (Bidigare et al., 1997) and surface sediments (Pagani, 2002) have been applied but rarely reach elevated pCO 2 levels like those encountered in the past.
Here we use an alternative approach by analyzing algal lipids near natural CO 2 seep systems. In tectonically active zones, volcanically induced seeps consistently bubble high pCO 2 concentrations into the surrounding water, substantially increasing the local CO 2 concentrations in the wa-ter and providing an environment referent to past and future high-CO 2 worlds. CO 2 seeps were previously overlooked for biological studies due to the typically high sulfide (H 2 S) concentrations associated with volcanic degassing that make these environments largely uninhabitable (Dando et al., 1999). However, certain CO 2 seep systems have been found to have low H 2 S concentrations, making them suitable for ocean acidification experiments (Hall-Spencer et al., 2008), prompting further research in, e.g., the Mediterranean (Boatta et al., 2013), Japan (Agostini et al., 2015), Papua New Guinea (Fabricius et al., 2011), and New Zealand (Brinkman and Smith, 2015). These sites may provide an ideal testing ground for the impact of isotopic fractionation on algal lipids where environmental conditions are at naturally balanced levels with the exception of the large gradient of CO 2 concentrations.
In our study, we explore the relationship between ε p and CO 2[aq] across several preestablished sites, with different (temporally consistent) levels of pCO 2 at the warm, temperate CO 2 seep in Mikama Bay, offshore of Shikine Island, Japan. We test this relationship using general algal biomarkers, i.e., compounds derived from a multitude of species and have rarely been used for ε p -based pCO 2 reconstructions despite their potential utility (Witkowski et al., 2018;Popp et al., 1989;Freeman and Hayes, 1992).

Sample site
The site is briefly described here. For further details, we refer to Agostini et al. (2018). Mikama Bay is located on the southsouthwest corner of Shikine Island, offshore of the Izu Peninsula, Japan (34.320865 • N, 139.204868 • E), and has several venting locations in the north of the bay (Fig. 1). The gas emitted from the seep contains 98 % CO 2 , and the bay has a spatially and temporally constant total alkalinity averaging at 2265 ± 10 µmol kg −1 . Samples were collected from three preestablished pCO 2 sites (Agostini et al., 2015), a "control pCO 2 " site in an adjacent bay outside the influence of the CO 2 seep (pCO 2 309 ± 46 µatm), a "mid-pCO 2 " site (pCO 2 ca. 460±40 µatm), and a "high-pCO 2 " site (pCO 2 769±225) ( Fig. 1). pCO 2 estimates are based on the carbonate chemistry parameters (pH NBS , temperature, salinity, and total alkalinity) of water in the bay and calculated using the program CO2sys Harvey et al., 2018). Temperature (annual range ca. 14 to 28 • C) and salinity (ca. 34 ‰ ) are relatively homogenous throughout the bay and do not differ between the elevated pCO 2 sites and control pCO 2 sites . Currents and wind were measured in October 2014 and April 2015 (Agostini et al., 2015). October 2014 measurements showed moderate turbulent winds (ranging from 0.6 to 11.5 m s −1 , with an average of 4.5 m s −1 ) associated with current velocities (ranging from 0 to 1.6 knots, average 0.4 knots) at 5 m in the surface water, whereas April 2015 measurements showed moderate north-northeast winds (1.5-8.6 m s −1 , average 5.1 m s −1 ) associated with low current velocities (0-0.2 knots, average 0.04 knots). Monthly surveys in the bay over the past 5 years show that these sites have similar annual mean values for temperature, salinity, and currents. Weather station data show that the severity of seasonal extreme weather event (e.g., typhoons) varies on an annual basis (Japan Meteorological Agency, https://www.jma.go.jp/en/typh/, last access: 1 April 2019).

Materials
Samples were collected in June and September of 2016 (indicated in Fig. 1). All samples were collected in at least triplicate for each site (control pCO 2 , mid-pCO 2 and high-pCO 2 site). Additional control sites (ca. 1.8 and 2.4 km away from the CO 2 seep) around the island were taken to ensure the fidelity of the control site closest to the seep. June sampling included surface waters for dissolved inorganic carbon (DIC) measurements, surface sediments, and benthic diatoms attached to surface sediment through extracellular polymeric substance production. In September, macroalgae and plankton tows were collected, in addition to surfaced water DIC and surface sediments, taken in triplicate at each site on four separate days.
