Comparison of cryoconite organic matter composition from Arctic and Antarctic glaciers at the molecular-level

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Geochimica et Cosmochimica Acta 104 (2013) 1–18 www.elsevier.com/locate/gca

Comparison of cryoconite organic matter composition from Arctic and Antarctic glaciers at the molecular-level Brent G. Pautler a, Ashley Dubnick b, Martin J. Sharp b, Andre´ J. Simpson a, Myrna J. Simpson a,⇑ a

Environmental NMR Centre and Department of Chemistry, University of Toronto, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4 b Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2E3 Received 16 August 2012; accepted in revised form 17 November 2012; available online 4 December 2012

Abstract Glacier surfaces are reservoirs that contain organic and inorganic debris referred to as cryoconite. Solar heating of this material results in the formation of water-filled depressions that are colonized by a variety of microbes and are hypothesized to play a role in carbon cycling in glacier ecosystems. Recent studies on cryoconite deposits have focused on their contribution to carbon fluxes to determine whether they are a net source or sink for atmospheric CO2. To better understand carbon cycling in these unique ecosystems, the molecular constituents of cryoconite organic matter (COM) require further elucidation. COM samples from four glaciers were analyzed by targeted extraction of plant- and microbial-derived biomarkers in conjunction with non-targeted NMR experiments to determine the COM composition and potential sources. Several molecular proxies were applied to assess COM degradation and microbial activity using samples from Greenland, the Canadian Arctic, and Antarctica. COM from Canadian (John Evans glacier) and Greenlandic (Leverett glacier) locations was more chemically heterogeneous than that from the Antarctic likely due to inputs from higher plants, mosses and Sphagnum as suggested by the solvent-extractable alkyl lipids and sterols and the detection of lignin- and Sphagnum-derived phenols after cupric oxide chemolysis. Solid-state 13C nuclear magnetic resonance (NMR) experiments highlighted the bulk chemical functional groups of COM allowing for a general assessment of its degradation stage from the alkyl/O-alkyl proxy whereas solution-state 1H NMR highlighted both microbial and plant contributions to base-soluble extracts from these COM samples. The dominance of 1H NMR signals from microbial protein/peptides in base-soluble extracts of COM from Antarctica (Joyce glacier and Garwood glacier), phospholipid fatty acid (PLFA) biomarker detection and the absence of plant-derived biomarkers in both the solvent and cupric oxide extracts suggests that this COM is dominated by microbial-derived material. These results indicate that COM carbon composition is dependent on the local glacier environment which may have a profound impact on carbon cycling and sequestration on glacier surfaces. Ó 2012 Elsevier Ltd. All rights reserved.

1. INTRODUCTION Polar and alpine glaciers and ice sheets are habitats for a high diversity of viruses and microbes that have adapted to surviving and functioning under low temperature conditions (Anesio and Laybourn-Parry, 2012). Biological activity on ⇑ Corresponding author. Tel.: +1 416 287 7234; fax: +1 416 287 7279. E-mail address: [email protected] (M.J. Simpson).

0016-7037/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.gca.2012.11.029

glacier surfaces (supraglacial environments) may alter the both the physical behavior of glaciers by changing surface reflectivity and the chemical nutrient cycling between the glacier surface and the surrounding atmosphere (Stibal et al., 2012a). Amplified climate change has resulted in a large decrease in the Earth’s ice volume in polar regions (Jacob et al., 2012) potentially disrupting these supraglacial ecosystems by altering food webs and biogeochemical fluxes (Vincent, 2010) while threatening the overall biodiversity of glacier fed watersheds (Jacobsen et al., 2012). Glacier sur-

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faces receive a large amount of wind-blown particulates that are deposited on glacier ablation zones (McIntyre, 1984). During the summer melt season, these particulates stimulate ice melting by locally decreasing the ice surface albedo, resulting in water-filled depressions known as cryoconite holes which are widely distributed on both polar and temperate glaciers (Hodson et al., 2008). The mixture of water and inorganic/organic debris in the cryoconite holes provides the inoculum, substrates and nutrients required to sustain microbial activity and biogeochemical cycling on glacier surfaces (Stibal et al., 2008; Cameron et al., 2012a). This cryoconite material may ultimately decrease and/or be transported by increased runoff to nearby aquatic environments (including the glacier bed) as glaciers melt and recede (Boon et al., 2003). Much of the current interest in cryoconite holes has focused on measuring carbon fluxes, rates of photosynthesis and respiration, and their overall biogeochemistry to examine their importance to both local and global carbon cycles (Foreman et al., 2007; Hodson et al., 2007, 2010a; Stibal et al., 2008, 2010, 2012b; Anesio et al., 2009, 2010). It has been suggested the large abundance and activity of photosynthetic microbial organisms within cryoconite holes may lead to the potential accumulation of up to 64 Gg of carbon per year globally and therefore glacier surfaces may be an unaccounted for global carbon sink (Anesio et al., 2009). However, the complex interactions between microbial activity and diversity, hydrology, and substrate availability may vary depending upon the composition, thickness and spatial heterogeneity of cryoconite. For example, the mineral and cryoconite organic matter (COM) composition has been shown to vary in samples collected from the same glacier depending on the location of cryoconite holes on the glacier surface (Langford et al., 2011). Cryoconite from holes in close proximity to the ice margin contained less biogenic material and displayed greater mineral diversity than cryoconite sampled from the glacier interior (Langford et al., 2011). The thickness of cryoconite debris has also been shown to influence microbial activity and carbon cycling, with net photosynthesis (CO2 fixation) being favored in holes with thinner debris layers and net heterotrophy (CO2 respiration) in holes with thicker debris layers (Cook et al., 2010; Telling et al., 2012). Furthermore DNA sequencing of rRNA genes of microbes isolated from Arctic and Antarctic cryoconite has revealed geographically distinct communities including unknown bacterial, eukaryotic and archael taxa (Cameron et al., 2012b). Thus, extrapolations of carbon dynamics from measurements on individual cryoconite holes to the global scale may be inaccurate due to COM and microbial community heterogeneity. These complexities need to be examined further for an accurate assessment of the role of cryoconites in the global carbon cycle. The composition of COM from a variety of locations can be explored through the application of several molecularlevel organic geochemical methods and proxies. Targeted molecular biomarker methods can provide valuable information on organic matter (OM) sources because their carbon skeleton is indicative of the natural product precursor (Simoneit, 2005) and/or microbial community structure

(Frostega˚rd and Ba˚a˚th, 1996; Volkman et al., 1998). Several molecular proxies based on biomarker abundance have been established to estimate reactivity and turnover of natural OM in the environment (Hedges et al., 1988; Kieft et al., 1994; Otto and Simpson, 2006). Although a large amount of information can be obtained using these targeted techniques, biomarkers may represent only a small portion of the total OM present in a sample (Ko¨gel-Knabner, 2000). To circumvent this limitation, nuclear magnetic resonance (NMR) spectroscopy has emerged as a powerful, non-selective approach to characterize natural OM from a variety of ecosystems and link its composition to global processes (Simpson et al., 2011). The overall composition of the bulk functional groups in COM can be examined by solid-state 13 C NMR, and the relative signal contributions can be applied as a proxy to estimate OM turnover (Preston et al., 1997; Simpson et al., 2008). Detailed molecular structural information can be obtained by employing multidimensional solution-state NMR techniques to base-soluble extracts (Simpson et al., 2011). The combination of biomarker methods with both solid and solution-state NMR techniques provides a comprehensive approach to fully characterize natural OM and postulate its sources, reactivity and potential contributions to the carbon cycle. A regional comparison of COM composition from polar glaciers has not yet been examined at the molecular-level. Individual chemical analyses have been limited to the examination of the inorganic granule composition of cryoconite from Svalbard glaciers (Cook et al., 2010; Langford et al., 2011), organic matter content from cryoconite sampled from Greenlandic glaciers (Stibal et al., 2010) and COM composition of a cryoconite sampled from a Canadian Alpine glacier (Xu et al., 2010). In this study we employ both biomarker and NMR methods to distinguish plant- (longchain alkyl lipids, sterols, lignin-derived phenols) and microbial-derived (short-chain lipids, sterols, phospholipid fatty acids; PLFAs) OM inputs to COM, and to estimate COM degradation stage and microbial activity using a variety of cryoconite samples from Greenland, the Canadian Arctic and Antarctica to provide the first regional comparison between polar glaciers. Analyses of COM composition, heterogeneity and potential substrate availability are required from a variety of geographic locations to fully understand the potential role of cryoconite in carbon cycling at both local and global scales. 2. EXPERIMENTAL 2.1. Glacier cryoconite sampling Samples were collected from four different glaciers in the Arctic and Antarctic (Table 1). Antarctic cryoconite samples were collected from the Garwood glacier and Joyce glacier which are cold-based polar glaciers in the Garwood Valley, with ice temperatures below the pressure-melting point of water. Cryoconite holes in Antarctic environments are often covered by lids of ice that are up to 30 cm thick during the extremely cold winters and persist through the summer melt season, restricting CO2 and N2 exchange. Such holes may persist for up to 10 years (Fountain et al.,

