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Reviews and Syntheses: Best practices for the application of marine
GDGTs as proxy for paleotemperatures: sampling, processing,
analyses, interpretation, and archiving protocols
Peter K. Bijl1*, Kasia K. Śliwińska2*, Bella Duncan3, Arnaud Huguet4, Sebastian Naeher5,6, Ronnakrit 5
Rattanasriampaipong7,8, Claudia Sosa-Montes de Oca9, Alexandra Auderset10, Melissa A. Berke11, Bum
Soo Kim12,13, Nina Davtian14, Tom Dunkley Jones15, Desmond D. Eefting16, Felix J. Elling17, Lauren
O’Connor1, Richard D. Pancost9, Francien Peterse1, Fenies Pierrick18, Addison Rice1, Appy Sluijs1,
Devika Varma19, Wenjie Xiao20, Yige Zhang21
10
1Department of Earth Sciences, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands. ORCID 000-000-002-
1710-4012 (Bijl) 0000-0003-1872-1853 (O’Connor) 0000-0001-8781-2826 (Peterse) 0000-0002-4507-6717 (Rice) 0000-
0003-2382-0215 (Sluijs)
2Geological Survey of Denmark and Greenland (GEUS), Department of geoenergy and storage, Copenhagen, Denmark.
ORCID 0000-0001-5488-8832 15
3Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand. ORCID 0000-0003-1108-6033
4Sorbonne Université, CNRS, EPHE, PSL, UMR METIS, Paris, 75005, France. ORCID 0000-0002-6124-2922
5GNS Science, Lower Hutt, New Zealand; ORCID 0000-0002-5336-6458
6School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Wellington, New Zealand,
ORCID 0000-0002-5336-6458
20
7University Corporation for Atmospheric Research, Boulder, CO, 80307 USA. ORCID 0000-0002-1425-8737
8Department of Geosciences, The University of Arizona, Tucson, AZ, 85721 USA
9Organic Geochemistry Unit, School of Earth Sciences, School of Chemistry, The Cabot Institute for the Environment,
University of Bristol, Bristol, UK. ORCID 0000-0003-4451-6458 (Sosa-Montes de Oca) ORCID: 0000-0003-0298-4026
(Pancost) 25
10School of Ocean and Earth Science, University of Southampton, Southampton SO14 3ZH, United Kingdom. ORCID:
0000-0002-6316-4980
11Department of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame IN
46556, USA. ORCID: 0000-0003-4810-3773
12Astromaterials Research and Exploration Science Division, NASA Johnson Space Center, Houston, TX 77058, USA. 30
13Amentum, JSC Engineering and Technical Support (JETS) Contract, NASA Johnson Space Center, Houston, TX 77058,
USA. ORCID: 0000-0002-0533-1330
14CEREGE, Aix-Marseille Université, CNRS, IRD, INRAE, Collège de France, Technopôle de l'Arbois, 13545 Aix-en-
Provence, France. ORCID: 0000-0002-3047-6064
15School of Geography, Earth and Environmental Sciences, University of Birmingham, B15 2TT, United Kingdom. ORCID 35
0000-0002-9518-8143
16GeoLab, Faculty of Geoscience, Utrecht University, Utrecht, the Netherlands
17Leibniz-Laboratory for Radiometric Dating and Isotope Research, Christian-Albrechts-University of Kiel, 24118 Kiel,
Germany. ORCID: 0000-0003-0405-4033
18Institute of Oceanography, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, 10617 Taipei, Taiwan. ORCID: 40
0000-0002-1183-9704
19Department of Marine Microbiology and Biogeochemistry, NIOZ Royal Netherlands Institute for Sea Research, Den Burg,
the Netherlands. ORCID 0000-0001-9707-6690 (Varma)
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20Department of Biology, HADAL & Nordcee, University of Southern Denmark, 5230 Odense M, Denmark. ORCID: 0000-
0002-4734-683X 45
21Guangzhou institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China. 0000-0001-7331-1246
* Both contributed equally to this work
Correspondence to: Peter K. Bijl (p.k.bijl@uu.nl) and Kasia K. Sliwinska (kksl@geus.dk)
Abstract. Marine glycerol dialkyl glycerol tetraethers (GDGTs) are used in various proxies (such as TEX86) to reconstruct 50
past ocean temperatures. Over 20 years of improvements in GDGT sample processing, analytical techniques, data
interpretation and our understanding of proxy functioning have led to the collective development of a set of best practices in
all these areas. Further, the importance of Open Science in research has increased the emphasis on the systematic
documentation of data generation, reporting and archiving processes for optimal reusability of data. In this paper, we provide
protocols and best practices for obtaining, interpreting and presenting GDGT data (with a focus on marine GDGTs), from 55
sampling to data archiving. The purpose of this paper is to optimize inter-laboratory comparability of GDGT data, and to
ensure published data follows modern open access principles.
Acronyms:
APCI: atmospheric pressure chemical ionization 60 AOM: anaerobic methane oxidation
ASE: accelerated solvent extraction
BIT: branched and isoprenoid tetraether
brGDGT: branched glycerol dialkyl glycerol tetraether
CCSF: core composite depth below sea floor 65
CL: core lipid
CN: cyano column
Cren: crenarchaeol
CSF: core depth below sea floor
DCM: dichloromethane 70
GP: gaussian process
GDGT: glycerol dialkyl glycerol tetraether
GDD: glycerol dialkyl diether
GMGT: glycerol monoalkyl glycerol tetraether
GTGT: glycerol trialkyl glycerol tetraether 75
HPLC: high-performance liquid chromatography
HPLC-APCI-MS: high performance liquid chromatography-atmospheric pressure chemical ionisation-mass spectrometry
IODP: International Ocean Discovery Program
IPL: intact polar lipid
isoGDGT: isoprenoid glycerol dialkyl glycerol tetraether 80
LC-MS: liquid chromatography-mass spectrometry
MI: Methane Index
MeOH: methanol
OH-GDGT: hydroxylated glycerol dialkyl glycerol tetraether
OMsoil: soil organic matter 85
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PFTE: polytetrafluoroethylene (commonly known as Teflon)
RI: ring index
RSE: relative standard error
SIM: selected ion monitoring
SPM: suspended particulate matter 90
SST: sea surface temperature
subT: subsurface temperature
TEX86: tetraEther index of 86 carbon atoms
TLE: total lipid extract
TOC: total organic carbon 95
1 Introduction
Glycerol Dialkyl Glycerol Tetraethers (GDGTs) are membrane lipids that are widely applied as indicators of past water and
air temperature, soil organic matter (OMsoil) input in marine settings, as well as soil pH (Schouten et al., 2013a; Fig. 1). They
are synthesized by a group of archaea (Nitrososphaerota; formerly Thaumarchaeota and Crenarchaeota) (De Rosa and
Gambacorta, 1988; Koga et al., 1993) and bacterial groups (Weijers et al., 2006b), including Acidobacteria and likely 100
additional sources (Weijers et al., 2009; 2010; Sinninghe Damsté et al., 2011; Halamka et al., 2021; 2023; Chen et al., 2022).
The GDGT ‘pool’ includes isoprenoid (isoGDGTs), and branched GDGTs (brGDGTs). Although iso- and brGDGTs are
synthesized in both terrestrial and marine settings, isoGDGTs are typically associated to marine production (Schouten et al.,
2000) and brGDGTs to terrestrial sedimentary settings (Sinninghe Damsté et al., 2000). The isoGDGTs are characterized by
their isoprenoid carbon skeleton and includes isoGDGTs-0 to -8 (where numerals refer to the number of cyclopentane 105
moieties), crenarchaeol, which has four cyclopentane rings and one cyclohexane moiety (De Rosa and Gambacorta, 1988;
Sinninghe Damste et al., 2002b). Next to these, hydroxylated GDGTs (OH-GDGTs) were recognized (Liu et al., 2012c) as
well as the much less studied glycerol trialkyl glycerol tetraethers (GTGTs; e.g., De Rosa and Gambacorta, 1988), glycerol
dialkyl diethers (GDDs; which is not a membrane-spanning lipid (Mitrović et al., 2023; Hingley et al., 2024; Coffinet et al.,
2015), and glycerol monoalkyl glycerol tetraethers (GMGTs), also known as H-shaped GDGT (Morii et al., 1998; Naafs et al., 110
2018; Baxter et al., 2019). It has been shown that the level of cyclization in marine isoGDGTs is correlated to mean annual
sea surface or subsurface temperature (Schouten et al., 2002), and represents a powerful paleotemperature proxy. BrGDGTs
have an alkyl backbone to which a total of four to six methyl branches can be attached. They are typically produced in terrestrial
settings and therefore much less common in marine settings than the isoGDGTs. BrGDGTs from soils are used to reconstruct
continental air temperatures and soil pH (De Jonge et al., 2024), also after transport to and deposition in marine depositional 115
settings (e.g., Weijers et al., 2007a; Pross et al., 2012; Pancost et al., 2013; Śliwińska et al., 2014; De Jonge et al., 2014b;
Willard, 2019; Bijl et al., 2018; 2021; Dearing Crampton-Flood et al., 2019). The relative abundance of brGDGTs in marine
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sediments is further used to reconstruct input of soil material to the marine environment (Hopmans et al., 2004), albeit with
care for marine brGDGT contributions (Peterse et al., 2009; Sinninghe Damsté, 2016).
120
The correlation between isoGDGTs and SST in marine sediments was first described by Schouten et al. (2002), who proposed
the TetraEther indeX consisting of 86 carbon atoms, known as the TEX86 index, to quantify the degree of cyclization of
isoGDGTs. The shape of the relationship between TEX86 and SST has been explored using a growing dataset of isoGDGTs in
core top sediments spanning the modern ocean (Fig. 1), and temperatures of the overlying sea surface and subsurface waters
(Schouten et al., 2002; Liu et al. 2009; Kim et al., 2010). Together with other geochemical proxies, both organic (alkenones, 125
diols) and calcite (δ18O, Δ47 and Mg/Ca measured on planktic foraminifera) -based, TEX86 is widely applied as SST proxy.
Paleo-applications of the proxy focus on the analysis of core lipids (CLs), i.e., GDGTs without their polar head groups. In
contrast, investigations of water and surface sediment sample investigations also include the analysis of intact polar lipids
(IPLs), i.e., with the polar headgroup still in place, indicating that these lipids are derived from living microbial cells (e.g.,
Harvey et al., 1986). Recently, OH-GDGTs have shown to have higher sensitivity in a lower temperature range than isoGDGTs 130
(Huguet et al., 2013; Lü et al., 2015; Varma et al., 2024a, b).
The analysis of GDGTs requires sensitive analytical equipment, capable of measuring small quantities (sub-micrograms) of
sample material. The relatively low concentration of GDGTs in environmental samples and geological archives can make their
analysis sensitive to contributions from contamination during sampling, sample preparation and analysis or workup, and thus 135
affect the reliability of GDGT-based paleoenvironmental reconstructions. Therefore, to optimize study-to-study
intercomparisons it is imperative to standardize the sampling, processing, and analytical procedures between laboratories, and
share best practices. In this paper we focus primarily on the isoGDGTs that are found in marine sediments and used for
reconstruction of past sea surface temperature, but where relevant, brGDGTs and IPLs will be discussed as well.
140
Several approaches have been used for GDGT extraction, workup, analysis, and data processing. Here, we describe and
compare these approaches and to assess their strengths and weaknesses. Through this study, combined with round robin studies
(Schouten et al., 2009; 2013b; De Jonge et al., 2024), we propose standardized procedures to ensure inter-laboratory
comparability, minimize the risk for contamination, maximize the reusability of processed sample material, and optimize data
reporting for a reliable generation of palaeoenvironmental reconstructions. 145
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Figure 1: Global map of core top samples for isoprenoid GDGTs is now comprise of data retrieved from previous
major compilation efforts (i) published during 2002-2014 (light grey) (Kim et al., 2008; 2010; Tierney and Tingley,
2014; 2015 and references therein) and (ii) published since 2015 (dark grey) (Rodrigo-Gámiz et al., 2015; Kusch et al., 150
2016; Richey and Tierney, 2016; Jaeschke et al., 2017; Ceccopieri et al., 2018; Chen et al., 2018; Schukies, 2018;
Lamping et al., 2021; Harning et al., 2019; Sinninghe Damsté et al., 2022; Hagemann et al., 2023; Varma et al.,
2024b)—along with the location of all TEX86 records in PhanSST (Judd et al., 2022 and references therein) in the
colour of the dominant geologic time interval represented.
155
Developments in Open Science in academia have increased awareness of scientific integrity in data reporting. The best-practice
of reporting scientific data follows FAIR (Findable, Accessible, Interoperable, and Reusable; Wilkinson et al., 2016) open
science principles, which were later translated into specific open access guidelines and objectives for the geosciences
community into ICON (Integrated, Coordinated, Open and Networked; Goldman et al., 2022). The advantages of applying
these principles are profound: they systematize the presentation of generated data that is connected to publications and ensure 160
that the right kind of data (including metadata) are correctly reported. In the longer term, the availability of properly archived
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data facilitates and stimulates its reuse by stakeholders, both in- and outside academia, with the generation of new insights far
into the future. A clear example of such an effort is the SST data compilation PhanSST (Judd et al., 2022; Fig. 1). The FAIR
Open Science principles greatly add value to the generated data and have ramifications for how the community best report
them. We however conclude that for isoGDGT analyses, the community has yet to agree on the common framework for data 165
presentation, and this paper is an important step towards that ambition.
The most common workflow in GDGT analysis consists of:
1. Sample selection and collection,
2. Sample storage, 170
3. Generation of the total lipid extract (TLE), including GDGTs, using various techniques,
4. TLE clean up procedures – column chromatography,
5. GDGT analysis using (Ultra) High Pressure Liquid Chromatography–Mass Spectrometry ((U)HPLC–MS),
6. GDGT peak integration,
7. Data interpretation, 175
8. Data reporting and archiving.
In this paper, we review and summarize best practices to generate, report and archive marine GDGT data, following the steps
above. We base our review on examples from the literature, empirical studies, and provide data that underpins over 20 years
of experience in the biomarker community. The purpose of this paper is to optimize inter-laboratory comparability of marine 180
GDGT data, and to ensure published data follows modern FAIR open access principles.
2 Sampling
2.1 Types of samples
Marine sediment samples for GDGT analysis can be obtained from sediment cores - mainly drill cores, piston/gravity cores,
multicore samples, grab samples - or outcrops. For studies on modern GDGTs, water column sampling is commonly 185
undertaken using sediment traps or with Niskin bottles and laboratory filtration, or using in situ pumps to collect Suspended
Particulate Matter (SPM) from the water column. As this paper focuses on sedimentary GDGTs, we do not discuss this further.
Surface marine sediment and shallow subsurface sampling (coring) happens via local/national marine cruises and expeditions,
whereas the International Ocean Discovery Program (IODP) and its legacy programs Deep Sea Drilling Project (DSDP) and
Ocean Drilling Program (ODP), are the only research programs to have recovered fully cored marine stratigraphic records (up 190
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to 2 km of sediment) across a full range of ocean depths (up to 8 km water depth). The IODP uses dedicated research vessels
and extensive international collaborations (e.g., the IODP Science plan: https://www.iodp.org/science-plan/127-low-
resolution-pdf-version/file and the 2050 IODP science framework: https://www.iodp.org/docs/iodp-future/1086-2050-science-
framework-full-document/file). Depending on the location of the drilling, IODP cores are stored and curated at specific
repositories at the University of Bremen (Germany), Gulf Coast Repository at Texas A&M University (USA) and Kochi Core 195
Center at Kochi University/JAMSTEC (Japan), see www.iodp.org/resources/core-repositories). In all three repositories
sediment cores are stored in a reefer (refrigerated storage area) maintained at a temperature of +4°C and controlled humidity
(www.iodp.org/resources/core-repositories). These repositories ensure availability of high-quality core material to member
states long after the completion of each expedition. Many national research facilities have repositories of cores and marine
samples as well, but the storing capacity and conditions can vary. Moreover, most sediment core repositories are not really 200
equipped to curate processed subsets of core samples, which leaves the responsibility for curating lipid biomarker fractions
and TLEs to the respective geochemistry laboratories that processed the material.
In order to study marine successions on land, two options are possible: onshore drilling for obtaining a continuous core record
or hand-sampling from outcrops. Sampling outcrops for marine GDGTs may lead to challenges related to the preservation of
the compounds. Sedimentary deposits on land, as in well-ventilated ocean basins, are typically exposed to various degrees of 205
oxic degradation (Huguet et al., 2008; Lenger et al., 2013). Furthermore, outcrop exposure can alter the autochthonous GDGT
signal through inputs from modern GDGTs (from e.g., modern soils) to an unknown amount, but this has never been quantified.