Water for the δ 13 C of DIC analysis was collected by overfilling glass vials with sea surface water and adding mercury chloride (0.5 %) before closing with a septa cap and sealing with electrical tape. Surface sediments were collected by divers using geochemical sample bags. Macroalgae and benthic diatoms were scraped off submerged rocks at each respective site. . A 25 µm mesh plankton net ("small plankton net", Rigo, Saitama, Japan) was towed 50 m three times per site and filtered using a portable hand aspirator on the boat over a single 0.7 µm GF/F (glass fiber filter; combusted prior to sampling for 4 h at 450 • C). All samples were immediately frozen; once back in the lab, these were freeze-dried and kept in a refrigerator.

Methods
Each seawater sample was measured for the δ 13 C of DIC in duplicate on a Thermo Scientific Gas Bench II coupled to a Thermo Scientific Delta V mass spectrometer. Prior to running samples, 12 mL vials were prepared with 100 µL of 85 % H 3 PO 4 and flushed with He. A total of 500 µL of seawater was added and left to react for at least 1 h prior to measuring the headspace. Standards were run at the start, end, and every six runs of a sequence. Standards were prepared with 0.3 mg of Na 2 CO 3 and 0.4 mg of Ca 2 CO 3 (all calibrated against NBS-19) flushed with He, injected with 500 µL of 85 % H 3 PO 4 , and reacted for 1 h. The headspace was measured, and average values and standard deviation er-rors reported are based on 6 measurements for June (3 at the high-pCO 2 site and 3 at the control) and 36 measurements for September (3 each at the high-pCO 2 site, mid-pCO 2 site, and control, collected on four separate days).
Silylated polar fractions were analyzed by GC-FID for quantification. Based on the quantities, fractions were diluted with ethyl acetate and ca. 1 µg of polar fraction was injected on-column for GC-MS to identify compounds and for GC-IRMS to measure the isotopic composition of specific compounds. Each instrument is equipped with the same CP-Sil 5 column (25 m × 0.32 mm; df 0.12 µm) and He is used as carrier gas. GC oven was programmed from 70 to 130 • C at 20 • C min −1 and then to 320 • C at 4 • C min −1 , which was held for 10 min. All three instruments use the same in-house mixture of n-alkanes and fatty acids to check chromatography performance at the start of each day (GC-standard). For compound-specific stable carbon isotope analysis using GC-IRMS, additional standards with known isotopic values (−32.7 ‰ and −27.0 ‰) of per deuterated (99.1 %) nalkanes (C 20 and C 24 , respectively), were co-injected with the GC-standard. Samples were also co-injected with the same GC-IRMS standards to monitor instrument performance. Every day, the Isolink II combustion reactor of the GC-IRMS was oxidized for at least 10 min, backflushed with He for 10 min, and purged for 5 min; a shorter version of this sequence is conducted in post-sample seed oxidation, which includes 2 min oxidation, 2 min He backflush, and a 2 min purge conditioning line. Longer oxidations were run weekly. Each derivatized compound was corrected for the δ 13 C of the BSTFA used in silylation (−32.2 ‰ ).

Results
Samples from the different matrices were collected at several control pCO 2 sites (309±46), at a mid-pCO 2 site (ca. 100 m from the venting area; 460 ± 40 µatm), and near the venting area (high-pCO 2 site; 769 ± 225 µatm) during June and September 2016 (Fig. 1), which included June-collected surface waters (for DIC), surface sediments, and benthic diatoms and September-collected surface waters (for DIC), surface sediments, plankton net tows, and macroalgae.
The δ 13 C of DIC demonstrated minimal change over the gradient of CO 2 and minimal change between the two seasons ( Table S1 in  The polar fractions of the extracts of the surface sediments, plankton, macroalgae, and benthic diatoms showed a similar suite of compounds, observed across all sites and during both seasons. The most prominent compounds were loliolide, phytol, C 14 -C 16 alkanols, and sterols, such as cholesta-5,22E-dien-3β-ol, cholesterol, 23-methylcholesta-5,22dienol, campesterol, stigmasterol, and β-sitosterol (e.g., Fig. 2). Terrestrial biomarkers, such as long-chain alcohols and triterpenoids were not detected. Loliolide, phytol, and cholesterol were targeted for stable carbon isotope analysis as the most abundant general algal biomarkers and with relatively good separation in the GC. The biological sources of these compounds will be discussed in Sect. 4.1.
