Evaluation of environmental proxies based on long chain alkyl diols in the East China Sea

Journal Pre-proofs Evaluation of environmental proxies based on long chain alkyl diols in the East China Sea Linghui He, Manyu Kang, Dongrong Zhang, Guodong Jia PII: DOI: Reference:

S0146-6380(19)30185-8 https://doi.org/10.1016/j.orggeochem.2019.103948 OG 103948

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Organic Geochemistry

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10 July 2019 24 October 2019 28 October 2019

Please cite this article as: He, L., Kang, M., Zhang, D., Jia, G., Evaluation of environmental proxies based on long chain alkyl diols in the East China Sea, Organic Geochemistry (2019), doi: https://doi.org/10.1016/ j.orggeochem.2019.103948

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Evaluation of environmental proxies based on long chain alkyl diols in the East China Sea

Linghui Hea, Manyu Kanga, Dongrong Zhanga,b, Guodong Jiaa,*

a State

Key Laboratory of Marine Geology, Tongji University, Shanghai 200092,

China b Key

Laboratory of Engineering Oceanography, Second Institute of Oceanography,

MNRC, Hangzhou 310012, China

*Corresponding author at: State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China. E-mail: [email protected]

ABSTRACT Long chain alkyl diols (LCDs) in marine environments are useful indicators of source organisms, ambient temperature, upwelling and nutrient conditions. However, the distribution of LCDs in the western Pacific marginal seas has been rarely reported, where wide shallow continental shelves and huge freshwater input from many rivers occur. In this study, we analyzed LCDs in surface sediments distributed from the Changjiang River estuary (CRE) to the East China Sea shelf to evaluate their sources and associated environmental proxies. Our results showed that the fractional abundance of C32 1,15-diol (FC32 1,15-diol) was highest in coastal area close to the CRE, with FC32 1,15-diol > 15% implying significant freshwater input. The C28 and C30 1,13diols showed a similar spatial distribution to the C32 1,15-diol, suggesting that the long chain diol index (LDI), a sea surface temperature (SST) proxy, may be biased by freshwater-derived diols. By excluding the freshwater-influenced samples (i.e., FC32 1,15-diol

> 15%), LDI reflected the autumn SST, yielding minimum temperature

residuals (0.2 ± 1.5 °C). The C28, C30 and C30:1 1,14-diols were abundant in the middepth (15–45 m) offshore environment that was affected only slightly by the Changjiang River plume, and decreased toward both the eutrophic estuarine and oligotrophic marine environments; whereas the C28:1 1,14-diol showed higher fractional abundances close to the CE. The spatial distribution of 1,14-diols is similar to that reported for Proboscia diatoms in this region, although the exact sources of 1,14-diol requires further study. Nutrient proxies based on 1,14-diols did not correlate

well with nutrient concentrations in this river-dominated marginal sea, where nutrient supply is dominated by the Changjiang River input.

Keywords: Long chain alkyl diols, biomarker proxies, estuary, East China Sea

1. Introduction Long chain alkyl diols (LCDs) are lipids that are composed of a long alkyl chain containing alcohol groups at C1 and a mid-chain position (C12, C13, C14, C15, etc.) (e.g., Balzano et al., 2018). LCDs were firstly identified in Black Sea sediments (de Leeuw et al., 1981) and include mainly C28 and C30 1,13-diols, C28 and C30 1,14diols, and C30 and C32 1,15-diols in marine environments (Rampen et al., 2012). With more culture and field observations, a lot of progress has been made in the source organisms of LCDs and related environmental proxies (Versteegh et al., 1997, 2000; Rampen et al., 2008, 2011, 2012, 2014a, 2014b; Willmott et al., 2010; Villanueva et al., 2014; de Bar et al., 2016; Balzano et al., 2017, 2018; Lattaud et al., 2017, 2018; Zhu et al., 2018; Gal et al., 2018). The C28 and C30 1,13-diols and C30 and C32 1,15-diols are believed to be biosynthesized by eustigmatophyte microalgae both in marine and freshwater environments (Gelin et al., 1997; Volkman et al., 1999; Méjanelle et al., 2003; Shimokawara et al., 2010; Lattaud et al., 2018a). However, due to the discrepancy of distributions of LCDs between cultured marine eustigmatophytes and marine

sediments, the biological sources of LCDs in marine environments remain uncertain (Volkman et al., 1992; Versteegh et al., 1997). Recent studies showed that Goniochloris species might be an important producer of 1,13- and 1,15-diols in some river systems (Lattaud et al., 2018a), and Chrysophyceae and Dictyochophyceae could be possible producers in the marine realm (Balzano et al., 2018). Rampen et al. (2012) noted that the ratio between C30 1,15-diol and 1,13-diols (i.e., the long chain diol index: LDI) is correlated with annual SST: LDI = [C30 1,15-diol]/[C28 1,13-diol + C30 1,13-diol + C30 1,15-diol]

(1)

LDI = 0.033  SST + 0.095 (R2 = 0.969, n = 162)

(2)

