The area offshore SW Taiwan locates in the transition zone of the Luzon Arc and the China passive continental margin subduction-collision system (Liu et al., 2004). The Kaoping Slope representing an accretionary prism has been developing since the late Miocene during the collision between the Luzon volcanic arc of the Philippine Sea Plate and the Asia continental margin of the Eurasian Plates (Huang et al., 2006b). A series of normal faults in this accretionary prism can provide good conduits for gas and fluid venting upward to the surface (Lin et al., 2006). In view of the geological characters of the Kaoping Slope, many investigations have been performed to assess potential active cold seep sites (Chi et al., 1998; Chuang et al., 2006; Huang et al., 2006a; Lin et al., 2006; Liu et al., 2006). Noticeably, this area also receives a high flux of terrigenous suspended sediments exported from the adjacent Kaoping (49.0 Mt/a), Erjen (30.2 Mt/a) and Tsengwen (25.1 Mt/a) rivers with a combined river particle load of 104 Mt/a (Dadson et al., 2003).

Remotely operated vehicle (ROV) surveys and sediment sampling were conducted during 2004–2007 in the Kaoping Slope (Fig. 1). On the base of these surveys, this study focused on two piston cores collected from high potential cold seep sites at the Kaoping Slope. Giant piston Core MD052911 (22°15.61′N, 119°51.08′E; 1 076 m water depth) was retrieved in 2005 by R/V Marion Dufrense. The length of Core MD052911 is 2 389 cm, and 29 samples from 294 cm to 809 cm depth were taken at 2–3 cm intervals for organic geochemical analysis, where the CH₄ concentration varied significantly (Yang T F, personal communication). Another piston Core ORI-860-22 (22°13.08′N, 119°50.63′E; 1 237 m water depth) was taken from the Good Weather Ridge during R/V OR-I, a cruise of chemosynthetic community investigation in 2007 by Taiwanese and Japanese scientists. The length of Core ORI-860-22 is 251 cm, and 24 samples from 16 cm to 235 cm depth were taken at 2 cm intervals and analyzed, crossing the sulphate-methane transition zone (Yang T F, personal communication).

C₂₃ to C₃₃ n-alkanes were found in all samples, with a strong odd to even number predominance and maximizing at n-C₂₉ or n-C₃₁. Two representative partial gas chromatograms of the hydrocarbon fractions are shown in Fig. 2. Biomarker content was normalized to TOC content in order to minimize the effect of varying sedimentation rate and degradations. The TOC normalized content of n-alkanes (C₂₇+C₂₉+C₃₁) in Core MD052911 varied from 688 to 1 215 ng/g with an average value of 971 ng/g; and the TOC normalized content in Core ORI-860-22 ranged from 1 387 to 2 126 ng/g with an average value of 1 674 ng/g (Fig. 3). From 807.5 cm to 545 cm depth in MD052911, the n-alkane (C₂₇+C₂₉+C₃₁) content decreased and reached a minimum, then increased and reached a maximum around 349 cm (Fig. 3a). In ORI-860-22 samples, n-alkane content increased gradually from the bottom and reached a maximum at 81 cm depth, afterward it began a decreasing trend toward the coretop (Fig. 3b).

A series of sterols and alkenones were detected in most sediment samples. Figure 4 shows partial chromatograms of the neutral fractions (aliphatic alcohols and alkenones) from Cores MD052911 (Fig. 4a) and ORI-860-22 (Fig. 4b). TOC normalized sum content of the three marine phytoplankton biomarkers (∑MB=brassicasterol+dinosterol+C₃₇-alkenones) in Core MD052911 varied from 844 to 2 123 ng/g with an average value of 1 598 ng/g, and the sum in Core ORI-860-22 ranged from 988 to 1 937 ng/g with an average value of 1 343 ng/g (Fig. 3). For Core MD052911, depth profiles of phytoplankton biomarkers showed similar trends (Fig. 3a), with minimum values at around 550 cm depth. After that, the biomarker contents generally increased toward the coretop. In Core ORI-860-22, brassicasterol and dinosterol contents also varied similarly, with generally higher values from 234 to 175 cm and lower values from 175 cm to the coretop (Fig. 3b). Alkenone content showed a different trend, with two higher value intervals at 234–200 cm and around 125 cm (Fig. 3b).

