Millennial-scale changes in the molecular composition of Posidonia australis seagrass deposits: Implications for Blue Carbon sequestration

Accepted Manuscript Millennial-scale changes in the molecular composition of Posidonia australis seagrass deposits: Implications for Blue Carbon sequestration Joeri Kaal, Oscar Serrano, Antonio Martínez Cortizas, Jeffrey A. Baldock, Paul S. Lavery PII: DOI: Reference:

S0146-6380(19)30128-7 https://doi.org/10.1016/j.orggeochem.2019.07.007 OG 3898

To appear in:

Organic Geochemistry

Received Date: Revised Date: Accepted Date:

25 April 2019 18 June 2019 14 July 2019

Please cite this article as: Kaal, J., Serrano, O., Martínez Cortizas, A., Baldock, J.A., Lavery, P.S., Millennial-scale changes in the molecular composition of Posidonia australis seagrass deposits: Implications for Blue Carbon sequestration, Organic Geochemistry (2019), doi: https://doi.org/10.1016/j.orggeochem.2019.07.007

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Millennial-scale changes in the molecular composition of Posidonia australis seagrass deposits: Implications for Blue Carbon sequestration

Joeri Kaala,b*, Oscar Serranoc, Antonio Martínez Cortizasa,d, Jeffrey A. Baldocke, Paul S. Laveryc

a

Ciencia do Sistema Terra, Departamento de Edafoloxía e Química Agrícola,

Universidade de Santiago de Compostela, Campus Sur s/n, 15782 Santiago de Compostela, Spain b

Pyrolyscience, 28015 Madrid, Madrid, Spain

c

School of Science, Centre for Marine Ecosystems Research, Edith Cowan University,

270 Joondalup Drive, Joondalup, WA 6027, Australia d Centro

de Estudios Avanzados de Blanes, Consejo Superior de Investigaciones

Cientificas, Blanes 17300, Spain e

CSIRO Agriculture and Food, Locked Bag 2, Glen Osmond, SA 5064, Australia

* Corresponding author: J. Kaal ([email protected]).

ABSTRACT Seagrass ecosystems are recognised for their role in climate change mitigation, due to their capacity to form organic-rich sediments. The chemical recalcitrance of seagrass organs is one characteristic driving carbon storage, but the molecular background of this feature is poorly understood. We assessed molecular composition changes of Posidonia australis sheaths (SH) and roots plus rhizomes (RR) along a sediment core, encompassing 3200 cal. yr BP, by means of nuclear magnetic resonance spectroscopy (13C NMR), conventional analytical pyrolysis (Py-GC–MS) and thermally assisted hydrolysis and methylation (THM-GC–MS). Significant trends with depth (age) in the composition of both SH and RR remains of P. australis were observed from all methods. In general terms, polysaccharides become depleted (degraded) and lignin enriched (selectively preserved) as age increases, and the minor constituents cutin, suberin and condensed tannin are also preferentially depleted during ageing in both fractions. Molecular changes with ageing were smaller in SH, especially regarding polysaccharides, indicative of a superior stability compared to RR. The molecular changes observed are most pronounced within the first 75 cm of the record, which reflects the recalcitrance of P. australis detritus once it is buried below that depth (corresponding to approximately 700 cal. yr BP). The capacity of P. australis to act as a long-term carbon sink seems to be mainly related to the resistance of buried lignocellulose materials to decomposition. The results on diagenetic effects on the molecular fingerprint of seagrass detritus contribute to our understanding of carbon sequestration in Blue Carbon ecosystems. Furthermore, data comparison of the methods applied using principal component analysis (PCA) allowed us to identify consistencies, discrepancies and complementarities.

Keywords: biogeochemical cycles, climate change, nuclear magnetic resonance spectroscopy, analytical pyrolysis, principal component analysis, coastal vegetated ecosystems, organic matter, degradation/preservation.

1. Introduction There is increasing interest in the assessment of organic carbon (C) dynamics in coastal vegetated ecosystems (i.e. mangrove, tidal marsh and seagrass), also known as Blue Carbon ecosystems, mainly due to their diverse ecosystem functions and important role in global C cycling (Duarte et al., 2013). Knowledge on the origin of C in Blue Carbon ecosystems is important to decipher biogeochemical cycles and underpin management and policy development (Mcleod et al., 2011). The C storage in seagrass sediments is influenced by interactions of biological (e.g., meadow productivity, cover and density), chemical (e.g., recalcitrance) and physical (e.g., hydrodynamic energy and accumulation rates) factors within the meadow (Serrano et al., 2016; Mazarrasa et al., 2018; Miyajima and Hamaguchi, 2019). Seagrass ecosystems can have an outstanding C sequestration capacity, estimated at 4.2–8.4 Pg C globally (Fourqurean et al., 2012). Among seagrasses, Posidonia spp. are unusual in their ability to capture carbon. Its growth dynamics and recalcitrant tissues form a highly organic, terraced structure known as mat, consisting of intertwined roots, rhizomes and sheaths trapped in the inorganic sediment that can extend many meters down into the sediment (sometimes referred to as soil) and persist for millennia (Pérès and Picard, 1964; Mateo et al., 1997; Lo Iacono et al., 2008). The C storage capacity of Posidonia spp. (30–410 kg C m-2; Mateo et al., 1997; Serrano et al., 2016) is large compared with that of terrestrial ecosystems (e.g., boreal, temperate and tropical forests), but similar to the C stocks of mangroves and tidal marsh ecosystems (Fourqurean et al., 2012). Most studies that have determined seagrass organic matter (OM) quality (composition) have focused on elemental parameters such as O/C, H/C or N/C ratios, and stable isotope parameters such as δ15N and δ13C. Studies of seagrass OM