The LDI covers temperatures ranging from −3 °C to 27 °C and has provided the first 43-kyr diol-derived SST record in a sediment core in the South Atlantic close to the Congo River outflow (Rampen et al., 2012). Since then, LDI has been applied to Quaternary and recent marine sediments worldwide (Naafs et al., 2012; Smith et al., 2013; Rodrigo-Gámiz et al., 2014; Plancq et al., 2015; de Bar et al., 2016; Lattaud et al., 2017, 2018b; De Bar et al., 2018, 2019; Zhu et al., 2018). As with other biotic SST proxies, the LDI-derived SST also shows seasonality associated with local conditions. For example, in oceanic waters in southeast Australia the LDI provided better estimates of winter SSTs (Smith et al., 2013), but in the western Mediterranean Sea, LDI-reconstructed SSTs were biased to warm seasons (Rodrigo-Gámiz et al., 2014). In contrast, in the Iberian Atlantic margin and a upwelling area of the coastal northern South China Sea, the LDI-derived SST matched better with annual mean

SST (de Bar et al., 2016; Zhu et al., 2018). Moreover, the LDI-derived SST in coastal regions may be affected by riverine diol input characterized by high abundance of C32 1,15-diol (Lattaud et al., 2017). Saturated and mono-unsaturated C28 and C30 1,14-diols have been identified in the marine diatom genus Proboscia which usually thrives in relatively high nutrient areas, such as upwelling regions (Sinninghe Damsté et al., 2003; Rampen et al., 2007, 2008). Indicators such as the Diol Index 1 (DI-1), Diol Index 2 (DI-2) and the Combined Diol Index (CDI) has been proposed to identify past upwelling and high nutrient conditions, defined as the relative abundance of saturated 1,14-diols with respect to 1,13-diols and/or the C30 1,15-diol (Rampen et al., 2008, 2014a; Willmott et al., 2010): DI-1 = ([(C28 + C30) 1,14])/([(C28 + C30) 1,14]+[C30 1,15])

(3)

DI-2 = ([(C28 + C30) 1,14])/([(C28 + C30) 1,14]+[(C28 + C30) 1,13])

(4)

CDI = ([(C28 + C30) 1,14])/([(C28 + C30) 1,14]+[(C28 +C30) 1,13] + [C30 1,15])

(5)

Recently, a new index, the nutrient diol index (NDI) was proposed by adding the C28:1 1,14-diol in the numerator as a quantitative proxy for nutrient levels (Gal et al., 2018): NDI = ([(C28 + C28:1) 1,14])/([(C28 + C28：1 + C30 + C30:1) 1,14]+[(C28 +C30) 1,13] + [C30 1,15])

(6)

However, the favorable conditions for Proboscia spp. remain unclear and appear specific for a particular region exhibiting stratification, early- or postupwelling, and varing nutrient levels (Rampen et al., 2014a). Besides, saturated C28,

C30, and C32 1,14-diols have been also reported to be produced by marine dictyochophyte Apedinella radians (Rampen et al., 2011), but the importance of Apedinella as a source of 1,14-diols in the ocean is still uncertain. Previous studies have suggested that regional differences in source organisms may influence the global diol distributions (e.g., de Bar et al., 2016). It is not clear whether or not diol distribution in the west Pacific region, where a series of marginal seas exists from the north (e.g., the Okhotsk Sea) to the south (e.g., the South China Sea), is consistent with the global distribution. In the South China Sea, LCD data in three short coastal sediment cores covering histories of several decades confirmed that the LDI may reflect annual SST and the DI-2 can reflect local summer wind-induced upwelling (Zhu et al., 2018). A study in the northwest Pacific indeed found something new, e.g., suggesting different types of Proboscia diatoms as compared with those in other areas for the source of 1,14-diols (Gal et al., 2018). In another study analyzing LCDs in 181 surface sediments in the marginal East China Seas, however, only C30 and C32 1,15-diols were identified (Yu et al., 2018), hence the occurrence of other LCDs and the applicability of LCD-derived proxies in the region is not clear. Here we provide additional LCD data and evaluate sources and proxies of LCDs in surface sediments in a region from the Changjiang estuary (CRE) to the East China Sea (ECS). This region is characterized by a large shelf sea, i.e. the ECS, receiving huge amounts of the Changjiang River input. It is an ideal place to examine the LCDderived proxies under various environmental gradients such as river plume influence,

nutrient level, and SST seasonality.

