# Introducing global peat-specific temperature and pH calibrations based on brGDGT bacterial lipids

A. Naafs, G.N. Inglis, Y. Zheng, J. Amesbury, H. Biester, R. Bindler, J. Blewett, A. Burrows, D. del Castillo Torres, F. Chambers, et al.

► To cite this version:

A. Naafs, G.N. Inglis, Y. Zheng, J. Amesbury, H. Biester, et al. Introducing global peat-specific temperature and pH calibrations based on brGDGT bacterial lipids. Geochimica et Cosmochimica Acta, 2017, 208, pp.285-301. 10.1016/j.gca.2017.01.038. insu-01474123

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Introducing global peat-specific temperature and pH calibrations based on brGDGT bacterial lipids

B.D.A. Naafs, G.N. Inglis, Y. Zheng, M.J. Amesbury, H. Biester, R. Bindler, J. Blewett, M.A. Burrows, D. del Castillo Torres, F.M. Chambers, A.D. Cohen, R.P. Evershed, S.J. Feakins, A. Gallego-Sala, L. Gandois, D.M. Gray, P.G. Hatcher, E.N. Honorio Coronado, P.D.M. Hughes, A. Huguet, M. Könönen, F. Laggoun-Défarge, O. Lähteenoja, R. Marchant, E. McClymont, X. Pontevedra-Pombal, C. Ponton, A. Pourmand, A.M. Rizzuti, L. Rochefort, J. Schellekens, F. De Vleeschouwer, R.D. Pancost

PII: S0016-7037(17)30052-2

DOI: http://dx.doi.org/10.1016/j.gca.2017.01.038

Reference: GCA 10132

To appear in: *Geochimica et Cosmochimica Acta*

Received Date: 2 June 2016

Revised Date: 14 January 2017

Accepted Date: 18 January 2017

Please cite this article as: Naafs, B.D.A., Inglis, G.N., Zheng, Y., Amesbury, M.J., Biester, H., Bindler, R., Blewett, J., Burrows, M.A., del Castillo Torres, D., Chambers, F.M., Cohen, A.D., Evershed, R.P., Feakins, S.J., Gallego-Sala, A., Gandois, L., Gray, D.M., Hatcher, P.G., Honorio Coronado, E.N., Hughes, P.D.M., Huguet, A., Könönen, M., Laggoun-Défarge, F., Lähteenoja, O., Marchant, R., McClymont, E., Pontevedra-Pombal, X., Ponton, C., Pourmand, A., Rizzuti, A.M., Rochefort, L., Schellekens, J., De Vleeschouwer, F., Pancost, R.D., Introducing global peat-specific temperature and pH calibrations based on brGDGT bacterial lipids, *Geochimica et Cosmochimica Acta* (2017), doi: http://dx.doi.org/10.1016/j.gca.2017.01.038

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Introducing global peat-specific temperature and pH calibrations based on
brGDGT bacterial lipids

B.D.A. Naafs¹,²*, G.N. Inglis¹,², Y. Zheng³, M.J. Amesbury⁴, H. Biester⁵, R. Bindler⁶,
J. Blewett¹,², M.A. Burrows⁷, D. del Castillo Torres⁸, F.M. Chambers⁹, A.D. Cohen¹⁰,
R.P. Evershed¹,², S.J. Feakins¹¹, A. Gallego-Sala⁴, L. Gandois¹², D.M. Gray¹³, P.G.
Hatcher¹⁴, E.N. Honorio Coronado⁸, P.D.M. Hughes¹⁵, A. Huguet¹⁶, M. Könönen¹⁷, F.
Laggoun-Défarge¹⁸, O. Lähteenoja¹⁹, R. Marchant²⁰, E. McClymont¹,²¹, X.
Pontevedra-Pombal²², C. Ponton¹¹, A. Pourmand²³, A.M. Rizzuti²⁴, L. Rochefort²⁵, J.
Schellekens²⁶, F. De Vleeschouwer¹², and R.D. Pancost¹,²

¹Organic Geochemistry Unit, School of Chemistry, University of Bristol, Bristol, UK
²Cabot Institute, University of Bristol, Bristol, UK
³State Key Laboratory of Continental Dynamics, Department of Geology, Northwest
University, Xi’an, PR China
⁴Geography, College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
⁵Institut für Geoökologie, AG Umweltgeochemie, Technische Universität
Braunschweig, Braunschweig, Germany
⁶Department of Ecology and Environmental Science, Umeå University, Umeå,
Sweden
⁷Australian National University, Acton, Canberra, Australia
⁸Instituto de Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Perú
⁹Centre for Environmental Change and Quaternary Research, School of Natural and
Social Sciences, University of Gloucestershire, Cheltenham, UK
¹⁰Department of Earth and Ocean Sciences, University of South Carolina, Columbia,
USA
¹¹Department of Earth Sciences, University of Southern California, Los Angeles,
USA
¹²ECOLAB, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France
¹³Pinelands Field Station, Rutgers University, New Lisbon, USA
¹⁴Department of Chemistry and Biochemistry, Old Dominion University, Norfolk,
USA
¹⁵Palaeoecology Laboratory, University of Southampton, Southampton, UK
35
36 Paris, France

16 Sorbonne Universités, UPMC, Univ Paris 06, CNRS, EPHE, UMR 7619 METIS,

37
17 Department of Forest Sciences, University of Helsinki, Finland

38
18 Université d'Orléans / CNRS / BRGM, ISTO, UMR 7327, Orléans, France

39
19 Department of Biology, University of Turku, Finland

40
41 York Institute for Tropical Ecosystems, Environment Department, Wentworth Way
University of York, York, UK

42
21 Department of Geography, Durham University, Durham, UK

43
44 Compostela, Santiago de Compostela, Spain

45
46 Division of Marine Geology & Geophysics, University of Miami - RSMAS, Miami,
USA

47
24 Department of Chemistry, Claflin University, Orangeburg, USA

48
49 Laval, Quebec City, Canada

25 Peatland Ecology Research Group (PERG), Centre for Northern Studies, Université

50
26 Department of Soil Science, University of São Paulo, Piracicaba, Brazil

51

52
* Corresponding author (david.naafs@bristol.ac.uk)

53

54 Abstract

Glycerol dialkyl glycerol tetraethers (GDGTs) are membrane-spanning lipids from
Bacteria and Archaea that are ubiquitous in a range of natural archives and especially
abundant in peat. Previous work demonstrated that the distribution of bacterial
branched GDGTs (brGDGTs) in mineral soils is correlated to environmental factors
such as mean annual air temperature (MAAT) and soil pH. However, the influence of
these parameters on brGDGT distributions in peat is largely unknown. Here we
investigate the distribution of brGDGTs in 470 samples from 96 peatlands around the
world with a broad mean annual air temperature (-8 to 27 °C) and pH (3-8) range and
present the first peat-specific brGDGT-based temperature and pH calibrations. Our
results demonstrate that the degree of cyclisation of brGDGTs in peat is positively
correlated with pH, pH = 2.49 x CBT<sub>peat</sub> + 8.07 (n = 51, R<sup>2</sup> = 0.58, RMSE = 0.8) and
the degree of methylation of brGDGTs is positively correlated with MAAT,
MAAT<sub>peat</sub> (°C) = 52.18 x MBT<sub>5me</sub> - 23.05 (n = 96, R<sup>2</sup> = 0.76, RMSE = 4.7 °C).
These peat-specific calibrations are distinct from the available mineral soil calibrations. In light of the error in the temperature calibration (~ 4.7 °C), we urge caution in any application to reconstruct late Holocene climate variability, where the climatic signals are relatively small, and the duration of excursions could be brief. Instead, these proxies are well-suited to reconstruct large amplitude, longer-term shifts in climate such as deglacial transitions. Indeed, when applied to a peat deposit spanning the late glacial period (~15.2 kyr), we demonstrate that MAAT<sub>peat</sub> yields absolute temperatures and relative temperature changes that are consistent with those from other proxies. In addition, the application of MAAT<sub>peat</sub> to fossil peat (i.e. lignites) has the potential to reconstruct terrestrial climate during the Cenozoic. We conclude that there is clear potential to use brGDGTs in peats and lignites to reconstruct past terrestrial climate.