composition at the molecular level, and especially of the macromolecular OM, are rare, even though some have highlighted their importance for long-term C storage (Torbatinejad et al., 2007; Trevathan-Tackett et al., 2015; Kaal et al., 2016, 2018a). The organs of Posidonia are composed predominantly of lignin and polysaccharides (e.g., Klap et al., 2000; Kaal et al., 2016). A recent study showed that there are major differences in lignin composition among Posidonia species (Kaal et al., 2018a), which adapted to life in the sea about 100 million years ago. Posidonia oceanica, an endemic species in the Mediterranean Sea, has the highest amount known in the plant kingdom of p-hydroxybenzoic acid (p-HBA) esterified to the lignin backbone, whereas P. australis, an endemic species in Australia, has a more typical herbaceous lignin consisting of predominantly guaiacyl and syringyl moieties (Kaal et al., 2018a). P. sinuosa, which is also endemic to Australia, has an intermediate lignin composition (slightly elevated p-HBA, but not as much as in P. oceanica), suggesting that differences in C storage capacity among Posidonia species (Mateo et al., 1997; Lavery et al., 2013) could be related to differences in their organic chemistry (Kaal et al., 2018b). Methods available for molecular characterization of OM in seagrass sediments – excluding those targeting only a specific fraction of the OM such as techniques based on chemical oxidation (CuO for lignin analyses, RuO4), amino acid or carbohydrate analyses (Geraldi et al., 2019), include infrared spectroscopy (FTIR, MIR, NIRS), nuclear magnetic resonance (NMR, especially solid-state 13C NMR) and techniques that use online pyrolysis (Py-GC–MS, THM-GC–MS, Py-FIMS). Their use in coastal vegetated Blue Carbon ecosystems is limited (for examples see Kaal, 2019) and thus far there has been no attempt to make a systematic (statistical) comparison among methods, identifying advantages, disadvantages, redundancies and complementarities.

The southern coast of Western Australia hosts extensive seagrass meadows, including thick P. australis mat deposits (Serrano et al., 2016). In this study, we analysed seagrass organs (sheaths and roots plus rhizomes), along a 1.5 m long P. australis core spanning the last 3200 cal. yr BP, by elemental and isotopic C analysis, 13C

NMR, Py-GC–MS and THM-GC–MS. The results are interpreted with the aid of

principal component analysis (PCA), and with two main objectives: (1) unravelling the diagenetic changes in seagrass OM composition during ageing and its implications for C sequestration, and (2) multi-methodological comparison to make recommendations on ideal combinations for the assessment of seagrass molecular composition.

2. Material and methods 2.1. Study site and sampling Sampling was conducted at Waychinicup Inlet (34°53' S, 118°19' E) in Western Australia (34° 54' S 118° 19' E; Fig. 1). Steep rocky shorelines are present along most of its 1300 m length, connecting with the sea through a 190 m wide opening. Rocky headlands maintain the opening and it is relatively exposed to strong oceanic swells and tides. The estuary is mostly marine but collects freshwater and land-derived organic-rich siliceous sediments from the Waychinicup River. The river carries nutrients and silt from a largely undisturbed catchment of 145 km2 (Hodgkin and Clark, 1990). Seagrass meadows are widespread throughout the Waychinicup Inlet although their distribution is generally limited to areas less than 400 m from the river mouth (Phillips and Lavery, 1997). Five species of seagrasses have been recorded in the estuary, with P. australis meadows being the largest and most abundant. A core was sampled in a 2 m deep dense and monospecific P. australis meadow at Waychinicup Inlet using manual percussion and rotation. The corer consisted in a 2 m

long PVC pipe with 75 mm inner diameter. The length of the corer and the retrieved core were used to correct the core lengths for compression effects (in this case, 30%) and all variables studied here are referenced to the uncompressed depths. The core was sealed at both ends, transported vertically and stored at 4 °C before processing in the laboratory.

2.2. Non-molecular analyses The core was segmented into 5 cm thick slices, which were oven-dried at 60 °C until constant weight to determine dry bulk density (g dw cm-3). Then, 14 selected samples along the 1.4 m core (focussing on relatively recent materials) were divided into two subsamples by quartering. Bulk sediment subsamples were milled and analysed to determine organic carbon content (C) and stable carbon isotope composition of the organic matter (δ13C). For C and δ13C analyses in bulk sediment, 1 g of ground sample was acidified with 4% HCl to remove inorganic carbon, centrifuged (3400 rpm for 5 min), and the supernatant with acid residues was removed by pipette avoiding resuspension. The pellet was then washed with ultrapure water, centrifuged and the supernatant removed. The residual samples were dried again and then encapsulated for C analysis using a Micro Cube elemental analyzer (Elementar Analysensysteme GmbH, Hanau, Germany) interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK) at the University of California Davis Facilities. The content of C was calculated for the bulk (pre-acidified) samples. Organic C isotope ratios (δ13C) are expressed as δ values in parts per thousand (‰) relative to the Vienna Pee Dee Belemnite standard. The other subsamples were re-suspended in water and wet-sieved (1 mm mesh). From the coarse fraction (> 1 mm), the P. australis sheath (SH) and root/rhizome (RR)

debris were sorted, rinsed in distilled water to remove attached particles, dried again and milled for subsequent C elemental and isotopic analyses, and molecular analyses (see below). For the analyses of elemental and isotopic C and N composition in SH and RR samples, a bulk non-acidified subsample was encapsulated and analyzed following the procedures described above. The low nitrogen content of the SH and RR samples in combination with the low sample availability implied that the results of elemental and isotopic analyses of N were unreliable (not presented). For the samples of SH and RR at 36 cm depth (decompressed), insufficient material was available for 13C NMR and isotope analyses. Four subsamples of P. australis debris (SH) were radiocarbon dated at AMS Direct and Beta Analytic laboratories following standard procedures (ISO 17025 and ISO 9001). An age-depth model was built using the Bayesian modelling approach Bacon 2.2 (Blaauw and Christen, 2011). Dates ages were calibrated using the marine 13.14C calibration curve (Reimer et al., 2013) and a local marine reservoir correction was applied (ΔR = 91 ± 35 years; Bowman, 1985). The age of the sediment-water interface was included (i.e. year of collection) with an error of ±5 years. Carbon stocks per unit area were calculated by multiplying dry bulk density (g dw cm-3) by the C concentrations in bulk samples, and then integrating to a 1 m thick deposit. The long-term C accumulation rates (g m-2 yr-1) were calculated by dividing the inventories in the sediment by the ages encompassed within the 1 m thick mat deposit. The Sequential Regime Shift Detection software (significance level = 0.1; Rodionov, 2004; Andersen et al., 2009) was used to detect discontinuities along the core depth of the properties measured, signalling the presence of ecological thresholds and in this case mostly ageing-induced shifts in molecular composition over the reconstructed period.