Among the sample matrices, the δ 13 C of loliolide ranges from −19.8 ‰ to −22.0 ‰ at the control sites, from −20.5 ‰ to −22.9 ‰ at the mid-pCO 2 site, and from −23.1 ‰ to −29.0 ‰ at the high-pCO 2 site ( Fig. 3a; Table S1). The δ 13 C of loliolide from June surface sediments shows the strongest change from the control site to the high-pCO 2 site (−21.2 ‰ to −29.0 ‰), followed by the δ 13 C of loliolide from September macroalgae (−21.3 ‰ to −25.7 ‰). A lesser δ 13 C shift is observed in the September surface sediment-derived loliolide (−19.8 ‰ to −23.1 ‰). The δ 13 C of the benthic diatom-derived loliolide (−20.2 ‰ to 23.6 ‰) and the plankton tow-derived loliolide show the smallest shifts from the control site to the high-pCO 2 site (−22.0 ‰ to −23.6 ‰).
Similar to the results of the δ 13 C of loliolide, the δ 13 C of phytol also consistently shows higher δ 13 C values in the control sites and lower δ 13 C values in the elevated pCO 2 sites among all samples types collected in both seasons ( Fig. 3b; Table S2). For the whole sample set, the δ 13 C of phytol ranges from −18.9 ‰ to −22.6 ‰ at the control site, from −19.4 ‰ to −22.4 ‰ at the mid-pCO 2 site, and from −22.6 ‰ to −27.8 ‰ at the high-pCO 2 site (Fig. 3b), similar ranges to those observed for loliolide. A similar shift in δ 13 C values of phytol is observed with increasing pCO 2 in the June surface sediments (−22.6 ‰ to −27.8 ‰), the June benthic diatoms (−18.9 ‰ to −24.4 ‰), and the September macroalgae (−21.5 ‰ to −26.9 ‰). Smaller changes in the δ 13 C of phytol are observed for September plankton (−21.7 ‰ to −24.4 ‰) and September sediment (−20.5 ‰ to −22.6 ‰).
The δ 13 C of cholesterol likewise shows a similar trend to the other two biomarkers but with a smaller shift in the δ 13 C values from the control pCO 2 sites to the elevated pCO 2 sites. Among the different sample matrices, the δ 13 C of cholesterol ranges from −21.2 ‰ to −25.1 ‰ at the control site, −22.1 ‰ to −23.4 ‰ at the mid-pCO 2 site, and −23.1 ‰ to −27.4 ‰ at the high-pCO 2 site ( Fig. 3c; Table S1). The strongest change in the δ 13 C of cholesterol with increased pCO 2 occurs in the June surface sediments from −22.6 ‰ in the control to −27.8 ‰ at the high-pCO 2 site. The June benthic diatoms also have a large isotopic shift in the δ 13 C of cholesterol (−21.2 ‰ to −25.8 ‰), as does the September macroalgae (−22.7 ‰ to −25.8 ‰). The September surface sediments (−22.2 ‰ to −23.1 ‰) and plankton tow-derived cholesterol (−25.1 ‰ to −26.7 ‰), however, have a smaller shift from the control to the elevated pCO 2 sites.