80

81 *Keyword: GDGT, biomarker, peatland, calibration, lignite*

82

83 Highlights:

84 - Analysis of brGDGT distributions in global peat dataset

85 - Correlation of brGDGT distributions with peat pH and mean annual air temperature

86 - Development of peat-specific temperature and pH proxies
1. Introduction

Although reconstructions of terrestrial environments are crucial for the understanding of Earth's climate system, suitable depositional archives (especially longer continuous sequences) are rare on land. Peatlands and lignites (naturally compressed ancient peat) are one exception and offer remarkable preservation of organic matter. Peats can be found in all climate zones where suitable waterlogged conditions exist. Typical peat accumulation rates are on the order of 1-2 mm/year (Gorham et al., 2003) and because they exhibit minimal bioturbation (although roots might be present) they are widely used as climate archives during the late Quaternary, predominantly the Holocene (e.g., Barber, 1993; Chambers and Charman, 2004). Peat-based proxies include those based on plant macrofossils, pollen, and testate amoebae (e.g., Woillard, 1978; Mauquoy et al., 2008; Väliranta et al., 2012), inorganic geochemistry (e.g., Burrows et al., 2014; Chambers et al., 2014; Hansson et al., 2015; Vanneste et al., 2015), (bulk) isotope signatures (e.g., Cristea et al., 2014; Roland et al., 2015) and organic biomarkers (e.g., Nichols et al., 2006; Pancost et al., 2007; Pancost et al., 2011; Huguet et al., 2014; Zocatelli et al., 2014; Schellekens et al., 2015; Zheng et al., 2015). Although these proxies can be used to provide a detailed reconstruction of the environment and biogeochemistry within the peat during deposition, an accurate temperature or pH proxy for peat is currently lacking (Chambers et al., 2012). This is particularly problematic because temperature and pH are key environmental parameters that directly affect vegetation type, respiration rates, and a range of other wetland features (e.g., Lafleur et al., 2005; Yvon-Durocher et al., 2014). The aim of this paper is to develop peat-specific pH and temperature proxies for application to peat cores as well as ancient peats from the geological record preserved as lignites.

We focus on using membrane-spanning glycerol dialkyl glycerol tetraether (GDGT) lipids. In general, two types of GDGTs are abundant in natural archives such as peats: 1) isoprenoidal (iso)GDGTs with *sn*-1 glycerol stereochemistry that are synthesized by a wide range of Archaea, and 2) branched (br)GDGTs with *sn*-3 glycerol stereochemistry that are produced by Bacteria (see review by Schouten et al., 2013 and references therein). A wide range of brGDGTs occur in natural archives such as mineral soils and peat; specifically, tetra-, penta-, and hexamethylated brGDGTs, each of which can contain 0, 1, or 2 cyclopentane rings (Weijers et al., 2006b). In addition, recent studies using peat and mineral soils have demonstrated that the additional methyl group(s) present in penta- and hexamethylated brGDGTs can
121 occur on either the α and/or ω-5 position (5-methyl brGDGTs) or the α and/or ω-6 position (6-methyl brGDGTs) (De Jonge et al., 2013; De Jonge et al., 2014).

123 brGDGTs are especially abundant in peat, in fact brGDGTs were first discovered in a Dutch peat (Sinninghe Damsté et al., 2000). The concentration of brGDGTs (as well as isoGDGTs) is much higher in the water saturated and permanently anoxic catotelm of peat compared to the predominantly oxic acrotelm, suggesting that brGDGTs are produced by anaerobic bacteria (Weijers et al., 2004; Weijers et al., 2006a; Weijers et al., 2011), potentially members of the phylum Acidobacteria (Weijers et al., 2009; Sinninghe Damsté et al., 2011; Sinninghe Damsté et al., 2014). Although the exact source organism(s) are/is currently unknown, in mineral soils (and potentially lakes) the distribution of bacterial brGDGTs is correlated with mean annual air temperature (MAAT) and pH (Weijers et al., 2007; Peterse et al., 2012; De Jonge et al., 2014; Loomis et al., 2014; Li et al., 2016). Over the past decade ancient deposits of mineral soils (e.g., Peterse et al., 2014) and peat (e.g., Ballantyne et al., 2010) have been used to reconstruct past terrestrial temperatures.

137 Mineral soils differ from peat as the latter are normally water saturated, consist predominantly of (partially decomposed) organic matter (the organic carbon content of peat is typically> 30 wt.%), are typically acidic (pH 3-6), and have much lower density. The combination of these factors means that peat becomes anoxic at relatively shallow depths, whereas mineral soils are typically oxic. Indeed, Loomis et al. (2011) showed that the brGDGT distribution in waterlogged soils is different from that in dry soils and Dang et al. (2016) recently provided direct evidence of moisture control on brGDGT distributions in soils. These differences suggest that microbial lipids in peat might not reflect environmental variables, i.e. pH and temperature, in the same way as they do in mineral soils.

147 Despite the high concentration of GDGTs in peats relatively few studies have examined the environmental controls on their distribution in such settings (Huguet et al., 2010; Weijers et al., 2011; Huguet et al., 2013; Zheng et al., 2015). Those studies found that the application of soil-based proxies to peats can result in unrealistically high temperature and pH estimates compared to the instrumental record. However, owing to the small number of peats that have been studied to date as well as the lack of peatland diversity sampled (the majority of peats sampled for these studies come from temperate climates in Western Europe), the correlation of temperature and pH
with brGDGT distribution in peats is poorly constrained. Notably, the lack of tropical peat brGDGT studies limits interpretations of brGDGT distributions in lignite deposits from past greenhouse climates (Weijers et al., 2011).

Here we compare brGDGT distributions in a newly generated global data set of peat with MAAT and (where available) *in situ* peat pH measurements. Our aim is to gain an understanding of the impact of these environmental factors on the distribution of brGDGTs in peat and develop for the first time peat-specific temperature and pH proxies that can be used to reconstruct past terrestrial climate.

## 2. Material and methods

### 2.1 Peat material

We generated a collection of peat comprising a diverse range of samples from around the world (Fig. 1). In total, our database consists of 470 samples from 96 different peatlands. In order to assess the variation in brGDGT distribution within one location, where possible we determined the brGDGT distribution in multiple horizons from within the top 1m of peat (typically representing several centuries of accumulation) and/or analyzed samples taken at slightly different places within the same peatland. A peat deposit typically consists of an acrotelm and catotelm, although marked heterogeneity can exist even over short distances (Baird et al., 2016). The acrotelm is located above the water table for most of the year and characterized by oxic conditions and active decomposition. The acrotelm overlies the catotelm, which is permanently waterlogged and characterized by anoxic conditions and very slow decomposition. Our dataset spans those biogeochemical gradients (e.g. acro/catotelm). Variations in peat accumulation rates differ between sites, implying that the ages of the brGDGT-pool might differ.