2. Materials and methods 2.1. Study area The ECS is one of the largest river-dominated marginal seas in the world. It occupies a relatively shallow, > 500 km wide continental shelf, with an average water depth of 60 m. The regional climate is affected by the East Asian monsoon, displaying humid and warm conditions in summer and dry and cold conditions in winter (Xu et al., 2018). The ECS receives a large amount of terrigenous materials from one of the largest rivers in the world, the Changjiang River, the annual sediment load of which was about 480 million tons from 1951 to 1968 (Yang et al., 2006). After the construction of the Three Gorges Dam, the sediment load from the Changjiang to the ECS has been significantly reduced by ~40% (Yang et al., 2006). The high flow season of the Changjiang River is from June to September, with river discharge up to 33467 m3/s (Li et al., 2017). The coastal zone, the mid-shelf and the deep Okinawa Trough (OT) are the three typical environments of the ECS (Liu et al., 2003, 2007), where the sediments are characterized mainly by silty clay and clayey silt, relic sand, and silt-clay, respectively. The East China Sea Coastal Current (ECSCC), which intensifies in winter and carries the Changjiang River water and sediments, flows southward along the inner shelf. Offshore in the mid-shelf, the saline Taiwan warm current (TWC)

flows northward and intensifies in summer under the prevailing southeast monsoon. Farther east, the saline and oligotrophic Kuroshio Current (KC) travels northward along the northwest slope of the OT and exchanges extensively with the ECS shelf water (Fig. 1).

2.2. Sampling In this study, a total of 72 surface sediments (< 5 cm depth; Fig. 1) collected using box corers were studied. Among them, 42 surface sediments with water depth from 6 m to 69 m were collected in the CRE and adjacent coastal sea in March 2018 during the cruise NORC2018-03 onboard the Kexue-III and Chuangxin-II. The other 30 surface sediments with water depth from 44 m to 1992 m, mainly on the ECS shelf, were collected in 2012 by the Qingdao Institute of Marine Geology (QIMG) in China. The sediment samples were well preserved at −20 °C upon collection and freeze-dried before analysis. Note that there is no overlap in samples between this study and Yu et al. (2018).

2.3. Sample extraction and purification A part of samples, located in the CRE and adjacent coastal sea, were previously extracted by Kang et al. (2019) using a modified Bligh-Dyer method (B&D method) as in previous studies (e.g., Pitcher et al., 2009; Bauersachs et al., 2014). An aliquot of stored total lipid extract (TLE) in that study was used for LCD

analysis here. For consistency, we treated the remaining sediment samples on the shelf and slope of the ECS using the same B&D method. Previously, the B&D method has been applied to river and marine suspended particulate matter (SPM) for LCDs extraction (de Bar et al., 2016). Briefly, a freeze-dried and powdered sediment sample was extracted ultrasonically using a solvent mixture of methanol (MeOH), dichloromethane (DCM) and phosphate buffer (8.7 g/L; 2:1:0.8, v/v/v) for 10 min. The supernatant was collected in a separatory funnel after centrifugation and the extraction procedure was repeated four more times. The resulting TLE was separated by adding DCM and phosphate buffer to achieve a final solvent of MeOH:DCM:phosphate buffer (1:1:0.9; v/v/v). The organic component was collected and the remaining aqueous phase was extracted two more times with DCM. The obtained organic extract was then dried by rotary evaporation and transferred to a glass vial. Following saponification with KOH/MeOH (6 g/100 mL) at 40 °C overnight, the neutral lipids were extracted using DCM and then fractionated using silica gel chromatography by elution with n-hexane/DCM (9:1; v/v) and DCM/MeOH (1:1; v/v), respectively. The alcohol component in the DCM/MeOH fraction was converted to trimethylsilyl derivatives with N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA) at 60 °C for 2 h prior to instrumental analyses.

2.4. Biomarker analysis LCDs analyses were performed on a Thermo Fisher TSQ8000 triple

quadrupole GC–MS/MS in both the selective ion monitoring (SIM) and multiple reaction monitoring (MRM) modes. The fraction containing diol derivatives was dissolved in n-hexane and 1 μL was injected into the injector maintained at 80 °C. The oven temperature was programmed from 80 °C to 130 °C at 20 °C/min and subsequently to 310 °C at 4 °C/min, and finally maintained at 310 °C for 25 min. The GC–MS/MS was equipped with a fused silica column (DB-5MS, 30 m  0.25 mm, film thickness 0.25 μm). Helium was used as carrier gas at 1.2 mL/min. The mass spectrometer was operated with an electron ionization (EI) energy of 70 eV. LCDs were identified from their characteristic mass spectra obtained in full scan mode with an m/z range of 50–600. For quantification of different diol isomers, scanning was performed in SIM mode with m/z 299 (C28 and C28:1 1,14-diols), m/z 313 (C28 1,13diol and C30 1,15-diol), m/z 327 (C30 and C30:1 1,14-diols) and m/z 341 (C32 1,15-diol) ions (cf. Versteegh et al., 1997; Rampen et al., 2012; Gal et al., 2018). For the MRM mode, m/z 299, 313, 327, 341 ions were selected as parent ions and the m/z 103 as product ions for all parent ions (de Bar et al., 2017). The abundance of each compound was expressed as the fraction in the sum of integrated areas of the total identified diols. FC32 1,15-diol was calculated as the percentage abundance of C32 1,15-diol to the total of 1,13- and 1,15-diols, as defined by de Bar et al. (2016). We used both SIM and MRM modes in LCD instrumental analysis for coastal samples in this study. Compared with the SIM mode, the MRM mode may substantially lower the background level and thus improve the detection of LCDs (de

Bar et al., 2017). The offshore samples were analyzed using the MRM mode only, because the background level was too high to accurately detect LCDs in the SIM mode. Results reported here were all based on the MRM mode. An aliquot of DCM/MeOH fraction was also analyzed for glycerol dialkyl glycerol tetraethers (GDGTs) in order to calculate the branched and isoprenoid tetraether (BIT) index, which is defined as the relative abundance of branched GDGTs to the sum of branched GDGTs and crenarchaeol (Hopmans et al., 2004). Some of these data has been published by Kang et al. (2019) and detailed procedure can be found there.