The ${\text{U}}_{37}^{{\text{K′}}}$ index is used to estimate sea surface temperatures (SST) by using Müller′s global equation (Eqs (1) and (2)) (Müller et al., 1998). ${\text{U}}_{37}^{{\text{K′}}}$-SST varied from 20.5°C to 25°C for Core MD052911 with an average of 23.8°C, while the range of SST variations in Core ORI-860-22 was 20.8°C to 23.7°C with an average of 22.8°C.

PMI is a diagnostic biomarker of MOA, and it was detected in only one sample (172 cm depth) in Core ORI-860-22 (Fig. 2b). The TOC normalized PMI content is 308 ng/g and the δ¹³C value (–123‰) is much more negative compared with those of n-alkanes (–32.8‰ to –22.3‰) in the sample. However, another MOA biomarker (crocetane) was below the detection limit in this sample.

Diagnostic biomarkers of MOA in the neutral fractions were also only detected in one sample of ORI-860-22 (Fig. 4b). At 172 cm depth, the TOC normalized content was 8 491 ng/g for archaeol and 182 ng/g for sn-2-hydroxyarchaeol, respectively. In the same sample, TOC normalized content of dinosterol reached a maximum (1 220 ng/g), whereas brassicaterol content was lower (150 ng/g), and alkenones were below the detection limit.

GDGT-0 to GDGT-3, Crenarchaeol and its regio-isomer were detected in all samples (Fig. 5). The TOC normalized content of total iso-GDGTs (GDGT-0 to 3, Crenarchaeol and Cre_isomer) was also plotted in Fig. 3, which revealed a broadly similar trend with those of the phytoplankton biomarkers in Core MD052911 (Fig. 3a), but a noticeably different trend from those of the phytoplankton biomarkers in Core ORI-860-22 (Fig. 3b). The TOC normalized iso-GDGTs content in Core MD052911 varied from 1 296 to 5 463 ng/g with an average of 3 527 ng/g. In Core ORI-860-22, there was an abnormal higher value (33 319 ng/g) for iso-GDGTs at 172 cm depth, but the iso-GDGTs content for the other samples varied in the range of 1 843–4 254 ng/g with an average of 2 881 ng/g.

TEX₈₆ temperatures were calculated using the global core-top equation (Eq. (3)) (Schouten et al., 2002; Kim et al., 2008). TEX₈₆ temperatures varied from 19.7°C to 29.3°C with an average of 23.4°C in Core MD052911, and from 10°C to 22.6°C with an average of 20.4°C (except the abnormal sample at 172 cm depth) in Core ORI-860-22.

Lipids in our records are dominated by a mixture of biomarkers, including n-alkanes with an odd-over-even predominance derived from terrestrial higher plants and brassicasterol, dinosterol and alkenones derived from marine phytoplankton (diatoms, dinoflagellates and haptophytes) (Boon et al., 1979; Marlowe et al., 1984; Volkman et al., 1998; Zhao et al., 2003). Recently, biomarker ratios have been increasingly used as proxies to distinguish sedimentary organic matter sources and to estimate the relative contributions of terrestrial and marine organic matter (referred as TOM and MOM, respectively). P_mar-aq, [C₂₃+C₂₅]/[C₂₃+C₂₅+C₂₉+C₃₁], was designed to be applied for coastal sedimentary organic matter source assessments, with low values (0.01–0.25) indicative of OM dominantly from terrestrial inputs, medium values (0.4–0.6) from emergent aquatic plants including mangroves and high values (>0.6) from aquatic plants and marine macrophytes (Sikes et al., 2009). The TMBR (terrestrial and marine biomarker ratio, Eq. (4)) index is based on the ratio between n-alkanes (C₂₇+C₂₉+C₃₁) and phytoplankton biomarkers (brassicasterol, dinosterol and C₃₇ alkenones, referred as B+D+A), and used to estimate the relative TOM contribution (Xing et al., 2014). We use 1/P_mar-aq to compare with TMBR and focus on estimating terrestrial OM inputs, with higher value (>4) indicating TOM domination and lower value (<1.7) indicating dominant contribution from aquatic and marine sources (Xing et al., 2011). One advantage of using these ratio-based proxies is that even though biomarker contents were significantly affected by biodegradation, sedimentary biomarker ratios were not significantly affected (Versteegh and Zonneveld, 2002).

Using n-alkanes and ∑MB as proxies for TOM and MOM contents, then lower average n-alkanes content in Core MD052911 (971 ng/g) than that in Core ORI-860-22 (1 674 ng/g) suggested lower TOM inputs in Core MD052911 (Figs 3a and b); and average ∑MB values in the two cores suggested slightly higher MOM inputs in MD052911 than that in Core ORI-860-22 (Figs 3a and b). Qualitatively, biomarker contents suggested that marine inputs were the dominant sources for organic matter in MD052911 sediments, while both terrestrial and marine inputs contributed to the total organic matter in ORI-860-22 sediments. The TMBR and 1/P_mar-aq indexes both revealed lower average values in Core MD052911 (0.38 and 2.6, respectively) relative to those in Core ORI-860-22 (0.56 and 5.05, respectively), which quantitatively indicated that MOM dominated OM sources in Core MD052911 and Core ORI-860-22 OM was mainly derived from terrestrial inputs. Both cores were retrieved from the Kaoping Slope off SW Taiwan with a high flux of terrigenous suspended sediments exported from the adjacent rivers with particle load of 104 Mt/a for today (Dadson et al., 2003), contributing the relatively high TOM contribution in both cores. Core ORI-860-22 is located south of Core MD052911 with a deeper water depth, it is inferred that different morphologies were the likely reason for the TOM percentage, with Core MD052911 locating at anticlinal strata and resulting in lower TOM percentage.

GDGTs results provide additional evidence for estimates of TOM and MOM contributions in these cores. iso-GDGTs biosynthesized by marine Crenarchaeota and a large number of membrane-metabolizing archaea are ubiquitous in marine environments (Sinninghe Damsté et al., 2002a, b; Schouten et al., 2013), and can be used as MOM indicators. TOC normalized content of iso-GDGTs in Core MD052911 is obviously higher than that in Core ORI-860-22 (excluding the abnormally high point), which is consistent with the higher MOM contributions in Core MD052911 estimated by ∑MB. Similar variations between iso-GDGTs and marine biomarkers (sterols and alkenones) in Core MD052911 suggested that these ether-bound lipids are derived from the archaea utilizing a carbon source unaffected by the oxidation of methane (Schouten et al., 2013). Whereas, different trends between iso-GDGTs and marine biomarkers in Core ORI-860-22 would indicate other sources for iso-GDGTs, such as from methane oxidation microbes (Blumenberg et al., 2004; Weijers et al., 2011; Zhang et al., 2011; Schouten et al., 2013). Quantitative estimates of TOM contribution can also be made using the BIT index, based on the ratio of Crenarchaeol and branched-GDGT compounds (referred as GDGT-I/-II/-III in Fig. 5c) (Eq. (5)) (Hopmans et al., 2004). Samples from open ocean settings contain very low amounts of brGDGTs, which resulted in low BIT values ranging from 0.01 to 0.08; and maximum BIT values of 0.98-1 are found for the samples of terrestrial origin, i.e., no aquatic input (Hopmans et al., 2004). BIT values in Core MD052911 samples ranged from 0.03 to 0.06 (0.04 on average), while BIT values ranged from 0.1 to 0.18 (0.14 on average) in Core ORI-860-22 samples. Lower TOM contribution in Core MD052911 indicated by the BIT index is consistent with TMBR estimates; but for quantitative estimates, BIT values indicated relatively low TOM percentage in comparison with TMBR estimates in both cores, probably due to the underestimate of TOM contribution by BIT index (Weijers et al., 2009). One reason for the difference is that BIT index only reflects soil OM input but TMBR represents both soil and plant OM input (Xing et al., 2014), because brGDGTs are only derived from anaerobic soil bacteria and transported to the coastal regions mainly by fluvial processes, while n-alkanes can be delivered by both rivers and aeolian dusts (Weijers et al., 2009; Xing et al., 2011, 2014; Smith et al., 2012; Schouten et al., 2013). Thus, the BIT results would suggest that river-transported terrestrial soil OM was not a major source of TOM of sedimentary OM around these settings, but a slightly higher contribution in Core ORI-860-22 than that in Core MD052911. On the other hand, degradation of brGDGTs during transport processes could also result in an underestimate of TOM contribution using the BIT index. The distributions of brGDGTs in sediments from the lower Changjiang River and East China Sea shelf indicated that ca. 95% of brGDGTs were degraded in the estuary region and the BIT proxy do not reflect the catchment environmental conditions (Zhu et al., 2011). Xing et al. (2014) also found that brGDGTs were deposited near estuaries and coasts had a lower content offshore, while a significant portion of n-alkanes could be deposited further offshore.

In Core ORI-860-22, three diagnostic MOA biomarkers (PMI, archaeol and hydroxyarchaeol) were present at relatively high concentrations for the sample at 172 cm, but these biomarkers were not detected in other analyzed samples. In addition, the δ¹³C value of PMI (–123‰) was much more negative compared with the δ¹³C values of n-alkanes (–22.3‰ to –32.8‰) from marine algae and terrestrial higher plants in the same sample. The co-occurrence of PMI, archaeol and hydroxyarchaeol with more depleted δ¹³C values is typical for many AOM settings (Elvert et al., 1999; Pancost et al., 2001; Pancost and Damste, 2003; Elvert et al., 2005; Pape et al., 2005; Guan et al., 2013). Therefore, our results could indicate significant AOM activities at or around 172 cm depth of Core ORI-860-22. However in Core MD052911, archaeal biomarkers were not detected in any of the analyzed sediment samples, suggesting absence of AOM activities. Previous studies of four sediment samples from the Dongsha Islands area of the South China Sea revealed that archaeal biomarkers PMI and crocetane only occurred below the sulfate-methane transition zone (SMTZ) with maximum concentrations close to the SMTZ (Yu et al., 2008). On the Northern European continental margin, archaeol concentration typically reached maximum values close to the SMTZ with decreasing concentration below the SMTZ at all studied sites, but was below detection limit in shallower sediments at most sites (Aquilina et al., 2010); hydroxyarchaeal and PMI were only present at and below the SMTZ at most sites (Aquilina et al., 2010). However, crocetane, another MOA biomarker, was not detected in any sediment samples in their study of brackish-marine sediments offshore Northern Europe (Aquilina et al., 2010). It has been suggested that the concentration of specific MOA biomarkers is influenced by the amount of upward transport of methane (Lee et al., 2013), our results thus indicated that the upward methane seepage was not continuous or the flux of methane was not sufficient to induce high AOM activities in our locations, resulting in below-detection concentrations of MOA biomarkers in Core MD052911 samples and most samples of Core ORI-860-22. On the other hand, the presence of high concentrations of allochthonous OM could obscure the detection of MOA biomarkers (Aquilina et al., 2010). TOM was an important component of sedimentary OM in both cores, but MOA biomarkers were detected in Core ORI-860-22 even though its TOM percentage was higher. As a result, TOM effect was not likely the main reason for the absence of MOA biomarkers in these sediment samples.

GDGTs based proxies can also be used to evaluate the influences of AOM on our samples. Previous research has indicated that AOM in a variety of settings can influence sedimentary iso-GDGT distributions, by producing a higher proportion of GDGT-1 and GDGT-2 (Weijers et al., 2011; Zhang et al., 2011). The different distributions of iso-GDGTs in Cores MD052911 and ORI-860-22 (Fig. 3) could indicate the AOM effects, specifically at 172 cm depth in Core ORI-860-22 where iso-GDGTs contents reached abnormal maximum value and dominated by GDGT-1 and GDGT-2 (Figs 3b and 5b). TEX₈₆ temperature differences between the cores also likely reflected the effects of AOM. TEX₈₆ temperatures calculated using the global core-top equation in Core ORI-860-22 (10–22.6°C, average 20.4°C) were significantly lower than those in Core MD052911 (19.7–29.3°C, average 23.4°C); in addition, TEX₈₆ temperatures were lower than the ${\text{U}}_{37}^{{\text{K′}}}$ temperatures (20.8–23.7°C, average 22.8°C) in Core ORI-860-22, while TEX₈₆ temperatures were similar to ${\text{U}}_{37}^{{\text{K′}}}$ temperatures (20.5–25°C, average 23.8°C) in Core MD052911. One possible reason for lower TEX₈₆ temperatures is the influence of terrestrial iso-GDGTs, which can be evaluated by comparing the BIT index. As discussed above, BIT values in all samples were below 0.2, indicating that river input of terrestrial iso-GDGTs could not bias the TEX₈₆ proxy (Weijers et al., 2006) in our samples. The more likely reason is the additional contributions to sedimentary iso-GDGTs from AOM activities, as previous studies have suggested that at high AOM activity sites, the application of TEX₈₆ was restricted due to the additional methanogens contribution to iso-GDGTs, and iso-GDGTs distribution pattern could no longer be explained by temperature-induced physiological responses (Schouten et al., 2002; Zhang et al., 2011). Thus, the different patterns of TEX₈₆ and ${\text{U}}_{37}^{{\text{K′}}}$ temperatures in these two sediment cores could indicate that AOM affected TEX₈₆ temperature estimates in Core ORI-860-22, but not significantly on TEX₈₆ temperature estimates in Core MD052911. This result is consistent with a previous study using samples from the hydrate-rich region in the Qiongdongnan Basin of the northern South China Sea, which also concluded that the lipids in the sediments were predominantly from planktonic archaea and the TEX₈₆-derived temperatures were unlikely to be biased by AOM activity due to the lower abundances of iso-GDGTs from methane-metabolizing archaea (Wei et al., 2014).

The Methane Index (MI), consisting of the relative distribution of iso-GDGTs (Eq. (6)), has been proposed to distinguish gas hydrate impacted and/or methane-rich deep sea environments from normal marine realm (Zhang et al., 2011). MI values range from 0 to 1, and 0.3 is proposed to mark the boundary between hydrate-impacted sediments and normal marine sediments. Higher MI values could indicate strong impact of AOM microbial communities, whereas low values characterize normal marine sedimentary conditions (Zhang et al., 2011).

There were clear differences in MI values between normal sediments and the sediment containing MOA biomarkers in our samples (Fig. 3). In Core ORI-860-22, the maximum MI value of 0.94 at 172 cm depth, dominated by GDGT-1 and GDGT-2. According to previous research, MI values close to 1 clearly showed significant contributions of methanotrophic archaeal community to sediment iso-GDGTs (Zhang et al., 2011), thus maximum MI value at 172 cm depth would indicate high AOM activities and be consistent with the detection of the three diagnostic MOA biomarkers (PMI, archaeol and hydroxyarchaeol). MI values for the other samples in Core ORI-860-22 ranged from 0.16 to 0.32 with most values lower than or around 0.3, indicating insignificant contribution of MOA iso-GDGTs and consistent with the absence of MOA lipid biomarkers in these samples. In Core MD052911, MI values ranged between 0.19 and 0.38, indicating insignificant contributions of iso-GDGTs from methanotrophic archaea, which is also consistent with MOA lipid biomarker results. However, the average MI value in Core MD052911 (0.25) is slightly higher than that in Core ORI-860-22 (0.19, excluding the value for 172 cm sample), and this is likely related to the temperature effect on MI. A previous study using 426 surface sediment samples indicated that MI values increase with increasing TEX₈₆ SSTs (Zhang et al., 2011), and this is likely caused by the slight SST dependence of MI values in planktonic Crenarchaeota, without involving methanotrophic Euryarchaeota (Zhang et al., 2011). Thus, in our results, both MI values and TEX₈₆ SSTs were higher in Core MD052911 than those in Core ORI-860-22.