Our database includes peats from six continents and all major climate zones, ranging from high latitude peats in Siberia, Canada, and Scandinavia to tropical peats in Indonesia, Africa, and Peru (Fig. 2). It covers a broad range in MAAT from -8 to 27 °C. Although most samples come from acidic peats with pH <6, the dataset includes several alkaline peats and overall our dataset spans a pH range from 3 to 8. All samples come from freshwater peatlands, except for the one from the Shark River peat (Everglades, USA) that is marine influenced. Unsurprisingly, given their global distribution, the peats are characterized by a wide variety of vegetation, ranging from
Sphagnum-dominated ombrotrophic peats that are abundant in high-latitude and temperate climates to (sub)tropical peats dominated by vegetation such as *Sagittaria* (arrowhead) and *Cyperaceae* (sedge), and forested tropical peatlands.

191

## 2.2 Environmental parameters

The distribution of brGDGTs was compared to MAAT and *in situ* pH. MAAT was obtained using the simple bioclimatic model PeatStash, which provides surface air temperatures globally with a 0.5 degree spatial resolution (for details, see Kaplan et al., 2003; Gallego-Sala and Prentice, 2013). The temperature data in PeatStash is obtained by interpolating long-term mean weather station climatology (temperature, precipitation and the fraction of possible sunshine hours) from around the world for the period 1931–1960 (Climate 2.2 data are available online http://www.pik-potsdam.de/~cramer/climate.html). Crucially, mean annual temperatures in peat are similar to MAAT, assuming that the peat is not snow-covered for long periods of time (McKenzie et al., 2007; Weijers et al., 2011). The temperature at the top surface of (high-latitude) peat can differ from the MAAT due to insolation by snow during winter and intense heating during summer. Despite this, the seasonal temperature fluctuations in peat are dampened at depth as temperatures converge to MAAT (Hillel, 1982; Laiho, 2006; McKenzie et al., 2007; Weijers et al., 2011). We assume that all peat horizons experienced MAAT (the only data available on a global basis). This is likely an oversimplification that introduces some additional uncertainty in our calibration.

Where available, pH data were obtained from measured values reported in the literature or our measurements during sampling. For peats, pH cannot be determined using dried material, as is normally done for soils (Stanek, 1973). Accurate pH measurements can only be obtained from *in situ* measurements, especially for groundwater-fed wetlands, and these are not available for all locations.

215

## 2.3 Lipid extraction

For the majority of samples (>430 out of 470), between 0.1 and 0.5 g of dried bulk peat were extracted with an Ethos Ex microwave extraction system with 20 mL of a mixture of dichloromethane (DCM) and methanol (MeOH) (9:1, v/v) at the Organic Geochemistry Unit (OGU) in Bristol. The microwave program consisted of a 10 min ramp to 70 °C (1000 W), 10 min hold at 70 °C (1000 W), and 20 min cool down.
Samples were centrifuged at 1700 rounds per minute for 3 to 5 min and the supernatant was removed and collected. 10 mL of DCM:MeOH (9:1) were added to the remaining peat material and centrifuged again after which the supernatant was removed and combined with the previously obtained supernatant. This process was repeated 3 to 6 times, depending on the amount of extracted material, to ensure that all extractable lipids were retrieved. The total lipid extract (TLE) was then concentrated using rota-evaporation. An aliquot of the TLE (typically 25%) was washed through a short (<2 cm) silica column using DCM:MeOH (9:1) to remove any remaining peat particles. The TLE was dried under a gentle nitrogen flow and then re-dissolved in hexane/iso-propanol (99:1, v/v) and filtered using 0.45 µm PTFE filters.

A small number of peats were extracted using different methods and either the TLE or polar fraction was analyzed for GDGTs (see Table S1). Samples from the Kyambangunguru peat in Tanzania were extracted using the Bligh-Dyer protocol. Previous work on peat demonstrated that the brGDGT distribution is similar using Bligh-Dyer extraction as Soxhlet extraction (Chaves Torres and Pancost, 2016). The TLE was cleaned over a short Si column at the OGU in Bristol. Both cleaned TLE and polar fractions were re-dissolved in hexane/iso-propanol (99:1, v/v) and filtered using 0.45 µm PTFE filters.

## 2.4 Analytical methods

All samples were analyzed for their core lipid GDGT distribution by high performance liquid chromatography/atmospheric pressure chemical ionisation – mass spectrometry (HPLC/APCI-MS) using a ThermoFisher Scientific Accela Quantum Access triplequadrupole MS. Normal phase separation was achieved using two ultra-high performance liquid chromatography silica columns, following Hopmans et al. (2016). Crucially this method allows for the separation of the 5- and 6-methyl brGDGT isomers. Injection volume was 15 µL, typically from 100 µL. Analyses were performed using selective ion monitoring mode (SIM) to increase sensitivity and reproducibility (m/z 1302, 1300, 1298, 1296, 1294, 1292, 1050, 1048, 1046, 1036, 1034, 1032, 1022, 1020, 1018, 744, and 653). The results were integrated manually using the Xcalibur software. Based on daily measurements of an in-house generated peat standard, analytical precession (σ) over the 12 months during which the data were analyzed is 0.01 for the proxy index we define below (MBT<sub>5me</sub>, eq. 2).
256 2.5 Proxy calculation

257 Guided by previous studies we used a range of proxies to express ratios of different
258 GDGTs and the nomenclature of De Jonge et al. (2014) (Fig. 1).

259 eq. (1) MBT

$$
= \frac{(Ia + Ib + Ic)}{(Ia + Ib + Ic + IIa + IIa' + IIb + IIb' + IIc + IIc' + IIIa + IIIa' + IIIb + IIIb' + IIIc + IIIc')}
$$

260 The original methylation of branched tetraether (MBT) index compared the relative
261 abundance of tetramethylated brGDGTs (compounds Ia-Ic) to that of penta-
262 (compounds IIa-IIc') and hexamethylated (compounds IIIa-IIIc') brGDGTs that have
263 one or two additional methyl groups (Weijers et al., 2007). It was recently discovered
264 that the additional methyl groups in penta- and hexamethylated brGDGTs can also
265 occur at the C6 position (6-methyl brGDGTs, indicated by a prime symbol; e.g.
266 brGDGT-IIa'): the 6-methyl penta- and hexamethylated brGDGTs (De Jonge et al.,
267 2013). Excluding the 6-methyl brGDGTs from the MBT index resulted in the
268 MBT<sub>5me</sub>' index. In the global soil database the application of MBT<sub>5me</sub>' led to an
269 improved correlation with temperature (De Jonge et al., 2014).

$$
\text{eq. (2) } MBT'_{5ME} = \frac{(Ia + Ib + Ic)}{(Ia + Ib + Ic + IIa + IIb + IIc + IIIa)}
$$

270 In addition to different number of methyl groups, brGDGTs can contain up to two
271 cyclopentane moieties (e.g., brGDGT-Ib and -Ic). CBT' is a modified version of the
272 original cyclisation of branched tetraether (CBT) index (Weijers et al., 2007) and in
273 soils CBT' has the best correlation with pH (De Jonge et al., 2014):

$$
\text{eq. (3) } CBT' = \log \left( \frac{Ic + IIa' + IIb' + IIc' + IIIa' + IIIb' + IIIc'}{Ia + IIa + IIIa} \right)
$$