2.5. Environmental data Annual mean SST, salinity, and nutrient (nitrate, phosphate, silicate) data in the offshore sea surface waters of study area were downloaded from https://www.nodc.noaa.gov/OC5/woa13/woa13data.html (0.25°  0.25° for SST and salinity; 1°  1° for nutrients). A Matlab scattered Interpolant method was applied to interpolate data for our study sites. Due to lack of data in the coastal area close to the CRE, data from Wang et al. (2013) were used here.

3. Results The ECS is a river-dominated marginal sea, showing regular gradients in salinity and nutrients from nearshore to offshore (Fig. 2). Nutrients are at high levels

in the CRE and adjacent seawater and show a general conservative behavior along the salinity gradient (Zhang, 1996; Wang et al., 2010; 2017). Due to high correlation between concentrations of nitrate, phosphate and silicate in the study area, nitrate is used here in discussion as nutrient representative. SST exhibits a northwest-southeast increase from ~15 °C to ~26 °C (Fig. 2). In LCDs, C30 1,15-diol was the dominant component in our samples, with fractional abundance ranging from 33.1% to 84.6% with a mean of 58.1% (Fig. 3). In coastal samples, especially those close to the CRE, the fractional abundances of C30 1,15-diol (33.1%–61.3% range, 46.5% average) were obviously lower than in offshore samples (64.3%–84.6% range, 75.4% average) (Fig. 3e). Such a spatial distribution is similar to the distribution of satellite-derived annual SST (Fig. 2a). The 1,14-diols were the second most abundant diols in this region. The C28 1,14-diol varied between 5.1% and 40.6%, with an average of 22.6%, and C30 1,14diol varied between 0.8% and 15.3%, with an average of 8.0%. Both diols showed low abundances in the CRE and shelf samples, but a roughly offshore increasing trend occurred in the coastal region (Fig. 3b, d). Smaller amounts of mono-unsaturated 1,14-diols were also detected in almost all samples. C28:1 and C30:1 1,14-diols were comparable in fractional abundance, showing average values of 1.2% (0–5.7% range) and 1.1% (0–2.6% range), respectively. The highest abundance of C28:1 1.14-diol occurred close to the CRE (Fig. 3g, h). The fractional abundance of C32 1,15-diol varied between 0% and 25.5%, with

an average of 5.0%. Higher abundances occurred close to the CRE and < 15.9% at sites immediately off the CRE (Fig. 3f). The abundances of the C28 1,13-diol (0–6.0% range, 2.0% average) and C30 1,13-diol (0–8.6% range, 1.9% average) were less than the C32 1,15-diol. As with the distribution of the C32 1,15-diol, the two 1,13-diols, especially C30 1,13-diol, showed slightly higher abundances close to the CRE and lower values off the CRE (Fig. 3a, c). The spatial distribution of diols can also be illustrated by a figure showing variations of factional abundance of diols with water depth of the sample sites (Fig. 4). As can be seen, C32 1,15-diol, as well as C28 1,13- and C30 1,13-diols, showed marked decreases from shallow sites to deep sites and remained low at sites > 50 m. The C28 and C30 1,14-diols exhibited higher abundances at middle depth (15–45 m) and lower abundances close to shallow CRE and in deep sites > 45 m. in contrast, the C30 1,15-diol was relatively abundant in deep offshore samples. We performed a principal component analysis (PCA) on the fractional abundances of all the diols and environmental factors. The first two principal components explained 81.7% of the variance of LCD abundances (Fig. 5). In the diagram, diols can be grouped into three groups: group 1 was the C30 1,15-diol that was close to SST; group 2 contained the C28 1,13-diol, C30 1,13-diol and C32 1,15diol, as well as C28:1 1,14-diol, which was close to nitrate and against salinity; and group 3 included C28, C30 and C30:1 1,14-diols, which was against 1,13- and 1,15diols.

BIT index varied between 0.02 and 0.80 with a mean of 0.08, and FC32 1,15-diol was between 0 and 36.9% with a mean of 7.7%. As shown in Fig. 2a and Fig. 3i, the spatial distribution of BIT index is similar to that of FC32 1,15-diol, both displaying a decreasing trends seaward. The BIT index showed a sharp decrease from 0.80 to 0.16 from the estuary to the near coast, and then remained below 0.1 in offshore sediments. Correspondingly, FC32 1,15-diol decreased from 36.9% to 15.6%, with less variations below 15%.