2.3. Molecular analyses 2.3.1. Solid state 13C NMR spectroscopy Solid state 13C NMR spectra were acquired for the prepared SH and RR samples using a Bruker 200 Avance spectrometer (Bruker Corporation, Billerica, MA, USA) equipped with a 4.7 T wide-bore superconducting magnet operating at a resonance frequency of 50.33 MHz. Weighed samples (30–300 mg) were packed into 7 mm diameter zirconia rotors with Kel-F end caps. For the smaller samples where the rotor could not be filled, Kel-F inserts were used to place the sample in the middle of the rotor. All samples were spun at 5 kHz. Chemical shift values were calibrated to the methyl resonance of hexamethylbenzene at 17.36 ppm, and a Lorentzian line broadening of 50 Hz was applied to all spectra. A cross polarisation (CP) and an inversion recovery (IR) 13C NMR experiment were performed on all samples. For the CP analysis a pulse of 3.2 ms, 195 W, and 90° pulse, a contact time of 1 ms, and a recycle delay of 1s was applied to all samples. The number of scans acquired for each sample varied between 5000 and 20,000 and increased as the amount of sample placed into the rotor decreased. The adequacy of the 1 s delay was confirmed by conducting an IR experiment for each sample using an array of inversion recovery times (0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4 s) and a recycle delay of 5 s. Values of T1H calculated from the IR experiment were all 5–10 times shorter than the 1 s recycle delay employed in the CP analysis indicating that saturation did not affect the acquired CP signal intensity. The Bruker TopSpin 3.5pl7 software was used to complete all signal processing. Signal intensities were corrected by subtraction of the background signal derived from an empty rotor. The spectra were integrated over the chemical shift limit ranges defined

by Baldock et al. (2013) and signal intensity derived from spinning side bands was allocated back to their parent signals according to Baldock and Smernik (2002).

2.3.2 Analytical pyrolysis For Py-GC–MS and THM-GC–MS, powdered samples were introduced into quartz tubes and embedded with deactivated quartz wool. The samples were analysed by a combination of a CDS Pyroprobe 5000 (pyrolysis), an Agilent 6890 (gas chromatography) and an Agilent 5975B (mass spectrometry). The pyrolysis interface was set at 325 °C (isothermal) and pyrolysis was performed for 20 s at 650 °C setpoint temperature (heating rate 10 °C ms-1). The pyrolysis unit was connected to the inlet of the GC, which was also isothermal at 325 °C, through a needle adapter. The GC was equipped with a HP-5MS non-polar column (length 30 m; internal diameter 0.25 mm; film thickness 0.25 µm). The oven temperature program was from 50 to 325 °C at 20 °C min-1. The transfer line between the GC and the MS was held at 325 °C. The MS was operated in electron ionization (EI) mode at 70 eV, with the ion source at 230 °C and the quadrupole detector at 150 °C and measuring fragments in the m/z 50–500 range. The same analytical conditions were used for Py-GC–MS and THM-GC–MS, with two exceptions: (1) for THM-GC–MS, prior to the analysis, a droplet of 25% (aq) tetramethyl ammonium hydroxide (TMAH, Sigma-Aldrich) was added to the samplecontaining quartz tubes and allowed to impregnate for one hour (at room temperature), and (2) the analysis started with a solvent delay period of 5 min and with the GC being held isothermal at 70 °C during that time. Relative proportions of Py-GC–MS and THM-GC–MS products were calculated as the percentage of the peak area of characteristic mass fragments (m/z) as the sum of peak areas of all compounds (% of total quantified peak area, TQPA).

2.4. Statistical analyses For data evaluation of the molecular approaches, the datasets from 13C NMR, Py-GC–MS and THM-GC–MS were evaluated statistically using principal component analysis (PCA), in correlation matrix mode and applying a non-rotated solution. The resultant PCs (PC1–PC2 of each dataset) were compared by means of linear correlation coefficients. For Py-GC–MS and THM-GC–MS, the relative proportions data of all individual products were used as input variables. For 13C NMR, we used the percentage of integrated areas (main chemical shift regions) as input. An alternative approach using the original data with all the chemical shift data points was also done: the results for PC1NMR (r2 = 0.90, P < 0.001) and PC2NMR (r2 = 0.80, P < 0.001) were very similar between the two NMR data evaluation approaches and therefore only the PCA based on the integrated regions is discussed. The PCs of the three methods were also compared with %C and δ13C of the corresponding SH and RR fractions. For the large datasets of Py-GC–MS and THM-GC–MS, relative proportions of the products from the main biomolecular constituents (lignin, polysaccharides, aliphatic biopolymers) were calculated to emphasise composition changes along the core of these types of OM.

3. Results and discussion 3.1. Biogeochemical characteristics of bulk sediment and P. australis organs The long-term accumulation rate based on the Bacon age-depth model (Supplementary Fig. S1) was estimated at 0.061 ± 0.028 cm yr-1 (mean ± SD). The core covered the last 3200 cal. yr BP (Table 1). The sediment beneath the P. australis meadow at Waychinicup Inlet had a dry bulk density of 1.2 ± 0.3 g cm-3, with 1.1 ±