274 The isomer ratio of 6-methyl brGDGTs (IR<sub>6me</sub>) reflects the ratio between 5- and 6-
275 methyl brGDGTs (Yang et al., 2015) with low (high) values indicative of a
276 dominance of 5-methyl (6-methyl) brGDGTs:

$$
\text{eq. (4) } IR_{6me} = \left( \frac{IIa' + IIb' + IIc' + IIIa' + IIIb' + IIIc'}{IIa + IIa' + IIb + IIb' + IIc + IIc' + IIIa + IIIa' + IIIb + IIIb' + IIIc + IIIc'} \right)
$$

277 The isomerization of branched tetraethers (IBT) is related to IR<sub>6me</sub> but reflects the
278 isomerization of brGDGT–IIa and –IIIa only (Ding et al., 2015):
$$eq. (5) IBT = -\log\left(\frac{IIa' + IIIa'}{IIa + IIIa}\right)$$

The branched versus isoprenoidal tetraether (BIT) index (Hopmans et al., 2004) reflects the relative abundance of the major bacterial brGDGTs versus a specific archaeal isoGDGT, crenarchaeol (Fig. 1), produced by *Thaumarchaeota* (Sinninghe Damsté et al., 2002):

$$eq. (6) BIT = \frac{Ia + IIa + IIa' + IIIa + IIIa'}{Ia + IIa + IIa' + IIIa + IIIa' + cren.}$$

Finally, the isoprenoidal over branched GDGT ratio ($R_{i/b}$), related to the BIT index, records the relative abundance of archaeal isoGDGTs over bacterial brGDGTs (Xie et al., 2012).

$$eq. (7) R_{i/b} = \frac{\sum isoGDGTs}{\sum brGDGTs}$$

## 2.6 Statistical methods

Temperature and pH calibrations were obtained using the average proxy value for each peat and Deming regressions. The software we used was RStudio (RStudio Team, 2015) and Method Comparison Regression (MCR) package (Manuilova et al., 2014), which are freely available to download<fnref n="1" />. The Rscript and data are available in the appendices.

Deming regressions differ from simple linear regression, which so far have been used in brGDGT proxy calibrations, as they account for error in the data on both the x- (e.g., proxy) and y-axis (e.g., environmental variable) (Adcock, 1878). We used the average proxy value for each peat to calculate Deming regressions, calibration errors (RMSE, see below), and calibration coefficients of determination (R²). The errors associated with proxy measurements (e.g. MBT₅me') and environmental parameters (MAAT/pH) are independent and assumed to be normally distributed. To calculate a Deming regression, the ratio of variances (δ) must be calculated. For MAAT we took a standard deviation (σ) of 1.5 °C based on the estimated mean predictive error of up to 1.4 °C for mean temperature in a similar dataset (New et al., 1999). For pH we took a standard deviation of 0.5 based on the average reported heterogeneity in pH for the peatlands used in the database (see Supplementary Table 1). For MBT₅me', CBT', and CBTₚeat we calculated the average

<footnote n="1">¹ https://www.rstudio.com and https://cran.r-project.org/web/packages/mcr/index.html</footnote>
standard deviation of each proxy from the entire peat data set (0.05, 0.25, and 0.2, respectively). This results in a ratio of variances of 0.0011 for the MBT<sub>5me</sub>/'MAAT calibration and 0.25 and 0.16 for the pH calibration based on CBT' and CBT<sub>peat</sub>, respectively. Residuals were calculated for the full dataset and using

$$eq. (8) \quad Residual_y = y_{observed} - y_{predicted}$$

The root mean square error (RMSE) for y, the predictive error for the environmental parameter of interest (MAAT or pH), was calculated for the average proxy value of each peat and using

$$eq. (9) \quad RSME_y = \sqrt{\frac{\sum_{x=1}^{n} (y_{x,observed} - y_{x,predicted})^2}{n} \times \frac{n}{df}}$$

Where df stands for degrees of freedom, which in this case is n-1.

## 3. Results

Although we did not calculate concentrations, based on changes in signal intensity the relative abundance of GDGTs was always higher at depth compared to the top (~0-20) cm of peat. BIT indices (eq. 6) range between 0.75 and 1, but 99% of the samples have a BIT value ≥0.95. Similarly, R<sub>i/b</sub> ratios are typically <0.5. Only three samples from the São João da Chapada peat in Brazil have a R<sub>i/b</sub> ratio >1.

The majority of brGDGTs are tetramethylated and 5-methyl penta- and hexamethylated brGDGTs. The most abundant brGDGTs in peat are brGDGT-Ia and IIa. By extension, the IR<sub>6me</sub> ratio (eq. 4) is low. brGDGTs containing cyclopentane moieties are much less abundant than acyclic brGDGTs and brGDGT-IIIb(') and -IIIc(') are either below detection limit or present at trace abundances (≤ 1% of total brGDGTs). Indeed, three brGDGTs dominate the entire global dataset: tropical peats contain almost exclusively brGDGT-Ia (up to 99% of total brGDGTs), whereas in high-latitude peats brGDGT-IIa and -IIIa are dominant (Fig. 3).

## 4. Discussion

The observation that R<sub>i/b</sub> ratios are low in most peats is consistent with previous observations that bacterial brGDGTs dominate over archaeal isoprenoidal GDGTs in peat (Schouten et al., 2000; Sinninghe Damsté et al., 2000; Pancost et al., 2003) and mineral soils (Hopmans et al., 2004).
337 **4.1 Shallow vs deep GDGT distributions**

338 The apparent increase in GDGT abundance with depth is consistent with previous
339 observations in peatlands (Weijers et al., 2004; Peterse et al., 2011) and reflects the
340 combined effects of preferential GDGT production in anaerobic settings and the
341 accumulation of fossil GDGTs over time at depth (Liu et al., 2010; Weijers et al.,
342 2011).

343 In one high-latitude peat (Saxnäs Mosse, Sweden) the distribution of both
344 intact polar lipids (compounds still containing a polar head groups) and core
345 brGDGTs (compounds having lost their polar head group) differed between the acro-
346 and catotelm and brGDGT abundances were much higher in the latter (Weijers et al.,
347 2009; Peterse et al., 2011). Based on these results Peterse et al. (2011) speculated that
348 microbial communities differed between the oxic acrotelm and anoxic catotelm. As
349 oxygen content can influence cellular lipid composition of bacteria, Huguet et al.
350 (2010) speculated that oxygen availability could be one of the factors directly
351 influencing the brGDGT synthesis by bacteria in peat, as opposed to influencing the
352 type of source organism(s). Studies from lakes also suggested that changes in lake
353 oxygenation state can influence the brGDGT distribution (Tierney et al., 2012;
354 Loomis et al., 2014).

355 Our dataset consists of a mixture of surface (0–15 cm) and deeper samples
356 that extend through the top one meter of peat. For the majority of peats there is no
357 detailed information available on water table depths and location of the acro/catotelm
358 boundary. Nonetheless, to provide a first order assessment on whether there is a
359 systematic and significant difference in core brGDGT distribution between the upper
360 (assumed to be generally oxic) and underlying anoxic peat, we compared the relative
361 abundance of the three most abundant brGDGTs (Ia, IIa, and IIIa) in the shallow
362 surface peat (top 15 cm) with that of the deep peat below 15 cm (Fig. 3), although we
363 acknowledge that this is likely an oversimplification.