4. Discussion 4.1. 1,13- and 1,15-diols and related proxies 4.1.1. Sources of 1,13- and 1,15-diols The source organisms for 1,13- and 1,15-diols are still not exactly clear, although culture studies show that eustigmatophyte algae isolated from various environments produce 1,13- and 1,15-diols (Volkman et al., 1999; Rampen et al., 2014a). This is especially true for marine sediments, which shows a dominance of C30 1,15-diol over other 1,13- and 1,15-diols and was also observed in most of our samples, while cultures of eustigmatophytes produce mainly the C32 1,15-diol (Volkman et al., 1992; Méjanelle et al., 2003; Rampen et al., 2012). Since the known marine representatives of eustigmatophytes, i.e. the genus Nannochloropsis, are never abundant (Andersen et al., 1998; Fawley and Fawley, 2007), it is likely that the major sources for LCDs in marine environment may not yet be available in culture, as suggested by

eustigmatophyte DNA study in an East African lake (Villanueva et al., 2014) and analysis of LCDs in river systems (Lattaud et al., 2018a). In contrast to the C30 1,15-diol distribution showing higher fractional abundances in offshore sites, the C32 1,15-diol, as well as C28 and C30 1,13-diols, showed a notable decreasing trend offshore. In the PCA diagram, the C32 1,15 diol exhibited an opposite direction to the C30 1,15-diol on PC1, suggesting a different source for the C32 1,15 diol. A higher fractional abundance of the C32 1,15-diol has been found not only in freshwater eustigmatophytes and river sediments (Wang et al., 2013; Rampen et al., 2014b; Lattaud et al., 2018a), but also in estuarine and coastal environments (de Bar et al., 2016; Lattaud et al., 2017); the latter suggests fluvial input of organic carbon to seas (de Bar et al., 2016; Lattaud et al., 2017). So, the spatial distribution of C32 1,15-diol observed here is consistent with previous studies and may also indicate fluvial input to the ECS. It is interesting that the C28 and C30 1,13-diols showed a similar spatial distribution as the C32 1,15-diol. In the PCA diagram, the three diols aggregated closely. These occurrences suggest their similar fluvial sources. In fact, culture studies have shown that some known families, such as Monopsidaceae, Eustogmataceae, and Goniochloridaceae, in the class of Eustigmatophyceae may produce C28-32 1,13- and 1,15-diols (Volkman et al., 1992, 1999; Rampen et al., 2014a; Balzano et al., 2018). Alternatively, the opposite response to ambient temperature of C30 1,15-diol to C28 and C30 1,13-diols (Rampen et al., 2012) might be another reason for the negative

loadings of C30 1,15-diol vs C28 and C30 1,13-diols in the PCA diagram. However, larger offsets between LDI derived temperatures and actual values close to estuarine and coastal areas observed elsewhere (e.g., Lattaud et al., 2017) and in this study (see below) suggest fluvial input of C28 and C30 1,13-diols.

4.1.2. FC32 1,15-diol as an indicator of fluvial organic matter (OM) contribution Relatively high amounts of the C32 1,15-diol has been reported in SPM and sediments in numerous estuarine environments (Versteegh et al., 1997; Rampen et al., 2014a; Lattaud et al., 2017; Zhu et al., 2018), suggesting that FC32 1,15-diol can potentially be used as a proxy for riverine OM input in shelf seas. For example, FC32 1,15-diol can reach up to 76% in SPM in the Amazon River and Yenisei River (Lattaud et al., 2017) and even up to 90% in the Pearl River in China (Zhu et al., 2018); whereas it is low (< 10%) in marine environments with no substantial continental input (Lattaud et al., 2017; Zhu et al., 2018). The distribution of the C32 1,15-diol in this study, showing a highest FC32 1,15-diol value of 37% in the CRE, a decreasing trend with increase of salinity and steady low values ~6% at the highest salinity in offshore environments (Fig. 7a), confirms previous conjecture. This can be further supported by the positive relationship (r = 0.89, p < 0.001) with the BIT index (Fig. 7b), because the BIT index is a proxy for the relative contribution of soil and riverine OM transported from land into the marine realm (Hopmans et al., 2004; Huguet et al., 2006; Walsh et al., 2008; Kim et al., 2009; Zell et al., 2013, 2014). Similar correlation between the two indices

has been also observed in many coastal and shelf areas, such as the Amazon shelf, Gulf of Lion, Berau delta, Kara Sea and New Jersey shelf (Lattaud et al., 2017; de Bar et al., 2019). However, BIT values in most offshore samples were < 0.1 and changed little, failing to trace subtle changes in the terrigenous contribution, likely due to the fact that BIT was dominated by variable marine crenarchaeol abundances in offshore settings (Kang et al., 2019). Correspondingly, FC32 1,15-diol remained low (< 12%) in these samples (Fig. 7b), similar to that observed in open marine environments and hence suggestive of little continental input (de Bar et al. 2016; Lattaud et al., 2017; Zhu et al., 2018). In coastal samples of this study, FC32 1,15-diol also correlated well with two other proxies indicating the relative amount of fluvial OM in coastal marine sediments, which are the stable isotope value of bulk organic carbon (δ13Corg) and the recently proposed FHG index based on heterocyst glycolipids of freshwater and marine diazotrophs (Kang et al., 2019; and Reference therein) (Fig. 7c, d). Collectively, FC32 1,15-diol

is a good tracer for the relative contribution of fluvial OM input into coastal

marine environments. However, as with the BIT index, variable marine biological production of 1,13- and 1,15-diols may complicate application of FC32 1,15-diol as an accurate proxy for fluvial input in marine-dominate environments, and hence multiproxy study might be preferable.