The δ 13 C differences in biomarkers among matrices and seasons
All three biomarkers, phytol, loliolide, and cholesterol, show a negative shift in δ 13 C values with increasing pCO 2 in each matrix and each season (Fig. 3), agreeing with the theory that higher pCO 2 conditions result in lower δ 13 C values in biomass (Farquhar et al., 1982). However, despite all having algal sources, the absolute isotope values vary for (1) each compound, (2) each matrix, and (3) both seasons, which we will now discuss. First, the absolute values of δ 13 C values vary among the three compounds. This may be expected given the different biosynthetic pathways leading to formation of each compound (Schouten et al., 1998), as well as the different contributors to each compound. Loliolide, considered a diatom biomarker in paleoreconstructions (e.g., Castañeda et al., 2009), is a diagenetic product of fucoxanthin (Repeta, 1989;Klok et al., 1984), a xanthophyll that contributes to approximately 10 % of all carotenoids found in nature (Liaaen-Jensen, 1978). Phytol, considered a photoautotroph biomarker in paleoreconstructions , is the side-chain of the vital and omnipresent pigment chlorophyll a that directly transfers sunlight energy into the photosynthetic pathway in nearly all photosynthetic organisms. Sterols, considered a general eukaryotic biomarker in paleoreconstructions, are the eukaryotic tetracyclic triterpenoid lipids used for critical regulatory roles of cellular functions, e.g., maintaining membrane fluidity (Nes et al., 1993). Although sterols are virtually restricted to eukaryotes, some exceptions have been found in bacteria (Wei et al., 2016). Here we only examine cholesterol, which is universally absent in prokaryotes and composes of up to 20 %-40 % of eukaryotic plasma membranes (Mouritsen and Zuckermann, 2004). Phytol and cholesterol may also have terrestrial sources, given that they are derived from all photoautotrophs and all eukaryotes, respectively. However, these samples were taken off the coast of a small island in the open ocean, and the absence of characteristic terrestrial biomarkers indicates that terrestrial contributions can be considered to be minimal. The close resemblance of the isotopic composition among all three compounds, including the primarily diatom-limited compound loliolide, suggests that these compounds share relatively similar source organisms. Cholesterol shows a lessened isotopic shift compared to the other two compounds from the ambient to elevated pCO 2 sites. Although we cannot fully exclude that this is due to terrestrial input, it is more likely due to the mobile eukaryotic zooplankton in the water column, which also contribute to the cholesterol signal.
Within the same biomarker and same season, some differences among matrices were observed. This difference may be due to the mobility of the matrix, as well as the algal assemblages. The plankton tow that captured free-floating surface water algae from that specific growth season is more readily transported by wind than the surface sediment, which likely reflects the culmination of multiple growth seasons throughout the water column. This is seen, for example, in the δ 13 C of cholesterol collected in September from the same control site where surface sediments are −22.2 ‰ and plankton tows are −25.1 ‰, where the latter has possibly been transported from sites with elevated pCO 2 levels. Similar differences among matrices are also observed in phytol and loliolide. The hypothesis of transportation affecting the isotopic signal in certain matrices is supported by the results from the macroalgae. The macroalgae, in contrast to the algae collected by plankton tows, were unaffected by transportation due to being fixed to the nearby rocks at each site. Thus, the isotopic composition of compounds of the macroalgae was similar to that of the surface sediments accumulated over a long period, e.g., −22.7 ‰ for the δ 13 C of cholesterol at the September control site.
Finally, there is a difference in the δ 13 C values for biomarkers between seasons. The June-collected surface sediments and algae yielded a larger difference in δ 13 C values along the CO 2 gradient than the September-collected surface sediments and algae. This seasonal difference may be due to extreme weather conditions experienced between the two sampling campaigns. Although typhoons are common in this region, in the weeks preceding the fieldwork in September, Shikine Island experienced an unusually high quantity of storms. The storms were also of unusual strength for this region of the Pacific, including typhoons Mindulle and Kompasu, the severe tropical storms Omais and Chanthu, and the long-lived, erratic Lionrock typhoon. This atypical abundance and severity of storms observably ripped corals out of the rocks around Shikine Island and thus likely resuspended and transported some sediment around the bay. This would explain the reduced δ 13 C difference between the control and high-pCO 2 site in the surface sediments collected in September, as well as the readily transportable algae collected by the plankton tow, and would explain why the rock-affixed macroalgae, also collected in September, maintained a strong δ 13 C change across the transect.

The ε p among general algal biomarkers
To further validate the impact of pCO 2 , we calculated the isotopic fractionation of algal biomass based on the δ 13 C of the three biomarkers. Here we focus on surface sediments as they are a close analogue to the geological sediment records. Although the macroalgae and benthic diatoms also show strong isotopic fractionation, they represent a limited number of species and a single growth season. Furthermore, we calculated the ε p from the June-collected surface sediments, which appear to be the least affected by typhoon activity and represent fractionation over multiple seasons.