364 There are some differences. In general the relative abundance of brGDGT-Ia
365 is slightly higher in the top 15 cm of a peat compared to the peat below 15 cm,
366 especially when its abundance is < 60%. Overall, however, the distributions plot
367 along the 1:1 line, indicating that there is no systematic difference in brGDGT
368 distribution between the (assumed) oxic surface and the peat below 15 cm (likely
369 anoxic). This does not preclude differences in brGDGT production between oxic and
370 anoxic conditions, but this appears to be primarily expressed via greater production of
brGDGTs under anoxic conditions as demonstrated by the higher abundance of GDGTs across the acro/catotelm boundary (Weijers et al., 2006a). These results provide indirect evidence that oxygen availability does not significantly impact the degree of methylation of (core) brGDGTs. One possible explanation for why oxygen availability does not affect distributions is that brGDGTs could be predominantly produced by anaerobes throughout the peat, in low abundance in anaerobic microenvironments in shallow peat and in high abundance in the anaerobic catotelm.

Several (high-latitude) peats, however, do appear to exhibit strong variations between deep and shallow sections of the peat. The down core records from Stordalen (Sweden) and Andorra (S. Patagonia), for example, are characterized by a large and abrupt shift in brGDGT distribution at depth (Fig. 4). The MBT<sub>5me</sub>' indices recorded at the very top of these high-latitude peats are between 0.8 and 0.6, as high as those found in mid-latitude and subtropical peats, but decrease to values between 0.2 and 0.4 below ~30 cm. Peats from temperate climates (e.g. Walton moss, UK) and the tropics (e.g. Sebangau, Indonesia) display much smaller or no change in brGDGT distribution with depth (Fig. 4 and 5). It appears that this offset in brGDGT distribution with depth is amplified in high-latitude peats. This is consistent with previous studies that indicated a difference in brGDGT-distribution between the acro- and catotelm in a high-latitude peat from southern Sweden (Weijers et al., 2009; Peterse et al., 2011).

We argue that the high MBT<sub>5me</sub>' values at the top of these high-latitude peats are heavily biased towards summer temperatures. At these settings winter temperatures are often below freezing for a prolonged period, likely causing bacterial growth and GDGT production to slow down significantly. Summer temperatures are much higher (e.g. mean warmest month temperature at Stordalen is around 13 °C), in-line with the observed relatively high MBT<sub>5me</sub>' values (e.g., 0.6-0.7 at Stordalen, see Figure 4). Deeper in the peat, seasonal temperature fluctuations are much less pronounced and temperatures rapidly converge to the MAAT (Vitt et al., 1995; Laiho, 2006; McKenzie et al., 2007; Weijers et al., 2011), likely accounting for the lower MBT<sub>5me</sub>' values in the deeper peat horizons. Moreover, the greater production of GDGTs in the anaerobic part of the peat will cause GDGT-based temperatures to rapidly converge on the deep peat growth temperature, overprinting the seasonal summer bias of fossil GDGTs synthesized at the surface.
This effect is diminished in temperate and especially tropical peatlands from around sea level, which we attribute to the lack of a preferred growing season in settings with smaller seasonal temperature ranges. In such settings temperatures are less frequently (or never) below freezing and brGDGT production in the top of the peat likely occurs for all or most of the year, such that GDGTs produced in both the shallow and deeper part of the peat record MAAT. This hypothesis needs further testing but indicates that 1) brGDGT production may be biased towards the warm season in the upper part of high-latitude/altitude peats; 2) care has to be taken when interpreting brGDGT-based trends in the top of such peats; and 3) the temperature signal in such peats is imparted at depth, such that downcore GDGT variations in ancient peat archives could potentially be temporally offset (precede) the climate events that caused them. However, as brGDGTs in long peat cores, and by extension ancient lignites (fossilized peats), are dominated by production at depth where temperature equals MAAT (see section 2.2) it is very unlikely that temperatures obtained from these archives are seasonally biased.

In the remainder of this work, for high-latitude peats that show a clear offset between the top and deeper part of the peat we use only the average GDGT distribution from below 20 cm, as the majority of change appears to occur in the top 20 cm. For the other peats we retain all data from the upper 1 m, not differentiating between the acro- and catotelm. To generate the temperature and pH calibrations we use the average brGDGT distribution for each peatland. For peats where multiple samples were analyzed, error bars indicate the deviation (1 σ) from the average.

## 4.2 Influence of temperature and pH on brGDGTs in peats

It is well established that in soils and lakes, environmental conditions such as temperature and pH are highly correlated with the brGDGT distribution (e.g., Weijers et al., 2007; Peterse et al., 2012; Schoon et al., 2013; De Jonge et al., 2014; Loomis et al., 2014; Xiao et al., 2015; Li et al., 2016). In the following sections we investigate the influence of these parameters on the brGDGT distribution in peat using the average proxy value (e.g. MBT<sub>5me</sub>) for each peatland.

### 4.2.1 Influence of peat pH on brGDGT distribution

Weijers et al. (2007) demonstrated that in a global mineral soil database the degree of cyclisation of brGDGTs is correlated to pH, with a higher fractional abundance of
438 brGDGTs that contain cyclopentane moieties in soils with a higher pH. Following the 439 discovery of 6-methyl brGDGTs (De Jonge et al., 2013), it was shown that the degree 440 of isomerization of brGDGTs, the ratio of 6-methyl versus 5-methyl brGDGTs, is also 441 correlated to soil pH, with a higher fractional abundance of 6-methyl brGDGTs in 442 soils with a higher pH (De Jonge et al., 2014; Xiao et al., 2015). Owing to the limited 443 pH range of the few peats used to study brGDGTs so far and because all of these 444 studies pre-date the recent analytical advances that allow for the separation of 5- and 445 6-methyl brGDGTs, it is unknown whether pH has an influence on brGDGTs in peats 446 or whether the dependence is similar to that found in soils. Our peat database spans a 447 pH range from 3 to 8, similar to that of the soil database, allowing us to assess the 448 influence of pH on the brGDGT distribution in such settings. 449

Although pH measurements are only available in 51 out of 96 peats, our 450 results indicate that 6-methyl brGDGTs are present at either only trace abundances 451 (IR<sub>6me</sub> <0.1) or are absent in acidic peats with pH <5.4 (Fig 6). Higher ratios occur in 452 peats with higher pH. The highest ratio (0.58) occurs in the marine-influenced 453 alkaline peat from the Everglades. Not surprisingly, the fractional abundances of the 454 three most common 6-methyl brGDGTs (brGDGT-IIa', -IIb', -IIIa') are significantly 455 correlated with pH with R-values between 0.4 and 0.6 (p<0.01) (Fig. 7). These results 456 are consistent with observations from soils that indicate a positive correlation between 457 the fractional abundance of 6-methyl brGDGTs and pH (De Jonge et al., 2014; Xiao 458 et al., 2015). 459

As a result, the IR<sub>6me</sub> as well as the related IBT index, both of which have 460 been used to reconstruct pH in soils (Ding et al., 2015; Xiao et al., 2015), are 461 correlated with pH in the peats (not shown). However, this comparison is complicated 462 by the fact that 6-methyl brGDGTs are absent in many of the peats. For IR<sub>6me</sub> the 463 absence of 6-methyl brGDGTs results in values that are 0, whereas IBT cannot be 464 calculated for samples that lack 6-methyl brGDGTs as the logarithm of zero is 465 undefined. 466

The abundance of 6-methyl brGDGTs is generally lower in peats than in 467 mineral soils with comparable pH. Indeed, 6-methyl brGDGTs are present in 99% of 468 all soils in the global soil database, including soils with pH <5 where IR<sub>6me</sub> ratios can 469 be as high as 0.4 (Fig. 6). Recent work has shown that in addition to pH the fractional 470 abundance of 6-methyl brGDGTs is negatively correlated with soil water content, 471 with fewer 6-methyl brGDGTs versus 5-methyl brGDGTs in soils with 60% water
content compared to soils with < 10% water content (Dang et al., 2016). It is likely that the negative correlation between soil water content and fractional abundance of 6-methyl brGDGTs can explain the overall lower IR<sub>6me</sub> in peats as these are generally water saturated.