For outcrop sampling it is thus crucial to retrieve fresh sediment material that is unaffected by weathering.
Moreover, deposits can be affected by variable degrees of heating and pressure which, if too intense, could affect GDGT
preservation. GDGTs have been shown to thermally degrade at temperatures >260°C (Schouten et al., 2004), making the 210
thermal history of especially older sediments an important consideration in the interpretation of their GDGT content. The
reaction kinetics of GDGTs are mainly governed by temperature and the duration of heating, making older sediments more
susceptible to the effects of thermal alteration. An understanding of the thermal history, including contact metamorphism from
igneous intrusions or lava flows, is possible through an estimation of the thermal maturity of organic matter in sediment
samples by using either optical (e.g., Thermal Alteration Index, vitrinite reflectance) or geochemical analysis of organic matter 215
(e.g., RockEval Pyrolysis), or an assessment of the hopane composition, and their stereochemical configuration (e.g., Schouten
et al., 2004).
In thermally mature sediments, the concentration of the original/autochthonous GDGT pool may either i) become biased due
to preferential preservation of GDGTs, with TEX86 decreasing with increasing maturity (Schouten et al., 2004; Schouten et
al., 2013a), where brGDGTs are preserved preferentially over isoGDGTs thus affecting the BIT index (Huguet et al., 2008; 220
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Schouten et al., 2013a), or ii) may decrease to below detection limit, even when the total organic carbon (TOC) content remains
high (Schouten et al., 2004). As temperature and pressure increases with burial depth, there is a risk that GDGTs degrade at
too deep depths. To date, the deepest-buried GDGTs were retrieved from North Atlantic Deep Sea Drilling Project Sites 398
and 603, from immature Lower Cretaceous sediments at depths of over 1.5 km below sea floor (Littler et al., 2011; Naafs and
Pancost, 2016). This provides evidence that GDGTs can be preserved even in relatively deeply buried strata, as long as 225
sediments do not surpass the temperature threshold (see above).
2.2 Contamination, storage, and initial processing of environmental samples
Sampling best practice seeks to avoid any addition of organic biomarkers or compounds to the target sample from external
sources (e.g., Brocks et al., 1999). Although this paper only concerns GDGTs, sample integrity should be maintained for all
lipid biomarkers where possible. When coring sediments using drilling mud/fluid, organic geochemists, whether present on-230
site or on-board a drilling vessel, are recommended to sample and process drill fluid as a reference where possible. Many
drilling muds might not contain biomarkers or GDGTs, but to minimize contamination risk, it is strongly recommended to
avoid using the external part of the core that has been in contact with drilling mud/fluids. Even though oil-contamination may
not have an effect on GDGTs, it might affect other lipid biomarkers, in other fractions. For legacy cores, it is recommended to
remove the outermost part of the core (which could have been contaminated with recirculating drilling mud/fluid). Once the 235
samples/cores are obtained, they need to be sealed from oxygen exposure and, to limit microbial activity, ideally stored in a
cold room (4ºC), or better yet, in a freezer (-20ºC), or freeze-dried immediately. One example that underlines the importance
of this comes from a recent study (Frieling et al., 2023), where the same lithological formation was sampled from an outcrop
and from a year-old sediment cores, located 3 km apart. The outcrop samples were collected recently, while the core which
was drilled 50 years ago, and stored in a non-refrigerated, uninsulated storage facility in Australia. GDGT extractions carried 240
out on age-equivalent samples from both outcrop and cores revealed orders of magnitude differences in their GDGT yield (see
data files connected to Frieling et al., 2023). It was concluded that the 5 decades of aerial exposure to temperature and humidity
swings had degraded the organic matter (the dinoflagellate cysts (Frieling et al., 2018) as well as the GDGT concentrations
(Frieling et al., 2023)) in the old cores, to the extent that dinocyst assemblages were significantly altered and GDGTs were no
longer quantifiable. Albeit extreme, this example highlights the fact that proper storage is crucial for the longevity of the 245
preservation of GDGTs in sample material.
Once collected from the core/outcrop, sediment samples are often stored in plastic bags, but this is not recommended for
samples taken for biomarker analyses. Plastic contamination (Grosjean and Logan, 2007) can be even more problematic with
older sample material, where plastic bags can disintegrate with time into microscopic flakes and mix with the sediment. 250
Although contamination from plastic will not necessarily be an issue for analysing GDGTs since it is done in selective ion
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monitoring mode (SIM) (i.e., coelution of contaminants will not be shown), it can cause analytical complications in other
fractions (e.g., Smith et al., 1993), and thus it limits reuse of extracts for analyses of non-polar biomarkers. We recommend
that new samples are either i) wrapped in aluminum foil and stored in a regular plastic sample bag, but note that aluminum foil
also disintegrates over time or ii) stored in a plasticizer-free sample bag dedicated to storage of samples for organic 255
geochemistry.
In order to minimize contamination, all metal tools. extraction cells, glassware, aluminum foil, aluminum oxide, silica, Na4SO2,
and glass wool must be furnaced at 450°C for 2 to 6 hours. If not furnaced, glassware and metal tools need to be cleaned, dried
and rinsed (3 times) with solvents before use. 260
Another potential contamination could come from non-pure solvents. The workup procedure of GDGT analyses requires
occasional dry-down of solvents, which has the potential to concentrate contaminants. Routine checks of batches of solvents
are therefore key to ensure that solvents are not contaminated. Procedural blank samples should be added to each sample batch
to confirm the absence of laboratory contamination. We recommend implementing a blank at the beginning of the laboratory 265
workflow; at lipid extraction (Soxhlet, microwave, accelerated solvent extractor, ultrasonic extraction, see section 3.3) and at
column separation, where different fractions are separated.
3 Processing samples for GDGTs
3.1 Drying samples
Prior to lipid extraction, any moisture or water content in the sediment sample is recommended to be removed for maximizing 270
the extraction efficiency (i.e., it will allow solvents to better penetrate sediments during extraction) while preventing the loss
of polar compounds bound to the water. Two sample drying methods are typically used when processing marine sediment
samples: i) freeze drying - samples are dried under vacuum and below -60ºC until dry; and ii) oven drying - samples are placed
in a laboratory oven (or drying device) for overnight or longer. Even though GDGTs will not be affected by temperatures
above 40 ºC, it is recommended to not exceed 40 ºC when drying samples in the oven, to prevent the degradation of sensitive 275
compounds that might occur at higher temperatures (Wiltshire and Du Preez, 1994; Rosengard et al., 2018). Freeze-drying is
the preferred option (Rosengard et al., 2018).
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3.2 Powdering samples
Aggregated or lithified sample materials, such as marine sediments, are usually homogenized (i.e., making sample materials
into powder) to increase surface area, maximizing lipid extraction efficiency. Powdering of the sediment can be done either 280
by hand in an agate mortar or ball mill. In both methods it is crucial to clean the mortar/mill between every sample (using
either: i) DCM/MeOH, ii) acetone, or iii) by grinding quartz sands that have been cleaned via combustion followed by a rinse
with solvents) to minimize cross-over contamination. Experience shows that the extraction efficiency increases when samples
are powdered to a finer size, although this has not been formally quantified.
3.3 Extractions of lipids 285
The extraction of organic compounds (including lipids) from natural samples (sediment, biomass, etc) is a crucial first step in
the analysis of biomarkers for paleoclimate research. The TLEs may contain thousands of various organic compounds, and
several of them can be used as proxies for seawater temperature reconstructions, such as long-chain (C37) ketones (alkenones)
(UKʹ37; Brassell et al., 1986), long chain diols (e.g., Rampen et al., 2012) and GDGTs (Schouten et al., 2002). Sample
preparation steps for all these lipids-based proxies, including marine GDGT analysis, usually consists of lipid extraction 290
followed by column separation. The amount of sediment to be extracted is generally inversely correlated to TOC content, i.e.,
smaller amounts are required from higher TOC samples. The sample size can therefore play a role in selecting the optimal
extraction technique (see below), as there is large overlap in solvent volume associated with each extraction method. The most
common protocols used in GDGT-targeted studies (which are also commonly applied for other biomarkers analysis) are
summarized below. 295
3.3.1 Accelerated Solvent Extraction (ASE)
ASE is widely used to automatize lipid extraction from sediments. The typical sample size is between 8 and 50 grams (up to
100 ml), depending on the TOC content and sample type. Powdered sediments are typically mixed with either combusted
quartz sand, diatomaceous earth, or glass wool (e.g., Huguet et al., 2010), to allow a better solvent flow through the cell and
avoid clumping. Subsequently samples are packed into a metal extraction cell. Total lipids are extracted from the sample using 300
a mixture of solvents, most commonly dichloromethane (DCM) and methanol (MeOH) in proportions 9 to 1 (vol:vol) under
high temperature (commonly 100 °C) and high pressure (>7.6 × 106 Pa; Huguet et al., 2006). Higher temperatures are used to
enhance the efficiency and speed of the extraction process. Samples can be extracted multiple times to optimize the extraction
yield (e.g., Lengger et al., 2012). The advantage of ASE is that it is a fully automated and fast extraction technique, but it uses
relatively large volumes of solvent for extraction and requires proper cleaning procedures for the extraction cells (see best 305
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practices in cleaning in Section 2.2). Notably, the method set for the extraction of GDGTs using ASE may destroy ladderane
fatty acids (due to the relatively high temperature and pressure).
3.3.2 Microwave-assisted extraction oven
The microwave-assisted extraction technique relies on the use of microwave energy applied to heat pressurized vessels
containing solvent and powdered sediment. Samples, typically between ~5 and 7 g, are placed into glass vials held by Teflon 310
tubes, before adding a mixture of DCM and MeOH (most commonly 9:1 or 3:1 (vol:vol); Schouten et al., 2013b), as for the
ASE. The microwave is programmed for the extraction, with controlled heating of the solvent and sediment followed by
cooling (e.g., Huguet et al., 2010 for examples of conditions). Like ASE, microwave extraction is fully automated, but it has
an advantage using less solvent and reducing carry-over contamination as it does not require tubing between samples. The
main disadvantage of microwave extraction relative to ASE is the additional step required to separate the solvent carrying total 315
lipid extract (TLE) from the sediment. For smaller models of microwaves, the glass vials containing sediment and solvent -
TLE mixture can be placed in a centrifuge to separate the aliquot from the sediment. In the case of larger microwave models,
the aliquot can be either transferred into a vial compatible with centrifugation or separated from the sediment by e.g., gently
pipetting the aliquot or pouring the aliquot into a clean vial. The second option will however require some extra time (1 to
several hours) to allow the sediment to settle. 320
ASE and microwave extraction methods yield similar results for GDGTs in terms of biomarker yield and extraction quality
(see Supplementary Table S1). However, it should be noted that specific settings of each method may impact the outcomes.
Comparisons between ASE and microwave extractions (Frieling et al., 2023) give comparable GDGT yield and quality with a
microwave heat setting of 70ºC, For the microwave-assisted extraction, significant biases in TEX86 indices (higher by <0.2 325
units) can occur when the temperature is set at 100-110ºC (see Supplementary Table S1). For both extraction methods, repeated
extractions of the same sample increases extraction yield.
3.3.3 Soxhlet
Soxhlet extraction is usually performed on larger samples (typically ~25–30 g, but not more than 100 g) compared to the
previously described methods. The powdered sediment, sometimes with an admixture of quartz to prevent channeling, is placed 330
into a pre-extracted cellulose thimble which is loaded into a Soxhlet apparatus; solvent is then heated until boiling (normally
less than 70°C for DCM:MeOH (2:1, v/v) mixture) under reflux for several hours/days (commonly 24 hrs; (e.g., Huguet et al.,
2010; Naeher et al., 2014b) or more (up to 48 hrs) for old sediment or low TOC) and flows through the sample, allowing the
extraction of lipids. As for the other techniques, the solvent mixture usually consists of DCM and MeOH (Schouten et al.,
2013b). The advantage of this technique is that extraction can be performed ultra-clean. The disadvantage is that the setup 335
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takes considerable amount of space and solvent for extraction and cleaning, is relatively slow (one instrument is used for
extraction of between one to ten samples between 24 and 48 hours), and complete recovery of all extracted lipids tedious,
making it less efficient than ASE or microwave for large batches of samples.
3.3.4 Ultrasonic extraction
Ultrasonic extraction method is usually applied for smaller samples (< 15 g) using an ultrasonic bath at ambient temperature, 340
to avoid overheating solvents. Samples are commonly mixed with solvent (typically a DCM/MeOH mixture) for a short time
(10–15 minutes, Yang et al., 2018) and subjected to centrifugation to retrieve the supernatant. The extraction is usually repeated
(3 to 5 times) and the supernatants are combined, forming the total lipid extract containing GDGTs (e.g., Zhang et al., 2012;
Yang et al., 2018). The final step includes a transfer of the TLE via a small Na2SO4 column (pipette) into a pre-weighed vial.
GDGT core lipids are traditionally extracted using the techniques mentioned above. The efficiency of these different extraction 345
techniques has been compared in multiple studies (e.g., Huguet et al., 2010; Lengger et al., 2012; Schouten et al., 2013b).
Schouten et al. (2007) compared Soxhlet, ultrasonic, and ASE extraction techniques for CL while Lengger et al. (2012)
compared Soxhlet, Bligh–Dyer (i.e., ultrasonic extraction modified from Bligh and Dyer (1959)) and ASE for extraction of
both IPL and CL. Both studies showed that the extraction efficiency of these methods for CL were not significantly influenced
by the allied method. In contrast, Huguet et al., (2010) suggested that CL GDGTs may be more efficiently extracted with 350
ultrasonic or Soxhlet extraction than with ASE. Nevertheless, an extensive round robin study of TEX86 and BIT analyses
involving 35 laboratories (Schouten et al., 2013b) revealed that neither TEX86 nor BIT index are substantially impacted by
sediment workup (extraction and processing), indicating that any of the aforementioned techniques could be used for the
determination of marine CL GDGT distributions.
355
Notably, in contrast to CL, IPL extraction usually follows a gentler method of an ultrasonic extraction modified from Bligh
and Dyer (1959). GDGT extraction efficiency in IPL was also suggested to be highly dependent on the applied extraction
technique (Huguet et al., 2010). The extraction yield for archaeal IPLs in cultures and environmental samples based on the
commonly used Bligh and Dyer protocol was reported to be low (e.g., Huguet et al., 2010; Weber et al., 2017). The
modification of the extraction protocol including the use of detergent was shown to increase the yield of archaeal lipids in 360
cultures and marine suspended particulate matter compared to the Bligh and Dyer methodology, even though no obvious
change in extraction efficiency was observed for marine sediments (Evans et al., 2022). It commonly uses a solvent mixture
of MeOH, DCM, and an aqueous buffer (2:1:0.8; v/v/v). Protocols for the extraction of IPL GDGTs based on the Bligh and
Dyer method were compared (e.g., Huguet et al., 2010; Evans et al., 2022) and yielded accurate comparative data for different
extraction methods. If the intact polar lipids need to be separated from core lipids, a modified protocol is needed (Pitcher et 365
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al., 2009; Lengger et al., 2012), where first the CL fraction is eluted with hexane/ethyl acetate 1:2 (v/v), followed by the IPL
fraction with MeOH.
The final step of the extraction is to evaporate the excess solvent mixture, in order to quantify the total lipid extract. As exposure
to oxygen at this step is to be avoided, drying of the solvent is typically done under flowing N2. For large volumes of solvent, 370
the solvent can be retrieved using a distillation setup under vacuum (Rotavap), whereas for smaller quantities the solvent is
evaporated under flowing N2 (Turbovap). In both cases it is recommended that the temperature, that serves as a heat source,
does not exceed 25°C. Notably, higher temperatures (up to 40°C) and long drying under N2 may not affect GDGTs, but can
cause the loss of volatile and semi-volatile compounds (e.g., pristane, phytane, short-chain alkanes and fatty acids).
3.4 Cleaning up - column separation techniques 375
In some cases, the TLE may contain traces of residue (might happen with microwave-assisted extraction). In this case it may
be necessary to filter the TLE over a pipette with either i) extracted cotton wool; ii) Na2SO4 column, or a iii) extracted paper
filter in a funnel. However, if the TLE is further separated into various fractions using column chromatography, the remaining
sediment fraction will stay on the column and be separated from the lipid extract. In that case, removing the residue may not
be strictly necessary, although the weight of the TLE will be overestimated due to the presence of the residue. 380
In extracts from anoxic marine sediments, elemental sulfur needs to be removed. To remove elemental sulfur from the TLE,
acid-activated copper is added (Smith et al., 1984); either i) to the solvent (DCM:MeOH) in the beginning to Soxhlet extraction,
or ii) to the TLE and stirred overnight, right after the ASE, microwave or ultrasonic extractions. However, this step can also
be applied after the separation of the TLE into fractions (see below), and then to the specific fraction that contains the elemental 385
sulfur.