Table 1 Summary of Cryoconite Sample Information and Selected Properties. Location

Coordinates

Date sampled

Sample label

Sample type

Coordinates

Elevation (m)

Total carbon%

Organic carbon (OC)%

Inorganic carbon (IC)%

Leverett

Greenland

67°030 N, 50°030 W

July, 2009

L1

Cryoconite (open) Cryoconite (open) Cryoconite (open) Cryoconite (open) Cryoconite (open) Cryoconite (open) Supraglacial mud

67°030 9100 N, 50°030 50300 W 67°030 74500 N, 50°020 33500 W 67°030 67500 N, 50°090 11400 W 67°030 94300 N, 50°030 50300 W 67°030 94300 N, 50°080 13300 W 67°030 95600 N, 50°080 35100 W 67°030 95600 N, 50°080 35100 W

354

0.4

0.4

nd

350

0.1

0.1

nd

335

0.1

0.1

nd

354

0.2

0.2

nd

393

0.2

0.2

nd

384

0.5

0.5

nd

384

0.9

0.9

nd

L2 L3 L4 L5 L6 LMud John Evans

Ellesmere Island, Canada

79°700 N, 74°520 W

July, 2002

JEG1

Cryoconite

79°060 3600 N, 74°090 200 W

387

7.1

7.1

nd

Garwood

Garwood Valley, Antarctica

78°020 S, 163°950 E

Dec., 2008

G1

Cryoconite (open) Cryoconite (closed) Cryoconite (closed)

78°000 92600 S, 163°550 43100 W 78°000 97400 S, 163°550 33000 W 78°010 03600 S, 163°550 05400 W

483

0.1

0.1

nd

492

0.6

0.2

0.4

452

0.5

0.2

0.3

Cryoconite (open) Cryoconite (closed) Cryoconite (closed) Cryoconite (closed) Supraglacial Pond

78°010 24200 S, 163°470 61700 W 78°010 28200 S, 163°460 97200 W 78°010 26700 S, 163°450 93800 W 78°010 22500 S, 163°470 62900 W 78°010 23500 S, 163°460 84500 W

440

0.4

0.1

0.3

468

0.4

0.1

0.3

499

0.2

0.2

nd

458

0.6

0.3

0.3

462

0.4

0.1

0.3

G2 G3 Joyce

Garwood Valley, Antarctica

78°020 S, 163°800 E

Dec., 2008

J1a J1b J2 J3 JSupra

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

Glacier

nd = not detected.

3

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2004). One cryoconite sample from the Garwood glacier (G2) and four from the Joyce glacier (J1b, J2, J3, JSupra) were obtained from holes with an ice lid at the time of sampling, and three (G1, G3, J1a) from holes that were open to the atmosphere at the time of sampling (Table 1). Each of the covered samples was opened with an ethanol-bathed, flame sterilized chisel. A sterile WhirlPak bag was turned inside out to reach in and collect the cryoconite debris. Debris from an ice covered-supraglacial pond (JSupra), that can be thought of as large cryoconite hole (or cryolake), was also collected in the same manner. Six cryoconite samples were collected from the Leverett glacier, located on the southwestern part of the Greenland Ice Sheet (Hodson et al., 2010a; Stibal et al., 2010). Each of the cryoconite holes sampled contained melt-water and were open to the atmosphere at the time of sampling (Table 1). Each sample was collected in the same manner as the Antarctic samples with sterile WhirlPak bags turned inside out. In addition a mud-like deposit (LMud) that was piled on the surface of the glacier (not in a water-filled depression) was collected for comparison to cryoconite samples. One cryoconite sample was collected from the John Evans glacier (JEG), Ellesmere Island, which is a polythermal glacier with a mix of ice at and below the pressure-melting point of water. Just prior to sampling, the JEG cryoconite hole that was initially open to the atmosphere had become frozen and buried by a summer snowfall. The snow and re-frozen melt-water were removed with an ethanol-bathed and flame-sterilized ice axe and the frozen cryoconite debris was collected from the interface between glacial ice and re-frozen melt-water. All cryoconite samples were kept frozen and in the dark during transport back to the laboratory to minimize biological and/or photochemical degradation. Each of the samples was freeze-dried and ground into a powder prior to further analyses. 2.2. Organic carbon, chemical extractions, and gas chromatography/mass spectrometry analysis The total carbon content of each cryoconite sample was determined in duplicate with a LECO SC-444 analyzer (University of Guelph, Ontario, Canada). Inorganic carbon was determined after ashing the samples at 475 °C for 3 h to remove organic carbon (OC) which was then calculated by difference between total carbon and inorganic carbon (IC). Previous measurements determined that the detection limit of the LECO method is 0.05% for carbon (Xu et al., 2010). Lipid biomarkers were extracted in triplicate by sonication of cryoconite samples (20 g) sequentially in 30 mL of CH2Cl2, CH2Cl2:CH3OH (1:1 v/v) and CH3OH followed by filtration (Whatman GF/F), rotary evaporation concentration, and drying under N2 (Otto et al., 2005). The alkaline cupric oxide (CuO) oxidation method (Hedges and Ertel, 1982) was used to liberate lignin-derived phenols and other aromatic compounds that may be associated with higher plant inputs and/or Sphagnum moss (Gon˜i and Hedges, 1990; Williams et al., 1998). The air-dried solvent-extracted cryoconite residue (7 g), 1.0 g of CuO (pre-extracted with CH2Cl2), 100 mg FeNH4(SO4)26H2O and 15 mL 2 M NaOH were loaded into a Teflon lined

bomb, purged with N2, sealed and heated at 170 °C for 2.5 h. The bombs were then cooled under running water and the supernatants were collected and acidified to pH 1 with 6 M HCl and left in the dark for an hour at room temperature to prevent the polymerization of cinnamic acids. After centrifugation (2500 rpm, 15 min) the supernatant was liquid–liquid extracted with anhydrous diethyl ether (3  30 mL), dried with Na2SO4 to remove any remaining water, concentrated by rotary evaporation and dried under N2. The solvent and CuO extracts were converted to trimethylsilyl (TMS) derivatives by reaction with 100 lL of N,O-bis-(trimethylsilyl)trifluoracetamide (BSTFA) and 10 lL anhydrous pyridine for 1 h at 70 °C. After cooling, 100 lL of hexane was added to dilute the extracts. PLFA biomarkers were extracted in triplicate from cryoconite samples (20 g) with 30 mL of a single phase mixture (1:2:0.8, v:v:v) of CHCl3, CH3OH, and citrate buffer (0.15 M, pH 4) by shaking for 24 h. The chloroform phase was collected by centrifugation (2500 rpm, 15 min) and fractionated into neutral lipids, glycolipids and polar lipids with 10 mL of CHCl3, 20 mL acetone and 10 mL of CH3OH by silica column chromatography respectively (Frostega˚rd and Ba˚a˚th, 1996; Feng et al., 2007). The polar lipid fraction containing the phospholipids were converted to fatty acid methyl esters by a mild alkaline methanolysis reaction (37 °C for 15 min), recovered with a hexane:CHCl3 mixture (4:1), and dried under a stream of N2. PLFA nomenclature is based on the number of carbon atoms and the number of double bonds followed by the position of the double bond from the methyl end of the molecule. The x(n) indicates that the first double bond starts on the nth carbon from the methyl ester. The prefixes i- and a- refer to iso-branched and anteiso-branched fatty acids respectively whereas the designation cy- indicates cyclopropane fatty acids. Gas chromatography/mass spectrometry (GC/MS) analysis of the derivatized extracts was performed using an Agilent model 6890N gas chromatograph coupled to an Agilent model 5973N quadrupole mass selective detector using the operating parameters described previously (Pautler et al., 2010a). Compounds were identified by comparison of the mass spectra to a MS library (Wiley275 MS library), and by comparison with authentic standards and with published data. Tetracosane, 1-docosanol (as TMS ester), and ergosterol (as TMS ester) were used as external quantification standards for the solvent extracts, while CuO oxidation products were externally quantified with vanillin and vanillic acid (as TMS esters) and PLFA extracts with oleic acid methyl ester. The GC/MS response has been shown to be linear over the concentration ranges for these samples (Xu et al., 2009) with a relative standard deviation 65% overall (Otto and Simpson, 2007). Individual biomarker concentrations were normalized to sample OC content. 2.3. NMR analysis Freeze-dried cryoconite (30 g) was repeatedly treated with 10% HF/HCl to concentrate the COM and to remove any paramagnetic minerals that can be problematic during