To calculate ε p in the June-collected surface sediments, we correct the δ 13 C of the organic matter (δ p ) for the δ 13 C of the inorganic carbon source for the producers of these compounds (δ d ) in Eq. (1): (1) δ p is calculated by correcting the δ 13 C for each individual biomarker for the offset with photosynthetic biomass caused by isotopic fractionation during biosynthesis. The isotopic offset between phytol and biomass is 3.5 ± 1.3 ‰, based on the average of 23 species compiled in Witkowski et al. (2018), and the isotopic offset between sterols and biomass is 4.5 ± 3.0 ‰ based on the average of eight algal species (Schouten et al., 1998). The isotopic offset for loliolide from biomass, however, has not been determined. Because isoprenoids are formed from the same biosynthetic pathway, here we average the offset of the other two iso-prenoids here (4.0 ‰) to estimate a value for the difference between loliolide and biomass. δ d is calculated by correcting the measured δ 13 C of DIC for temperature (Mook, 1974) and pH (Madigan et al., 1989), which considers the relative contribution of different inorganic carbon species to the measured DIC. Based on the equations of Mook et al. (1974), we correct for the temperature-dependent carbon isotopic fractionation of dissolved CO 2 with respect to HCO − 3 using the annual mean sea surface temperature for Shikine Island of 20.4 • C . Based on the equations of Madigan et al. (1989), we corrected for the δ 13 C of HCO − 3 and δ 13 C of CO 2[aq] mass balance calculation that accounts for the relative abundance of these inorganic carbon species based on pH (Lewis and Wallace, 1998) at the high-pCO 2 site (7.81 pH T ) and mid-pCO 2 site (7.99 pH T ), relative to the ambient control (8.14 pH T ). The corrected δ d values yield −10.1 ‰ at the control site, −10.0 ‰ at the mid-pCO 2 site, and −9.5 ‰ at the high-pCO 2 site (Table S2).
ε p values consistently yield much higher values at the elevated pCO 2 sites than the ambient control sites for all three biomarkers, which share similar trends and absolute values ( Fig. 4; Table S3). ε p derived from loliolide averages 7.2 ± 1.6 ‰ at the control, 9.2 ± 1.6 ‰ at the mid-pCO 2 site, and 15.9±1.6 ‰ at the high-pCO 2 site, ε p derived from phytol at 8.6±0.4 ‰, 8.6±0.9 ‰, and 14.9±1.0 ‰, respectively, and ε p derived from cholesterol at 7.6 ± 3.0 ‰, 9.2 ± 3.1 ‰, to 13.7±3.1 ‰, respectively, where errors represent the standard deviation of the triplicate samples taken at each site. These results show that CO 2 has a profound impact on ε p as it is the only variable with a large gradient in the bay. Given that maximum fractionation for algae species is ca. 25 ‰ to 28 ‰ in laboratory cultures (Goericke and Fry, 1994), the CO 2 seep values suggests strong fractionation, but does not approach maximum fractionation (ε f ) at the high-CO 2 site. This may be due to presence of carbon concentrating mechanism in phytoplankton, which utilize 13 C-enriched bicarbonate, or possibly due to the presence of rubisco types with different ε f values than previously assumed (Thomas et al., 2018).

pCO 2 reconstructed from general algal biomarkers
We estimate pCO 2 from the ε p values, a relationship first derived for higher plants (Farquhar et al., 1982(Farquhar et al., , 1989 and later adapted for algae (Jasper et al., 1994;Rau et al., 1996) in Eq. (2): where ε f reflects the maximum rubisco-based isotopic fractionation, b reflects species carbon demand per supply such as growth rate and cell-size (Jasper et al., 1994), and K 0 reflects a constant to convert CO 2[aq] to pCO 2 based on temperature and salinity (Weiss, 1974). ε f for algal species range from 25 ‰ to 28 ‰ in laboratory cultures (Goericke and Fry,Figure 4. The ε p of general algal biomarkers in sediments. Loliolide (triangle), phytol (circle), and cholesterol (square) from the control, mid-pCO 2 and high-pCO 2 sites during June sediment collection. 1994); we use an average 26.5 ‰ with an uncertainty of 1.5 ‰ uniformly distributed for these general algal biomarkers (Witkowski et al., 2018). The b value is difficult to estimate as it is a catchall for factors other than pCO 2 that affect fractionation and is particularly difficult to estimate for general algal biomarkers because they are derived from a multitude of species. Previous studies using phytol's diagenetic product phytane as a pCO 2 proxy (Bice et al., 2006;Sinninghe Damsté et al., 2008;van Bentum et al., 2012) have used a mean value of 170 ‰ kg µM −1 , similar to the mean of alkenone producers. This is supported by a compilation of the δ 13 C values of modern surface sediment