0.5% organic C (Table 1). The organic C stock in the upper meter was estimated as 10.4 kg C m-2 and the C long-term accumulation rate (over the last 3200 cal. yr BP) was estimated as 5.3 g organic C m-2 yr-1. The δ13C signatures of the bulk OM averaged – 17.0 ± 2.2‰ (Table 1). The characteristics of the P. australis mat studied are similar to those found elsewhere in south-west Australia, confirming that Posidonia meadows rank amongst the seagrass ecosystems with the highest C storage potential (Serrano et al., 2016). The results of elemental analysis (%C) and stable isotope composition (δ13C) of the SH and RR fractions are shown in Fig. 2. For both fractions, C content ranged between 35 and 65% of dry weight (46.4 ± 6.3%). In the RR fraction, a decreasing trend in C content can be observed within the first 50 cm. The δ13C of most SH and RR samples was close to –10‰ (–10.0 ± 1.4‰), with the exception of the RR sample at 42 cm, which was close to –15‰. In general, δ13C is slightly more negative in the RR than in the SH fraction but remained fairly constant with depth in both fractions. This implies that the effects of ageing do not cause significant 13C-depletion, unlike previous observations that showed selective preservation of 13C-depleted organic compounds (e.g., Lehmann et al., 2002). The relatively stable δ13C values of SH and RR over 3200 years after burial can indicate the general stability and thus C storage capacity of Posidonia detritus (Fourqurean et al. 2012). However, significant changes in the molecular composition of SH and RR debris along the core were observed with NMR and pyrolysis (see below), suggesting that δ13C has limited capability to decipher shifts in OM composition during diagenesis of Posidonia remains.

3.2. 13C NMR spectroscopy

The normalised areas of the main chemical shift regions of the 13C NMR signal show that polysaccharides constitute the main type of organic matter in both SH and RR seagrass organs, with a sum of O-alkyl C and di-O-alkyl C (which are strongly correlated, r2 = 0.73, P < 0.001) that exceeded 40% of the normalised areas in all samples (Fig. 3a). The signals of aryl C and O-aryl C were strongly correlated (r2 = 0.78, P < 0.001), suggesting that both correspond to resonances of lignin. The correlation between N-alkyl C/methoxyl C signal and aryl C (r2 = 0.64, P < 0.001) supports the hypothesis that lignin is the source of these signals as well. The sum of lignin signals (aryl C, O-aryl C and methoxyl C; Fig. 3b) ranged between 30% and 50% in both SH and RR organs. From the balance between the signals from polysaccharides (Ps) and lignin (Lg) it was concluded that, throughout the core, SH had relatively high polysaccharide content (Ps/Lg = 1.9–2.5), whereas RR were enriched in lignin (Ps/Lg = 1.0–2.1). The relative content of polysaccharides showed a stronger decline with age in the RR than in the SH, and the opposite was observed for the lignin resonances (stronger increase for RR). Furthermore, the Ps/Lg ratio decreased twice as fast in RR than in SH. The relative intensity of carbonyl C (ketone, amide, carboxyl) was between 6% and 10%, and that of alkyl C between 2% and 8%. The alkyl C was more pronounced in the RR organs (Fig. 3c and 3d). The PC1 from NMR (PC1NMR), which explained 63% of total variance, had higher scores for RR than SH samples (Fig. 4a), especially in deeper layers of the deposit, and RR also showed a stronger trend with depth (r2 = 0.91, P < 0.001) than SH (r2 = 0.75, P < 0.001). The regime shifts in PC1NMR were detected at 60 cm (~750 cal. yr BP) for SH and at 100 cm (~2050 cal. yr BP) for RR. The loadings profile (Fig. 5a) indicated that PC1NMR had strong positive values (> 0.7) for ketone C and lignin (aryl C, O-aryl C and N-alkyl/methoxyl C) signals, and negative loadings (< –0.7) in the region

between 60 and 110 ppm chemical shift, i.e. the regions of O-alkyl and di-O-alkyl C from polysaccharides. This showed that the higher content and stronger increase with depth of lignin in RR is the main source of variance in the NMR dataset. We interpret this to indicate a selective depletion of polysaccharides relative to lignin during diagenesis, especially in RR. An alternative explanation would be that the lignin in RR is more labile thereby mitigating the signal of selective degradation of polysaccharides, but this interpretation is unlikely considering the evidence from Py-GC–MS (see below). For the scores of PC2NMR (Fig. 4b), which explained 22% of total variance, both SH and RR showed a decrease with depth with trends shifting in both cases at 75 cm or 1000 cal. yr BP. The positive loadings (> 0.7) of the alkyl and carboxyl/amide C regions (Fig. 5a) and the declining scores with depth could be indicative of a loss of aliphatic (e.g., fatty acids) material. In summary, PC1NMR and PC2NMR reflect diagenetic processes that cause depletion in polysaccharides (PC1NMR) and aliphatic components (PC2NMR), and retention of lignin (PC1NMR).

3.3. Pyrolysis-GC–MS The pyrolysis fingerprints were composed primarily of products of polysaccharides and lignin. The polysaccharide products accounted for 46.8 ± 9.1% of TQPA. There was a clear decreasing trend with depth in the proportion of carbohydrate compounds in both RR and SH (Fig. 6a). Levoglucosan, which is generally considered as a marker of relatively intact structural polysaccharides such as cellulose (Pouwels et al., 1989), was the most abundant polysaccharide product in most SH and RR samples, followed by 3/2-furaldehyde (Supplementary Table S1). These results are indicative of the large proportion of structural polysaccharides in both RR and SH, which were more abundant in SH than in RR along the core.