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

NMR acquisition (Schmidt et al., 1997; Gelinas et al., 2001). Treatment of environmental samples with HF has been shown not to significantly alter the overall OM composition (Rumpel et al., 2006). After treatment, the samples were washed several times with deionized water and freezedried. HF-treated cryoconite (100 mg) was packed into a 4-mm zirconium rotor with a Kel-F cap for solid-state 13C cross-polarization – magic angle spinning (CP-MAS) NMR analysis. Spectra were acquired with a 500 MHz Bruker Avance III spectrometer, equipped with a 4-mm H-X MAS probe, using a ramp-CP pulse program (Conte et al., 2004) with a spinning rate of 13 kHz, a contact time of 1 ms, a 1 s recycle delay and processed with a line broadening of 100 Hz. The spectra were integrated into the following chemical shift regions: alkyl carbon (0–50 ppm); O-alkyl carbon including alcohols, carbohydrates, ether, methoxy and acetal carbon (50–110 ppm); aromatic and phenolic carbon (110–165 ppm); carboxyl and carbonyl carbon (165–210 ppm; Baldock et al., 1992; Preston et al., 1997; Simpson et al., 2008). All chemical shifts were calibrated using an external glycine standard. The HF-treated cryoconite samples were exhaustively extracted with 0.1 M NaOH, filtered through a 0.22 lm Millipore Durapore membrane filter, cation exchanged with Amberjet 1200H ion exchange resin and freeze-dried. The base-soluble extracts (100 mg) were further dried over P2O5 to remove any residual water, then re-constituted in DMSO-d6 (0.75 mL) and transferred to a 5 mm NMR tube for analysis. All solution-state NMR spectra were acquired on a Bruker Avance 500 MHz spectrometer equipped with a QXI probe with an actively shielded Z-gradient at 298 K. One-dimensional (1-D) solution-state 1H NMR experiments were acquired using 128 scans, a recycle delay of 2 s with 32,768 time domain points. All spectra were apodized through multiplication with an exponential decay corresponding to 3 Hz line broadening in the transformed spectrum and a zero-filling factor of 2. 1-D diffusion edited (DE) 1H NMR experiments were carried out using a bipolar pulse longitudinal encode-decode sequence (Wu et al., 1995). Scans (1024) were collected using 2.5 ms, 53.5 gauss/cm gradient pulses and a 200 ms diffusion time. Total correlation spectroscopy (TOCSY) spectra were acquired in the phase-sensitive mode, with a mixing time of 80 ms. Heteronuclear single quantum coherence (HSQC) spectra were collected in the phase-sensitive mode using Echo/Antiecho-TPPI gradient selection and an average 1J 1 H–13C of 145 Hz. Scans (1024) were collected for each of the 196 increments in the F1 dimension while a total of 1024 data points were collected in F2 and a relaxation delay of 2 s was employed. Both TOCSY and HSQC spectra F2 planes were multiplied by an exponential function corresponding to a 15 Hz line broadening, while the F1 dimensions were processed using sine-squared functions with a p/2 phase shift and a zero-filling factor of 2. Chemical shift assignments are based upon previously published data (Fan et al., 2000; Kelleher and Simpson, 2006; Simpson et al., 2007a,b; Xu et al., 2010; Simpson et al., 2011) and confirmed by two-dimensional (2-D) NMR experiments and NMR spectral predictions using Advanced Chemistry Development’s ACD/SpecManager and ACD/2-D NMR

5

Predictor using Neural Network Prediction algorithms. NMR parameters such as spectrometer frequency, sweep width and line shape were accounted for in the HSQC spectral prediction of a portion of a microbial peptide sequence containing aromatic amino acid residues tyrosine and phenylalanine from Ralstonia eutropha that has been isolated from soil (Benndorf et al., 2007) to confirm their assignments in COM base-soluble extracts. 3. RESULTS AND DISCUSSION 3.1. Carbon analysis and organic matter biomarkers The measured carbon contents of the cryoconite samples are listed in Table 1; the relative abundance of OC was low in all of the samples from the Leverett, Garwood, and Joyce glaciers whereas the cryoconite from JEG1 was enriched in OC. IC was not detected in any of the Arctic cryoconite samples, but it was often more abundant than OC in the Antarctic cryoconite. Significant variations between OC and IC were not observed within or between glacial ecosystems with the exception of the OC rich JEG1 cryoconite. Solvent-extractable components included a variety of n-alkanols, n-alkanoic acids, n-alkenoic acids, n-alkanes, sterols, and simple sugars (Table 2). The extractable sugars (glucose, mannose, sucrose, and trehalose) were the most abundant constituents observed in the Antarctic (Garwood and Joyce) cryoconite and JEG1 extracts. These sugars were less abundant in the Leverett cryoconite samples relative to the remaining extractable constituents. Although most simple extractable sugars are produced by many organisms and are therefore not source specific (Otto et al., 2005), mono- and di-saccharides detected in OM may be degradation products of cellulose (Simoneit et al., 2004). Trehalose is a reserve carbohydrate that functions as stress protectant in fungi and bacteria (Koide et al., 2000; Arnold et al., 2003; Feng et al., 2010) and is the most abundant biomarker identified in the Antarctic cryoconite samples. A series of n-alkanes (C18–C33) with an odd over even carbon number predominance was detected in all Leverett and JEG1 cryoconite samples. Mid-chain (C23–C25) n-alkanes, which are typically derived from aquatic macrophytes, mosses and/or Sphagnum (Baas et al., 2000; Ficken et al., 2000; Nott et al., 2000) were present in high concentrations, followed by the long chain (C29–C31) n-alkanes that are mainly derived from higher plant waxes (Eglinton and Hamilton, 1967), and short-chain (C21–C23) n-alkanes which may originate from either plants or microbes (Harwood and Russel, 1984; Volkman et al., 1998). Long and mid-chain n-alkane homologues were only detected in two cryoconite samples from Antarctica (G1 and J1b). Both long chain n-alkanols (>C20), mainly derived from vascular plants (Harwood and Russel, 1984) and Sphagnum (Baas et al., 2000), and short-chain n-alkanols (
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Table 2 Concentration of solvent-extractable compounds (mg g1 OC) and proxies in cryoconite and glacier mud samples. Leverett

n-Alkanols C16–C19 C20–C32 sec-Alkanols C29-10-ol n-Alkanoic acids C12–C19 C20–C30 n-Alkenoic acidsb n-Alkanes C18–C30 (even) C19–C33 (odd) C19–C21 C23–C25 C29–C31 Sterols Cholesterol Ergosterol Campesterol Stigmasterol b-Sitosterol Sugars Glucose Mannose Sucrose Trehalose n-Alkane proxies C23/(C23 + C29) C31/C29 C29/(C27 + C29 + C31)