In order to minimize the degradation of the LC column as well as to concentrate and analyze the abundance of GDGTs it is
optimal to separate the polar fraction from the total lipid extract using column chromatography. The polar fraction usually
contains alcohols, including isoGDGTs, brGDGTs (Section 8.2) and OH-GDGTs (Section 8.1) (Fig. 3). The separation is 390
typically achieved by passing the TLE over either activated alumina (e.g., Huguet et al., 2006) or partly deactivated silica
column (e.g., Naeher et al., 2014a) as the stationary phase.
However, underivatized fatty acids will be lost when using an activated alumina column. A silica column can also absorb FAs
if they are not derivatized first. Therefore, one option is to derivatise TLEs with diazomethane, the less hazardous 395
trimethylsilyldiazomethane or boron trifluoride methanol to convert free FAs into methyl esters prior to column separation.
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Alternatively, fatty acid fractions can be separated through saponification from neutral fractions, which also contain GDGTs
and which can further be separated from other neutral compounds following the same silica or alumina column separation
(e.g., Naeher et al., 2012). In brief, TLEs are saponified using 6% KOH in MeOH and neutrals extracted by liquid-liquid
extraction with n-hexane. FAs are then recovered the same way following acidification with HCl. This procedure also leads to 400
the cleavage of wax esters, so in addition to clean separation of FAs from other compounds, this is also a suitable approach to
study FAs and alcohols that are released by saponification of intact plant waxes, particularly in samples dominated by terrestrial
OM. An alternative approach to saponification is the separation of free FAs (without wax ester decomposition) from other
compounds using NH2 columns (Hinrichs et al., 2003).
405
Commonly applied solvents are hexane or hexane/DCM (9:1 vol:vol) to obtain an apolar fraction and DCM/MeOH (1:1
vol:vol) to obtain a polar fraction. Depending on other compounds that may be of interest, additional fractions can be eluted.
For instance, an intermediately polar fraction is eluted using hexane/DCM 1:1 or 1:2 (vol:vol) to obtain ketones (e.g., Grant et
al., 2023), in particular long-chain ketones or alkenones which are alternative paleoclimate proxies for sea surface temperature
estimation (i.e., UKʹ37 index; e.g., Prahl and Wakeham, 1987; Herbert, 2014). When the polar fraction shows many co-eluting 410
compounds, an additional fraction using an ethyl acetate/DCM solvent mixture (1:1 vol:vol) can be applied to further purify
the polar fraction (e.g., Bijl et al., 2013). Deviating from these typical procedures there are numerous other fraction separation
routines, all of which eventually end up with a polar fraction containing the GDGTs.
Polar fractions are most commonly stored refrigerated. However, dry storage at room temperature for up to 20 years has been 415
shown not to affect the quality of GDGT analyses (e.g., Sluijs et al., 2020).
3.5 Standards
To enable quantification of GDGTs, a synthetic C46 glycerol trialkyl glycerol tetraether (GTGT) is commonly used as an
internal standard (Huguet et al., 2006; see 5.3 for further explanation), although quantification is not required for the calculation
of TEX86 and other GDGT-indices. The C46 GTGT, or any other standard, can be added in known amount, either (i) to the 420
sediment before the lipid extraction (e.g., Ceccopieri et al., 2019), (ii) to the total lipid extract (Huguet et al., 2006), or (iii) just
before the analysis of the polar fraction obtained after column separation. Since lipids can be lost during the extraction process,
it is important to be explicit in the methodology section about when the standard is added, and to keep this consistent throughout
the workup for all samples in a dataset. This ensures clarity and reproducibility in the experimental procedures (see also in
Section 5.3 on quantification). 425
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3.6 Filtering of the polar fraction
Not all polar lipids that eluted from the column using DCM:MeOH dissolve in the solvent mixture that is used during UHPLC-
MS analysis (in n-hexane-isopropanol 99:1 vol:vol). In order to remove any particulates before analysis, the polar fraction
obtained after column separation and containing the GDGTs is classically filtered through a 0.45 μm pore size PTFE
(polytetrafluoroethylene) filter prior to injection (Huguet et al., 2006). This is done using the solvent mixture with which the 430
analyses will be performed, so that after filtration the sample is ready to be measured. Partial dry-down to concentrate the
samples is to be avoided at this stage, as it affects the solvent mixture composition of the sample, and thus the elution. We thus
recommend complete dry-down and then resuspend to concentrate the sample. Therefore, the filtering is recommended with
GDGTs in the required concentration. The optimal concentration of the polar fraction for the UHPLC-MS is about 1 mg mL-
1. Total dry-down of the sample at this stage requires refiltering, as occasionally polar lipids do not redissolve. Brief 435
ultrasonication may redissolve lipids at this stage. Alternatively, the redissolved polar fraction can be centrifuged before
analysis (Coffinet et al., 2014).
3.7 Contamination during sample processing
Carry-over of GDGTs from one sample to another, either in preparation (powdering) or in the various instruments that are
used for extraction. For ASE, post-extraction cell cleaning is tedious and could introduce carry-over contamination if not done 440
well, particularly when high-TOC samples are followed by low-TOC samples. Moreover, the system itself could induce carry-
over contamination through the tubing that is used to transport the extract from cell to vial (See section 2.2). Routine
measurements of blanks and strict adherence of cleaning protocols minimizes risks of carry-over.
4 Analysis
4.1 LC-MS analysis 445
Following sample preparation (see Section 3), the filtered polar fractions containing GDGTs are analyzed by liquid
chromatography mass spectrometry (LC-MS). High performance liquid chromatography (HPLC) or ultrahigh performance
liquid chromatography (UHPLC) systems are typically used to separate GDGTs. Following compound separation, single,
tandem, and high-resolution mass spectrometers are suitable for the detection, identification, and quantification of different
compounds (Liu et al., 2012a; Lengger et al., 2018). Round-robin interlaboratory comparison studies (Schouten et al., 2013b; 450
De Jonge et al., 2024) have investigated the effects of sample preparation and analytical differences across various LC-MS
instruments available in different laboratories worldwide. These studies found no apparent systematic impacts on GDGT-based
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indices, despite differing extraction and analysis protocols. However, minor discrepancies in absolute GDGT concentrations
were noted due to several factors such as instrumentation settings and human error (i.e., manual integration).
455
The most commonly applied method for the analysis of GDGTs in marine sediments is reported in Hopmans et al. (2016),
sometimes with minor modifications. In brief, the separation of GDGTs is achieved using two UHPLC silica columns (Acquity
BEH HILIC columns, 2.1 × 150 mm, 1.7 μm; Waters) that are fitted in series and use a guard column of the same material
(Acquity BEH HILIC pre-column, 2.1 × 5 mm; Waters). Normal phase separation of GDGTs is based on mixtures of n-hexane
and isopropanol. The mobile phase is typically composed of mixtures of solvent A, which is 100% n-hexane, with solvent 460
mixture B comprising n-hexane/isopropanol (9:1, vol:vol). Typically, GDGTs are eluted isocratically for 25 min with 18% B,
followed by a linear gradient to 35% B in 25 min, then a linear gradient to 100% B in 30 min. The flow rate is low with 0.2
ml/min and the column temperature is maintained at 30°C. The typical runtime is 90 min but might be adjusted depending on
user requirements (e.g., maximizing peak resolution, required target compounds to be determined, throughput efficiency).
About 20 min should be included at the end of each run to return to the initial solvent mixture prior to injection of the next 465
sample, to prevent contamination and to ensure equilibration of the composition of the mobile phase. It was shown that
injection volume should not be too high (max 50 μL) when n-hexane-isopropanol (99:1, v/v) is the sample solvent (Wang et
al., 2022). Any remaining polar fraction after the LC-MS analysis should be dried under N2, properly labeled and stored.
Overall, this method achieves the separation of isoGDGTs, which elute first, followed by branched GDGTs (brGDGTs) and 470
then hydroxylated GDGTs (OH-GDGTs; see Fig. 3). The maximum operating pressure for these columns is approximately
600 bar, but analysis is usually undertaken at much lower operating pressures, commonly ramping up from ca. 180 to 220 bar.
The method described by Hopmans et al. (2016) has largely replaced previous approaches using cyano (CN) columns which
did not achieve the same degree of separation of several isomers (Hopmans et al., 2000; Schouten et al., 2013b). However,
since this development does require some investment, not all laboratories have yet adopted the double-column technique. 475
Based on the new analytical developments, new indices and new paleoclimate calibrations have been proposed that yield lower
analytical errors in temperature reconstructions using GDGT-based proxies (e.g., Hopmans et al., 2016). Therefore, it is
recommended to use the newer approaches and proxies.
Following column separation, the eluting compounds are ionised by atmospheric pressure chemical ionisation (APCI) using 480
positive polarity mode, which yields protonated molecular ions ([M+H]+) of the target compounds. In single quadrupole
systems, the spray chamber is operated with gas and vaporizer temperatures of 200°C and 400°C, respectively. The drying gas
flow is typically set to 6.0 L min-1 and the nebula pressure to 60 psig. However, all these settings may differ depending on the
instrument used and may require individual modifications. The quadrupole temperature is usually set to 100°C.
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The mass selective detector is either a single quadrupole, tandem or high-resolution mass spectrometers. However, only one-485
dimensional MS mode is typically used to obtain diagnostic M+ ions of different GDGT homologues and isomers. The
abundances of GDGTs are monitored using selective ion monitoring mode (SIM), compounds are identified by comparing
mass spectra and retention times with those in the literature. For isoGDGTs, selected ion fragmentograms are m/z 1302.3
(GDGT-0), 1300.3 (GDGT-1 and -1’), 1298.3 (GDGT-2 and -2’), 1296.3 (GDGT-3 and -3’), 1294.3 (GDGT-4 and -4’, not
commonly targeted since not in the TEX86 index, but should be) and 1292.3 (Crenarchaeol and stereoisomer - cren’) (Hopmans 490
et al., 2016; Schouten et al., 2013a). In some environments, isoGDGT homologues up to isoGDGT-8 (m/z 1286), particularly
in hydrothermal and extremophilic environments (e.g., Schouten et al., 2013). For brGDGTs, target ions are m/z 1050.0,
1048.0, 1046.0, 1036.0, 1034.05, 1032.0, 1022.0, 1020.0, 1018.0 (De Jonge et al., 2014a; Hopmans et al., 2016), as well as
brGMGTs at 1048.0, 1034.0 and 1020.0 (e.g., Baxter et al., 2019; Sluijs et al., 2020; Bijl et al., 2021). OH-isoGDGTs are
monitored using their M+ ions at m/z 1318.3, 1316.3 and 1314.3, and as well as dehydrated ions at m/z 1300.3, 1298.3 and 495
1296.3 for OH-GDGT-0, -1 and -2 respectively (Liu et al., 2012c; Fietz et al., 2016; Varma et al., 2024b). The C46 GTGT
standard is monitored at m/z 743.8. For all ions, a mass window of 1.0 is generally maintained.
The analysis of the large number and diversity of GDGT derivatives and related ether lipids, such as GDDs, GMGTs, GTGTs,
can be analyzed with similar LC-MS methods, commonly adapted from the GDGT method of Hopmans et al. (2016). This 500
method can be shortened or extended dependent on which of these compounds are targeted and mainly differ based on the
target ions that need to be recorded for identification and quantification (e.g., Coffinet et al., 2015; Naafs et al., 2018; Baxter
et al., 2019; Mitrović et al., 2023; Hingley et al., 2024). For instance, H-isoGDGTs, if present in sample, are already recorded
in the mass fragmentograms of isoGDGTs, which are m/z 1300 (H-isoGDGT-0), 1298 (H-isoGDGT-1), 1296 (H-isoGDGT-
2), 1294 (H-isoGDGT-3), 1292 (H-isoGDGT-4) and eluted later than isoGDGTs using the Hopmans et al. (2016) method. For 505
isoprenoid GDDs (isoGDDs), the following mass fragmentograms are used: m/z 1246 (isoGDD-0), 1244 (isoGDD-1), 1242
(isoGDD-2), 1240 (isoGDD-3), 1238 (isoGDD-4) and 1236 (isoGDD-Cren), which corresponds to a mass difference of 56
relative to the equivalent isoGDGTs. Similarly, for branched GDDs (brGDDs), target ions are m/z 966 (brGDD-Ia), 964
(brGDD-Ib), 962 (brGDD-Ic), 980 (brGDD-IIa), 978 (brGDD-IIb), 976 (brGDD-IIc), 994 (brGDD-IIIa), 992 (brGDD-IIIb)
and 990 (brGDD-IIIc). IsoGDDs and brGDDs elute later than isoGDGTs and brGDGTs if samples are analysing as reported 510
in Hopmans et al. (2016). In contrast, up to three different isomers of bacterial GMGTs can be detected in each selected ion
fragmentogram using m/z 1020, 1034 and 1048, which elute elute after brGDGTs.
Instrument performance (e.g., solvent purity and aging, leaks, blockages, pump functions and pressure control, etc) should be
regularly checked and the use of check tunes to regularly evaluate the mass spectrometer performance is recommended. Blanks
(processing blanks in each sample batch and clean solvent injections), laboratory standards and known reference samples 515
should be regularly analyzed as part of sample sequences to ensure precision and accuracy of the obtained results. As an
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example, routine in-house reference sample (Arabian Sea extract mixed with Rowden soil extract) measurements since 2019
on the same instrument (“LC1”) in the GeoLab of Utrecht University, show the variability of TEX86 (Fig. 2; data in
Supplementary Table S2). The reference sample measurements during ‘normal’ performance of the UHPLC-MS (we removed
measurements during maintenance) have a standard deviation that is much smaller than the calibration error, and also smaller 520
than any paleoceanographic signal that is reconstructed (0.005 TEX86 units and 0.008 BIT index units). This doesn’t come by
itself, and is the result of finetuning and constant monitoring of machine performance. The routine analysis of the reference
sample ensures reproducibility and allows monitoring of machine drift, so that when needed machine settings can be adjusted.
Ideally, laboratories use more than one reference sample for robust calibration of the analytical error, and analyse it in replicate
over a short time span. Also, for optimal machine intercalibration, different labs should use the same reference samples so that 525
machines are optimally tuned to each other. Note also the absence of user (i.e., integrator) difference in the results (Fig. 2),
this reflects in part the strict in-house training and intercalibration. integration results between integrators can differ
considerably, particularly when integration “hygiene” (i.e., how the peak tails are trimmed) is inconsistent. Integration
intercomparisons between analysts is strongly recommended when datasets from different integrators are combined.
Combining datasets from different machines require at least the cross comparison of a few samples on each machine, to ensure 530
both machines give comparable results for the same samples. Long-term trends in the standard data are probably the result of
tuning changes, performance of the pump system and column replacements. Although Fig. 2 demonstrates that the impact of
such adjustments to the equipment on GDGT analyses are small, it is recommended that adjustments to the machine are
carefully logged.
535
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Figure 2: The TEX86 results of over 250 routine analyses of the internal GDGT reference sample (Arabian Sea +
Rowden soil) in the Geolab of Utrecht University, over the past 5 years. Colour represents the name of the integrator
of the results. Blue line represents a loess smooth through the data, black horizontal line represents the average result
of the internal standard, grey bar represents the 1 sigma of all measurements. The standard deviation in TEX86 index 540
units equates to about 0.3°C in mid-range temperatures for all calibrations. Measurements of standards during
UHPLC-MS maintenance were deleted from this dataset. For BIT results (see section 6 for details about BIT), relative
abundances of individual GDGTs and their standard deviation, see data in Supplementary Table S2.
5 GDGT peak integration
5.1 Integration guidelines 545
Integrating GDGT peak areas is the first step in interpreting GDGT data. Here, we provide an integration guide, with examples
from seafloor core top sediments in basins with both warm (above 26 °C/77 °F, typical for tropical and subtropical regions)
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and cold (below 10 °C/50 °F, typical for polar and subpolar regions) surface waters, to illustrate temperature-dependent
differences in chromatograms and their impact on integration (Figure 3). Traditionally, GDGT-0 through to GDGT-3,
crenarchaeol and cren’, along with at least the three brGDGTs used in the calculation of the BIT index (see section 6.1), have 550
been reported for marine samples (see Section 8.2). The utility of a wider array of GDGT-like compounds, including an
extended array of brGDGTs, OH-GDGTs, GMGTs, GTGTs (see Section 8 for details) is increasingly recognized, and we
recommend that these are reported where possible, and that it is explicitly stated if not possible (i.e., where abundances of
compounds are below detection limit). Figure 3 also demonstrates integrations for GDGT-4 and OH-GDGTs.