In addition to 6-methyl brGDGTs, the fractional abundances of brGDGTs containing cyclopentane moieties (brGDGT-Ib and -I1b) are also significantly correlated to pH (R = 0.73 and 0.56, p<0.01, respectively) (Fig. 7a and 7c). The other brGDGTs are not significantly correlated to pH. These observations are consistent with those from soils, where both 5- and 6-methyl brGDGTs containing cyclopentane moieties are more abundant at higher pH (Weijers et al., 2007; Peterse et al., 2012; De Jonge et al., 2014). Consequently, and similar to soils (De Jonge et al., 2014; Xiao et al., 2015), CBT' (eq. 3) in peat can be modeled as a function of pH (Fig. 8):

$$eq. (10) pH = 2.69 \times CBT' + 9.19 \quad (n = 50, R^2 = 0.44, RMSE = 1.0)$$

The slope of this calibration is different (higher) from that found in soils (see supplementary information), but the coefficient of determination is lower, and the RMSE is higher. A stronger correlation is obtained by using only compounds that are significantly correlated to pH in the numerator, CBT<sub>peat</sub>:

$$eq. (11) CBT_{peat} = \log\left(\frac{Ib + IIa' + IIb + IIb' + IIIa'}{Ia + IIa + IIIa}\right)$$

$$eq. (12) pH = 2.49 \times CBT_{peat} + 8.07 \quad (n = 51, R^2 = 0.58, RMSE = 0.8)$$

Although the coefficient of determination increases and RMSE decreases using CBT<sub>peat</sub>, the calibration uncertainties are still larger than those reported for soils (see supplementary information).

It is noteworthy that in peats the correlation between brGDGT distributions and pH is much weaker than that with MAAT (see below). This contrasts with mineral soils, for which the correlation of CBT' with pH (R<sup>2</sup> = 0.85), is stronger than that of MAT<sub>mr</sub> with MAAT (R<sup>2</sup> = 0.68) (De Jonge et al., 2014). The weaker correlation can partly be explained by the smaller sample set used for the peat calibration (n = 51) versus soil calibration (n = 221). However, taking 51 random mineral soils from the latter still yields a stronger correlation between CBT' and pH than we obtain for the peat data set. In addition, the coefficient of determination of a calibration based only on peats with pH ≥ 5 is ~0.5 for CBT<sub>peat</sub>, similar to that of the complete data set. We argue that the difference could be related to the observation that in mineral soils water content also influences the brGDGT distribution, especially
that of 6-methyl brGDGTs (e.g., Menges et al., 2014). Recently Dang et al. (2016) showed that CBT<sub>(5me)</sub> is higher in dry soils compared to wet soils. Because alkaline soils are often also dry whereas acidic soils are often wet, this effect could enhance the correlation between CBT' and pH in soils. As peats are typically water saturated, the additional effect of soil water content is lacking, which may explain the weaker correlation between CBT' and pH in peats compared to mineral soils.

### 4.2.2 Influence of MAAT on brGDGTs in peats

In mineral soils the distribution of brGDGTs is influenced by MAAT, with the degree of methylation decreasing as temperature increases (Weijers et al., 2007; De Jonge et al., 2014). A temperature effect on the brGDGT distribution was recently also found in one peatland (Huguet et al., 2013). Although the producers of brGDGTs are currently unknown, such a response is consistent with homeoviscous adaptation (Weijers et al., 2007). Here we investigate whether temperature has a significant correlation with brGDGTs in peats on a global scale.

When plotted against MAAT, only 5-methyl brGDGTs lacking cyclopentane moieties (brGDGT-Ia, -IIa, and -IIIa) have significant correlations with MAAT (Fig. 9). brGDGT-Ia is positively correlated with MAAT (R = 0.72, p<0.01), whereas brGDGT-IIa (R = 0.82, p<0.01), and -IIIa (R = 0.63, p<0.01) are negatively correlated with MAAT. These correlations are significantly higher than those found in the global soil data set (De Jonge et al., 2014). The degree of methylation of 5-methyl brGDGTs is captured in the MBT<sub>5me</sub>' index (eq. 2). As such we use the MBT<sub>5me</sub>' index to construct a peat-specific temperature proxy (Fig. 10):

$$ \text{eq. (13) } \text{MAAT}_{\text{peat}} (^{\circ}\text{C}) = 52.18 \times \text{MBT}_{5\text{me}}' - 23.05 \quad (n = 96, R^2 = 0.76, \text{RMSE} = 4.7^{\circ}\text{C}) $$

Crucially, no correlation is observed between MBT<sub>5me</sub>' and pH (R<sup>2</sup> = 0 and p > 0.8) and we observe no trend in the residuals. The coefficient of determination (R<sup>2</sup>) of MAAT<sub>peat</sub> is higher compared to a Deming regression of the expanded soil dataset (R<sup>2</sup> = 0.60, see supplementary information) as well as that of the linear MBT<sub>5me</sub>' calibration (R<sup>2</sup> = 0.66) suggested by De Jonge et al. (2014). Crucially, because the slope of the MAAT<sub>peat</sub> calibration is steeper, it could have greater utility for the reconstruction of tropical temperatures (MAAT<sub>peat</sub> reaches saturation at 29.1 °C), although these maximum temperatures are higher than the maximum MAAT in the
modern calibration data set which is 26.7 °C. In contrast, the Deming MBT<sub>5me</sub>' soil calibration reaches saturation (i.e. MBT<sub>5me</sub> = 1) at a temperature of 24.8 °C (see supplementary information), while the linear MBT<sub>5me</sub>' calibration suggested by De Jonge et al. (2014) has a maximum of 22.9 °C.

537

### 4.3 Implications for paleoclimate reconstructions and future work

Compared to the natural archives previously used to reconstruct past terrestrial temperature change (e.g., riverine, lacustrine, and marine sediments), peats have a major advantage. For example, the brGDGTs in peat are mainly derived from *in situ* production. Mixing of brGDGT source areas, which complicates the application of GDGTs in sediments that represent a large catchment area (e.g., Zell et al., 2014; De Jonge et al., 2015; Sinninghe Damsté, 2016), is unlikely to be a problem. In addition, peats are overall characterized by anoxic conditions and the preservation potential of organic compounds such as brGDGTs is high. Finally, as peats are water saturated, especially the catotelm where the majority of brGDGT production occurs, the additional influence of changes in moisture content (Menges et al., 2014; Dang et al., 2016) is also negligible. Nevertheless, there are limitations to this proxy that need to be considered when evaluating suitable palaeoclimate applications, and we explore those below.

552

#### 4.3.1 Late Holocene climate

Here we provide peat-specific temperature and pH proxies that could be used to reconstruct terrestrial climate over a broad range of time scales, including the late Holocene. However, the estimated variation in terrestrial temperature of most places on earth during the last millennium is typically less than 1°C (Mann et al., 2009; Pages 2k Consortium, 2013), although there could be local exceptions. Such temperature change is much smaller than the calibration error (RMSE of ~ 4.7 °C). Although based on different lipids produced by different organisms, GDGT proxies can potentially record temperature changes smaller than the calibration errors when utilized within a highly constrained site-specific study (Tierney et al., 2010), although this interpretation was recently contested (Kraemer et al., 2015).