JEGa

Garwood

Joyce

L1

L2

L3

L4

L5

L6

LMud

JEG 1

G1

G2

G3

J1a

J1b

J2

J3

JSupra

nd 2.3

0.1 4.0

0.3 5.1

0.1 1.3

0.1 0.9

0.3 4.9

0.1 1.9

nd 4.7

nd nd

nd nd

nd nd

nd nd

nd nd

nd nd

nd nd

nd nd

0.1

0.2

0.3

nd

nd

nd

0.3

nd

nd

nd

nd

nd

nd

nd

nd

nd

1.3 3.3 2.2

3.6 0.3 1.5

4.8 0.2 3.2

1.0 0.4 0.6

1.6 0.8 0.9

1.7 1.9 0.2

0.6 0.8 0.4

1.5 3.3 2.8

2.2 nd nd

0.6 nd 0.2

0.4 nd nd

0.8 nd nd

2.9 nd 0.4

0.4 nd nd

0.3 nd 0.1

1.6 nd nd

0.8 2.3 0.2 1.0 0.9

1.8 2.6 0.3 2.1 nd

5.1 8.3 1.0 3.7 3.3

0.5 1.1 0.1 0.7 0.2

0.8 1.6 nd 0.9 0.8

1.6 3.0 0.9 1.5 0.8

1.8 2.6 0.9 1.4 0.8

0.4 0.6 0.1 0.3 0.3

2.5 4.2 nd 1.1 2.2

0.1 nd nd nd nd

nd nd nd nd nd

nd nd nd nd nd

nd 0.7 nd 0.2 0.2

nd nd nd nd nd

nd nd nd nd nd

nd nd nd nd nd

0.4 nd 0.6 0.2 3.4

nd nd 0.5 nd 2.8

0.4 nd 0.5 0.5 0.4

nd nd 0.2 nd 1.3

nd nd 0.3 nd 1.3

0.1 0.6 0.2 0.2 0.4

0.2 0.6 0.3 0.3 0.8

nd 0.2 1.0 0.7 1.7

nd nd 0.3 nd 0.8

0.2 nd 0.2 nd 0.4

0.1 nd 0.2 nd 0.3

0.2 nd nd nd 0.3

nd nd nd nd 0.4

nd nd nd nd 0.1

0.2 nd nd nd 0.2

nd nd nd nd 0.4

4.3 0.5 4.8 4.3

nd 1.1 1.4 2.2

nd 0.3 0.6 3.9

5.8 0.4 6.7 0.5

nd 0.8 1.1 1.7

0.5 0.2 0.4 0.2

nd nd 0.04 0.1

5.0 0.4 5.4 1.9

nd 0.2 0.1 15.6

nd 0.1 0.2 7.0

0.1 0.2 0.2 2.0

nd 0.4 nd 10.3

nd 0.3 0.6 7.8

nd 0.1 0.1 5.7

0.1 nd 0.02 3.0

nd 0.2 0.1 3.6

0.46 0.84 0.45

1.00 – –

0.44 0.59 0.38

0.60 – 0.37

0.45 0.70 0.40

0.68 0.93 0.32

0.63 1.15 0.29

0.35 0.67 0.32

– – –

– – –

– – –

– – –

– – –

– – –

– – –

– – –

nd = not detected. a JEG, John Evans glacier. b n-Alkenoic acids includes: C16:1, C18:1 in all samples; C15:1, C18:2, C19:1, C20:1 were also detected in Leverett and JEG samples.

could be derived from both microbes and/or plants/mosses (Harwood and Russel, 1984; Volkman et al., 1998; Baas et al., 2000). These compounds were present in much higher concentrations in Leverett and JEG cryoconite samples compared to their long chain counterparts (>C20), but still displayed the typical biosynthetic even over odd carbon number predominance. n-Alkanols and long chain n-alkanoic acids were not detected in cryoconite samples for either Antarctic glacier. The secondary (sec) alkanol, nonacosan10-ol (C29-10-ol), which has been found to be the most abundant sec-alkanol in epicuticular waxes of higher plants (Osborne and Stevens, 1996; Prugel and Lognay, 1996; Koch et al., 2006), was detected in three Leverett cryoconite samples along with the sampled Leverett glacier mud. Mono- and di-unsaturated acids (C16:1, C18:1, C18:2) were also detected in the Arctic cryoconite samples which are likely attributed to fresh microbial inputs (Frostega˚rd and Ba˚a˚th, 1996; Zelles, 1999) as these acids are sensitive to microbial and photo-degradation (Canuel and Martens, 1996). A variety of sterols were detected in the solvent extracts of the cryoconite samples (Table 2). Cholesterol (cholest-5-en-3b-ol) was observed in various samples from

both the Arctic and Antarctic and could be an indication of eukaryotic microbes (Volkman, 2003) but since it is produced by plants as well (Harwood and Russel, 1984; Hartmann, 1998), it cannot be used to clearly distinguish OM sources. Campesterol (24-methyl-cholesterol), b-sitosterol (24-ethyl-cholesterol) and stigmasterol (24-ethyl-cholesta-5,22-dien-3b-ol) are common sterols found in higher plants (Harwood and Russel, 1984; Hartmann, 1998). However several studies related to sterol biosynthesis in microbes suggest that b-sitosterol and stigmasterol are also produced by other eukaryotic organisms such as microalgae (Volkman, 2003) and have also been detected in moss collected from the Antarctic Dry Valleys (Feng et al., 2010). Ergosterol (ergosta-5,7,22-trien-3b-ol) commonly produced by fungi was detected in samples from the Leverett glacier and JEG1 and has been used as an important marker related to fungal degradation of OM in soil environments (Ruzicka et al., 2000; Feng et al., 2008). Several biomarker ratios have been proposed to distinguish between the major OM tissue inputs to soils and sediments. For example, the n-alkane ratio of C23 over the sum of C23 and C29 homologues [C23/(C23 + C29)] has been used to distinguish between the relative contribution of OM

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

from aquatic macrophytes and mosses (including Sphagnum) from higher plants (Xu et al., 2010). Values of this ratio for samples from the Leverett glacier (0.45–1.0) are higher than those reported for higher vascular plants (Table 2; Eglinton and Hamilton, 1967), indicating that mosses may be the dominant inputs to COM. The relative (and likely minor) inputs of higher plant species to COM can be further differentiated by the application of the ratio of [C29/(C27 + C29 + C31)] n-alkanes to distinguish between grasses/broad leaf and conifer derived tissues which have been shown to have average values of 0.2–0.35 and 0.35– 0.5 respectively (Lei et al., 2010). In addition, the proxy ratio of C31/C29 n-alkanes can be applied to distinguish between dominant OM input sources from grass or tree vegetation because grass tissues are dominated by C31 and C33 n-alkanes whereas tree tissues have a larger abundance of C29 and C27 n-alkanes (Schwark et al., 2002; Jansen et al., 2006; Bai et al., 2009). The absence of all or some of these n-alkane biomarkers in the Antarctic cryoconite samples suggests that higher plants are not major inputs to these COM samples (Tables 2). Applying each of these proxies to the Arctic cryoconite samples suggests that vascular plant inputs to the JEG1, L6, and Leverett glacier mud samples are largely from grasses/broad leaves, with the sample of Leverett glacier mud dominated by grasses rather than trees (Table 2). The remainder of the samples from Leverett cryoconite (L1, L3 and L5) likely have a vascular plant contribution to OM derived from conifer trees (Table 2). Biomarkers extracted by the alkaline CuO oxidation reaction of the solvent-extracted COM residues are indicative of potential vegetation sources (Johansson et al., 1986; Gon˜i and Hedges, 1992). Their relative composition can be used to differentiate between OM inputs such as lignin (Hedges and Ertel, 1982), oxidation products of cutin, polysaccharides and/or proteins (Gon˜i and Hedges, 1990; Gon˜i et al., 2000), tannins and other flavanoids (Dickens et al., 2007), and Sphagnum moss (Williams et al., 1998) as well as to discriminate between specific tissue inputs such gymnosperms versus angiosperms, and woody versus non-woody plants (Hedges and Mann, 1979; Gon˜i and Hedges, 1992). The major lignin-derived phenol monomers (vannyl, syringyl, cinnamyl) were not detected in any of the Antarctic cryoconite samples, which further suggests that higher plant vegetation is not a dominant contributor to COM in this region (Table 3). The absence of these lignin-derived phenols was also observed in COM from a temperate glacier in the Canadian Rocky Mountains (Xu et al., 2010). Cinnamyl (C; p-coumaric and ferulic acid) monomers that are only produced by non-woody gymnosperm and angiosperm tissues were below detection limits in all samples. Both syringyl (S; syringic acid, syringaldehyde, acetosyringone, syringylglyoxalic acid) and vanillyl (V; vanillic acid, vanillin) monomers were detected in the JEG1 cryoconite sample suggesting a dominant woody angiosperm tissue input to COM. V monomers were the only lignin-derived phenol biomarkers detected in Leverett cryoconite suggesting a predominance of woody gymnosperm tissue inputs and/or degradation of the other lignin phenol tissues (discussed further later in this section; Table 3). Hydroxy-