Several criteria are in use to determine the detection limit of GDGTs, e.g., below (or above) which quantification of the GDGT 555
is no longer reliable. On the lower end, a signal-to-background noise ratio of 3 is commonly applied, while other laboratories
use a minimum peak area cutoff (103). On the higher end, cutoffs are less well-defined, but in general samples with peaks with
‘blunt’ maxima are to be diluted and rerun.
Multiple software packages are currently available for automate peak integration (e.g., Dillon and Huang, 2015; Fleming and
Tierney, 2016). The pilot results of these programs are impressively close to human integration. These automated approaches 560
can systematize objective choices for the baseline and tail cutoff, can save considerable amount of time when handling large
datasets, and reduce the risk of human error in data integration and transfer. However, it is important to note that the inspection
of chromatograms for potential coelutions and the verification of proper GDGT concentrations should still be performed
manually by an expert.
565
570
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Figure 3: Example chromatogram and
integration guide for two modern
samples analyzed using the method
described by Hopmans et al. (2016): a) a
warm sea surface temperature site
(mean annual SST ~26.5 °C) in the
Coral Sea, and b) a cold sea surface
temperature site (mean annual SST ~-
1°C) in the Ross Sea. a) and b) display
the total ion current (TIC) for each
sample. The mass chromatograms
(derived from selective ion monitoring
(SIM)) for the six commonly integrated
isoGDGTs, as well as GDGT-4 are
displayed for the site with warm surface
water in c) and for the site with cold
surface water in d) (note further
discussion on the integration of GDGT-
4 in Section 8.3). Mass chromatograms
for the three integrated OH-GDGTs are
displayed for the site with warm surface
water in e) and for the site with cold
surface water in f). Grey shaded panels
in c), d), e) and f) represent the
recommended integration for each
GDGT. g) Zoom-in on the baseline of 3
isoGDGTs showing how the tails of
these peaks should be trimmed.
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5.2 Possible challenges in peak integration
In addition to temperature, the distributions of GDGTs (and likely also GMGTs) can be impacted by spatial variations in
archaeal communities, occasionally leading to unusual or challenging peaks to integrate. An example of this is GDGT-2,
particularly observed in sediment samples from the Southern Ocean and Antarctica, where an isomer of GDGT-2 can precede 575
and partially coelute with the main isomer peak (Fig. 3e), leading to a shoulder or double peak. This can be seen in the mass
chromatogram for the sample collected from the cold SST site in Figure 3. We recommend that this peak/shoulder should be
excluded from integration of the main isomer peak and be noted in the methods section.
In samples where overall GDGT abundances, or abundances of a specific GDGT, are below detection limit it is recommended 580
to report the area limit at which quantification is no longer possible (where detection limit is defined as the point at which a
peak is indistinguishable from background noise). Different laboratories are likely to have an area value (i.e., an area of 3000)
or signal-to-noise ratio (i.e., >3) as cutoffs, and while these values will vary between LC systems and software we recommend
that integration limits are stated in publications. Below this limit, it is recommended to refer to a peak as NQ, standing for non-
quantifiable, to indicate that the peak may still be present but is not abundant enough to be confidently integrated. 585
5.3 Quantification of GDGTs
GDGTs can be quantified by comparing their peak areas to those of a standard (Huguet et al., 2006; see also Section 3.6).
Typically, this is expressed in ng/g, by comparing either to the dry weight of extracted sediment (ng/g dry weight) or to TOC
(ng/g TOC) data. Quantification of GDGTs helps in identifying shifts in GDGT concentration and preservation, which is 590
important given that preservation changes could qualitatively affect the GDGT results (Ding et al., 2013). Although a linear
response of the mass spectrometer for all GDGT compounds for quantification is generally assumed, studies on the
reproducibility of TEX86 and BIT analysis between different MS systems (Escala et al., 2009) and different laboratories
(Schouten et al., 2009; 2013b) reveal that this response varies substantially with MS settings. Further, quantifications using
C46 or any non-authentic standard will be semi-quantitative, as the response between C46 and GDGT can vary over time and 595
between instruments. This leads to large interlaboratory offsets particularly in the BIT index, as this proxy comprises
compounds over a large m/z range (Table 1) (Schouten et al., 2009). Similarly, laboratory-specific MS settings are assumed to
cause large (several orders of magnitude) differences in the absolute quantification of GDGTs between laboratories worldwide
(De Jonge et al., 2024). These interlaboratory comparison studies have called for the introduction of a community-wide
standard mixture with established GDGT proxy values that can be used to calibrate MS instruments (Escala et al. 2009; 600
Schouten et al., 2009; 2013b; De Jonge et al., 2024). Until this has been accomplished, laboratories should perform regular
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reruns of an in-house GDGT standard mixture to monitor the stability of their MS instrument, and consistency in integration
performance, and thus the accuracy of their measurements (see Section 4.3). Note, however, that the offsets between
instruments in the quantification of GDGTs does not impact individual records generated on the same instrument, i.e.,
individual instruments, when maintained well, show high/sufficient accuracy and precision in both TEX86 and BIT estimations 605
(see Section 4.2).
6 Data interpretation: non-thermal overprints
Along with sea surface temperature, marine sedimentary GDGT distributions are governed by a range of factors that provide
insight into past environments but can confound simple interpretation of only one environmental parameter. For example,
marine GDGT-based paleothermometry is complicated by a range of sources for isoGDGTs beyond near-surface ammonia-610
oxidizing Nitrososphaerota. Various screening mechanisms and indices (Table 1) have been developed to assess the impact of
non-thermal effects or contributions from alternative sources of isoGDGTs. These screening methods typically focus on
identifying additional non-surface Nitrososphaerota contributions to sedimentary GDGTs, e.g., methanogens or anaerobic
oxidisers of methane (methane index; MI) or terrestrial inputs (BIT). The impact of degradation on GDGT distributions (and
associated indices) are not as well developed. However, degradation impacts appear to be minimal, with no evidence that 615
degradation of GDGTs alters their distribution or TEX86 values during herbivory (Huguet et al., 2006), in the water column
(Kim et al., 2009), or in sediments deposited under different redox conditions (Schouten et al., 2004). The most obvious impact
of degradation, therefore, is that the pelagic signal is diluted relative to sedimentary archaeal or soil contributions (Huguet et
al., 2009). For example, Hou et al. (2023) and Kim and Zhang (2023) showed that high values of the MI, Delta Ring index
(!"#) and BIT index corresponded with an interval of lower absolute concentrations of GDGTs, and in particular a 620
disproportionate decline in the concentration of crenarchaeol. Because these impacts remain incompletely understood or
perhaps offer additional environmental insights, we recommend that samples that are removed by screening should still be
reported in the data report but excluded from the subsequent temperature reconstruction. Bijl et al. (2021) provided an R script
that follows standardized steps in GDGT data evaluation and interpretation. Although these screening methods have clearly
defined cutoff values, we recommend careful consideration of the depositional setting when these screening methods are used. 625
Specifically, concentration changes of GDGTs must be considered for those samples whereby screening methods suggest non-
thermal impacts.
Table 1: Summary of screening methods assessing anomalous GDGT distributions.
Index to
identify
Equation
Description
Reference
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anomalous
sample
Branched and
Isoprenoid
tetraether
(BIT) index
$#%&'()*+*%,#-.'##-.'###-
'/()*+*%,#-.'##-.'###-.'0)12-/
Indicates organic matter derived
from soil (OMsoil) input into a
marine environment where 0
implies no OMsoil and 1 is no
crenarchaeol. Higher values may
cause temperature bias from
OMsoil-derived isoGDGTs.
Hopmans et al.
(2004)
Methane Index
(MI)
3#&'*+*%,4-.'5-.'6-
'*+*%,4-.'5-.'6-.'0)12-.'0)127-/
Indicates contribution of
methanotrophic Archaea, where
higher values (>0.3–0.5)
indicate larger contribution.
Zhang et al.
(2011)
%GDGT-0
8'*+*%,9- &:'*+*%,9-
'*+*%,9-.'0)12-;<499/
'*+*%,9-='0)12-/
Indicates contribution of
methanogenic Euryarchaeota,
where values >67% or ([GDGT-
0])/([Cren]) >2 indicate larger
potential methanogenic input.
Blaga et al.
(2009),
Sinninghe
Damsté et al.
(2012a)
Δ Ring Index
(ΔRI)
!"#&"#!"# ,"#$%&'()/
Where:
"#!"# &,9>??<%@A*+ .6>65<B%@A*+C,.4>DE
And
𝑅𝐼!"#$%& = 0 × [𝐺𝐷𝐺𝑇 − 0]+ 1 × [𝐺𝐷𝐺𝑇 − 1]+ 2 × [𝐺𝐷𝐺𝑇 − 2]+ 3 × [𝐺𝐷𝐺𝑇 − 3]+ 4 × [𝑐𝑟𝑒𝑛]
+ 4 × [𝑐𝑟𝑒𝑛']5
And:
%@A*+
&'*+*%,5-.'*+*%,6-.'0)12--
'*+*%,4-.'*+*%,5-.'*+*%,6-.'0)12--/
TEX86 and RI correlate with
temperature. ΔRI measures if a
sample's GDGT distribution
deviates from the modern ocean
TEX86-RI relationship. If ΔRI
lies outside the 95% confidence
interval of the modern
regression (±0.3 ΔRI units), then
non-thermal factors are
indicated.
Zhang et al.
(2016)
GDGT-
2/GDGT-3
'*+*%,5-
'*+*%,6-/
Elevated values (>5) indicate
greater contribution from
subsurface GDGTs.
Taylor et al.
(2013) (see
also Hurley et
al., 2018)
fcren
F./)0-!:./)0-!2./)0 &'0)12--
'0)12-.'0)12G-/
High values (>0.25) indicate
anomalous GDGT distribution
impacted by non-thermal
factors.
O’Brien et al.
(2017)
%GDGTRS
8*+*%3$ &:'0)12--
'*+*%,9-'.'0)12--;<499/
>30% Identifies a ‘Red Sea-
type’ distribution, but this
cannot be distinguished from a
high temperature signal
Inglis et al.
(2015)
Dnearest
Distance metric based on a Gaussian Process emulator
covariance matrix
High values (>0.5) indicate a
GDGT distribution substantially
different from the “nearest
neighbour” within the
calibration data set (i.e., non-
analogue to modern core tops)
Dunkley Jones
et al. (2020)
630
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6.1 Terrestrial input
IsoGDGTs form a minor component in terrestrial/aquatic GDGT distributions (e.g., Blaga et al., 2009), but in settings with
significant soil organic matter input into the marine realm, terrestrially derived isoGDGTs have been shown to bias
reconstructed temperatures based on TEX86 values (Weijers et al., 2006a). The Branched and Isoprenoid Tetraether (BIT)
index was developed to assess the contribution of GDGTs from terrestrial soils transported by rivers into marine sediments 635
(Hopmans et al., 2004). It is based on the abundance of the three dominant brGDGTs (brGDGT-I, brGDGT-II, brGDGT-III)
compared to crenarchaeol, which is predominantly produced in marine settings (Hopmans et al., 2004). An index value of 0
implies no brGDGT input, while a value of 1 represents no crenarchaeol input. The BIT index is useful in marginal marine
and lake sediments (Hopmans et al., 2004). A study on the marine surface sediments of the Congo Fan found that in samples
where BIT exceeded 0.3, temperature estimates were biased by >2°C, implying that terrestrial isoGDGT contributions affect 640
the marine sedimentary isoGDGT pool (Weijers et al., 2006a).
There are however many complications with the use of BIT for terrestrial input. Firstly, BIT index values are not consistent
between laboratories (see 5.3; Schouten et al., 2009). Secondly, the application of a threshold above which temperature bias is
likely to occur is locality dependent because 1) it is influenced by the difference between the TEX86 values of terrestrially 645
sourced isoGDGTs, and marine sourced GDGTs, and 2) the abundance of crenarchaeol, which is typically more abundant at
higher temperature, also influences the BIT index (Schouten et al., 2013a). Albeit typically in small amounts, marine
production of brGDGTs can impact BIT index values, although the molecular composition of marine-produced brGDGTs
differs strongly from those produced in soils (Peterse et al., 2009; Sinninghe Damsté, 2016). Various indices have been
developed to assess marine or terrestrial brGDGT production (see section 8.2) (Huguet et al., 2008; Weijers et al., 2014; Xiao 650
et al., 2016). Furthermore, brGDGTs tends to be preferentially preserved over isoGDGTs during syn- and/or post-sedimentary
oxic degradation (Peterse et al., 2009). The preferential degradation occurs in particular under anoxic conditions. As a
compromise, some studies chose to discard TEX86 as SST proxy when BIT and TEX86 values correlate, and a location-specific
threshold has been established if a correlation existed, or if substantial scatter or anomalous values in TEX86 occurs above a
certain BIT threshold (e.g., Schouten et al., 2009; Bijl et al., 2013; Schouten et al., 2013b; Davtian et al., 2019). Other studies 655
question the use of the BIT index for estimating marine vs. terrestrially derived organic matter, as samples from an increasing
range of environments are found to have in situ produced brGDGTs (e.g., Peterse et al., 2009; Dearing Crampton-Flood et al.,
2019; Bijl et al., 2021). A study of the Mississippi River Delta has also shown that river-derived brGDGTs are not transported
far into the marine system (Yedema et al., 2023). Moreover, brGDGTs detected in marine sediments, even those close to shore,
show distributions that are fundamentally different from those in modern soils or peats (Sinninghe Damste, 2016; Hollis et al., 660
2019; Bijl et al., 2021). With the documentation of isoGDGT contributions from land, and the production of brGDGTs in the
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marine system, the BIT index proxy as purely indicative of terrestrial organic matter input becomes problematic. We
recommend caution in overinterpreting BIT as proxy for terrestrial input and as criterion for evaluating TEX86, particularly
when brGDGT distributions diverge from those of modern soils and peats. The degree of cyclization in the brGDGTs reflects
this best, as soil-derived brGDGTs have much less cyclization than those produced in marine sediments (Sinninghe Damste, 665
2016; Dearing Crampton-Flood et al., 2019). This means that some of the older TEX86 data that were discarded because of
high BIT might actually accurately reflect local temperature.