Regardless of calibration issues, application of the MAAT<sub>peat</sub> calibration to late Holocene palaeoclimate remains problematic. A potential seasonal bias in the top of some high-latitude peats, as well as a potential difference between oxic and anoxic
567 production, appear to prevent application of this proxy to shallow peat sediments.
568 Indeed, our downcore profiles spanning the top 1 meter of peat exhibit changes in
569 brGDGT distributions equivalent to temperature variations of up to several degrees
570 Celsius, larger than the expected climate variations. Moreover, as discussed above,
571 GDGTs appear to be predominantly generated at depth, and although this evidently
572 ensures they record MAAT it does mean that their reconstructed temperature signals
573 start in deeper peat horizons, i.e. stratigraphically preceding the climate changes that
574 caused them.

575 Future work should determine whether these peat-specific proxies can be used
576 to reconstruct small amplitude and/or short-lived temperature variation. However we
577 currently urge caution in applying the peat-specific proxies to shallow peat cores to
578 reconstruct late Holocene climate (e.g., Little Ice Age or Medieval Warm anomaly).
579

### 4.3.2 Application to the last glacial

580 We envision these proxies are well-suited to reconstruct large amplitude and more
581 long-term temperature excursions such as those associated with the last glacial
582 termination and early Holocene. Such transitions are recorded in some particularly
583 long peat cores at several places around the world (e.g., McGlone et al., 2010;
584 Vanneste et al., 2015; Zheng et al., 2015; Baker et al., 2016). To test whether the
585 novel peat-specific temperature calibration can be used to reconstruct
586 glacial/interglacial temperature variability, we applied this proxy to samples from the
587 Hani peat sequence (Fig. 2). Hani peat is located in northeastern China and in places
588 is up to 10 meters thick, spanning ~16,000 cal yrs (Zhou et al., 2010). We analyzed
589 two samples from ~840 cm depth (corresponding to the late glacial at around 15.3
590 kyr), and compared MAAT<sub>peat</sub> with that of two samples from around 100 cm depth
591 (corresponding to the late Holocene with an age of 700-1000 yrs). Using MAAT<sub>peat</sub>
592 we obtained an average temperature of around -0.8 °C for the late glacial (15.3 kyr).
593 For the late Holocene (0.7-1 kyr) we obtained an average temperature of around 4.6
594 °C (Table 1).
595

596 Taking the calibration error of ~4.7 °C into account the reconstructed late
597 Holocene temperatures (4.6 °C) are close to the observed modern-day MAAT of
598 around 4 °C at this locality (Zhou et al., 2010). In contrast, applying soil calibrations
599 to reconstruct MAAT at this site results in significantly higher values (up to 11 °C;
600 Table 1). MAAT<sub>peat</sub> (as well as the soil MBT<sub>5me</sub> calibration) indicates that
temperatures increased from the late glacial to the late Holocene by around 5 °C. In contrast the MAT<sub>mr</sub> mineral soil calibration indicates a smaller increase of around 3 °C. A ~ 5 °C increase is similar to that observed in east Asian loess-paleosol sequences (Peterse et al., 2014), although that is based on the MBT(’)/CBT method. In addition a 5 °C deglacial temperature increase is similar to those of several sea surface temperature records available from similar latitudes in the Sea of Japan (Lee, 2007). The next step should be multiproxy temperature reconstructions in a variety of locations to test the new calibration and to determine whether absolute temperatures obtained using MAAT<sub>peat</sub> are reliable. Nonetheless, this initial analysis indicates that MAAT<sub>peat</sub> yields temperature estimates that are consistent with both modern day observations and other proxy estimates for the last glacial.

612

613 *4.3.3 Deep time application*

We see considerable scope for future work with this proxy to reconstruct terrestrial temperature during past greenhouse periods and across hyperthermals (e.g. Paleocene/Eocene Thermal Maximum; PETM). These events are recorded in lignite deposits. For example the PETM is documented in lignites from the UK (Collinson et al., 2003; Pancost et al., 2007). Importantly, lignites are the lowest (maturity) rank of coal and have not experienced significant burial and associated temperature and pressure that leads to the loss of GDGTs (Schouten et al., 2004, 2013). Due to their low thermal maturity, lignites are thought to retain their original brGDGT distribution over geological timescales. For example, brGDGTs have been reported in an immature late Paleocene lignite from the USA (Weijers et al., 2011), early Eocene lignites from Germany (Inglis et al., 2017), as well as Miocene lignite from Germany (Stock et al., 2016). Although analyzed using the classical analytical method that did not separate 5 and 6-methyl brGDGTs, the brGDGT distribution in a late Paleocene lignite from North America is dominated by brGDGT-Ia (Weijers et al., 2011), similar to that seen in modern peats from the tropics and suggesting high terrestrial temperatures. This is consistent with our overall understanding of terrestrial climate during the greenhouse world of the late Paleocene and early Eocene (Huber and Caballero, 2011).

As the brGDGT distribution in peat deposits is dominated by production in the anoxic catotelm below the water table where the seasonal temperature cycle is muted (see section 4.1) brGDGT-based temperatures obtained from lignite deposits can be
considered to reflect MAAT. We envision that future studies applying our new peat-
specific calibrations to immature lignites will provide valuable new insights into
terrestrial climate during the geological past. In addition, the GDGT concentrations in
peats are generally much higher than those found in soils. We therefore propose that
for studies of brGDGT distributions in (marine) sediments with a peat-dominated
catchment area (e.g. Siberia (Frey and Smith, 2005)) or that contain independent
evidence for the input of peat-derived material (e.g. high concentration of C31 αβ-
hopanes or palynologic evidence for the input of typical peatland vegetation), the
majority of GDGTs is likely derived from peatlands. In such settings it is more
appropriate to use a peat-specific calibration rather than a mineral soil calibration.

645

646 **5. Conclusions**

647 Using 470 samples from 96 peatlands from around the world we explored the
648 environmental controls on the bacterial brGDGT distribution in peats. We
649 demonstrate that brGDGT distributions are correlated with peat pH and especially
650 mean annual air temperature (MAAT). We develop for the first time peat-specific
651 brGDGT-derived pH and temperature calibrations. In addition to their application in
652 ancient peat-forming environments, we also suggest that these calibrations could be
653 preferable to the available mineral soil calibration in marginal marine settings when it
654 is clear that brGDGTs are predominantly derived from peats. We suggest caution in
655 applying this proxy to late Holocene peat (e.g., covering the Medieval Climatic
656 Anomaly and/or Little Ice Age) as both the calibration error and downcore variation
657 appears to be larger than expected climate signals during this period. Taken together
658 our results demonstrate that there is clear potential to use GDGTs in peatlands and
659 lignites to reconstruct past terrestrial climate, opening up a new set of sedimentary
660 archives that will help to improve understanding of the climate system during the
661 geological past.

662

663 **Acknowledgements**

664 This research was funded through the advanced ERC grant “the greenhouse earth
665 system” (T-GRES, project reference 340923), awarded to RDP. All authors are part of
666 the “T-GRES Peat Database collaborators” collective. RDP also acknowledges the
667 Royal Society Wolfson Research Merit Award. We thank D. Atkinson for help with
668 the sample preparation. We acknowledge support from Labex VOLTAIRE (ANR-10-
669 LABX-100-01). Peat from Patagonia and Tierra del Fuego were collected thanks to a
670 Young Researcher Grant of the Agence National de la Recherche (ANR) to FDV,
671 project ANR-2011-JS56-006-01 "PARAD" and with the help of Ramiro Lopez,
672 Andrea Coronato and Veronica Pancotto (CADIC-CONICET, Ushuaia). Peat from
673 Brazil was collected with the context of CNPq project 482815/2011-6. Samples from
674 France (Frasne and La Guette) were collected thanks to the French Observatory of
675 Peatlands. The Canadian peat was collected in the context of the NSERC-Discovery
676 grant of L. Rochefort. Peats from China were obtained under a National Natural
677 Science Foundation of China grant (No. 41372033), awarded to Y. Zheng. We thank
678 the editor, 3 anonymous reviewers, and Jess Tierney for valuable comments.