7

phenol (P; p-hydroxybenzaldehyde, m-hydroxybenzoic acid, p-hydroxybenzoic acid) monomers, which have been suggested to originate from plant-derived proteins (Gon˜i et al., 2000) or from polyphenols resembling a hydrolysable tannin structure found in the Sphagnum cell wall (Rasmussen et al., 1995; Kuder and Kruge, 1998; Williams et al., 1998), were detected in all cryoconite samples. 3,5-Dihydroxybenzoic acid, which is derived from vascular plants (Dickens et al., 2007), was also detected in JEG1 COM (Table 3). In addition to the phenolic compounds which provide taxonomic information on COM sources, the relative distribution of the lignin-derived monomeric phenols has been found to be a valuable indicator of OM degradation state for soils and sediments (Otto and Simpson, 2006). Lignin biodegradation, mainly by white- and brown-rot fungi (Hedges et al., 1988; Opsahl and Benner, 1995), modifies the side chains of the monomers, mainly by the demethylation of methoxyl groups (Ertel and Hedges, 1984). This has been shown to lead to an enrichment of the P (unmethoxylated) and V (1 methoxyl group) monomers relative to S (2 methoxyl groups) monomers (Kuder and Kruge, 1998; Zaccone et al., 2008). Therefore, ratios of P/V and S/V monomers may be related to COM degradation state and that the observed P and V monomers may be a mixture of diagenetically altered lignin monomers and/or phenols derived from Sphagnum (Zaccone et al., 2008). Biodegradation of terrestrially-derived OM has been suggested as the predominant degradation pathway in soils with photooxidation contributing to alterations of litter chemistry and substrate destabilization (Feng et al., 2011). However, cryoconite holes often fill with water (Hodson et al., 2008) and are exposed to extreme climate conditions including extended sunlight for months out of the year which may increase the importance of chemical photooxidation of lignin phenols (Benner and Kaiser, 2011). Changes in the oxygen functionality of the lignin phenol side chains have also been used to assess the diagenetic alteration (increase of acid monomers relative to aldehyde monomers; Ad/Al) of a wide variety of geochemical samples (Ertel and Hedges, 1984; Opsahl and Benner, 1995; Kuder and Kruge, 1998; Otto and Simpson, 2006; Zaccone et al., 2008; Pautler et al., 2010a; Woods et al., 2011). The calculated values of (Ad/Al)V of the vanillyl monomers for Leverett COM are relatively high compared to those for the Leverett glacial mud and JEG1 COM samples (Table 3), suggesting either a more advanced stage of lignin oxidation within these samples or deposition of extensively degraded material. Overall, the relatively low concentrations of detected ergosterol and fungal PLFA biomarkers suggests that photooxidation may be an important degradation pathway of lignin-derived phenols in this COM. The lower (Ad/Al)V value calculated for the JEG1 sample is suggestive of fresher or relatively less degraded COM and may indicate that this cryoconite hole contains recently deposited or fresher material that has not undergone substantial biodegradation and/or photooxidation. The distribution of PLFA biomarkers is valuable for assessing microbial community structure (White et al., 1979; Frostega˚rd and Ba˚a˚th, 1996; Zelles, 1999) and are

Leverett

8

Table 3 Concentration of CuO products (mg g1 OC) and proxies in cryoconite and glacier mud samples. JEGa

Garwood

Joyce

L2

L3

L4

L5

L6

LMud

JEG 1

G1

G2

G3

J1a

J1b

J2

J3

JSupra

0.3 0.1 1.0 1.4

0.6 0.4 2.5 3.6

1.1 0.6 4.4 6.1

0.3 0.2 1.3 1.8

0.5 0.2 1.9 2.5

1.2 0.5 4.2 5.8

0.2 0.2 1.2 1.5

0.1 0.3 0.4 0.8

0.2 0.02 0.3 0.6

0.2 0.01 0.2 0.3

0.3 0.01 0.2 0.5

0.4 0.01 0.2 0.6

0.4 0.01 0.2 0.6

0.4 0.01 0.2 0.5

1.4 0.03 0.5 1.9

0.4 0.01 0.2 0.6

Other benzenes 3,5-Dihydroxybenzoic acid

nd

nd

nd

nd

nd

nd

nd

0.6

nd

nd

nd

nd

nd

nd

nd

nd

Lignin monomers Vanillin Acetovanillone Vanillic acid Total vanillyls (V)

0.1 nd 0.2 0.3

0.7 nd 2.2 2.9

0.8 nd 3.0 3.8

0.4 nd 0.9 1.3

0.4 nd 1.1 1.5

0.2 nd 0.6 0.8

0.1 nd 0.1 0.2

0.2 nd 0.1 0.3

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

Syringaldehyde Acetosyringone Syringic acid Syringylglyoxalic acid Total syringyls (S)

nd nd nd nd –

nd nd nd nd –

nd nd nd nd –

nd nd nd nd –

nd nd nd nd –

nd nd nd nd –

nd nd nd nd –

0.3 0.1 0.2 0.1 0.7

nd nd nd nd –

nd nd nd nd –

nd nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

nd nd nd –

p-Coumaric acid Ferulic acid Total cinnamyls (C) Total lignin monomers

nd nd – 0.3

nd nd – 2.9

nd nd – 3.8

nd nd – 1.3

nd nd – 1.5

nd nd – 0.8

nd nd – 0.2

nd nd – 1.5

nd nd – –

nd nd – –

nd nd – –

nd nd – –

nd nd – –

nd nd – –

nd nd – –

nd nd – –

Total benzenes and phenols

1.7

6.1

9.9

3.1

4.0

6.6

1.7

2.8

–

–

–

–

–

–

–

–

5.06 – 3.47 –

1.20 – 3.06 –

1.61 – 3.57 –

1.35 – 2.36 –

1.69 – 2.65 –

7.34 – 3.40 –

8.07 – 0.52 –

2.97 4.70 0.69 0.46

– – – –

– – – –

– – – –

– – – –

– – – –

– – – –

– – – –

– – – –

CuO product proxies P/V S/V (Ad/Al)v (Ad/Al)s nd = not detected. a JEG, John Evans glacier.

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

L1 Hydroxyphenol products p-Hydroxybenzaldehyde m-Hydroxybenzoic acid (3-OH) p-Hydroxybenzoic acid (4-OH) Total (P)

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

used as a proxy for living microbes and microbial activity (Evershed et al., 2006; Webster et al., 2006) in soils and sediments because these biomarkers are only present in viable microbial cells and decay rapidly upon cell death (White et al., 1979). A wide variety of PLFAs were detected in all cryoconite samples, with the highest concentrations of gram negative (16:1x7, cy17:0, 18:1x7 and cy19:0) and gram positive (i14:0, i15:0, a15:0, i16:0, i17:0, a17:0, i18:0, a18:0) bacterial PLFAs detected in the Antarctic cryoconite holes that were open to the atmosphere at the time of sampling (G1, G3, J1a) and in the Leverett glacier mud. The highest concentration of fungal PLFA (18:2x6) was detected in JEG1 (Fig. 1). The concentrations of bacterial PLFAs detected in the open cryoconite holes in Antarctica indicate a higher rate of microbial activity, likely due to the increased availability of oxygen relative to the covered cryoconite holes, where microbial degradation or cycling of COM may occur at a slower rate (Bagshaw et al., 2007; Fountain et al., 2008; Hodson et al., 2008; Anesio et al., 2010). The detection of fungal PLFA and ergosterol biomarkers in JEG1 is likely linked to the contribution of fresh lignin-derived material and carbohydrates detected in this sample suggesting that this fungal activity may be associated with larger availability of suitable substrates (Martinez et al., 2011). Conversely, much higher (Ad/Al)V ratios in the Leverett COM samples suggest that the lignin-derived phenol substrates are at an advanced stage of oxidation. The lower abundance of fungal PLFAs, the detection of ergosterol in only one sample (L6) and absence in the remaining suggests that photooxidation may have contributed to the extensive lignin degradation observed. The detection of both bacterial and fungal PLFAs in all samples suggests that cryoconite holes contain active microbial communities, and their quantified abundance is on the same order of magnitude as has previously been observed in polar soil samples (Pautler et al., 2010b; Aislabie et al., 2012), although it is lower than detected in COM from

160

Gram Negative Bacteria PLFAs Gram Positive Bacteria PLFAs Fungal PLFAs

g/g(OC)

120

80

40

L1 L2 L3 L4 L5 LM L6 JE ud G 1 G 1 G 2 G 3 J1 J1 a b J2 JS J up 3 ra

0

Fig. 1. PLFA biomarker distributions in COM, glacial mud, and supraglacial pond sediments of gram negative bacteria (16:1x7, cy17:0, 18:1x7 and cy19:0), gram positive bacteria (i14:0, i15:0, a15:0, i16:0, i17:0, a17:0, i18:0, a18:0) and fungi (18:2x6).