6.2 Methanogenic input
Marine and sedimentary archaeal communities can also contain methanogenic Euryarchaeota, which can synthesize GDGT-
0, and to a lesser extent, GDGT-1, GDGT-2, and GDGT-3 (e.g., Pancost et al., 2001; Blaga et al., 2009; Sinninghe Damsté et 670
al., 2012a; Inglis et al., 2015). The impact of Euryarchaeota on a GDGT distribution can be assessed using %GDGT-0 (Table
1), where values >67% indicate that a sample contains a substantial contribution from methanogenic sourced GDGTs
(Sinninghe Damsté et al., 2012a). The ratio of GDGT-0 to crenarchaeol is also sometimes used, with values above 2 indicating
a methanogenic source (e.g., Blaga et al., 2009; Naeher et al., 2012; 2014b). Other indicators of methanogenic archaeal
contributions include archaeol, hydroxyarchaeol, pentamethylicosenes and crocetene (and their derivatives), which can be 675
detected using GC-MS analysis (Hinrichs et al., 2000; Niemann and Elvert, 2008; Naeher et al., 2014b). The impact of
methanogenic input on a sedimentary GDGT distribution likely varies by depositional and oceanographic setting, and has
typically only been found to have a minor impact in marine sediments (i.e., Inglis et al., 2015; O'Brien et al., 2017). In any
case, the likelihood of a methanogenic overprint in a specific oceanographic setting must be assessed when screening to detect
non-thermal overprints in GDGTs. 680
6.3 Methanotrophic input
Post-depositional production of isoGDGTs also occurs during anaerobic oxidation of methane by anaerobic methanotrophic
archaea (Zhang et al., 2011). Methanotrophic archaea, especially those of group ANME-1, preferentially produce GDGT-1,
GDGT-2 and GDGT-3, and can bias reconstructed temperatures in sediments where they are active, such as around cold seeps
or areas with gas hydrate occurrences (Pancost et al., 2001; Zhang et al., 2011). The Methane Index (MI; Table 1) assesses the 685
relative contribution of methanotroph-produced GDGTs to those produced in the water column by non-methanotrophic
Nitrososphaera (Zhang et al., 2011). A range of >0.3–0.5 is considered to indicate a significant contribution from a source
other than normal marine production (Zhang et al., 2011; Kim and Zhang, 2023). We recommend that samples with MI values
>0.3 are used with caution when estimating SST, acknowledging that similar to the BIT (see above), the MI value may be
locality dependent and should be used as a guideline rather than a firm cut off. The sensitivity of the MI has been debated, 690
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with it previously unclear whether small amounts of diagenetic methane in porewater could impact MI values, or if a high flux
of methane associated with gas hydrates was required to elevate MI levels (Weijers et al., 2011; Kim and Zhang, 2023). Recent
research indicates that MI is quantitatively related to sedimentary methane diffusive flux, with high MI values strongly
associated with high methane fluxes and shallow depths of the sulfate-methane transition zone, where the activity of anaerobic
methane oxidation is mostly concentrated (Kim and Zhang, 2023). MI values at the lower end of the range (i.e., >0.3) appear 695
to be associated with high methane flux in polar settings, while this value is closer to >0.5 in non-polar regions (Kim and
Zhang, 2023). Additional indicators of methanotrophic archaea include archaeal lipids described in 6.2, which are
distinguished from methanogenic sources by their 13C-depleted composition (δ13C values ranging from -40 to low as -120‰;
e.g., Hinrichs et al., 2000; Niemann and Elvert, 2008; Naeher et al., 2014b). Furthermore, methanotrophic bacteria commonly
occur in conjunction with methanotrophic archaea and are distinguished from heterotrophic bacteria by 13C-depleted signature 700
of bacterial biomarkers such as fatty acids and hopanoids (Hinrichs et al., 2003; Birgel and Peckmann, 2008; Naeher et al.,
2014b). Therefore, MI may not be an effective indicator at sites with additional sources of GDGTs, such as soil-derived GDGTs
in coastal settings (Zhang et al., 2011). In these instances, δ13C measurements of bacterial biomarkers such as hopanes (Pancost,
2024), or, in a more elaborate workup scheme, directly on the GDGTs (Pearson et al., 2016; Keller et al., 2025) could be used
to assess methanotrophic contributions. 705
6.4 Ring index and Δ Ring Index
Different strains of archaea have been found to display variable TEX86 values, despite having been cultured at the same
temperatures (Elling et al., 2015; Qin et al., 2015). This suggests that growth temperature is not the sole control on changes in
the TEX86 ratio, and that other factors including Thaumarchaeal community composition can play an important role. A more
linear relationship was found between growth temperature and the Ring Index (RI, Table 1), which measures the weighted 710
average of cyclopentyl moieties, across all strains of archaea in culture experiments (Elling et al., 2015; Qin et al., 2015). The
slope and strength of this relationship varies between Thaumarchaeal strains, suggesting that community composition can still
impact the relationship between RI and temperature (Elling et al., 2015). Higher values of RI indicate higher temperatures. In
the modern ocean, TEX86 and RI are correlated, and RI can be calculated from TEX86 using a regression (Zhang et al., 2016).
If a sample’s RI deviates from the calculated RI outside of the 95% confidence interval of the modern regression (±0.3 ΔRI 715
units), then the TEX86 value for that sample is considered to be potentially influenced by non-thermal factors and/or deviates
from modern analogues. These factors include the impact of GDGTs derived from soil, methanogenic and methanotrophic
archaea as described above, variations in community composition, or potentially other non-thermal impacts on GDGT
biosynthesis such as archaeal growth rates (Zhang et al., 2016).
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6.5 Surface vs subsurface GDGT production (GDGT-2/GDGT-3 ratio) 720
One of the known complications of the GDGT-based temperature proxy relates to the depth of GDGT production and export.
Nitrososphaerota live throughout the water column, are most abundant near the nutricline, and less abundant in the uppermost
~50 meters of the water column (Wuchter et al., 2005; Hurley et al., 2018). Sedimentary GDGTs have typically been considered
to best represent surface or shallow subsurface conditions (Schouten et al., 2002), possibly due to preferred export of
Nitrososphaerota from these depths in aggregates like fecal pellets, as speculated in Wuchter et al., (2005). Evidence for this 725
includes the observation that sedimentary TEX86 values statistically best fit surface temperature (Kim et al., 2008; although
below we explain that this might be a statistical artefact caused by the fact the total temperature range is larger in the surface
than in the subsurface), and that those sedimentary relationships are only slightly offset from those derived from SPM of
surface water (<100 m) (Wuchter et al., 2005; Taylor et al., 2013; Schouten et al., 2013a).
730
The GDGT literature suffers from imprecise definition of the qualitative terms surface and (shallow) subsurface. Sediment
trap work has indicated that most GDGTs are exported from the surface (e.g., Wuchter et al., 2005). However, in such studies
the uppermost trap is typically located at 500 meters of depth, proving nothing more than dominant export from the upper 500
meters of the water column, which includes mixed layer and thermocline. For proxy calibration and application, the major
factor is whether GDGTs are exported from above, within or below the (permanent) thermocline. Because in many ocean 735
regions the thermocline and nutriclines are related, we might expect that a portion of GDGT export might occur from close to
the thermocline rather than only from above.
Interestingly, two dominant clades of Nitrososphaerota are present in the water column, of which one is present in the upper
~200 of the water column (shallow clade), and other resides typically deeper than 100 m (Francis et al., 2005; Villanueva et 740
al., 2015). The GDGT distribution in the membranes of the shallow clade adjusts to temperature, but GDGTs from below 100
m water depth do not in the same way (Hurley et al., 2018; Schouten et al., 2002; Taylor et al., 2013; Turich et al., 2007;
Wuchter et al., 2005; Zhu et al., 2016). GDGTs derived from the deeper clade thus affect the temperature signal preserved in
marine sediments. The two clades are distinctive in their ratio of GDGT-2/GDGT-3: SPM collected from above the permanent
pycnocline are typically <5 (e.g., Hernández-Sánchez et al., 2014; Hurley et al., 2018), while deeper SPM has values up to 40. 745
The exact GDGT-2/GDGT-3 value of deep clade Nitrososphaerota remains elusive, however, as SPM also includes organic
matter exported from the surface ocean. Nonetheless, the essentially bimodal distinction between the two clades, also
encountered in the water depth domain, implies that GDGT-2/GDGT-3 ratio values can be used to differentiate between
contribution from ‘shallow’ (~0-200 m depth) and ‘deep’ (> ~100 m depth) clades of archaea (Hurley et al., 2018;
Rattanasriampaipong et al., 2022). The ratio of GDGT-2/GDGT-3 can thus be used as a method of assessing whether a 750
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sediment sample displays a contribution from archaea living in or below the surface waters and this shows that many (notably
deep ocean) samples in the calibration data set includes contributions from the deep clade (e.g., Taylor et al., 2013; Kim et al.,
2015; van der Weijst et al., 2022). This implies that calibration to surface ocean temperature has likely led to an overestimate
of the proxy response slope and that integrated (shallow) subsurface calibrations are more appropriate (Ho and Laepple, 2015)
Yet, in many oceanographic settings, there is a strong correlation between SST and subT variability through time (e.g., Ho and 755
Laepple, 2015) which implies that TEX86 – calibrated to an integrated shallow subsurface depth – may still serve as a proxy
for surface temperature variability, if this assumption can be substantiated (Fokkema et al., 2024).
6.6 Other non-thermal overprints (fcren and Red Sea-type)
Crenarchaeol and cren’ are produced by marine Nitrososphaerota. Some studies (Sinninghe Damsté et al., 2012a; O'Brien et
al., 2017) suggested that a substantial increase in the proportion of the cren’ relative to crenarchaeol, referred to as fcren, could 760
indicate a non-thermal impact on a GDGT distribution. For instance, Group I.1a Nitrososphaerota produce much less cren’
(compared to crenarchaeol) than Group I.1b Nitrososphaerota (Pitcher et al., 2010), which means that community changes in
Nitrososphaerota will have impact on the GDGT distributions (and thus overprint values and TEX86) irrespective of
temperature, and overprint values. In the modern core-top dataset, fcren values vary between 0 and 0.16. If a sample displays
higher values (i.e., >0.25) it could indicate shifts in archaeal communities. This ratio might be a useful indicator of non-thermal 765
overprints in settings which are warmer than the basins with the warmest sea temperature today. In cultures, fcren variations
are strongly temperature controlled, but archaeal ecology might also play a role (Bale et al., 2019).
Core-top samples collected from the modern Red Sea display unusual GDGT distributions and TEX86 values that do not align
with observed temperatures, potentially caused by an endemic Nitrososphaerota clade (Trommer et al., 2009). Distributions in 770
this highly saline, warm, and low nutrient environment are typically characterized by a low abundance of GDGT-0 relative to
the cren’ leading Inglis et al. (2015) to suggest that ‘Red Sea-type’ distributions in the geological past could be detected by the
abundance of cren’ relative to that of GDGT-0 (%GDGTRS). Values of %GDGTRS >30 could indicate ‘Red Sea-type’
distributions, although Inglis et al. (2015) note that these values are also expected to also occur under very high temperature
(>30 °C) surface waters. In the Red Sea surface samples, where ecological factors are at play, fcren is high as well, but the fact 775
that the basin water is also hot makes it impossible to separate an ecology effect from a temperature effect.
6.7 D_nearest
The OPTiMAL method of calibrating GDGT paleothermometry (Dunkley Jones et al., 2020; Table 1) uses a Gaussian process
(GP) emulator to determine the relationship between sea surface temperature and all six of the main isoGDGTs (GDGT-0, -1,
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-2, -3, cren and cren’). This study also quantifies how similar a fossil GDGT assemblage is to the modern core top calibration 780
data set. OPTiMAL measures the distance between the fossil assemblage and its nearest neighbours within the modern
calibration data, in GDGT space. This distance is small if the fossil assemblage is “within” the modern calibration space and
will grow the further the fossil assemblage is outside the modern calibration. The weighting coefficients learned by the GP
emulator allow for a measurement of this “distance” - termed Dnearest. If a GDGT input coordinate has a minimal effect on
temperature prediction (it is relatively insensitive to temperature), then points with a large absolute difference in GDGT-values 785
from this coordinate will still be near in Dnearest space. If, however, a GDGT coordinate is more significant in temperature
prediction, differences between samples in that coordinate will contribute more to Dnearest. A threshold Dnearest value of 0.5 was
proposed, above which fossil samples are considered to be significantly ‘non-analogue’ with the modern calibration, on the
basis of the inflection point of rapidly increasing uncertainty in the GP estimator of temperature (Dunkley Jones et al., 2020).
As noted in Dunkley Jones et al. (2020) there are two (not exclusive) potential categories of non-analogue GDGT samples; the 790
first consists of samples with anomalous GDGT distributions, such as fossil samples with one or more of high BIT, ΔRI, MI
and/or %GDGT-0, that also show high Dnearest; and the second consists of GDGT assemblages that formed at temperatures
beyond the temperature range of the modern calibration. For a GP emulator that is agnostic about the form of the temperature
- GDGT relationship, there is ‘no information’ outside of the modern calibration space and it can make no meaningful
temperature prediction for samples in this category. However, calibrations that are confident about the form of the GDGT-795
temperature relationship, and its conservation at temperatures >30ºC, would use this as the basis for temperature prediction at
higher Dnearest values. Even in these cases, Dnearest is a means of checking how far these extrapolations have gone beyond the
constraints of the calibration data. However, OPTiMAL is not applicable outside the modern range and has therefore no use
in paleoclimate studies outside of the modern temperature range.
7 Temperature calibrations 800
7.1 GDGT core top dataset
GDGT distributions in samples from a global core top dataset have been critical in calibrating GDGT relationships with
temperature. The calibration dataset was initially composed of 44 core top samples (Schouten et al., 2002) and has been
expanded several times since then (Kim et al., 2008; 2010), to the most recent iteration of 1095 samples in Tierney and Tingley
(2015). More core top data points/sites have been reported since 2015, in some cases substantially expanding some areas of 805
the global ocean that are underrepresented in the data set of Tierney and Tingley (2015), for example from the Mediterranean
Sea, Southern Ocean and Antarctica (e.g., Kim et al., 2015; Jaeschke et al., 2017; Lamping et al., 2021) (Fig. 1). However,
despite these efforts, several regions of the global ocean remain poorly represented: subtropical gyres, deep water settings and
other areas of the open ocean distal from land (Fig. 1). On top of this, core top data was assembled while the proxy was still
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in the development phase. This means that it initially included only the six primary isoGDGTs (GDGT-0, GDGT-1. GDGT-810
2, GDGT-3, crenarchaeol and the cren’) and the three brGDGTs included in the BIT index (brGDGT-I, brGDGT-II and
brGDGT-III), and did not exclude core topdata with overprints. Also, it misses more recent developments in the proxies, e.g.,
in the interpretations of brGDGTs that are marine-in situ produced, and the inclusion of GDGT-4. Following our
recommendations in Section 10 and in Table 4, we envision that an open access repository in which all cope top data are easy
accessible and stored with metadata, will enable the community to iteratively improve the calibrations. 815
GDGTs are commonly applied to paleo/geological samples, where following information/data are typically generated e.g.,:
sediment fraction (clay, silt), mineralogical composition, carbonate content, X-ray Fluorescence (XRF), TOC, or/and
depositional setting (shallow marine, hemipelagic, etc.). To ensure better understanding of GDGT distributions in ancient and
recent samples, future expansion of the core top data set as well as critical evaluation of the existing core top data should
include the following information: 820
- Core top GDGT data should be reported as peak areas, with quantified concentrations where available, as well as the
used detection limit (e.g., signal-to-noise ratio, peak area, etc). We also suggest expanding the range of GDGTs
beyond those used for TEX86 (see Section 8). This will optimize the interoperability and reusability of core top data
as different indices or calibration methods are developed over time, that include more than the six primary isoGDGTs
applied in the first top core calibration in 2002. 825
- Samples displaying unusual distributions (i.e., that fail the screening methods described above) should still be reported
but flagged as possibly impacted by non-thermal processes. As an example of how this could be done, the R-script of
Bijl et al. (2021) indicates in columns with logical values which samples show unusual distributions based on
threshold values for overprint criteria.
- Currently sea surface temperature and water depth to seafloor are reported for each core top site. We recommend that 830
a larger range of environmental metadata is included, for example, distance to shore, water column structure/water
mass, oxygen concentrations, and any information that can guide an interpretation of core top sediment age.
- Core-top and surface sediment is often assumed to be representative of modern conditions, but variability in sediment
dynamics, bottom currents and even sampling methods may lead to these samples representing thousands of years of
sediment accumulation (Mekik and Anderson, 2018). Where possible, an estimate of sediment age (i.e., based on 835
microfossils or 210Pb dating) should be reported.
- Surface sediment data will need to be scrutinized regarding data quality and confounding factors to arrive at proper
proxy calibrations. For example, there may be some doubt regarding data quality of some samples included in the
current dataset that must be overcome by data reproduction in different laboratories (i.e., round-robins). Another issue
is that most surface samples from the Mediterranean Sea are compromised by large contributions of deep-dwelling 840
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32
archaea and should possibly not be used for calibrations to SST. The community must define a way forward for
defining proper analytical conditions and use of surface sediment data for the development of proxies.
7.2 Calibration equations
Currently, a large range of GDGT-based temperature calibrations exist, as a result of method development and improved
mechanistic understanding of proxy functioning (see GDGT review paper). We summarize key developments in global 845
calibrations, and when/how to use these calibrations in Table 2. We note that the current understanding of GDGT synthesis
and taphonomy prevents choosing one calibration that suits all purposes, geologic time intervals, and geographic settings.
Table 2. Summary of key developments in GDGT-based global calibrations (where ‘n’ refers to the number of
coretop/surface sediment samples, RSE - residual standard error). 850
Calibration
Equation
Calibration
error
Description
Reference
Status
Linear TEX86,
n=44
𝑇𝐸𝑋!" = 0.015× 𝑇 + 0.28-
Where
𝑇𝐸𝑋!"
=[𝐺𝐷𝐺𝑇 −2]+[𝐺𝐷𝐺𝑇 − 3]+ [𝑐𝑟𝑒𝑛#]
[𝐺𝐷𝐺𝑇 −1]+[𝐺𝐷𝐺𝑇 − 2]+[𝐺𝐷𝐺𝑇 −3]+ [𝑐𝑟𝑒𝑛#]-
And T= annual mean SST in °C.