The other groups of pyrolysis products were dominated by those that can be attributed to polyphenolic materials: the sum of the methoxyphenols (guaiacols, syringols and 4-vinylphenol), was 32.7 ± 8.1% of TQPA (Fig. 6b). These compounds can be ascribed to different types of building blocks of lignin (Ralph and Hatfield, 1991) and lignin-like (cinnamyl alcohols) derivatives which occur in low molecular weight structures often bound to carbohydrate monomers, i.e. in glycosylated form. The sum of these products increased with depth and was more abundant in RR matter. The sum of the other phenolic compounds (phenol, C1- and C2-alkylphenols, catechol and methylcatechols) was 12.8 ± 2.4% (Fig. 6c) with no consistent trends with depth. These phenols are products of lignin and tannin, even though contributions from other sources such as carbohydrates and proteins cannot be ruled out (Tsuge and Matsubara, 1985). Catechols are probably products of tannin and degraded lignin. The polymethylene compounds (or methylene chain compounds, MCC) accounted for 2.3 ± 1.2% of TQPA (Fig. 6d). This group included homologous series of n-alkanes, n-alkenes, n-alkanoic acids, several fatty acid methyl esters (FAMEs) and prist-1-ene. These compounds are associated with epicuticular waxes and biopolymers cutin and suberin (see section on THM products; Nierop and Verstraten, 2004). The sum of these compounds was larger in RR than in SH and decreased due to diagenetic processes in both seagrass fractions. Minor products included toluene as the only aromatic hydrocarbon (1.3 ± 0.5%), diketodipyrrole as the only nitrogen-containing compound (0.2 ± 0.2%) and unidentified compounds (3.8 ± 1.0%) (Supplementary Table S1). The results of the PCA of the Py-GC–MS fingerprints provided a PC1PY and PC2PY that explained 31% and 27% of variance, respectively. They both exhibited clear depth trends in RR (PC1PY: r2 = 0.85, P < 0.001; PC2PY: r2 = 0.52, P < 0.005) as well as

in SH fractions (PC1PY: r2 = 0.38, P < 0.05; PC2PY: r2 = 0.50, P < 0.01), indicative of their relation to selective ageing effects (Fig. 4c and d). The positive loadings of many lignin products and negative loadings of polysaccharide products (Fig. 5b) showed that polysaccharides decreased due to ageing, whereas lignin products accumulated, in agreement with 13C NMR data. We interpret this as a dominant effect of gradual selective degradation as the materials age, which was stronger for the RR than the SH fraction. The shifts in PC1PY were detected at 60 cm (~750 cal. yr BP) for RR and at 75 cm (~1000 cal. yr BP) for SH. The dataset of the sum of carbohydrates set to 100% showed that the relative proportions of 2-methylfuran, 3/2-furaldehyde, 5-methyl-2-furaldehyde, levoglucosenone and (2H)-furan-3-one increased with depth in RR (r2 > 0.4, P < 0.05), whereas those of furan carboxylic acid, 4-hydroxy-5,6-dihydro-(2H)-pyran-2-one and levoglucosan decreased with depth in RR (r2 > 0.4, P < 0.05) (not shown). These combinations of compounds reflect the effects of ageing on polysaccharide composition, as previously described for mineral soils, peat deposits, lacustrine sediments or shipwreck wood (Schellekens et al., 2009; Kaal et al., 2015; Traoré et al., 2017). Strikingly, for the SH samples, none of the polysaccharide products had a significant trend with depth, which is a strong indication that in this fraction they were indeed more recalcitrant than in RR. The same calculations for the lignin products showed that in RR the products of p-coumaric and ferulic acid (4-vinylphenol and 4vinylguaiacol) decreased with depth whereas that of 4-methylguaiacol, 4methylsyringol and 4-ethylsyringol increased (r2 > 0.4, P < 0.05), which might indicate a selective depletion of glycosylated phenylpropanoids, perhaps due to the loss of the bound carbohydrates. Again, in SH a completely different pattern was observed, with depleted 4-propan-2-one-guaiacol and an increase with depth of the three isomers of

C3:1 (propenyl)-guaiacols, indicative of side-chain alteration of guaiacyl lignin (r2 > 0.4, P < 0.05). PC2PY had strong positive loadings for the MCC (Fig. 5b), which implies that their proportions declined with depth due to PC2-type diagenetic alterations. The shifts in PC2PY were detected at 60 cm (~750 cal. yr BP) for RR and at 75 cm (~1000 cal. yr BP) for SH. Among the lignin products, syringyl moieties tended to have positive loadings on PC2PY whereas the guaiacols had predominantly negative loadings, suggesting that this process also depleted syringyl lignin (closer inspection of the data showed that this was valid for SH materials, not for RR). The negative loadings of several phenolic products of unknown origin (alkylbenzaldehydes and tentatively identified 4-hydroxycinnamaldehyde) might suggest that the source of these compounds accumulated due to PC2PY-type changes. Finally, the MCC plotted in two sections in the PC1PY-PC2PY plane, one with very high PC2PY loadings (> 0.85) and low PC1PY (nalkanes and n-alkenes), and another with moderate negative loadings on PC1PY and moderately positive on PC2PY (saturated and unsaturated fatty acids, C16-C26 range).

3.4.THM-GC–MS Polysaccharide products accounted for only 7.2 ± 3.7% of TQPA (Fig. 7a) and their sum did not show a clear trend with depth in RR or SH. Clearly, there is a strong bias against the polysaccharides and towards the polyphenolic products in the THMGC–MS data when compared to the NMR and Py-GC–MS data. This can be explained by the low THM yield of structural polysaccharides (Estournel-Pelardy et al., 2011). The erratic depth profile of the sum of these products is also an indication of the difficulties of THM to provide meaningful information on polysaccharide abundance in seagrass organs.

The THM fingerprints were strongly dominated by the products of lignin and cinnamic acids (mostly derivatized ferulic acid, i.e. G18), combined accounting for 69.0 ± 9.9% of TQPA (Fig. 7b). These compounds have monomethoxy- (p-hydroxyphenyl, H), dimethoxy- (guaiacyl, G) and trimethoxybenzene (syringyl, S) groups. With the available data, we could not distinguish methoxyl moieties with a native methyl group from methoxyl groups that formed upon methylation of hydroxyl groups during the THM reaction (Filley et al., 1999). This implies that the lignin products without a side chain group typical of lignin, such as 3,4-dimethoxybenzoic acid methyl ester (G4) or 3,4,5-trimethoxybenzoic acid methyl ester (S6), may also originate from catechol or pyrogallol groups in tannins (Filley et al., 1999). However, considering the dominance of methoxyphenols over catechols (and absence of pyrogallols) from Py-GC–MS, and the link between methoxyl C and aryl C from NMR, the contribution of tannin to the THM compounds with two or three adjacent methoxyl groups was assumed to be small. More unambiguous THM products of tannin were the 1,3,5-trimethoxybenzenes, which originate from condensed tannin A-rings (Nierop et al., 2005). These compounds accounted for 3.6 ± 2.3% of TQPA (Fig. 7c). The presence of the highest proportion of tannin products in the surface of the RR sequence (0–15 cm; 6–15%) suggested that tannin was more abundant in the RR (also observed for P. oceanica; Kaal et al., 2016), but only in relatively fresh tissues. It can be concluded that condensed tannin was not a significant pool of recalcitrant OM in deep seagrass sediment layers. The MCC compounds accounted for 10.8 ± 6.9% (Fig. 7d). As usual, these compounds were strongly dominated by different types of FAMEs. The FAMEs were subdivided into: (1) markers of cutin (an aliphatic biopolymer present in leaf cuticles) such as 9,16- and 10,16-dimethoxy C16 FAME and 9,10,18-trimethoxy C18 FAME (Kolattukudy, 2001; Nierop and Verstraten, 2004), (2) long-chain FAMEs and ω-