9

the Athabasca glacier in the Canadian Rockies (Xu et al., 2010). 3.2. Nuclear magnetic resonance (NMR) spectroscopy 3.2.1. Structural elucidation of the major cryoconite organic matter constituents Several non-targeted advanced NMR techniques were used to determine the overall OM composition of whole environmental samples (solid) and base-soluble extracts (liquid) because they can complement the information obtained from biomarker measurements (Simpson et al., 2008; Pautler et al., 2010b; Xu et al., 2010). A suite of NMR experiments was performed on HF/HCl treated cryoconite debris (solid-state 13C CP-MAS) and base-soluble extracts (solution-state 1H 1-D, DE, TOCSY and HSQC). The major chemical constituent assignments derived using these complementary techniques are shown in detail in Fig. 2, using the JEG1 COM sample as an example which were then used to assign COM components in the 1D NMR spectra for the remaining samples. The solid-state 13C CP-MAS NMR spectrum provides an overview of the major types of bulk chemical functional groups of COM (Fig. 2A). O-alkyl carbon (50–110 ppm) consists predominantly of oxygen substituted aliphatic structures such as those found in carbohydrates or peptides (65–95 ppm), a-carbons from peptides (shoulder: 50– 60 ppm), and the anomeric carbon from carbohydrates (105 ppm), whereas the alkyl carbon region (0–50 ppm) signals terminal CH3 groups (15 ppm) along with straight chain methylene (CH2) structures from proteins, lipids and/or waxes (Baldock et al., 1992; Simpson et al., 2008). The contribution from the aromatic carbon region (110– 165 ppm) is often derived from aromatic amino acid residues in peptides and/or natural products derived from plant polyphenolic biomolecules, and the carboxylic carbon region (165–210 ppm) indicates contributions from carboxylic acid and/or keto-type structures (Preston and Trofymow, 2000; Quideau et al., 2001). In addition to determination of the major COM functional groups, the relative sample degradation can be approximated from solid-state 13C CP-MAS NMR spectra by calculating the alkyl/O-alkyl ratio because it increases with progressive biodegradation of OM (Baldock et al., 1992; Simpson et al., 2008). The measured alkyl/O-alkyl ratio for JEG1 of 0.23 provides further evidence that JEG1 COM composition is mainly comprised of fresh, non-degraded material. The application of solution-state NMR spectroscopy to the base-soluble COM fraction facilitates a wider array of experiments and allows for the acquisition of better resolved spectra and subsequent assignments of chemical structures that typically cannot be performed in the solidstate due to overlapping resonances (Simpson et al., 2011). Fig. 2B highlights a standard 1-D 1H NMR spectrum for the base-extractable COM from JEG1 as an example with several chemical shift assignments that include peptide amino acid side chains, aliphatic compounds, and carbohydrates that were confirmed by 2-D NMR experiments (Fig. 2D–F) and NMR spectral prediction (Fig. 2G). COM constituents that exhibit a large amount

10

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

A

D

O-Alkyl C Region

3 1

3

2

O-substituted rings (carbohydrates)

3 3

5

1

Anomeric C (carbohydrates)

ppm

4

2 3

6 7

Alkyl C Region

8

Carboxylic C Region 8

Aromatic C Region

6

4

2

0

ppm

E

Aliphatic 20

α CH (peptides) 140

120

100

B

80

60

40

20

DMSO (solvent)

ppm

40

(CH2)n

Carbohydrates

60

Anomeric CH (carbohydrates)

ppm

160

CH2 β to COOH

80

CH2 (carbohydrates) 100

Phe Anomeric 1H (carbohydrates) α 1H (peptides)

CH2 γ to COOH

Aromatic Amino Acid SC

8

F

N-H in peptides

CH (carbohydrates)

Tyr

Terminal CH3 6

4 ppm

120

2

N-Acetyl in PG

4

10

SC

15

2 1 9.0

8.0

7.0

6.0

5.0

4.0

3.0

2.0

20 25

ppm

ppm

180

30

C

Carbohydrates

5

35 40

3 Peptide Peptide CH3

45

DMSO

CH2

2.5

2.0

1.5

1.0

0.5

ppm

Anomeric 1H (carbohydrates)

G

Amino Acid SC

α 1H (peptides)

5 20

115 40

PG

80

6

5

4 ppm

3

2

6.0

5.0

4.0

3.0

2.0

ppm

O NH

NH NH

HN O

135

7.00 ppm

6.75

O

NH

H3C O NH NH CH3

4

O NH O

O

NH O NH

5 OH

4

NH2 5

O CH3

6.50

H2N

23

2 O 3

H2N

3 7.25

1

O

7.0

2

120

1

8.0

130

100

7

9.0

125

4

ppm

120

1

ppm

Aromatic Amino Acid SC N-H in peptides

60

NH2

HO

O

OH

Fig. 2. NMR spectroscopic analysis of COM from the JEG1; (A) Solid-state 13C CP-MAS NMR spectrum highlighting the major carbon functional group regions, (B) Solution-state 1H NMR spectrum and structural assignments of the base-soluble extracts for COM highlighting the major 1H chemical shift assignments from the literature (Simpson et al., 2011) and supporting 2-D NMR experiments; (C) DE 1H NMR spectrum in which small molecules exhibiting large diffusion are gated from the final spectrum leaving 1H signals from large/rigid domains; (D) TOCSY spectrum highlighting the major 1H–1H couplings which are outline in detail in the text; (E) HSQC spectrum highlights the major H-C couplings from microbial protein/peptide amino acids, carbohydrate structures, and aliphatic groups (Simpson et al., 2007a, b); (F) Expanded HSQC spectrum of the aliphatic region, spectral assignments in addition to the N-acetyl group from peptidoglycan are outlined in the text; (G) HSQC simulation of a peptide fragment (shown below the spectra) isolated from soil bacteria (Ralstonia eutropha; Benndorf et al., 2007) confirming the assignments of aromatic C–H coupling from Phe (labeled 1, 2 and 3) and Tyr (labeled 4 and 5) protein/peptide side chains further highlighting the microbial dominance in the extracts. Abbreviations: PG = peptidoglycan, SC = amino acid side chains, Tyr = tyrosine, Phe = phenylalanine.

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18

of motion (diffusion) during acquisition are edited out of DE 1H NMR spectra which highlights 1H signals that arise from large macromolecular and/or rigid compounds only (Fig. 2C; Wu et al., 1995; Simpson et al., 2007b). Chemical shift assignments that can be used to distinguish between plant- and microbial-derived inputs to base-extractable COM were confirmed by 2-D NMR techniques. A TOCSY spectrum (Fig. 2D) revealed amide-a–b–c along with a–b, b–c and longer range 1H–1H couplings from microbial protein/peptide amino acid side chains (Fan et al., 2000; Kelleher and Simpson, 2006; Simpson et al., 2007a) and 1H–1H couplings for less source specific carbohydrate structures (Fan et al., 2000; Kelleher and Simpson, 2006; Simpson et al., 2007a,b). The acquisition of HSQC NMR facilitates the 1H chemical shift assignments for 1-D experiments of base-soluble COM extracts by exploiting the increased chemical shift dispersion of 13C nuclei (Simpson et al., 2011). Fig. 2E and F reveal the major C–H couplings from microbial protein/peptide amino acid side chains, carbohydrates and aliphatic groups (Simpson et al., 2007a,b). The assignments of the aromatic protein/peptide amino acid side chains in the spectrum (as opposed aromatic 1H nuclei resonating from other potential natural product inputs), was confirmed by HSQC spectral simulation of a peptide fragment (shown below the spectra, Fig. 2F) from a soil bacterium (R. eutropha) protein extract (Benndorf et al., 2007). The simulated spectrum was compared to the aromatic signals of the COM extract allowing for the specific assignment of tyrosine and phenylalanine amino acid side chain residues (Fig. 2F). Further microbial-derived COM contributions are supported by the signal consistent with the N-acetyl functional group likely derived from peptidoglycan and/or chitin cell walls (Lorenz et al., 2007; Simpson et al., 2007b). This signal has previously been applied as an additional proxy to assess microbial contribution in soils and sediments based on its relative contribution to the overall spectrum (Pautler et al., 2010b; Szpak et al., 2012). A notable absence from the HSQC spectrum is the large lignin-derived methoxyl (OCH3) signal derived from lignin (C–H correlation of d13C: 58 ppm and d1H: 3.8 ppm; Kelleher and Simpson, 2006) despite the detection of lignin-derived phenols after the alkaline CuO biomarker extraction in the Arctic samples. It is likely that the overall contribution of lignin to COM is very low since it is below the NMR limit of detection. 3.2.2. Comparison of COM composition between glaciers The relative contributions of the major chemical shift regions from 13C CP-MAS NMR can be used to compare COM composition between glaciers (Table 4). The contributions of each of the bulk carbon functional groups appear to be variable and specific trends are difficult to observe using results from this technique alone. However, the composition of JEG1 COM appears to differ the most from the remaining samples, as its spectrum is dominated by oxygen substituted aliphatic structures such as those found in carbohydrates (accounting for 78% of the total signal; Table 4). The calculated alkyl/O-alkyl ratios are more variable in COM from the Leverett glacier (0.49– 1.29) than in COM from the Garwood (0.7–0.82) and Joyce