RSE ±2.0 °C
Original linear
SST calibration
based on a global
core top dataset
Schouten et
al. (2002)
Superseded by Kim
et al., (2008)
TEX86’
n=104
TEX86=0.016×SST+0.20
Where
TEX86=
([GDGT-2] +[GDGT-3]
+[cren])/
([GDGT-1] +[GDGT-2]
+[cren])
RSE: none
given
Modified version
of TEX86 used in
Paleogene Arctic
samples with high
relative
abundances of
GDGT-3
Sluijs et al.,
(2006)
No longer in use,
see discussion in
Sluijs et al., (2020)
Updated linear
TEX86
n=223
𝑆𝑆𝑇 =56.2× 𝑇𝐸𝑋!" −10.8-
RSE ±1.7 °C
Updated linear
SST calibration
Kim et al.
(2008)
Superseded by Kim
et al., (2010)
Reciprocal
TEX86
n=287
𝑆𝑆𝑇 = −16.3× : 1
𝑇𝐸𝑋!";+ 50.5-
68.2%
confidence
interval
Non-linear
(reciprocal), high
temperature
calibration
Liu et al.
(2009)
Not recommended
since it lacks
underlying
mechanistic
understanding
𝑇𝐸𝑋!"
$-
Exponential
n=255
𝑇𝐸𝑋!"
$=𝑙𝑜𝑔-(𝑇𝐸𝑋!")-
And 𝑆𝑆𝑇 =68.4× (𝑇𝐸𝑋!"
$)+38.6-
𝑇𝐸𝑋!"
$-
Logarithmic (Kim
et al., 2010) or
exponential;
(Tierney and
Tingley, 2014)
calibrations;
𝑇𝐸𝑋!"
$ was
recommended for
temperature >
15°C. Surface
waters (0–20m)
Kim et al.
(2010)
In use, but
𝑇𝐸𝑋!"
$suffers from
statistical
shortcomings,
notably regression
dilution and
residuals at the
warm end of the
calibration.
(Tierney and
Tingly, 2014)
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33
𝑇𝐸𝑋!"
% and
Exponential
n=396
𝑇𝐸𝑋!"
%
=𝑙𝑜𝑔:[𝐺𝐷𝐺𝑇 −2]
[𝐺𝐷𝐺𝑇 −1]+[𝐺𝐷𝐺𝑇 − 2]+ [𝐺𝐷𝐺𝑇 − 3];-
And 𝑆𝑆𝑇 =67.5× (𝑇𝐸𝑋!"
%)+46.9-
RSE ±4.0 °C
for 𝑇𝐸𝑋!"
%
±2.5°C
𝑇𝐸𝑋!"
% was
recommended for
temperature
below 15°C.
Surface waters
(0–20m)
Kim et al.
(2010)
𝑇𝐸𝑋!"
% has
mechanistic flaws
and is no longer
recommended for
use.
Linear 𝑇𝐸𝑋!"
$
n=21
𝑇 = 52.0 × (𝑇𝐸𝑋!"
$)+42.0-
RSE ±3.4 °C
Linear calibration
of 𝑇𝐸𝑋!"
$ in
mesocosms.
Kim et al.
(2010)
Not used because of
the lack of analogy
between mesocosm
and core top GDGT
distributions
BAYSPAR
n=1095
(samples north
of 70° N
removed)
Bayesian, spatially varying regression based on
𝑇𝐸𝑋!"
90th percent
confidence
intervals
Linear calibration.
Used in
‘Standard’ or
‘Analogue’ mode
depending on
whether
oceanographic
conditions at the
site were
analogue to
modern. Can
reconstruct SST
or SubT
(weighted 0–200
m water depth,
weights given by
gamma
probability
density function).
Tierney and
Tingley
(2014,
2015)
In use, particularly
for applications in
modern-like
temperature ranges
and analogue
settings
Subsurface
𝑇𝐸𝑋!"
$
n=255; >15°C
𝑇 = 40.8 × (𝑇𝐸𝑋!"
$)+22.3-
95%
confidence
intervals
Recalibration of
𝑇𝐸𝑋!"
$. A least
squared
regression was
performed for
depth-integrated
temperatures
between 0-1000m
water depth, and
an ensemble
(SUBCAL)
derived for a
subsurface
temperature
calibration.
Ho and
Laepple
(2016)
In use by the
community because
the paper presents a
useful range of
integrated export
depths. The
assumption of
deeper export
reduces the proxy
response slope and
dampens variability
in downcore
records.
OPTiMAL
Machine learning-based Gaussian process
regression model
95%
confidence
intervals, root
mean square
uncertainty
±3.6 °C
SST model based
on machine
learning using
relative
abundances of all
6 isoGDGTs.
Dunkley
Jones et al.
(2020)
In use, but assumes
surface signal. It
cannot be used to
predict
temperatures
outside of the
modern calibration
range of SSTs (>30
ºC).
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34
An initial calibration between GDGTs and temperature was developed by Schouten et al. (2002) on a core top dataset of 44
samples (Table 2). They found the best correlation with annual mean SST was a linear regression using the TEX86 ratio, which
includes GDGT-1, GDGT-2, GDGT-3 and cren’, but excludes the abundant GDGT-0 and crenarchaeol to avoid these
compounds having an overwhelming influence on the index. This was also driven by concerns that GDGT-0 had many 855
alternative sedimentary sources (see Section 6). Nonetheless, Zhang et al. (2015) confirmed a strong relationship between SST
and the weighted average RI. A modified version of TEX86, termed TEX86’, based on an expanded data set of 104 core top
sites, was used by Sluijs et al. (2006) for samples from the Paleogene succession from the Arctic which contained high relative
abundances of GDGT-3, possibly related to high terrestrial input. This index removed GDGT-3 from the denominator of
TEX86, but proportionally high GDGT-3 was not commonly found in other sample sets, and the index is no longer in use 860
(Sluijs et al., 2020).
The linear TEX86-based calibration of Schouten et al. (2002) was subsequently updated by Kim et al. (2008) based on an
expanded global core top data set of 223 samples (Table 2). Kim et al. (2008) noted that the core top samples from high latitude
sites showed significant scatter, and that the TEX86 calibration had limited utility below 5°C. Liu et al. (2009) expanded on the 865
concepts introduced by Schouten et al. (2002) acknowledging the challenges in extrapolating the core top calibration above
the limit of the modern day (i.e., above ~30°C and TEX86 values of ~0.73) (Table 2). Liu et al. (2009) developed a calibration
based on the reciprocal of TEX86 that reduced the slope of the TEX86-temperature relationship for samples from warm water
pool.
870
The modern core top data set was expanded further by Kim et al. (2010), who also observed the relative insensitivity of TEX86
to temperature in cold regions and investigated variations in GDGT ratios to improve the calibration at both the cold and warm
ends of the spectrum (Table 2). The authors concluded that a logarithmic form of TEX86, referred to as %@A*+
4 was most optimal
as it exhibited the highest R2 and the smallest residual error (which was their prime quality criterion) for samples from sites
with moderate to high surface water temperature (15–28°C) when tested on a core top dataset with subpolar and polar samples 875
removed, and recommended this calibration was applied for sites with expected temperatures above 15°C. For the whole
temperature spectrum, and especially below 15°C, the authors found a logarithmic form of a GDGT ratio without the cren’
resulted in the best correlation (%@A*+
5). %@A*+
5/HI/IJHKK/HL/MIN/OI a proxy for polar sea surface or subsurface temperature, as it
seems to provide the most plausible absolute values (e.g., Ai et al., 2024). However the ratio used in %@A*+
5 does not correlate
with an increase in cyclopentane rings, i.e., it lacks a physiological basis and can be easily biased by water depth-driven 880
variations as expressed by the GDGT-2/GDGT-3 ratio (Taylor et al., 2013). Thus, this calibration of TEX86 has been widely
discarded by the community, and we recommend it not be used.
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Kim et al. (2010) also investigated the validity of %@A*+
4 and %@A*+
5 using mesocosm experiments, and found that %@A*+
4 (i.e.,
log TEX86) provided the strongest correlation to incubation temperatures in culture data, albeit with a slightly different intercept
and slope reflecting a reduced amount of the cren’ in cultures than present in core tops. Incubation studies have since 885
highlighted that TEX86-temperature correlations vary across archaeal strains, while RI appears to have a more linear
relationship (Elling et al., 2015). Despite this, global core tope values of RI have not yet been calibrated to temperature.
Several temperature calibrations have focused on the fact that sedimentary GDGTs may represent a subsurface rather than near
surface temperature signal (e.g., Taylor et al., 2013). Kim et al. (2008) statistically compared the fit of sedimentary TEX86 to 890
temperatures from various depths, as outlined above, and based on that proposed that TEX86 correlates best to SST. Later,
mounting evidence suggested that TEX86 is more representative of a subsurface signal. Ho and Laepple (2016) employed a
calibration ensemble between 0 and 1000 m water depth, assuming the majority of GDGTs are exported from between 100
and 350 m. Tierney and Tingley (2014, 2015) expanded the core top dataset and developed a Bayesian, spatially varying
method to generate linear calibrations (BAYSPAR) to surface (0–20m) or subsurface (0–200m), and recognized a regression 895
dilution bias in the warm end of %@A*+
4. An approach taken by van der Weijst et al. (2022) combines modern observations of
water column structure, and additional microfossil and GDGT-based proxies (i.e., the ratio of GDGT-2/GDGT-3) to assess
changes in the export depth of GDGTs through a 15 million year long equatorial Atlantic record, enabling authors to determine
which depth-integrated calibration is most appropriate to use at in the investigated core site.
900
BAYSPAR can also spatially weight a calibration to core tops near a sample site, recognizing that archaeal communities vary
through the global ocean. As well as BAYSPAR, several regional calibrations have been developed to take account of this
spatial variance, with examples including a calibration for the Baltic Sea (Kabel et al. 2012) or Sea of Okhotsk (Seki et al.,
2014) and a subsurface calibration for offshore Antarctica (Kim et al., 2012). More recently, improvements to low temperature
calibrations have focused on the inclusion of OH-GDGTs alongside isoGDGTs (e.g., Fietz et al., 2016; Varma et al., 2024b) 905
(see section 8.1).
Machine learning-based approaches (e.g., OPTiMAL, Dunkley Jones et al., 2020) take an agnostic view of the form of the
relationship between GDGT abundances and temperatures, using a Gaussian Process emulator to optimize temperature
estimation from the modern core-top calibration data set of Tierney and Tingley (2015). This approach uses the fractional 910
abundances of all six GDGTs to reconstruct SSTs (Dunkley Jones et al., 2020) and generates prediction uncertainty estimates
that include uncertainty about the learned function (Dunkley Jones et al., 2020). The disadvantage of this approach is that no
SST estimations can be made outside of the range of the calibration space - the GP emulator can only make SST predictions
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with any degree of confidence where it is constrained by data, so for SSTs >30ºC and <5 ºC. For these temperatures, other
compounds such as the OH-GDGTs seem to have a higher sensitivity to temperature (Varma et al., 2024b) (see Section 8.1). 915
Choosing the most appropriate calibration can be a complicated process, creating at least two particularly acute challenges to
their interpretation (e.g., Fokkema et al., 2024). First, although evidence for a dominant subsurface signal is mounting, it
remains unclear which water depths sedimentary TEX86 exactly represents at certain oceanographic conditions. This means
that this may also be spatially, or temporally varying (van der Weijst et al., 2022; Cartagena-Sierra et al., 2021). Second,
although calibration choice may not strongly impact reconstructed temperatures when applied to indices that fall within the 920
modern calibration dataset, the assumed mathematical relationship (linear or logarithmic) between TEX86 and temperature has
a profound impact when applied to ancient climates with indices higher than those observed in modern oceans. It is important
to note that there is not necessarily one ‘correct’ calibration to use, as calibration choice will depend on factors such as a sample
set location and the studied time period. It is critical to determine the mathematical relationship between temperature and
GDGT distributions, including whether that is properly represented by TEX86 at all temperatures (i.e., as opposed to a weighted 925
averaged ring index). We must also improve our understanding of the mechanistic relationship between GDGTs and
temperature (see GDGT review paper), including the effect of local oceanographic conditions, community structure, export
dynamics and changes therein. Therefore, when publishing the GDGT-derived temperature record, justification of the selected
calibration should be provided. Stating the rationale behind the choice of the calibration will give a better understanding for
the community, who may not be experts in the study area, why a particular calibration has been deemed as the most appropriate 930
for the given sample set. It is imperative to disclose GDGT data in full for appropriate reuse and recalibration of existing data
(see section 6). Moreover, there is now abundant evidence to suggest that although several good calibration approaches
calibrate GDGTs to sea surface temperature, with also good proxy-to-proxy intercomparison, the majority of GDGTs are
produced in the subsurface (50–200m; Hurley et al., 2018). At the same time, dominant depth of export remains difficult to
constrain, and for deeper time, substantial part of the TEX86 proxy records are derived from settings with relatively shallow 935
water depths (Tierney et al., 2017). Proxy records should be interpreted with that in mind; our lack of understanding about
how temperature affects GDGT distributions of the Archaea living in sub-thermocline waters and responsible for unusual
GDGT-2/GDGT-3 ratios. For samples from the most recent geological past, where oceanographic conditions could be assumed
to be relatively similar to modern, investigating how different calibrations predict temperature for nearby core-tops may also
help to inform an appropriate calibration to use. For the moment, there is no perfect calibration that could provide reliable 940
temperature reconstructions in cold temperatures, warmer-than-modern climates, and that takes full account of the depth of
production of GDGTs, also given the fact that that depth may vary per oceanographic setting and through time. Considering
known overprints, depth of production and oceanographic settings that are important for reliable temperature reconstructions
from GDGTs, the community has a way forward for further proxy development and improving calibrations.
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8 Other marine GDGT proxies 945
8.1 OH-GDGTs
OH-GDGTs are, like isoGDGTs, widespread in marine environments. They contain one or two hydroxy groups attached to
their biphytanyl chains, and were first identified in marine sediment by Liu et al. (2012). OH-GDGTs increase in abundance
at higher latitudes (Huguet et al., 2013). Initially, OH-GDGTs were used to improve accuracy of the SST reconstructions in
(sub)polar regions where TEX86 residual standard error increases. Using a dataset of 77 samples collected from the water 950
column, marine surface sediments, as well as marine and freshwater downcore sediments, Huguet et al. (2013) found that the
relative abundance of OH-GDGTs compared with isoGDGTs (%OH) shows a weak negative correlation with annual SST.
They reduced the dataset to marine surface samples only (n = 38; Table 3) in order to improve the correlation and reduce the
error. By extending the initial dataset of Huguet et al. (2013) with sea surface sediments from the Southern Ocean (Ho et al.,
2014) (n = 52) and empirically searching for a better calibration, Fietz et al. (2016) established a new temperature calibration 955
including GDGT-1, GDGT-2, GDGT-3, the cren’ and OH-GDGTs (OHC) that shows a better correlation and lower residual
standard error than %OH with annual SST, summer SST and SST 0 – 200m. Recently, Varma et al. (2024b) compiled OH-
GDGT from an extended array of surface sediments, including data analysed at NIOZ ‘NIOZ dataset’ (n=575) and data
analysed in other laboratories (n=297). Data that failed either the screening methods described in Section 6 (i.e., high BIT
index values), or where abundances of OH-GDGT-1 and/or OH-GDGT-2 were below the detection limit, were excluded from 960
further analysis, leaving n=469 in the NIOZ dataset. Varma et al. (2024b) found interlaboratory offsets for OH-GDGT-based
proxies between the NIOZ dataset and datasets from other laboratories. The offset was especially large for indices which
combine both iso- and OH-GDGTs (i.e., %OH), indicating OH-GDGT response may vary on different analytical equipment.
To circumvent this, authors obtained calibration results based only on the NIOZ dataset, but suggested that a round robin study
is necessary to determine the extent of interlaboratory differences. Authors found a better correlation between annual SST and 965
%OH compared with earlier studies, but with no significant improvement for the OHC calibration. Varma et al. (2024b) also
proposed a new OH-GDGT-based temperature calibration by adding OH-GDGT-0 to the denominator of the TEX86 equation
(TEX86OH) (Table 3). This new calibration has a stronger correlation with SST than TEX86, showing no flattening of the
relationship below 15°C, remaining linear down to around 5 °C. Interestingly, the TEX86OH calibration has a similar
correlation with ocean temperatures between 0–200 m (R² = 0.89, n = 470) as with SST (R² = 0.88, n = 513), suggesting that 970
TEX86OH might be more suitable for reconstructing subsurface temperatures than surface temperatures, although as of yet it
is not known where in the water column OH-GDGTs are produced.