4.1.3. Application of LDI

The LDI in the literature was based on SIM-derived data (e.g., Rampen et al. 2012), whereas in this study we use MRM-derived data. For the comparability of our LDI values with literature values, reliable LCD data from the SIM mode in this study, mostly from the estuarine and coastal samples, were calculated for LDI, and then compared with LDI-derived from MRM data. We found a strong linear relation between them (Fig. 6), formulated as below: LDISIM = 1.65  LDIMRM − 0.66

(r = 0.98, p < 0.001, n = 31)

(7)

As a result, all our LDIMRM data were converted to LDISIM, and then were used as below. Note that this relation is not 1:1, which could be due to the relative peak areas of the LCDs are different in the MRM compared to SIM due to the different fragment yields of m/z 103 from the target ions (m/z 299, 313, 327 and 341) (de Bar et al., 2017). Note that the slope here is significantly higher than the slope (i.e., 1.19) observed by de Bar et al. (2017). Since de Bar et al. (2017) used different instruments from ours, i.e., Agilent GC–MS(/MS) systems, we think that the difference in slope was potentially caused by instrument-specific factors. Using the World Ocean Atlas (WOA) 2013 dataset (https://www.nodc.noaa.gov/OC5/woa13/woa13data.html), we downloaded annual and seasonal SST data (0.25°  0.25°) for every study site, showing an annual SST range of 15.4–25.5 °C. LDISIM values in this study ranged from 0.20 to 0.99 with an average of 0.87. SSTLDI based on eq. (2) varied between 3.2 °C and 27.1 °C, with an average of 23.4 °C. The temperature residual, calculated as the difference between

SSTLDI and SSTWOA, for annual SST varied between −15.0 °C and 8.4 °C with an average of 2.5 ± 3.5 °C (Fig. 8a), indicating a slightly warm bias of estimated SST relative to actual annual SST. For seasonal SSTs, the lowest mean residual, −0.7 ± 2.9 °C (−17.9 °C to 3.0 °C range), occurred in autumn (September-OctoberNovember) (Fig. 8a), with the second lowest mean residual of −1.9 ± 3.6 °C (−21.4 °C to 4.0 °C range) in summer (June-July-August). However, the mean standard errors for the autumn and summer SST residuals (2.9 °C and 3.6 °C, respectively) are still higher than that from the global dataset (2.0 °C; Rampen et al., 2012). We found that the greater scatter was mainly caused by several marked negative residuals (< −3.5 °C). Interestingly, corresponding to these marked negative residuals, FC32 1,15-diol values were > 15% (Fig. 8a; with only one exception). This suggests freshwater-derived diols may have caused the abnormally negative SSTLDI biases. Similar findings have been observed in several studies. For example, de Bar et al. (2016) observed large negative discrepancies (−3 to −9 °C) near the coast, especially around the estuary, in the Iberian Atlantic margin. Underestimated SST were also observed in the Berau delta (up to −10 °C) and the Gulf of Lion (up to −5 °C) (Lattaud et al., 2017). By excluding samples significantly influenced by freshwater input (i.e., FC32 1,15-diol

> 15%), the scatter in estimated SSTLDI was greatly reduced. The mean annual,

spring, summer, autumn and winter residuals were 3.3 ± 2.6 °C (−2.5–8.4 °C range), 6.7 ± 3.9 °C (0.1–13.5 °C range), −0.8 ± 2.1 °C (0.1–13.5 °C range), 0.2 ± 1.5 °C

(−5.1–3.1 °C range), and 7.1 ± 3.4 °C (1.3–13.1 °C range), respectively. So, minimum residuals and scatter occurred in autumn. In addition, LDISIM and satellite annual SST were moderately positively correlated (r = 0.57, p < 0.001, excluding samples with FC32 1,15-diol > 15%). When putting the annual SST-LDI data points into the figure containing global data of Rampen et al. (2012), we found they were generally compatible with the global data, except for some data lying outside the 95% prediction (Fig. 8b). However, when the annual SST data were replaced by autumn temperature, all the data were well within the 95% prediction of the global dataset (Fig. 8b) and the correlation between autumn SST and LDI became better (r = 0.65, p < 0.001). These results indicate that the LDI may reflect autumn SST in the study region. This is basically consistent with the global dataset showing higher correlation coefficients between LDI and SST for late summer and early autumn and lower correlation for winter months, and hence potentially indicating proliferation of their source organisms mainly during the warm months (Rampen et al., 2012). Similar conclusion was also made in subpolar Kara Sea by Lattaud et al. (2017). However, differences exist in different regions perhaps due to varying local conditions. For example, SSTLDI agreed better with winter SST along the coast in front of the Douro and Mondego (de Bar et al., 2016), whereas in the coastal northern South China Sea, SSTLDI was much closer to annual mean SST (Zhu et al., 2018). Nevertheless, our conclusion is based on sediment analysis, and further studies using sediment traps and SPM, are needed to reveal any potential seasonality effect.