11

(0.67–0.90) glaciers. This suggests a greater heterogeneity of constituents and/or microbial activity between in the Leverett samples than in those from Antarctica, which is consistent with the biomarker measurements. JEG1 COM has the lowest alkyl/O-alkyl ratio, which suggests that this sample is the least degraded and contains the largest amount of labile carbon of all the samples analyzed in this study. Solution-state 1H NMR spectroscopy of base-soluble natural OM extracts has emerged as a complementary technique to the traditional solid-state 13C CP-MAS NMR technique and has been shown to be very sensitive to subtle changes in OM chemistry with higher spectral resolution and/or dispersion (Simpson et al., 2007b; Feng et al., 2008; Pautler et al., 2010b). It is important to note that solution-state 1H NMR spectroscopy can only be used to analyze the soluble OM constituents, which likely represent between 50–80% of the total OM in the samples (Simpson et al., 2007a). However, when applied in tandem with solid-state 13C NMR, this method provides a more complete level of specificity and molecular detail for source determination and structural elucidation of OM (Clemente et al., 2012). Examination of the 1H NMR spectra of COM samples from JEG and the Leverett glacier (Figs. 2 and 3) allowed the assignment of the major chemical constituents present in the soluble fraction. The 1H NMR spectrum of JEG1 highlights contributions from both alkyl and O-alkyl constituents whereas the 1H NMR spectrum of L4 (example of Leverett COM) appears to be dominated by aliphatic constituents relative to carbohydrate constituents. The application of DE 1H NMR of L4 however reveals major 1 H signals derived from microbial protein/peptides and attenuates a large amount aliphatic 1H signals (Fig. 3B) whereas signals in the O-alkyl carbohydrate region are not attenuated suggesting that these structures are also major constituents to the overall large/rigid structures. DE 1H NMR spectra of extracted microbes and microbial-dominated OM possess a larger CH3 signal relative to the (CH2)n signal and much lower abundance of carbohydrates (Simpson et al., 2007a; Feng et al., 2010; Xu et al., 2010) whereas vascular plant rich OM shows the opposite trend (Deshmukh et al., 2003, 2005; Simpson et al., 2003). The larger intensity of (CH2)n signal and O-alkyl (carbohydrate) constituents in the DE 1H NMR spectrum of L4 deviate from DE 1H NMR spectra of pure microbes and is therefore suggestive of an inputs from plant aliphatic material to these samples which is consistent with both biomarker and 13C CP-MAS NMR observations for samples from the Leverett glacier. Upon confirmation of chemical assignments, DE 1H NMR and 13C CP-MAS NMR spectra provide a powerful approach for comparison of COM samples from different glaciers. Fig. 4 compares 13C CP-MAS NMR and DE 1H NMR spectra of samples from each of the glaciers. The overall molecular composition as shown by 13C CP-MAS NMR are retained by DE 1H NMR. JEG1 and L1 appear to have a larger contribution from carbohydrate (both spectra) and aliphatic (CH2)n signals (DE 1H NMR spectra), which suggests a larger input of plant material. Both G3 and J3 have smaller carbohydrate contributions relative to the rest of the spectra and a more dominant CH3 ali-

0.84 0.73 0.67

3.3. Cryoconite organic matter biogeochemistry

0.90 0.82 0.82 0.70 0.23 1.14 1.29 0.76 0.82 1.09 JEG, John Evans glacier.

1.0 0.49 Alkyl/O-alkyl

a

33 43 16 8 36 40 12 6 37 45 11 7 29 35 23 13 32 46 17 5 18 78 2 2 41 36 13 10 40 31 17 12 32 42 15 11 31 38 20 11 36 33 18 13 38 38 19 13 26 53 15 6 Alkyl C O-Alkyl C Aromatic C Carboxylic C

LMud L6 L5 L4 L3 L2

phatic 1H NMR signal compared to (CH2)n, highlighting the microbial-dominant nature of these COM samples (which is supported by the assignments of 1H signals from microbial protein/peptide amino acid side chains and peptidoglycan/chitin (Simpson et al., 2007a; Feng et al., 2010) and the larger amount of extractable biomarkers in JEG1 and Leverett COM relative to Garwood and Joyce COM samples).

0.77

36 49 9 6 32 48 14 6

JSupra J3 J1b Joyce

J1a G3 G2

Garwood

L1

G1

JEG

JEG 1

Leverett

a

Table 4 Relative contribution (%) of the alkyl (0–50 ppm), O-alkyl (50–110 ppm), aromatic (110–165 ppm) and carboxylic (165–210 ppm) signals in the solid-state

13

J2

37 44 14 5

B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18 C CP-MAS NMR spectra of COM.

12

The biomarker and NMR analyses of cryoconite samples from different geographic locations suggest that both allochthonous and autochthonous material are important contributors to COM from Arctic glaciers, whereas COM from the Antarctic glaciers (Garwood and Joyce glacier) is dominated by autochthonous material. Microbes are shown to be important contributors to all COM samples in this study as shown by the large contribution of shortchain lipid biomarkers in the solvent extracts and the microbial fingerprint observed in DE 1H NMR. Previous studies have shown that cyanobacteria are important atmospheric N-fixation and photosynthetic organisms in cryoconite holes (Telling et al., 2011). However biomarkers and 1 H NMR signals specific to a cyanobacterial mat (Feng et al., 2010) were not observed in these samples. It has been shown that cyanobacteria may account for only 2.2% of the microbial biomass in COM from Svalbard (Kastovska et al., 2007) therefore their contribution to COM is likely below the analytical detection limits in comparison to the other microalgae biomarkers observed, consistent with results obtained previously for COM from the Athabasca glacier, Canada (Xu et al., 2010). Cryoconite sampled from Garwood and Joyce glaciers is comprised mainly of microbial constituents with a lower abundance of solvent-extractable compounds. Solvent-extracts mainly consisted of short-chain n-alkanoic acid biomarkers and some simple sugars while a DE 1H NMR spectral fingerprint consisting of protein/peptide and carbohydrates that is typically observed for microbes and microbial-dominated OM was observed (Simpson et al., 2007a; Feng et al., 2010). The sterol biomarkers detected in these samples likely originate from eukaryotic organisms such as mosses and/or microalgae rather than vascular plants due to the absence of plant-derived lipid biomarkers. This is also supported by the high abundance of trehalose and the detection of P phenolic monomers (in conjunction with the absence of lignin-derived phenol monomers) after CuO oxidation extractions which are thought to originate from mosses such as Sphagnum. The low concentrations of the fungal derived PLFA biomarker (18:2x6) and absence of ergosterol suggests that fungi are not the only contributors to the sterols and trehalose biomarkers detected in these samples. The alkyl/O-alkyl ratios calculated from 13C CPMAS NMR (Table 4) are suggestive of COM degradation by either within the cryoconite holes or prior to deposition. The overall enrichment of alkyl carbon relative to O-alkyl carbon has correlated to overall degradation of OM samples (Baldock et al., 1990, 1992); the O-alkyl constituents remaining after biodegradation are likely associated with

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O-Alkyl

Amide & Aromatic

13

Alkyl

A

carbohydrates

B

(CH2)n

peptide α-H

CH3

anomeric 1H (carbohydrates) CH2 amide N-H

10 1

9

LP CH2

Phe Tyr

8

7

6

5

4

3

2

1

ppm

1

Fig. 3. H NMR spectra of L4 COM sample; (A) 1-D H NMR spectrum showing the larger contribution of alkyl aliphatic 1H constituents over the O-alkyl (mainly carbohydrates) and aromatic constituents; (B) DE 1H NMR spectra with major 1H constituents assigned. The aliphatic (CH2)n signal is larger relative to the terminal (CH3) signal which is suggestive of vascular plant inputs in conjunction with microbial proteins/peptides (Simpson et al., 2003, 2007a; Feng et al., 2010). Abbreviations: PG = peptidoglycan, LP = lipoprotein, Tyr = tyrosine, Phe = phenylalanine.