679

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Figure captions

Fig. 1: Structures of brGDGTs (with numbering) as well as isoprenoidal GDGT crenarchaeol (cren), following (De Jonge et al., 2014). Roman numbers indicate tetra-(I), penta-(II), and hexamethylated (III) brGDGTs, whereas letters indicate the absence (a), presence of one (b), or two (c) cyclopentane rings. Prime symbols indicate 6-methyl brGDGTs in which the additional methyl groups of the penta- and hexamethylated brGDGTs occur at the α and/or ω-6 position instead of α and/or ω-5 position of 5-methyl brGDGTs.
1056

1057 Fig. 2: Map with the location of all peats used in this study. The star indicates the
1058 location of the Hani peat sequence in NE China.

1059

1060 Fig. 3: Fractional abundances of the three main brGDGTs in the top 15 cm of each peat (assumed to be representative of the oxic acrotelm) versus the fractional abundance of these brGDGTs between 15 and 100 cm in the peat (assumed to be representative for the anoxic catotelm). For peats where multiple samples were analyzed, error bars represent 1σ from the average fractional abundance.

1065

1066 Fig. 4: Down core record of MBT<sub>5me</sub>’ in four peats: a high-latitude peat from Sweden (blue squares), high-latitude peat from Patagonia (orange squares), temperate peat from the UK (green triangles), and tropical peat from Indonesia (purple diamonds).
1067
1068
1069 (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
1070

1071

1072 Fig. 5: Standard deviation of MBT<sub>5me</sub>’ for each low-altitude (< 1000 m) peat versus latitude. The four peats used in figure 4 are highlighted.

1074

1075 Figure 6: Ratio of 6-methyl over 5-methyl brGDGTs (IR<sub>6me</sub>) versus pH for peat samples (black squares) together with the IR<sub>6me</sub> in the top 10 cm of mineral soils (orange circles) (De Jonge et al., 2014; Ding et al., 2015; Xiao et al., 2015; Yang et al., 2015; Lei et al., 2016). Vertical error bars on the peat data represent 1σ and are based on the analysis of multiple horizons from the same peat. Horizontal error bars represent the spread in pH reported for each peat. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

1082

1083 Figure 7: Fractional abundance of brGDGT versus pH for those compounds with a r-value greater than 0.45 A) brGDGT-Ib, B) brGDGT-IIa', C) brGDGT-IIb, D) brGDGT-IIb', and E) brGDGT-IIIa' (p < 0.01 for all compounds). Samples with fractional abundances <0.001 are not included. Vertical error bars represent 1σ and are based on the analysis of multiple horizons from the same peat. Horizontal error bars represent the spread in pH reported for each peat.

1089
1090 Fig. 8: A) Average CBT' for each peat versus pH (black circles) and C) average
1091 CBT<sub>peat</sub> for each peat versus pH (black circles). Solid blue lines in A and C represent
1092 the Deming regression used to obtain the calibrations, while dashed black lines reflect
1093 simple linear regressions. Horizontal error bars represent 1σ and are based on the
1094 analysis of horizons samples from the same peat. Vertical error bars represent the
1095 spread in pH reported for each peat. Also shown is the residual pH for all analyzed
1096 peat samples (yellow circles), obtained by subtracting the estimated pH using the
1097 CBT' (B) and CBT<sub>peat</sub> (D) deming calibrations from the observed pH. (For
1098 interpretation of the references to color in this figure legend, the reader is referred to
1099 the web version of this article.)

1100
1101 Fig. 9: Fractional abundance of the three main brGDGT versus MAAT A) brGDGT-Ia, B) brGDGT-IIa, and C) brGDGT-IIIa (p < 0.01 for all compounds). Samples with fractional abundances <0.001 were not included. Vertical error bars represent 1σ and are based on the analysis of multiple horizons from the same peat.

1105
1106 Fig. 10: Average MBT<sub>5me</sub> for each peat versus MAAT (black circles). The solid blue
1107 line represents the Deming regression, whereas dashed lines reflect the simple linear
1108 regression. Horizontal error bars represent 1σ and are based on the analysis of
1109 multiple horizons from the same peat. Also shown is the residual MAAT of all
1110 analyzed peat samples (yellow circles) obtained by subtracting the estimated MAAT
1111 using the MBT<sub>5me</sub> Deming calibration from the observed MAAT. (For interpretation
1112 of the references to color in this figure legend, the reader is referred to the web
1113 version of this article).
<table><thead><tr><th rowspan="2">Depth (cm)</th><th rowspan="2">Age (yr)</th><th rowspan="2">MBT<sub>5ME</sub>'</th><th>MAT<sub>mr</sub> soil (RMSE 4.6 °C)</th><th>MAT<sub>5me</sub> soil (RMSE 4.8 °C)</th><th>MAAT<sub>peat</sub> (RMSE 4.7°C)</th></tr><tr><th>De Jonge, 2014</th><th>De Jonge, 2014</th><th>This study</th></tr></thead><tbody><tr><td>86</td><td>~700</td><td>0.53</td><td>6.6</td><td>10.9</td><td>4.5</td></tr><tr><td>102</td><td>~1000</td><td>0.53</td><td>6.6</td><td>11.3</td><td>4.8</td></tr><tr><td>838</td><td>~15,100</td><td>0.46</td><td>4.4</td><td>6.7</td><td>1.2</td></tr><tr><td>846</td><td>~15.400</td><td>0.39</td><td>2.8</td><td>5.4</td><td>-2.7</td></tr><tr><td></td><td></td><td>Δ MAAT</td><td>3.0 °C</td><td>5.0 °C</td><td>5.4 °C</td></tr></tbody></table>

<figure><img src="image_2.png" /></figure>
## Tetramethylated brGDGTs

<figure><img src="image_3.png" /><figcaption>Ib</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Ic</figcaption></figure>

<figure><img src="image_5.png" /></figure>

## Pentamethylated brGDGTs

<figure><img src="image_6.png" /><figcaption>IIa'</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>IIb</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>IIb'</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>IIc</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>IIc'</figcaption></figure>

<figure><img src="image_11.png" /></figure>

## Hexamethylated brGDGTs

<figure><img src="image_12.png" /><figcaption>IIIa'</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>IIIb</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>IIIb'</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>IIIc</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>IIIc'</figcaption></figure>

<figure><img src="image_17.png" /></figure>

## Crenarchaeol

<figure><img src="image_18.png" /><figcaption>cren</figcaption></figure>
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>
<figure><img src="image_21.png" /></figure>
<figure><img src="image_22.png" /></figure>
<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /></figure>
Relative abundance

<figure><img src="image_25.png" /></figure>

R = 0.73

<figure><img src="image_26.png" /></figure>

R = 0.39

<figure><img src="image_27.png" /></figure>

R = 0.56

Relative abundance

<figure><img src="image_28.png" /></figure>

R = 0.55

<figure><img src="image_29.png" /></figure>

R = 0.46

<figure><img src="image_30.png" /></figure>
<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>
<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>
<figure><img src="image_37.png" /></figure>