Another approach is to generate OH-GDGT calibrations independent of isoGDGTs, also circumventing the interlaboratory
differences for proxy indices that incorporate both OH-GDGTs and isoGDGTs (Varma et al., 2024b). Lü et al. (2015) analyzed 975
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the correlation between OH-GDGT cyclization and SST using a dataset of 107 samples from the global dataset of Huguet et
al. (2013), the Nordic Seas (Fietz et al., 2013) and new sediment samples from the South and East China Seas. Lü et al. (2015)
proposed two new calibrations: RI-OH using only OH-GDGT-1 and OH-GDGT-2, recommended for SST > 15°C, and RI-
OH' in which OH-0 is added to the denominator of the RI-OH equation, recommended for SST < 15°C. Subsequently, the
correlation of RI-OH' with SST was improved by Fietz et al. (2020) by adding surface sediment data from the Baltic Sea 980
(Kaiser and Arz, 2016), observing a better correlation to SST (R² = 0.76) than in the original equation (R² = 0.75) of Lü et al.
(2015). Recently, Varma et al. (2024b) updated the equations of these two calibrations, improving the correlation of RI-OH
with SST (R² = 0.79) but showing a poorer correlation for RI-OH’ (R² = 0.64).
Although the main factor influence on OH-GDGT distributions is temperature, several studies have shown the impact of non-985
thermal factors. Xiao et al. (2023) observed that the production of OH-GDGTs by benthic archaea can have a large impact on
RI-OH’, which dictates caution to apply this proxy on sediments from deep ocean basins. Other confounding factors include:
i) the influence of seasonal phenomena such as the extension of sea ice cover (Wu et al., 2020), or changes in the monsoon
regime (Wei et al., 2020), ii) inputs of terrestrial sediments that often have a higher relative abundance of OH-GDGT-2
compared to marine sediments (Kang et al., 2017; He et al., 2024) iii) freshwater inputs modifying how the archaea adjust the 990
OH-GDGT composition of their membrane (Sinninghe Damsté et al., 2022), iv) a difference in the archaeal community as a
function of water column depth (Zhu et al., 2016; Lü et al., 2019; Liu et al., 2020; Varma et al., 2023; 2024a), v) changes in
dissolved oxygen concentration or nutrient abundance (Harning et al., 2023; Harning and Sepúlveda, 2024) vi) seasonal biases
(Lü et al., 2015; Davtian et al., 2019), and vii) difficulty in quantifying OH-GDGTs when abundance is low, especially in
tropical regions with temperatures > 25°C (Varma et al., 2024a; 2024b). 995
As with TEX86, selecting a calibration and an equation is a complicated process that is generally carried out on a case-by-case
basis, depending on the characteristics of the study area (e.g., ice cover, variability of terrestrial inputs, depth of the nutricline,
archaeal community differences) and the location of the samples (e.g., water depth, distance from the river mouth). Importantly,
a brief justification of the choice of calibration is necessary to allow the reader to understand the rationale behind this decision.
1000
Table 3. Temperature calibrations using OH-GDGTs (RMSE - root mean square error)
Calibration
Equation
Error
Description
Reference
% OH
∑[𝑂𝐻 −𝐺𝐷𝐺𝑇𝑠]
∑[𝑂𝐻 −𝐺𝐷𝐺𝑇𝑠] + ∑[𝑖𝑠𝑜 −𝐺𝐷𝐺𝑇𝑠]-
𝑆𝑆𝑇- =- −0.24× %𝑂𝐻 + 8.3-
RMSE = 9.7 °C
95% confidence
interval
Original global linear SST
calibration based on a
marine surface sediment
dataset of 38 samples.
Huguet et al.
(2013)
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RI-OH
[𝑂𝐻 −1] + 2× [𝑂𝐻 − 2]
[𝑂𝐻 −1] + [𝑂𝐻 −2] -
𝑅𝐼 −𝑂𝐻 = -0.018×𝑆𝑆𝑇 + 1.11-
RMSE = 6 °C
Global linear calibration
based on marine surface
sediment dataset of 107
samples, for use above 15
°C.
Lü et al. (2015)
RI-OH’
[𝑂𝐻 −1] + 2× [𝑂𝐻 − 2]
[𝑂𝐻 −0] + [𝑂𝐻 −1] + [𝑂𝐻 −2]-
𝑅𝐼 −𝑂𝐻′ = -0.0382×𝑆𝑆𝑇 + 0.1--
RMSE = 6 °C
Global linear calibration
based on marine surface
sediment dataset of 107
samples, for use below 15
°C.
(Lü et al., 2015)
OHC
[𝐺𝐷𝐺𝑇 − 2] + [𝐺𝐷𝐺𝑇 − 3] + [𝐶𝑟𝑒𝑛′] + [𝑂𝐻 − 0]
[𝐺𝐷𝐺𝑇 − 1] + [𝐺𝐷𝐺𝑇 − 2] + [𝐺𝐷𝐺𝑇 − 3] + [𝐶𝑟𝑒𝑛′] + ∑[𝑂𝐻 −𝐺𝐷𝐺𝑇𝑠]-
𝑂𝐻𝐶 = -0.0266- × 𝑆𝑆𝑇 −0.144-
RMSE = 3.9 °C
Fietz et al.
(2016)
𝑇𝐸𝑋!"
&$-
[𝐺𝐷𝐺𝑇 −2] + [𝐺𝐷𝐺𝑇 −3] + [𝐶𝑟𝑒𝑛′]
[𝐺𝐷𝐺𝑇 −1] + [𝐺𝐷𝐺𝑇 −2] + [𝐺𝐷𝐺𝑇 −3] + [𝐶𝑟𝑒𝑛′]+ [𝑂𝐻 − 0]-
𝑇𝐸𝑋!"
&$ = 0.023 × SST +0.03
𝑇𝐸𝑋!"'()*(('+
&$ = 0.026 × 𝑆𝑆𝑇()*((++0.09
NIOZ dataset
𝑇𝐸𝑋!"
&$ = 0.021 × SST +0.08
𝑇𝐸𝑋!"'()*(('+
&$ = 0.025 × 𝑆𝑆𝑇()*((++0.11
Complete dataset
Standard
deviation of
residuals (NIOZ
dataset): 3.2 °C
(SST)
and 2.8 °C (SST
0–200m)
Standard
deviation of
residuals
(complete
dataset): 3.7 °C
(SST) and 2.9
°C (SST 0–
200m)
Increases the temperature
sensitivity of the index,
especially for temperatures
from 5 to 15°C. Two
equations available: surface
and subsurface (0–200m).
Calibrations obtained using
the NIOZ dataset and
complete dataset.
Varma et al.
(2024b)
𝑅𝐼 −𝑂𝐻′,--
SST = 15.2 × 𝑅𝐼 −𝑂𝐻′*((++5.0
for SST reconstruction between 0 – 200 m
RMSE = 2.1 °C
The contribution of deep-
water archaea may alter
OH-GDGT distributions,
with increased OH-GDGT-
0 production at greater
depths due to colder
temperatures. This effect is
more pronounced in low
latitudes, where the
surface-to-bottom water
temperature gradient is
stronger. The RI-OH’
equations proposed here
attempt to consider the
impact of water depth.
Xiao et al.
(2023)
𝑆𝑆𝑇- = -21.2- × -𝑅𝐼 −𝑂𝐻′./012/)3&-
+0.0013- × -𝑊𝑎𝑡𝑒𝑟-𝐷𝑒𝑝𝑡ℎ-− -0.3---
RMSE = 5.2 °C
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8.2 Branched GDGTs
The distribution of brGDGTs in terrestrial environments is mainly linked to changes in temperature and pH (Weijers et al.,
2007b). As brGDGTs are predominantly produced in terrestrial environments, their presence in marine sediments is often
associated with the input of terrigenous material, notably soils (Hopmans et al., 2004; Schouten et al., 2013a). However, studies 1005
carried out in a variety of environments including open oceans (Weijers et al., 2014), fjords (Peterse et al., 2009), continental
shelves and rivers (Zhu et al., 2011; Zell et al., 2014), and the deepest hadal trenches (Xiao et al., 2020) showed significant
differences in the distribution of brGDGTs between terrestrial and marine sediments, leading to the hypothesis of in situ marine
production. The distribution of in situ brGDGTs in marine environments remains however poorly understood. Currently, three
approaches are used to differentiate the origin of brGDGTs in marine sediments: the abundance of hexamethylated (sum of 1010
hexamethylated brGDGTs- ΣIIIa) over pentamethylated (ΣIIa) brGDGTs (ΣIIIa/ΣIIa) (Xiao et al., 2016), the degree of
cyclisation of tetramethylated brGDGTs (#ringstetra) (Sinninghe Damsté, 2016) and comparison of the relative abundance of
tetramethylated, pentamethylated and hexamethylated brGDGTs (Sinninghe Damsté, 2016).
The ΣIIIa/ΣIIa ratio was derived from a global dataset comprising 1,354 terrestrial and 589 marine samples. Notably, 90% of 1015
marine sediments exhibited a ΣIIIa/ΣIIa >0.92, while 90% of terrestrial sediments had ΣIIIa/ΣIIa of <0.59 (Xiao et al., 2016).
In their study, Xiao et al. (2016) combined IIIa and IIIa' as the majority of the available data at that time did not distinguish
between the isomers. As a result, the proposed proxy was predominantly based on data lacking isomer separation. With
improved compound separation achieved by the HPLC method of Hopmans et al. (2016), now both 5- and 6-methyl brGDGTs
are incorporated in the calculation. 1020
The #ringstetra approach is based on the comparison between the global soil dataset and sediments from a variety of open sea,
coastal and river environments, which shows that the #ringstetra value in soils is always <0.7, suggesting that brGDGTs in
marine sediments with values >0.7 have a purely marine origin (Sinninghe Damsté, 2016). It was also observed that the relative
abundance of tetra-, penta- and hexamethylated brGDGTs in soils followed a clear trend when plotted in a triplot, and that 1025
datapoints derived from (coastal) marine sediments plot increasingly offset from this trend depending on the contribution of
in situ produced brGDGTs in marine sediments (Sinninghe Damsté, 2016). The observed discrepancies in the degree of
methylation and cyclisation of brGDGTs in marine sediments and soils have been attributed to pH differences between soils
and marine waters, and to colder temperatures in the deep ocean than in soils (Sinninghe Damsté, 2016; Xiao et al., 2016).
These new approaches supplement the use of the BIT index as a tracer of terrestrial brGDGT inputs to marine sediments (see 1030
Terrestrial inputs), particularly in coastal regions where primary productivity is controlled mainly by nutrient inputs from
rivers, for example, in Chinese coastal seas (Liu et al., 2021). In this configuration, the increase in marine isoGDGTs
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production due to increased Nitrososphaerota productivity offsets the terrestrial brGDGTs inputs from the rivers, resulting in
a low BIT index despite high inputs from land (Liu et al., 2021).
1035
Identifying the source of brGDGTs in marine environments is crucial for applying both terrestrial or marine
paleothermometers. In the case of significant in situ marine production of brGDGTs in marine settings, the MBT’5ME index
should be applied with care (cf., De Jonge et al., 2014b), or corrected for the marine contribution prior to reconstructing mean
annual air temperatures from marine sediments (Dearing-Crampton-Flood et al., 2018). Although in situ production of
brGDGTs in marine environments complicates their use as proxies for terrestrial environmental conditions, recent studies 1040
suggest that their distribution can provide information about other marine environmental conditions, such as oxygen conditions
(Liu et al., 2014; Xiao et al., 2024).
8.3 GDGT-4
Although GDGT-4 is produced by marine Nitrososphaera, it is rarely quantified and reported with other GDGTs because it is
not included in the TEX86 equation. However, its presence in various environments, including cultures (Pitcher et al., 2010; 1045
Elling et al., 2015; 2017; Bale et al., 2019), SPM (Zhu et al., 2016; Hurley et al., 2018; Besseling et al., 2019), core tops (Wei
et al., 2011), ancient sediments (Zhang et al., 2014; Zhuang et al., 2017; De Bar et al., 2019; Crouch et al., 2020; Cavalheiro
et al., 2021) and hydrothermal systems (Hernández-Sánchez et al., 2024) suggests that GDGT-4 contributes to membrane
adaptation, and may be an important component of membrane lipids, especially in warm climates. Although GDGT-4 is not
currently included in GDGT-derived indices, quantifying it and including it in the core top database will future-proof data for 1050
potential inclusion in future indices. Accurate quantification of GDGT-4 hinges on full chromatographic separation of
crenarchaeol (or any crenarchaeol isomer) and GDGT-4. If crenarchaeol and GDGT-4 are not chromatographically separated,
correction of the apparent GDGT-4 peak area is needed, which may be done by subtracting the isobaric interference of the +2
Da isotope peak of crenarchaeol (1294.2601), which occurs at 45.97% of the intensity of crenarchaeol (1292.2444) at natural
isotopic abundance (Sinninghe Damste et al., 2012b). 1055
8.4 GTGTs, GMGTs and GDDs
Glycerol trialkyl glycerol tetraethers (GTGTs) and glycerol dialkanol diethers (GDDs) have been identified in cultured archaea
(e.g., Bauersachs et al., 2015; Elling et al., 2014; 2017) and marine sediments (Liu et al., 2012b; Liu et al., 2018; Xu et al.,
2020). GDD have been speculated to be either biosynthetic intermediates (e.g., Meador et al., 2014) or degradation products
(Coffinet et al., 2015). In fact, a degradation pathway was proposed by Liu et al. (2016) suggesting that isoGDDs are formed 1060
from isoGDGTs. Recent discoveries in the isoGDGT biosynthetic pathway (Zeng et al., 2019; Lloyd et al., 2022) do not
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consider the involvement of GDDs as intermediates in the biosynthesis, giving further support to GDDs as degradation
products. Alternatively, it has been shown that GDDs could be degradation products of GDGTs (Coffinet et al., 2015; Naafs
et al., 2018; Baxter et al., 2019; Mitrović et al., 2023; Hingley et al., 2024). Glycerol monoalkyl glycerol tetraethers (GMGTs;
also known as H-shaped GDGTs (H-GDGTs) were first identified in a hyperthermophilic methanogen (Morii et al., 1998), but 1065
later appeared to also occur in sediments from low-temperature marine and lacustrine environments (e.g., Schouten et al.,
2008; Liu et al., 2012c), where they were inferred to possibly be derived from Euryarchaeota. Their relative increase with
temperature in marine hydrothermal sediments suggests that they may play a role in thermal regulation for their archaeal source
organism (Sollich et al., 2017; Hernández-Sánchez et al., 2024), as recently supported by a mechanism linking GMGTs to high
temperatures using molecular dynamics simulations (Garcia et al., 2024; Zhou and Dong, 2024). Next to isoGMGTs, branched 1070
GMGTs (brGMGTs) also exist, and are found in marine sediments of modern (Liu et al., 2012c) to late Cretaceous (e.g., Bijl
et al., 2021), where their distributions, including methylation, strongly, but not consistently, vary in response to environmental
change, likely temperature and/or water column oxygenation (Sluijs et al., 2020; Bijl et al., 2021; Kirkels et al., 2022) although
in all these applications, brGMGTs have a different relationship to temperature. BrGMGTs furthermore occur in oxygen
minimum zone SPM from the eastern Pacific (Xie et al., 2014), which agrees with the identification of the enzyme that 1075
synthesizes GMGTs, which is associated with obligate anaerobic archaea in oxygen-deficient (O2 < 25 µM) environments (Li
et al., 2024). While GTGTs, GDDs, and GMGTs are currently not commonly investigated or reported in marine sediments,
future research may explore their potential applications in paleoclimate studies.
9 Best practices for sample, site and proxy intercomparisons and the presentation of error and uncertainty
Studies using GDGTs for temperature reconstruction can span or compile multiple sites (e.g., O’ Brien et al., 2020; Auderset 1080
et al., 2022; Hou et al., 2023), or different depositional settings within an individual study site, e.g., fully marine to shallow
marine or glaciomarine (e.g., Śliwińska et al., 2019; Duncan et al., 2022). It is important to consider variability in the
depositional setting through a record, or in multi-site compilations, as this can influence GDGT preservation, or the water
depth GDGTs have been exported from (e.g., Huguet et al., 2008; Taylor et al., 2013; Duncan et al., 2022). Integration of
GDGT distributions and screening indices with the wider sedimentological and depositional history of a site should play an 1085
important role in interpreting a GDGT record. Likewise, other proxy methods for environmental reconstruction, such as those
based on microfossils or other molecular fossils, can serve to support or help interpret a GDGT temperature record.