methoxy FAMEs which probably originated from suberin (Del Río and Hatcher, 1998), and (3) short-chain FAMEs (C12–C18) which may originate from multiple precursors including free fatty acids. Interestingly, the cutin products were most abundant in the SH samples from the surface of the core (Supplementary Table S1), whereas deeper sections of SH materials were depleted in cutin products. For the RR materials, the proportions of these compounds were negligible at the surface (< 0.1%) and increased with depth. The likely products of suberin were more abundant in the surface of both RR and SH sequences. They were higher in the RR fraction, although the difference was smaller than expected (suberin is abundant in root and perhaps rhizome, but not in foliar tissues such as sheaths). The role of these aliphatic products in C storage of the P. australis detritus is limited. Other THM products groups were benzene carboxylic acids (3.4 ± 0.8%), nitrogen-containing compounds (i.e. a derivatized alkylamide and an unidentified compound; 2.7 ± 1.9%) and unidentified compounds (3.2 ± 1.6%) (Supplementary Table S1). The PC1THM (33% of total variance) was also controlled by ageing effects, as shown by the correlation between PC1THM scores and depth (Fig. 4e), even though this trend was only significant for the SH fraction (r2 = 0.49, P < 0.001). The strong positive PC1THM loadings for the long-chain FAMEs and ω-methoxy-FAMEs, and negative loadings for several of the dominant lignin products (G4, G6, S6) (Fig. 5c), suggested a preferential loss of suberin causing enrichment of lignin. We found no significant trends in the acid/aldehyde guaiacyl oxidation proxy G6/G4, suggesting that ageing had negligible effects on the G-type lignin. Indeed, even markers of very well-preserved G and S lignin products (G14, G15, S14, S15; Filley, 2003) showed positive depth trends in both fractions (negative loadings on PC1THM), supporting a limited effect of ageing on lignin. The loadings of the cutin markers on PC1THM were lower, suggesting that

ageing has a smaller effect on cutin abundance (Supplementary Table S1). In fact, the ratio of cutin to suberin markers increased with depth (r2 = 0.46, P <0.01) in the RR fraction (not shown). These results agree with Armas-Herrera et al. (2016) who found that above-ground aliphatic OM (cutin) was more resistant to degradation than the suberin in roots. The PC2THM (15% of variance) scores increased significantly with depth for RR (Fig. 4f; r2 = 0.64, P < 0.001) but not for SH. The PC2THM exhibited positive loadings for mainly lignin products and unidentified compounds (Fig. 5c).

3.5. Methods comparison The compositional information provided by 13C NMR and Py-GC–MS was in good agreement. Firstly, both methods showed that polysaccharides were the most abundant biomolecular component (40–60%), that SH materials had larger proportions of polysaccharides than RR (and vice versa for lignin), and that the proportions of polysaccharides decreased with depth in the core in both OM fractions, but with a stronger decline in RR than in SH. Furthermore, the sum of the O-alkyl and di-O-alkyl regions from 13C NMR were correlated to the relative proportions of Py-GC–MS carbohydrate products (r2 = 0.80, P < 0.001). For lignin, which fluctuates between approximately 20% and 40%, similar trends were observed for both methods (r2 = 0.67, P < 0.001), which were opposite to the trends in polysaccharide content. Furthermore, both techniques showed that polymethylene aliphatic OM, reflected by alkyl C from 13C NMR and MCC from Py-GC–MS, had an estimated relative proportion of 1–8% and was more abundant in RR than in SH, and declined with depth (but the correlation is rather weak, r2 = 0.18, P < 0.05). The methods were thus consistent in terms of general molecular fingerprinting, and complementary in the sense that 13C NMR provides support of the percentage data from Py-GC–MS (which is quantitatively less robust than

13C

NMR) and carboxylic groups (which are largely destroyed during pyrolysis), that

Py-GC–MS allowed to confirm that the aryl and O-aryl signal from NMR indeed corresponded to lignin and no other polyphenolic sources, and that the alkyl C was largely of polymethylenic nature and not from resonance of other aliphatic groups such as lignin’s C3 side chain. For THM-GC–MS, these correlations were much weaker: the strongest correlation was between total aryl signal from NMR and total lignin plus tannin from THM-GC–MS (r2 = 0.26, P < 0.01). Fig. 8 compares the PC1 and PC2 from 13C NMR and Py-GC–MS. Considering that PC1 and PC2 both reflect ageing effects, in particular selective loss/preservation dynamics, the strong correlations between the parameters for the two methods, for RR (PC1 and PC2) and for PC2 of the SH material, are indicative of good agreement. For these three combinations (Fig. 8a, c and d), not only general trends but also many minor deviations often coincided for 13C NMR and Py-GC–MS, showing that the techniques were identifying the same underlying processes, i.e. PC1-type depletion of polysaccharides, causing lignin enrichment, and PC2-type preferential loss of cutin and especially suberin. The relatively weak correlation for PC1 in the SH fraction could be explained by the relatively low values of PC1 for both methods: selective depletion of polysaccharides (PC1) was only a minor ageing pathway for the SH fraction, which points towards recalcitrance. For THM-GC–MS, the correlations were much weaker, but still consistent with the interpretation of the PCs from Py-GC–MS and 13C NMR. For the SH fraction, PC1THM, which reflected preferential loss of aliphatic sources (cutin and suberin), was correlated with PC2NMR (r2 = 0.60, P < 0.005) and PC2PY (r2 = 0.47, P < 0.001). Hence, loss of aliphatic OM was the major source of variation in the THM dataset, probably due to the weak signal of polysaccharides. For PC2THM, correlations were found for