organic matter mean average of 168 ± 43 ‰ kg µM −1 (Witkowski et al., 2018) and a single study on phytol in the equatorial Pacific Ocean (Bidigare et al., 1997). We apply this average, rounded to 170±50 ‰ kg µM −1 , to all three general algal biomarkers. The resulting reconstructed pCO 2 estimations show the expected values in the control sites and much higher values in the elevated CO 2 sites among all three biomarkers ( Fig. 5; Table S3). Loliolide shows the biggest shift, from 239 + 50/ − 49 µatm at the control, 266 + 57/ − 54 µatm at mid-pCO 2 site, and 437 + 113/96 µatm at the high-pCO 2 site. Phytol has a similar but slightly smaller shift in pCO 2 estimates to loliolide, with estimations of 264 + 55/ − 54, 291 + 56/ − 53, and 444 + 98/ − 87 µatm at the control, mid-pCO 2 site, and high-pCO 2 sites, respectively. Cholesterol Figure 5. Reconstructed pCO 2 from general algal biomarkers. pCO 2 reconstructed from the δ 13 C of loliolide (triangle), phytol (circle), and cholesterol (square) in June-collected sediments versus the actual pCO 2 measured at each location Harvey et al., 2018). shifts in a similar manner to the other two biomarkers with 244 + 67/ − 54, 266 + 77/ − 61, and 358 + 136/ − 90 µatm, respectively. These reconstructed values closely match each other and trend in the same direction as the actual values.
The reconstructed pCO 2 values derived from the δ 13 C of general algal biomarkers closely match the actual measured pCO 2 values of the control (Fig. 5), i.e., 309±46 µatm Harvey et al., 2018), when considering the uncertainty in the reconstructed estimations. However, the proxies underestimate the absolute values measured at the elevated pCO 2 sites ( Fig. 5; Table S3), i.e., 460±40 µatm at the mid-pCO 2 site and 769 ± 225 µatm at the high-pCO 2 site Harvey et al., 2018). There are several possible explanations as to why there is an underestimation. As discussed above, carbonate concentration mechanisms may be operating in a large number of phytoplankton, such that they become relatively enriched in 13 C and thus lead to lower reconstructed pCO 2 values (Stoll et al., 2019;Badger et al., 2019). There is also a large uncertainty in the b value applied, which may be much lower than the value assumed here. However, if this is the case, then pCO 2 values reconstructed for past times may be much higher, leading to considerable discrepancies with other pCO 2 proxies (cf. Witkowski et al., 2018). A simple explanation for this underestimation may be some site limitations. The high variability of pCO 2 at these sites could have impacted the reconstructed values, as these algae could have been exposed to much different, and perhaps lower, levels than those observed during the times that pCO 2 values were measured. Furthermore, there is a strong possibility of allochthonous marine input of sediment at the mid-pCO 2 and high-pCO 2 sites, i.e., input from sediment outside of the bay area. This allochthonous input seems likely given the intense weather conditions that occur annually in this small bay in which lateral transport of sediment could bring algal material grown in ambient pCO 2 conditions into the bay and dampen the overall pCO 2 signal picked up in the biomarkers. Future research conducted at another CO 2 seep setting with different weather and current conditions could illuminate this.

Conclusions
We analyzed the δ 13 C of general algal biomarkers in surface sediments, plankton, benthic diatoms, and macroalgae collected in a transect from a CO 2 vent during two seasons. The strong δ 13 C change between the control and elevated pCO 2 sites suggest that the increased CO 2 concentrations in the seawater does indeed influence fractionation of photoautotrophic biomass and validates previous pCO 2 reconstructions, which have considered utilizing general algal biomarkers for this purpose. Reconstructions correctly estimate control values, though reconstructions at the elevated pCO 2 sites show underestimations of the actual pCO 2 , possibly due to the allochthonous input from nearby marine sediments deposited under normal pCO 2 levels caused by the intense annual typhoon activity in this region. Our results show that CO 2 seeps may offer testing grounds for exploring new pCO 2 proxies under natural conditions at high pCO 2 levels like those encountered in the geological past.
Data availability. All data are present in the paper and/or in the Supplement.
Author contributions. CRW, SS, and JSSD designed the study. SA, BPH, and CRW collected field samples. CRW analyzed samples and wrote the manuscript. CRW, MTJvdM, JSSD, and SS interpreted the data.