While the analytical error in GDGT-based temperature proxies is very small (see Figure 2), the calibration error imposes
uncertainty on the absolute reconstructed SST values. This calibration error includes variability caused by known factors which 1090
could bias TEX86 and its relationship to temperature, such as overprints, water column factors etc. One way to reduce the
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calibration error is by more careful selection of subsets from the core top calibration that, based on what we now know, most
accurately reflects temperature (e.g., as in Fokkema et al., 2024). In any case, we recommend that graphs wherein GDGT-
based temperature records are presented should indicate the calibration error. We note that the calibration error is occasionally
taken to represent the uncertainty in the reconstructed SST from sample to sample, and interpreted to mean that only SST shifts 1095
larger than the calibration error can be interpreted from any proxy record, whereas this error addresses the uncertainty of the
record as a whole. In fact, many available reconstructions show predictable (e.g., relative to other temperature proxies) sample-
to-sample TEX86 variability, well within the range of the calibration error (e.g., Bijl et al., 2021; Hou et al., 2023). This
distinction is important, because despite some level of uncertainty in the absolute temperature reconstruction derived from
GDGTs, the temperature trends and sample-to-sample variability within the calibration error still hold paleoceanographic 1100
significance. One way to assess the significance of predictable or expected (e.g., relative to other temperature proxies or sites
subject to similar conditions) temperature trends is to examine downcore variations in temperature proxy values and compare
them with the analytical error before proxy conversion into SST. Indeed, the cumulative effect of all non-thermal effects at the
spatial scale (e.g., global or regional) of the selected calibration may not apply to one specific site, so the calibration error
should be viewed as an upper-bound of the uncertainty attributable to the non-thermal effects relevant to this site (Davtian et 1105
al., 2019). Therefore, applying the calibration error as uncertainty on all samples (e.g., via an envelope, or by a temperature
error bar on each sample) gives the false impression that downcore trends within the calibration error might not be significant.
We therefore recommend visualizing the calibration error separate from the individual data points, e.g., as bars in the corner
of the plot.
10 Data reporting and archiving 1110
10.1 Towards a common approach in GDGT data reporting and archiving
Since the development of the high performance liquid chromatography-mass spectrometry (HPLC-MS) technique for analysis
of GDGTs (Hopmans et al., 2000), research into the environmental occurrence of GDGTs synthesized by archaea and bacteria
has greatly expanded. To date, tens of thousands of GDGTs determined from laboratory experiments (cultures and
mesocosms), modern environmental archives (marine waters and sediments), and ancient sedimentary sequences, have been 1115
reported in published literature. Additionally, the recent improvement of analytical techniques and methodology in lipid
determination allowed scientists to discover newer classes of GDGT compounds.
Published temperature records derived from GDGTs are largely accompanied by raw data. Researchers commonly use online
archiving systems like World Data Center for Paleoclimatology and Pangaea to make their data publicly available. However, 1120
data reporting and accessibility have never been fully systematized. To date, there is no agreed-upon standard for reporting
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GDGT information upon publication. As the number of GDGT measurements carried out by the community has grown over
the years, the effort to analyze, integrate, and/or synthesize such large datasets requires a significant amount of time to manually
compile and vet each data record individually (cf. Judd et al., 2022; PhanSST database). In this section, we aim to provide a
list of recommended items for reporting GDGT information in publications. The recommendations are based on the Linked 1125
Paleo Data (LiPD; Mckay and Emile-Geay, 2016) data standards and architecture, ensuring adherence to FAIR open access
principles and facilitating the interchange.
10.2 Data components
Following LiPD data reporting framework, six possible types of information should be included when reporting GDGT data
in any publications, including (1) root metadata, (2) geographic metadata, (3) publication metadata, (4) funding metadata, (5) 1130
paleodata information, and (6) geochronological information (e.g., age-depth models). Scientific journals and/or online
archiving systems may require some data components during the peer review process, making sure to have all the data
components listed here available at publication will ensure the integrity of the data for future use. Table 4 provides further
descriptions of each data component.
1135
Table 4: Description of data components needed to be considered when reporting GDGT information. Please note
that the list provided here is not exhaustive.
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No.
Data
Component
Description(s)
Elements in each component
Level of Importance
(Required, Preferred, Optional)
1
Root
Metadata
(Dataset
Metadata)
This contains basic information
of the reporting dataset,
including but not limited to:
i
Dataset name
Required
ii
Author(s)/Investigator(s)
Required
iii
Sample request number(s)/ID(s)
Required
iv
Cruise name/ID
Required
v
Link(s) to published dataset(s)
Required - This information will be available after the
author(s) publishing the dataset with an online archive,
such as PANGAEA online database
2
Geographic
Metadata
This contains geographical
information of study sites,
including but not limited to:
i
Coordinate(s) (modern
latitude/longitude)
Required
ii
Site name(s)
Required
iii
Descriptive information such as:
● Country, State,
Province
● Ocean basin/region
Preferred - especially for studies with samples from
geological outcrops
Optional - for marine samples, providing site names
with coordinates is sufficient
3
Publication
Metadata
This contains publication
information of GDGT data
retrieved from previously
published datasets, including
but not limited to:
Not all information will be
required, but the authors need
to make sure that the
publication metadata is
sufficient for readers to be able
to track back to the original
publications of the compiled
information.
i
Author(s)
Required
ii
Title
Preferred
iii
Journal name
Optional
iv
DOI
Preferred
v
Year of publication
Required
vi
Link(s) to original publication(s)
Optional
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No.
Data
Component
Description(s)
Elements in each component
Level of Importance
(Required, Preferred, Optional)
4
Funding
Metadata
This is applicable when the
research that produced the
data was funded. The
metadata includes:
i
Funding agency
Generally required by journal(s)/publisher(s)
ii
Funding grant number(s)/ID(s)
5
PaleoData
Information
All measured and inferred
paleoenvironmental data,
including but not limited to:
Note: GDGT abundances and
material information are
commonly reported in data
tables and/or spreadsheets.
GDGT Abundances
i
Raw peak intensities including
the standard
Required
ii
Absolute abundances (required
material information)
Optional
iii
Fractional abundances
Optional
If reported, the authors MUST explicitly describe all
the fractions used for the fractional abundance
calculation, i.e., all fractions that will give the sum to 1.
Material Information
iv
Sample information, including:
● For IODP samples
○ Site, Hole,
Core,
section,
interval,
depth (and
which depth
scale), age
(and whose
age model)
● For non-IODP/outcrop
samples
○ Hole/Core/S
ection/Interv
al
Required
v
Sample weight(s)
Required when reported absolute abundances
vi
Amount of spiked standard
Required when reported absolute abundances
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No.
Data
Component
Description(s)
Elements in each component
Level of Importance
(Required, Preferred, Optional)
vii
Sample description(s) (usually
optional):
Colour, texture, key features
from core images
Optional
Sample Preparation Information
vii
Sample preparation information
Lipid extraction and purification
method(s) used
Required - usually described in the “Methods and
Materials” section in scientific reports/publications.
See section 10.2.2 for details.
6
Geochronolo
gical
Information
This contains information used
to infer the age of individual
GDGT samples.
i
Age-depth models
● Tie points
● Age determination
approach
○ Linear
interpolation
between tie
points
○ etc.
Required if sample age is reported.
1138
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10.2.1 Non-GDGT information (metadata)
This section includes a list of recommended metadata of non-GDGT information to be reported alongside 1140
GDGT data. We have categorized the importance of each metadata item into three levels: required,
recommended, and optional. Required information must be publicly accessible at time of publication.
Additionally, we discuss best practices for reporting this information and provide guidance on handling
missing information from published literature when necessary.
● Sample Name and/or Sample ID (required): Samples must be identified with unique and 1145
unambiguous sample names and/or sample identifications (IDs). Crucially, this label must provide
the explicit link between published data and vial/sampleID. For example, the double-column
development in GDGT analyses on UHPLC-MS (Hopmans et al., 2016) required that previously
measured GDGTs had to be re-measured, highlighting the importance of proper sample labeling
and storage. GDGTs can be stored for decades in their vials, which yields an opportunity for future 1150
remeasurements when labeling is adequate and links to original datasets.
● Sample Request Number and/or Sample Request ID (required when applicable, in e.g.,
IODP regime): This provides a direct way to link GDGT data with the original source of the
sample information that is curated at the core repository.
● Sample Information (required). The majority of GDGT research is done on core material, for 1155
instance from the IODP regime and its predecessors, but also from piston coring expeditions with
national seagoing expeditions and onshore drilling campaigns. Reporting GDGT data without
sample metadata (Site, Hole, Core, Section, Interval) makes it impossible to apply an updated
depth or age scale to the data, and thus limits future reuse of the data.
● Sampling depth (required). Depth scales can change with a revision of the splice, the 1160
stratigraphic correlation, or to correct errors. For sediment samples from ocean drilling programs,
various depth scales are developed, including CSF-A, CSF-B, and CCSF (prior to stratigraphic
correlation, after stratigraphic correlation, depth from the splice, etc.), and the reporting of sample
depths should include a reference to the scale used (e.g., original site reports or similar), especially
when using an revised post-expedition composite depth scale. More information: 1165
https://www.iodp.org/policies-and-guidelines/142-iodp-depth-scales-terminology-april-2011/file.
● Modern water depth (recommended).
● Age (recommended).
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o Provide reference for the applied age model used and type - e.g., biostratigraphic,
magnetostratigraphic, cyclostratigraphic or others 1170
o Specify how sample ages are interpolated between tie points
● Paleo-location (optional, but required for Bayesian calibrations).
o When reporting paleolatitude/longitude, authors must report plate rotation models and
refer to the source of the information.
1175
10.2.2 Sample Information
GDGT abundances can be reported in three different formats as described below:
● Raw peak areas: This format represents the area under integrated GDGT peaks (intensity x time)
obtained using LC-MS.
● Absolute concentrations: Areas of individual peaks peak that are above a set and well-defined 1180
detection limit can be quantified when a known amount of a standard is used (i.e., the most
common is a synthesized C46 GDGT; cf. Huguet et al. (2006)) and dry weight of extracted sediment
(and TOC) are reported; usually reported as nanograms of GDGT per gram of sediment dry weight
(ng/g dry weight and/or per g TOC).
● Fractional abundances: Abundance of individual GDGTs relative to the whole suite of GDGTs 1185
of interest (i.e., six common isoGDGTs) is commonly referred to as “fractional abundances.”
Reporting GDGT distributions in the fractional abundance format is likely the most common
approach, as we can compare GDGT distribution patterns across all samples regardless of the
absolute amount of GDGTs. Franctional abundances can be reported in either a fraction (0-1) or a
percentage format (0-100%). The sum of all GDGT fractional abundances should add up to 1 or 1190
100%. It must be explicit which components are included in the fractional abundance calculations,
as with the additional reporting of additional GDGT-like compounds this may become ambiguous.
Moreover, reporting GDGT abundances in this format can cause subsequent biases in GDGT-
based index values if the reported numbers are rounded up with too few decimal places. See a
discussion on the potential impact of the decimal rounding below. 1195
Best practices in reporting GDGT abundances:
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● We recommend that authors always report peak areas from the LC-MS. Although these values are
inherently dependent on instrument sensitivity and method settings, peak areas are the root of the
analyses and as such more interoperable and reusable. The peak areas can be used for the 1200
subsequent quantification of absolute and/or fractional GDGT abundances. Although retention
times of each individual peak is information that is not commonly reported in published literature,
we recommend the authors to keep track of these metrics as they are useful to evaluate integration
consistency, machine functioning, and column lifespan.
● Despite ongoing progress in understanding the relationship between GDGTs and temperature, the 1205
mechanistic constraints on GDGT proxies require further improvement. Therefore, we recommend
reporting all available GDGT data (e.g., isoGDGTs, brGDGTs, OH-GDGTs, GTGTs, GDDs, and
GMGTs, even those not currently used in indices) to enable re-evaluation in subsequent research.
If these data can not be archived upon publication, the authors should as minimum make it clear
which GDGT compounds have been detected and which have not. 1210
● When reporting fractional abundances, we recommend not rounding the calculated fractions, as
rounding can introduce biases in GDGT-based ratios and indices. Figure 4 illustrates the impact
of varying degrees of rounding on calculated TEX86 values. This issue derived from rounding
decimals is commonly found in early studies where GDGT information is reported in printed data
tables without supplementary spreadsheets. If printed data tables require significant decimal 1215
rounding due to limited space, we recommend that the authors must provide the supplementary
spreadsheets where the non-rounded GDGT fractional abundances are available. We also
recommend that authors explicitly describe the method used to calculate fractional abundances
(i.e., which GDGTs are included in the fractional abundance calculation). A lack of information
on this complicates the calculation of indices that utilize GDGT compounds from different series; 1220
for example, BIT uses isoGDGTs and brGDGTs.
● Lastly, we recommend that authors should explicitly provide remarks for compounds that are
either absent or in (too) low concentration. GDGT compounds that are not analysed (i.e., “NA”)
should be clearly distinguished from compounds with below quantification limit compounds (i.e.,
“NQ”). 1225
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Figure 4: The sensitivity of TEX86 values to different rounding factors. The rounding factor 1230
indicates the last decimal place that the numbers are rounded up to (i.e., rounding factor of 2
means that the numbers are rounded up to the 2nd decimal place). Changes in data distribution
with rounding factors of four (A–F), three (G–L), and two (M–R), are presented from top to
bottom. From left to right, plots A, G, M show the scatter plots between SST from the nearest grid
cells from the 2009 World Ocean Atlas database (retrieved from Tierney and Tingley, 2015) and 1235
the TEX86 values calculated from rounded GDGT fractional abundances (blue) as well as the non-
rounding original TEX86 values (grey). Histograms of binned TEX86 values (plots B, H, N) and each
isoGDGT (plots C–F, I–L and O–R).
10.3 Recommended templates for data reporting and archiving 1240
We provide a sample template that lists all recommended column names for GDGT reporting of core tops
and downcore samples (Supplementary Table S3 and S4, respectively).
11 Concluding remarks
This paper represents the consensus of a large part of the lipid biomarker community on the best practices,
guidelines and recommendations for the entire process of generating marine GDGT data with the purpose 1245
of reconstructing past ocean temperature. Following these guidelines of analyses and reporting provides a
level of certainty that data is reproducible, comparable, findable, accessible, interoperable and reusable.
This maximizes the use and thus the value of the generated data, now and in the foreseeable future. This
paper serves as an accessible reference for best practice in future studies.
Supplements 1250
Supplementary Table S1: comparison of extraction efficiency and quality between ASE and microwave
extractions and with different temperature settings.
Supplementary Table S2: integration data of the UU TEX+BIT GDGT standard measurements.
Supplementary Table S3: A template for coretop samples
Supplementary Table S4: A template for downcore samples 1255
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53
Data availability
Data that was used to draft Figure 1, 3 and 4 are available via the cited papers in the captions. Data that was
used to draft Figure 2 is presented in Supplementary Table S2.
Conflict of Interest 1260
One of the co-authors, Sebastian Naeher, is associate editor for Biogeosciences
Author contributions
Tier 1: PKB and KKS coordinated the compilation of the paper, assembled and coordinated with the writing
teams for the sections, streamlined the separate sections, co-wrote Chapters 1–16; arranged alphabetically
Tier 2: BD, AH, SN, RR and CSMdO led the writing of the main sections of the paper (Chapters 2–10); 1265
arranged alphabetically
Tier 3: AA, MB, BSK, ND, TDJ, DDE, FE, LO’C, RDP, Fpe, Fpi, AR, AS, DV, WX and YZ contributed
to the writing of the sections of the paper (Chapters 2–10); arranged alphabetically
Chapter 1, 11–16: all 1270
Chapter 2: CSMdO, SN, MB, PKB, KKS
Chapter 3: AH, CSMdO, FE, ND, PKB, DE
Chapter 4: SN, CSMdO, FE, ND, PKB, DE
Chapter 5–9: BD, AR, AA, LO’C, SN, TDJ, MB, FPe, DV, FE, RR, ND, WX, RDP, KKS, YZ, FPi, BSK
Chapter 10: RR, LO’C, AS, BD, FPi, DV, ND, KKS 1275
Acknowledgements
This paper has been constructed after fruitful discussions during the 2nd International GDGT workshop in
Zurich, 6–8 September 2023, which was organised with funding from SNSF Scientific Exchanges 2023,
IZSEZ0 220372 to main organiser Cindy de Jonge. We also thank the Netherlands Earth System Science
Center (NESSC; grant no. work20232-1), and the Dutch Research Council (NWO-Vidi grant no. 192.074) 1280
https://doi.org/10.5194/egusphere-2025-1467
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54
to FP. The authors wish to thank the organisers of that workshop for facilitating these discussions. FJE
acknowledges funding from the Deutsche Forschungsgemeinschaft (grant 441217575)
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