PC1NMR (r2 = 0.59, P < 0.005) and PC2NMR (r2 = 0.37, P < 0.05), and with PC1PY (r2 = 0.53, P < 0.005) and PC2PY (r2 = 0.44, P < 0.01), but only for the RR fraction. This is indicative of accumulation of lignin and depletion of polysaccharides, which was indeed more pronounced in RR. None of the PCs of any method was correlated with the C content of the Posidonia tissues. This suggests that ageing did not have a significant effect on the proportion of C in the RR and SH, but more data on elemental composition (O/C, N/C and H/C ratios) would be needed to elaborate further on this hypothesis. The δ13C ratio was inversely correlated to PC2NMR (r2 = 0.50, P < 0.001) and PC2PY (r2 = 0.37, P < 0.001). This showed that δ13C is affected by preferential loss of aliphatic sources (cutin/suberin), which are known to have a strongly negative δ13C in comparison with lignocellulose biomass (Feng et al., 2010). These correlations became slightly stronger if the outlier sample with δ13C of –15‰ is omitted and were stronger for SH than for RR if separate correlations for the two fractions were calculated. Hence, δ13C of the seagrass detritus (Fig. 2) was controlled by aliphatic OM preservation in the studied Posidonia tissues, whereas the more intense effect of ageing, i.e. the change in the balance between lignin and carbohydrates, had no significant effect on δ13C. This implied that diagenesis of Posidonia tissues had little influence on δ13C values supporting its use as a proxy of source materials in Blue Carbon ecosystems (seagrass vs algae and seston etc.). Indeed, the δ13C of the bulk sediment (Table 1) had much stronger variations and is more negative (–17‰ against –10‰ on average), which cannot be ascribed to decay processes in SH and RR materials. Further studies are required to understand the processes involved in the decrease in δ13C values of bulk OM with ageing, which could either be related to diagenetic processes of non-seagrass

sources or to environmental drivers affecting the inputs and sources of OM through time.

3.6. Consequences for Blue Carbon dynamics The results of the present study showed that the SH and RR fractions were composed predominantly of a mixture of polysaccharides and polyphenolic components, the former of which were more abundant in the SH fractions, and the latter in the RR fraction. In the RR fraction, there was a clear selective loss of polysaccharides upon ageing, as indicated by the decrease in their relative proportions (13C NMR and Py-GC–MS) and the alteration of the polysaccharides revealed by Py-GC–MS. In the SH materials, the preferential loss of polysaccharides and enrichment of lignin was much less pronounced and alteration of the polysaccharides (i.e. changes in relative proportions among polysaccharide products) was not detected. A secondary ageing effect was associated with the loss of suberin and to a minor extent cutin, in both tissue fractions. The decrease with depth of cutin and suberin, which are often among the more stable forms of OM in terrestrial soil systems (and have barely been studied in marine sediments), and the high proportion of lignin products, provided additional support of the key role of lignin in C sequestration in Posidonia ecosystems (Kaal et al., 2018a). Ageing also affected the balance between syringyl and guaiacyl lignin, which moved towards the latter as ageing advanced. Finally, condensed tannin moieties, which were more abundant in RR, decrease rapidly due to ageing as well. It is important to note that for both 13C NMR and Py-GC–MS, and to a minor extent also THM-GC–MS, the decrease in PC1 and PC2 with depth was not uniform: PC1-type ageing effects were more pronounced in the upper 75 cm of the record, corresponding to 1000 cal. yr BP after burial. For SH this change was of minor

importance, indicative of a more stable polysaccharide component; PC2 scores suggested that the effects of ageing were most pronounced between 60 and 100 cm (600–1000 cal. yr BP), implying that loss of mostly cutin and suberin, and to some extent syringyl lignin (in SH tissue), required some time to become detectable by the methods applied (~600 years), but was then quite rapidly completed (~1000 years). Hence, the OM alteration of the Posidonia tissue in general, or at least in the time range studied here, seemed to be completed after 1000 years of burial, after which the changes were relatively small. This is further evidence of the long-term stability of OM in Posidonia detritus once it was buried sufficiently deep below the sediment surface. It should be noted that the aliphatic biopolymers such as suberin, and also condensed tannin, which were preferentially lost upon ageing (negative trends with depth), are usually (i.e., in terrestrial systems) considered as relatively recalcitrant compounds (e.g., Derenne and Largeau, 2011). In the P. australis mat material, this was not the case: here the most persistent constituent of the OM is lignin in general (in RR) or G-type lignin (in SH). The decrease of polysaccharides with depth was less surprising: polysaccharides are among the more labile materials in most depositional environments (e.g., Baldock et al., 1997; Kögel-Knabner and Rumpel, 2018, and References therein), but whether the decrease in relative proportion of the polysaccharides with depth was associated with leaching of low molecular weight carbohydrates in, for example, ferulate complexes (which could explain the decrease in G18 abundance) or due to selective microbial decay of polysaccharides, or a combination of both mechanisms, remains unknown. Either way, the polysaccharides of the SH materials were more stable than those of the RR. The results of the present study are in contrast to those obtained for P. oceanica (Kaal et al., 2016) and underlying mat deposits, which had remarkably high contents of

p-HBA linked to a lignin backbone, whereas P. australis did not (Kaal et al., 2018a), and this difference was confirmed by the low proportion of phenol and methylphenols in the P. australis record studied here. Differences in p-HBA content between P. oceanica and P. australis may partly explain the higher organic C storage capacity of the former (Serrano et al., 2016; Kaal et al., 2018a) There was no evidence of a significant production of condensed aromatic structures as an effect of photo-oxidation of seagrass organs (neoformation) (cf. Waggoner et al., 2015). Also, there was no indication of the accumulation of a significant microbial component in the seagrass organs either (that would be reflected by N-containing pyrolysis products and N-alkyl C signal). It may be anticipated that such processes left important signals on the record, but that these signals were anchored in the fine sediment OM fraction rather than the physically preserved seagrass organs studied here.