4.2. 1,14-diols and related proxies 4.2.1. Sources of 1,14-diols In the PCA diagram (Fig. 5), the saturated and mono-unsaturated C28 and C30 1,14diols were loaded opposite to C30 1,15-diols on PC2, suggesting different sources for 1,14-diols compared to those probably derived from marine eustigmatophytes. Previous studies have shown that 1,14-diols are mainly produced by Proboscia diatoms that usually thrive in upwelling areas at low sea water temperature while another potential 1,14-diol producer A. radians is predominantly present in estuarine and brackish non-upwelling areas (Sinninghe Damsté et al., 2003; Rampen et al., 2007, 2009, 2011, 2014a). However, A. radians does not produce C28:1 and C30:1 1,14diols (Rampen et al., 2011), and the C32 1,14-diol, present in A. radians (Rampen et al., 2011), was not detected in our samples. In addition, the occurrence of A. radians in the CRE and adjacent sea has not been reported. Hence, Proboscia diatoms seem to be the major producers of the 1,14-diols detected in this study. However, the occurrence and ecology of Proboscia diatoms have only rarely been studied in the ECS and, to our knowledge, only P. alata has been reported (Luan et al., 2007; He et al., 2009; Luan and Sun, 2010; Yang et al, 2014; Noman et al., 2019). P. alata in the ECS usually occur in summer in offshore waters only slightly influenced by the Changjiang River plume, increasing with the increase of salinity and decrease of nitrate and turbidity (Luan et al., 2007; Luan and Sun, 2010). The distribution of P.

alata in this study area could be due to that the species does not rely heavily on nutrients such as N and Si (Sakka et al., 1999; Annett et al., 2010), which is different from diatoms such as Skeletonema costatum that are abundant in the CRE and the Changjiang plume (Luan et al., 2007; Luan and Sun, 2010; Yang et al., 2014). There are also rare reports on the occurrence of P. alata on the shelf and in the slope area of the ECS, perhaps due to serious nutrient limitation. Thus, the distribution of 1,14diols, except for the C28:1 1,14-diol, showing relatively high fractional abundances in offshore middle depth (15–45 m), is generally coincident with the occurrence of P. alata in this area. However, the C28:1 1,14-diol, which is abundant in P. alata (Sinninghe Damsté et al., 2003), exhibited its highest fractional abundance close to the CRE; and the abundance of the C30:1 1,14-diol, comparable in fractional abundance with the C28:1 1,14-diol here, has been reported to be quite low in P. alata (Sinninghe Damsté et al., 2003; Rampen et al., 2007, 2009). Besides, although both saturated and mono-unsaturated C28 and C30 1,14-diols are detected in about equal amounts in P. indica (Sinninghe Damsté et al., 2003; Rampen et al., 2007, 2009), the species has not been reported in the ECS. Such phenomena are similar to those observed in the northwestern Pacific region, where the exact source of 1,14-diols is still to be determined (Gal et al., 2018). Diol distributions in this study are different from those observed in the Iberian Atlantic margin, showing more C30 and C30:1 1,14 diols close to coastline, bur more C28 1,14-diol in offshore environments. Given that the ecology of Proboscia spp. is not clear at present and appears specific for a

particular region (de Bar et al., 2016), the exact source of 1,14-diols also needs to be determined in this area.

4.2.2. Evaluation of nutrient proxies In our results, C28 and C30 1,14-diols, as well as the C30:1 1,14-diol, did not correlate with nitrate (Fig. 5). The low coefficients of correlation (< 0.2) of nitrate with both DI-1 and CDI (Fig. 9a, c) indicate that the two proxies are not applicable as nutrient indicators in this study area. Similar results were also observed in SPM samples in the northwestern Pacific region (Gal et al., 2018). Interestingly, the DI-2 was negatively correlated with nitrate concentration (Fig. 9b), which has not been reported elsewhere. We think the DI-2 here was largely determined by the distribution of C28 and C30 1,13-diols in the denominator, which happened to be similar to nutrient distribution exhibiting higher values close to the CRE (Fig. 3a, c). Note that the NDI proxy also exhibited a low correlation with nitrate (Fig. 9d), although the C28:1 1,14-diol, abundant close to the CRE (Fig. 3g) and positively correlated with nitrate (Fig. 5), is included in its numerator. This could be due to the minor contribution of C28:1 1,14diol compared with the C28 1,14-diol, another component of the numerator. Collectively, our data indicate that nutrient indices derived using the 1,14-diol are not valid in our study area. The failure of these indices to indicate nutrient conditions in the ECS might be associated with the river-dominated nature of the ECS. As opposed to open ocean environments, where conditions are usually