microbes (Simpson et al., 2007a; Feng et al., 2011) possibly biomacromolecules produced during degradation known as extracellular polymeric substances which are primarily composed of polysaccharides with smaller amounts of lipids and nucleic acids (Wingender et al., 1999). The largest amount of bacterial PLFA biomarkers observed in this study was found in Antarctic cryoconite holes that were open to the atmosphere at the time of sampling (G1, G3, J1A), indicating that exposure to the atmosphere may stimulate microbial activity and lead to further COM degradation in these samples. As climate change progresses in Antarctica, more cryoconite holes may be open to the atmosphere for longer periods of time which would result in larger contributions and feedback to the carbon cycle. COM sampled from the Arctic was found to be more structurally complex than that in the Antarctic samples. Microbes are considered to be important contributors to the samples from the Leverett glacier based on the extraction of short-chain lipid biomarkers and the identification of

microbial protein/peptide amino acid side chain signals in solution-state 1H NMR experiments and absences of lignin-derived peaks in the HSQC spectra. However, the larger aliphatic (CH2)n 1H signals relative to CH3 along with a larger abundance of 1H signals from carbohydrates in DE 1H NMR suggests additional plant-derived inputs. The solvent-extractable lipid distributions and the detection of hydroxyphenolic monomers after CuO oxidation suggest that the majority of the plant inputs are likely from mosses and/or lichens. The n-alkane distribution, detection of C2910-ol, and V lignin-derived phenol monomers indicate possible inputs from grasses/broad leaves to L6 and Leverett glacier mud, whereas the remainder of the samples possess OM derived from conifer trees, likely deposited from long range atmospheric transport of OM particulates (Simoneit, 2006). The high (Ad/Al)V and alkyl/O-alkyl ratios (Table 4) indicate that the COM is degraded to varying extents. The limited amount of the fungal PLFA biomarker and the absence of ergosterol in nearly all the samples

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B.G. Pautler et al. / Geochimica et Cosmochimica Acta 104 (2013) 1–18 13C

CP-MAS NMR

Carboxyl Aromatic O -Alkyl

DE 1H NMR Amide & Aromatic

Alkyl

O-Alkyl

Alkyl

carbohydrates

JEG1

(CH2)n

O-substituted 13C ring structures (carbohydrates)

CH3

peptide α-H anomeric 1H (carbohydrates)

anomeric 13C (carbohydrates)

Amino Acid SC PG

Tyr amide N-H Phe

carbohydrates

L1 peptide α-H anomeric 1H (carbohydrates)

LP CH2

Tyr

amide N-H

CH2 PG

Phe

CH3 (CH2)n

LP CH2

G3

peptide α-H Tyr

amide N-H

(CH2)n CH3

CH2 PG

Phe

SC

CH3

J3 amide N-H

LP (CH2)n CH2 CH2 peptide α-H

Tyr Phe

200 180 160 140 120 100 80

60

40

20 ppm

10

9

8

7

PG

6

5

4

3

2

1 ppm

Fig. 4. Comparison of 13C CP-MAS NMR and DE 1H NMR spectra of JEG1 COM (JEG1), Leverett COM (L1) to Antarctic cryoconite sampled from the Garwood glacier (G3) and Joyce glacier (J3) with major structural regions and assignments assigned. JEG1 and L1 appear to have a larger contribution from carbohydrates (both spectra) and aliphatic (CH2)n signal (DE 1H NMR spectra) which suggests an addition contribution of plant material. G3 and J3 have smaller carbohydrate contributions (similar to that of purely extracted microbes) relative to the rest of the spectra and the more dominant CH3 aliphatic 1H NMR signal compared to (CH2)n highlighting the microbial-dominant nature of these COM samples (Simpson et al., 2007a; Feng et al., 2010; Xu et al., 2010). Abbreviations: PG = peptidoglycan, SC = amino acid side chains, LP = lipoprotein, Tyr = tyrosine, Phe = phenylalanine.

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suggests current fungal degradation of lignin phenols and carbohydrates is not the primary mode of degradation; highlight the potential importance of photochemical degradation pathways. The sole detection of vanillyl monomers may suggest woody gymnosperm tissue inputs, however OM samples that have already undergone extensive degradation may result in the enrichment of vanillyl (and possibly hydroxyphenol) monomers over syringyl monomers (Zaccone et al., 2008). If these COM samples are in fact highly diagenetically altered as hypothesized, the source information obtained from these biomarkers alone may be inaccurate. The COM from JEG1 displayed NMR signals and couplings from protein/peptide amino acid side chains, peptidogylcan/chitin and PLFA biomarkers suggesting that living microbes are important constituents to the overall composition. However, a high contribution of O-alkyl constituents (mainly carbohydrates) was observed by both solid and liquid-state NMR spectra. The biomarker proxies suggest dominant inputs from mosses/lichens and/or microalgae (presence of hydroxyphenolic monomers after CuO oxidation, sterols and trehalose biomarkers) and grass/broad leaf inputs (presence of plant lipids, vanillyl and syringyl ligninderived phenols and plant-derived 3,5-dihydroxybenzoic acid). The low alkyl/O-alkyl and (Ad/Al)V,S suggest fresh COM that has not undergone significant diagenesis at the time of sampling. These complementary molecular-level techniques demonstrate that COM samples contain active microbial communities and several potential OM substrates. The observed differences in COM composition and microbial activity could be related to substrate bioavailability where COM becomes encapsulated into microstructures potentially leading to carbon sequestration on glacier surfaces (Hodson et al., 2010b; Langford et al., 2010). Microbial growth often results in the production of extracellular polymeric substances that contain several functional groups that are available to interact with granule surfaces, encapsulating available OM substrates into micro-aggregates (Omoike et al., 2004; Spence and Kelleher, 2012). Therefore bioavailability of substrates may be an important contributor to COM sequestration on glacier surfaces and should be investigated in greater detail. 4. CONCLUSIONS This examination of COM from both Arctic and Antarctic glaciers with a variety of organic geochemical techniques has highlighted its heterogeneity, suggesting differences in sources, degradation and overall biogeochemistry. COM from Antarctic glaciers appeared to be mainly autochthonous, with some inputs of moss material, whereas Arctic COM contained a wider diversity of inputs such as OM derived from microbes, mosses, and vascular plants, likely transported to the glaciers by atmospheric processes. Mud found on the surface of the Leverett glacier appeared to contain OM with a different molecular signature to COM from the same glacier, and is likely not the major source of all COM samples from this region. Microbial community abundance (approximated by PLFA biomark-

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ers) appears to be similar in magnitude to that in both Arctic and Antarctic soils, but lower than was previously observed from Athabasca glacier COM. As glaciers continue to recede as the result of climatic warming, these ice surface habitats may initially undergo a stimulation in microbial activity, although the surface area for them to colonize may be reduced and COM may be washed away more frequently by runoff. Collectively these data highlight the complex heterogeneity of COM which may be an important mechanism that promotes active bacterial colonies and sequestration of carbon on glacier surfaces. However, caution should be applied in extrapolations of local results to the global scale because the COM heterogeneity and complex substrate/microbial interactions vary for each cryoconite ecosystem and thus, the subsequent role cryoconite holes may play in the carbon cycle. ACKNOWLEDGMENTS David M. Wolfe is thanked for the assistance with biomarker extractions. M.J. Simpson thanks the Natural Science and Engineering Research Council (NSERC) of Canada for support via a Discovery Grant. B.G. Pautler thanks NSERC for a Canada Graduate Scholarship along with Dr. O. Pisani and Dr. J.W.H. Weijers for helpful comments and discussions. M.J. Sharp acknowledges support from NSERC in the form of Discovery and RTI grants and an Undergraduate Summer Research Award for A. Dubnick and Environment Canada for support from the Science Horizons. Prof. Sean Fitzsimons and Prof. Jemma Wadham are thanked for fieldwork assistance which was funded by Antarctic New Zealand for S. Fitzsimons and NERC for J. Wadham. We also thank Maya Bhatia for assistance with transporting some of the samples.

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