4. Conclusions This study has presented molecular records of organic matter (OM) characteristics in a seagrass Blue Carbon ecosystem. The analyses of Posidonia organs, instead of bulk sediments, helped avoid the influence of matrix effects (inorganic materials) and maintained a focus on the Posidonia residues alone, simplifying the interpretation of the acquired analytical data. Within the coarse organic matter fraction (sheaths and roots/rhizomes), the main effect of ageing was the enrichment of lignin and loss of carbohydrates, probably as a result of selective decay and/or leaching of the latter. The sheath materials were less affected by ageing than the roots and rhizomes including the polysaccharides component of the OM. There was no evidence of enrichment of black carbon-like, microbial or other recalcitrant organic matter within

the seagrass organs studied. The results obtained contribute to our understanding of the organic chemistry behind the capacity of Blue Carbon ecosystems to store organic carbon. Future studies will be concerned with bulk samples of the area to elucidate the contribution of other potential sources of sediment OM, such as microbial biomass and allochthonous sources (e.g., seston, algae and terrestrial OM), which, globally, had been estimated to constitute 50% of the organic carbon stored in seagrass sediments (Kennedy et al., 2010).

Acknowledgements This work was supported by the ECU Silver Jubilee Award. O.S. was supported by the Australian Research Council (DE170101524). We are grateful for the time and comments of Laurent Grasset and an anonymous reviewer.

Associate Editor–Geoffrey Abbott

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Table 1. Sample depths before and after decompression corrections (see text) and bulk (hence, not the isolated sheath and root/rhizome fractions) properties of the core from Waychinicup Inlet. Estimated ages were obtained from a Bacon age/depth model (Supplementary Fig. S1). dw = dry weight. Depth (compressed) (cm) 0–5 5–10 10–15 15–20 20–25 25–30 30–35 35–40 40–45 45–50 55–60 75–80 95–100 110–121

Average depth (decompressed) (cm) 3 10 17 23 30 36 43 50 56 63 76 103 129 152

Age (cal. yr BP) 32 96 160 223 287 352 434 544 656 768 1007 2065 2735 3199

Dry bulk density (g dw cm-3) 0.80 1.41 0.89 0.64 1.80 1.35 1.65 1.21 1.04 1.45 1.36 1.23 1.37 1.16

Organic C content (%dw) 1.50 0.88 1.84 1.55 1.28 1.44 1.67 0.86 0.82 0.49 0.58 0.98 0.86 0.43

δ13C (‰) –12.99 –13.85 –15.76 –14.59 –18.32 –19.54 –16.22 –16.62 –19.42 –18.89 –16.63 –16.97 –18.92 –19.54

Figure Captions

Fig. 1. Study site showing the location of the analysed seagrass deposit.

Fig. 2. (a) Carbon content and (b) δ13C of the sheath (SH) and root/rhizome (RR) fractions from the studied core, plotted against depth.

Fig. 3. Results from 13C NMR of the sheath (SH) and root/rhizome (RR) fractions from the studied core, plotted against depth. Normalised abundances of the main chemical shift regions, i.e. (a) O-alkyl + di-O-alkyl C from polysaccharide, (b) aryl, O-aryl and methoxyl C from lignin, (c) carbonyl C and (d) alkyl C.

Fig. 4. Results from principal components analysis (PCA) of the datasets from the three techniques applied to the sheath (SH) and root/rhizome (RR) fractions. PC1/PC2 = principal component 1/2. Data from: (a and b) 13C NMR spectroscopy, (c and d) PyGC–MS and (e and f) THM-GC–MS.

Fig. 5. Loadings from principal component analysis (PCA) on PC1 (x-axis) and PC2 (yaxis) for: (a) 13C NMR, (b) Py-GC–MS, and (c) THM-GC–MS. For lignin products, a subdivision is made for p-hydroxyphenol (H), guaiacyl (G) and syringyl (S) moieties. Note that for THM-GC–MS, guaiacyl and syringyl correspond to dimethoxy- and trimethoxybenzenes, respectively (we suggest that they are predominantly G and S products, not methylated catechols or pyrogallols).

Fig. 6. Summary of results from Py-GC–MS of the sheath (SH) and root/rhizome (RR) fractions. Relative proportions of: (a) carbohydrate products, (b) lignin phenols (4vinylphenol, guaiacols, syringols), (c) phenols (phenols and catechols, from tannin and degraded lignin) and (d) methylene chain compounds (MCC; aliphatic materials).

Fig. 7. Summary of results from THM-GC–MS of the sheath (SH) and root/rhizome (RR) fractions. Relative proportions of: (a) carbohydrate products, (b) lignin products (mono-, di- and trimethoxybenzenes typical of lignin or lignin-like cinnamyl structures), (c) tannin products (condensed tannin A rings), and (d) fatty acid methyl esters (FAMEs = polymethylene aliphatics).

Fig. 8. Comparison of the results from PCA of the 13C NMR spectroscopy and Py-GC– MS data of the sheath (SH) and root/rhizome (RR) fractions: (a) PC1 for NMR (yellow) and Py-GC–MS (green) for the root/rhizome fraction, (b) same for the sheath fraction (SH), (c) PC2 for RR, (d) PC2 for SH.

Figure 1

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AUSTRALIA

Graphical abstract

HIGHLIGHTS -

Molecular analysis of 3200 yr Posidonia australis seagrass soil record (Australia) Tracing diagenetic effects on sheaths and root/rhizome detritus Ageing-induced selective degradation of polysaccharides and enrichment of lignin Minor constituents cutin, suberin and tannin also preferentially depleted Overall C storage capacity associated with lignocellulose chemical recalcitrance