oligotrophic and nutrient supply is mainly controlled by upwelling or stratification conditions, the nutrients in our study area are modulated by the Changjiang River input. Mixing between the eutrophic freshwater and the oligotrophic seawater gives rise to strong salinity and nutrient gradients from nearshore to offshore. Most diatoms outcompete other taxa only when silicate is present in excess, but Proboscia grows at low levels of silicate (Sinninghe Damsté et al., 2003), which could be the reason for their occurrence at the edge of the Changjiang River plume in the offshore sea, where nutrient levels decline significantly (Luan et al., 2007; Luan and Sun, 2010; Yang et al., 2014). Consequently, Proboscia diatoms, as well as their diol lipids, should not positively reflect nutrient levels in such circumstances.

5. Conclusions In surface sediments distributed from the CRE to the ECS shelf, LCDs including C28 and C30 1,13-, 1,14- and 1,15-diols, C32 1,15-diol, and C28:1 and C30:1 1,14-diols were detected, with C30 1,15-diol and C28 1,14-diol the two dominant components. The C32 1,15-diol, as well as 1,13-diols, was clearly more abundant close to the CRE, indicating substantial freshwater contribution. FC32 1,15-diol = 15% can be applied as a threshold, above which the influence of fluvial input was appreciable. By excluding freshwater influenced samples, the LDI reflected the autumn SST, and to a lesser extent the summer, suggesting proliferation of the contributing microalgae during the warm months in the ECS. The relative abundances of 1,14-diols, except for

the C28:1 1,14-diol, were higher in offshore middle depths (15–45 m), where nutrient levels are moderate relative to the eutrophic estuarine and coastal region and oligotrophic shelf region. Such a distribution pattern is consistent with the distribution of Proboscia alata reported in this area, although the exact sources of 1,14-diol require further study. We posit that in this river-dominated marginal sea, where nutrient supply is dominated by the Changjiang River input, proxies based on 1,14diols appear to be unsuitable to indicate nutrient variations, because Proboscia diatoms do not positively respond to nutrient levels.

Acknowledgments We appreciate the captain and crew of cruise NORC2018-03 based on R/V Kexue-III and Chuangxin-II for sample collection. We thank Yin Fang for providing us an opportunity to participate in the cruises, and Liang Dong for useful advice. Junjian Wang and Lingdi Chen gave great help in sediment processing and lipid analyses. Two anonymous reviewers are thanked for their valuable comments on this paper. This work was supported by the State Key R&D project (No. 2016YFA0601104).

Associate Editor–Stefan Schouten

References

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Figure captions Fig. 1. Location of sampling sites in the East China Sea. Arrows indicate the main surface currents in East China Sea: Kuroshio Current (KC); Yellow Sea Coastal Current (YSCC); East China Sea Coastal Current (ESCC); Taiwan Warm Current (TWC). Fig. 2. Spatial distributions of (a) annual SST, (b) salinity, (c) nitrate, (d) phosphate, (d) silicate and (e) BIT index in this study. The map was made by Ocean Data View software. Fig. 3. Spatial distributions of fractional abundances of LCDs and FC32 1,15-diol index. The map was made using Ocean Data View software. Fig. 4. Fractional abundances of diols as a function of water depth of sediment samples. The water depth increases from left to right. Fig. 5. PCA plot based on LCD fractional abundances (black arrows, n = 69), BIT index, annual satellite SST, salinity and nitrate (red arrows). SST, salinity and nitrate data were derived from WOA13. Fig. 6. Relationship between MRM-derived LDI and SIM-derived LDI in this study. Fig. 7. Correlation between FC32 1,15-diol and (a) annual salinity, (b) BIT, (c) δ13Corg, and (d) FHG. δ13Corg and FHG data were from Kang et al. (2019).

Fig. 8. (a) Annual and autumn SST residuals for estimated LDI temperatures with the change of FC32 1,15-diol. Autumn data are shown here as they produced minimal mean residuals relative to other seasons. Samples with FC32 1,15-diol > 15% produced consistently negative residuals for autumn data. Horizontal dashed lines denote ± 2.0 °C limits; (b) Scatter plots of SIM-derived LDI versus satellite SST. Global dataset were from Rampen et al. (2012). SSTsatellite-SON and SSTsatellite-annual in the ECS represent satellite derived autumn and annual SSTs, respectively. Data in the ECS with FC32,1,15-diol >15% are not included. Red curves are 95% prediction of the global calibration (black line). Fig. 9. Relations between nitrate and indices of: (a) DI-1, (b) DI-2, (c) CDI and (d) NDI.

Highlights FC32 1,15-diol >15% indicates appreciable freshwater-derived diols LDI proxy reflects the autumn SST in the East China Sea Nutrient indices based on 1,14-diols are not suitable in the study area

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: