Evaluation of bacterial glycerol dialkyl glycerol tetraether and ²H–¹⁸O biomarker proxies along a central European topsoil transect

Molecular fossils, like bacterial branched glycerol dialkyl glycerol tetraethers (brGDGTs), and the stable isotopic composition of biomarkers, such as δ²H of leaf wax-derived n-alkanes (δ²H_n-alkane) or δ¹⁸O of hemicellulose-derived sugars (δ¹⁸O_sugar), are increasingly used for the reconstruction of past climate and environmental conditions. Plant-derived δ²H_n-alkane and δ¹⁸O_sugar values record the isotopic composition of plant source water (δ²H_source-water and δ¹⁸O_source-water), which usually reflects mean annual precipitation (δ²H_precipiation and δ¹⁸O_precipiation), modulated by evapotranspirative leaf water enrichment and biosynthetic fractionation (ε_bio). Accuracy and precision of respective proxies should be ideally evaluated at a regional scale. For this study, we analysed topsoils below coniferous and deciduous forests as well as grassland soils along a central European transect in order to investigate the variability and robustness of various proxies and to identify effects related to vegetation. Soil pH values derived from brGDGTs correlate reasonably well with measured soil pH values but are systematically overestimated (ΔpH = 0.6±0.6). The branched vs. isoprenoid tetraether index (BIT) can give some indication whether the pH reconstruction is reliable. Temperatures derived from brGDGTs overestimate mean annual air temperatures slightly (ΔT_MA=0.5 ^∘C ± 2.4). Apparent isotopic fractionation (ε_{n-alkane/precipitation} and ε_{sugar∕precipitation}) is lower for grassland sites than for forest sites due to signal damping; i.e. grass biomarkers do not record the full evapotranspirative leaf water enrichment. Coupling δ²H_n-alkane with δ¹⁸O_sugar allows us to reconstruct the stable isotopic composition of the source water more accurately than without the coupled approach (Δδ²H = $\sim -\mathrm{21}$ ‰ ± 22 ‰ and Δδ¹⁸O = $\sim -\mathrm{2.9}$ ‰ ± 2.8 ‰). Similarly, relative humidity during daytime and the vegetation period (RH_MDV) can be reconstructed using the coupled isotope approach ($\mathrm{\Delta }{\mathrm{RH}}_{\mathrm{MDV}}=\sim -\mathrm{17}\pm \mathrm{12}$). Especially for coniferous sites, reconstructed RH_MDV values as well as source water isotope composition underestimate the measured values. This can likely be explained by understorey grass vegetation at the coniferous sites contributing significantly to the n-alkane pool but only marginally to the sugar pool in the topsoils. Vegetation-dependent variable signal damping and ε_bio (regarding ²H between n-alkanes and leaf water) along our European transect are difficult to quantify but likely contribute to the observed underestimation in the source water isotope composition and RH reconstructions. Microclimate variability could cause the rather large uncertainties. Vegetation-related effects do, by contrast, not affect the brGDGT-derived reconstructions. Overall, GDGTs and the coupled δ²H_n-alkane–δ¹⁸O_sugar approach have great potential for more quantitative paleoclimate reconstructions.

1 Introduction

Information about the variability and consequences of past climate changes is a prerequisite for precise predictions regarding the present climate change. Molecular fossils, so-called biomarkers, have great potential to enhance our understanding about variations in past climate and environmental changes. Lipid biomarkers in particular are increasingly used for paleoclimate and environmental reconstructions (e.g. Brincat et al., 2000; Eglinton and Eglinton, 2008; Rach et al., 2014; Romero-Viana et al., 2012; Schreuder et al., 2016). However strengths and limitations of respective proxies need to be known (Dang et al., 2016). For this, calibrations using modern reference samples are essential.

One famous and widely applied lipid biomarker group are terrestrial branched glycerol dialkyl glycerol tetraethers (brGDGTs). They are synthesised in the cell membranes of anaerobe heterotrophic soil bacteria (Oppermann et al., 2010; Weijers et al., 2010) and have great potential for the reconstruction of past environmental conditions (e.g. Coffinet et al., 2017; Schreuder et al., 2016; R. Zech et al., 2012), although some uncertainties exist. Calibration studies suggest that the relative abundance of the individual brGDGTs varies with mean annual air temperature (T_MA) and soil pH (Peterse et al., 2012; Weijers et al., 2007), at least across large, global climate gradients or along pronounced altitudinal gradients (Wang et al., 2017). However, in arid regions the production of brGDGT is limited, while isoprenoidal GDGTs (iGDGTs) produced by archaea provide the dominant part of the overall soil GDGT pool (Anderson et al., 2014; Dang et al., 2016; Dirghangi et al., 2013; Wang et al., 2013; Xie et al., 2012). The ratio of brGDGTs vs. isoprenoid GDGTs (BIT) can be used as an indication of whether a reconstruction of T_MA and pH will be reliable. Moreover, Mueller-Niggemann et al. (2016) revealed an influence of the vegetation cover on the brGDGT producing soil microbes. From field experiments, it is known that vegetation type and mulching practice strongly effect soil temperature and moisture (Awe et al., 2015; Liu et al., 2014). Thus, multiple factors can be expected to influence soil microbial communities and GDGT production. So far, little is known about the variability of GDGT proxies on a regional scale, and a calibration study with a small climate gradient but with different vegetation types might be useful.

Concerning paleohydrology proxies, compound-specific stable hydrogen isotopes of leaf wax biomarkers, such as long-chain n-alkanes (δ²H_n-alkanes), record the isotopic signal of precipitation and therefore past climate and environmental conditions (Sachse et al., 2004, 2006). However, various influencing factors are known, e.g. the moisture source to leaf waxes (Pedentchouk and Zhou, 2018 and Sachse et al., 2012, for review). Next is the evapotranspiration of leaf water (Feakins and Sessions, 2010; Kahmen et al., 2013; Zech et al., 2015), which is strongly driven by relative air humidity (RH; e.g. Cernusak et al., 2016 for review). In addition, a strong precipitation signal is known to be incorporated into long-chain leaf waxes (Hou et al., 2008; Rao et al., 2009; Sachse et al., 2004). In paleoclimate studies, it is often not feasible to disentangle between the evapotranspirative enrichment and the precipitation signal. Zech et al. (2013) proposed coupling δ²H_n-alkane results with oxygen stable isotopes of hemicellulose-derived sugars (δ¹⁸O_sugar). Assuming constant biosynthetic fractionation (ε_bio) for the different compound classes (n-alkanes and hemicellulose sugars), this coupling enables the reconstruction of the isotopic composition of leaf water, RH, δ²H and δ¹⁸O of plant source water (≈ δ²H and δ¹⁸O of precipitation; Tuthorn et al., 2015). So far, a detailed evaluation of this approach on the European scale, as well as related effects concerning vegetation changes, is missing.

We analysed topsoil samples under coniferous, deciduous and grassland vegetation along a central European transect in order to estimate the variability of the biomarker proxies. More specifically, we aim to test whether

• i.

  the vegetation type has an influence on the brGDGT proxies, the δ²H_n-alkane and the δ¹⁸O_sugar stable isotopic composition, and the reconstructed δ²H_source-water, δ¹⁸O_source-water and RH;

• ii.

  the published brGDGT proxies used for reconstructing mean annual temperature and soil pH are sensitive enough to reflect the medium changes in temperature and soil pH along our transect;

• iii.

  the coupled δ²H_n-alkane–δ¹⁸O_sugar approach enables a δ²H and δ¹⁸O of precipitation and RH reconstruction along the transect.

2 Material and methods

2.1 Geographical setting and sampling

In November 2012, we collected 29 topsoil samples (0–5 cm depth) from 16 locations along a transect from southern Germany to southern Sweden (Fig. 1a). We distinguished between sites with coniferous forest (con, n=9), deciduous forest (dec, n=14) and grassland (grass, n=6) vegetation cover (for more details see Schäfer et al., 2016, and Table S1 in the Supplement).

2.2 Database of instrumental climate variables and isotope composition of precipitation

Climate data were derived from close-by weather observation stations operated by regional institutions (Deutscher Wetterdienst (DWD) for Germany, Danmarks Meteorologiske Institut (DMI) for Denmark and the Sveriges Meteorologiska och Hydrologiska Institute (SMHI) for Sweden). The DWD provides hourly data for each station (DWD Climate Data Center, 2018b), enabling not only the calculation of T_MA, but also of the mean annual relative air humidity (RH_MA), mean temperature and relative air humidity during the vegetation period (T_MV and RH_MV), and daytime temperature and relative humidity averages over the vegetation period (T_MDV and RH_MDV). In addition, annual precipitation observations were used to derive the mean annual precipitation amount (P_MA; DWD Climate Data Center, 2018a). From the DMI, the respective climate variables were derived from published technical reports (Cappelen, 2002; Frich et al., 1997; Laursen et al., 1999). The SMHI provides open data from which we derived the climate variables for the Swedish sites (Swedish Meteorological and Hydrological Institute, 2018). For more details about the climate database used for calculations and comparisons, the reader is referred to Table S2.

Figure 1 (a) Sample locations (red dots, map source: US National Park Service), (b) variations of mean annual air temperature (T_MA, red dots and line) and mean annual precipitation (P_MA, blue bars) derived from close-by climate station data, and (c) hydrogen and oxygen stable isotope composition of precipitation (δ²H_GIPR,OIPC and δ¹⁸O_GIPR,OIPC, respectively) as derived for the sampled transect locations (see Sect. 2.2 GIPR δ²H and δ¹⁸O generation procedure). The reader is referred to Sect. 2.2 (and Tables S1 and S2) for database and reference information of data plotted in (b) and (c).

For comprising German precipitation (δ²H and δ¹⁸O) along the transect, we realised a regionalisation (called δ²H_GIPR and δ¹⁸O_GIPR) using online available data from 34 German GNIP stations, four Austrian ANIP stations and the Groningen GNIP station (van Geldern et al., 2014; IAEA/WMO, 2018; Stumpp et al., 2014; Umweltbundesamt GmbH, 2018), following the approach of Schlotter (2007). However, instead of the multivariate regression procedure applied by Schlotter (2007), we used a random forest approach (Hothorn et al., 2006; Strobl et al., 2007, 2008) to describe the relationship of squared latitude, latitude, longitude and altitude vs. long-term weighted means of precipitation δ²H and δ¹⁸O and realised the prediction for each site (see the Supplement for more information). For the Danish and Swedish sites, such a procedure was not possible. Hence, the annual precipitation δ²H and δ¹⁸O values were derived from the Online Isotopes in Precipitation Calculator (OIPC, version 3.1), therefore called δ²H_OIPC and δ¹⁸O_OIPC (Bowen, 2018; Bowen and Revenaugh, 2003; IAEA/WMO, 2015). The final used δ²H_GIPR,OIPC and δ¹⁸O_GIPR,OIPC data are given in Table S1.

The T_MA along the transect ranges from 5.3 to 10.6 ^∘C, and P_MA ranges from 554 to 1769 mm (Fig. 1b). Precipitation δ²H and δ¹⁸O shows moderate changes along the transect, δ²H_GIPR,OIPC varies between −52 ‰ and −79 ‰, and δ¹⁸O_GIPR,OIPC ranges from −7.4 ‰ to −10.9 ‰ (Fig. 1c).

Correlations between δ¹⁸O_GIPR,OIPC and P_MA, altitude of the locations, and T_MA are given in the Supplement (Figs. S1 to S3), along with a δ²H_GIPR,OIPC vs. δ¹⁸O_GIPR,OIPC scatter plot (Fig. S4).

2.3 Soil extractions and analysis

2.3.1 GDGTs and pH

A detailed description of sample preparation for lipid analysis can be found in Schäfer et al. (2016). Briefly, 1–6 g of freeze-dried and ground soil sample was microwave extracted with 15 mL dichloromethane (DCM) ∕ methanol (MeOH) 9 : 1 (v:v) at 100 ^∘C for 1 h. Extracts were separated over aminopropyl silica gel (Supelco, 45 µm) pipette columns. The nonpolar fraction (including n-alkanes) was eluted with hexane and further purified over AgNO₃-coated silica pipette columns (Supelco, 60–200 mesh) and zeolite (GHGeochem Ltd.). The GDGT-containing fraction was eluted with DCM : MeOH 1 : 1 (v:v), redissolved in hexane ∕ isopropanol 99 : 1 (v:v) and transferred over 0.45 µm PTFE filters into 300 µL inserts. For quantification, a known amount of a C46 diol standard was added after transfer. The samples were analysed at ETH Zurich using an Agilent 1260 Infinity series HPLC–atmospheric chemical pressure ionisation mass spectrometer (HPLC-APCI-MS) equipped with a Grace Prevail Cyano column (150 mm × 2.1 mm; 3 µm). The GDGTs were eluted isocratically with 90 % A and 10 % B for 5 min and then with a linear gradient to 18 % B for 34 min at 0.2 mL min⁻¹, where A is hexane and B is hexane ∕ isopropanol (9 : 1, v:v). Injection volume was 10 µL and single ion monitoring of [M+H]⁺ was used to detect GDGTs.

The pH of the samples was measured in the laboratory of the Soil Biogeochemistry group, Institute of Agronomy and Nutritional Sciences, Martin Luther University Halle-Wittenberg, using a pH meter in a 1 : 3 soil : water (w∕v) mixture.

2.3.2  δ²H _n-alkane

The hydrogen isotopic composition of the highest concentrated n-alkanes (n-C₂₅, n-C₂₇, n-C₂₉, n-C₃₁ and n-C₃₃) was determined using a TRACE GC Ultra gas chromatography connected to a Delta V Plus isotope ratio mass spectrometer via a ²H pyrolysis reactor kept at 1420 ^∘C (GC-²H-Py-IRMS; Thermo Scientific, Bremen, Germany) at ETH Zurich (Christoph et al., 2019). For more details about n-alkane quantification the reader is refereed to Schäfer et al. (2016). The compound-specific ²H∕¹H ratios were calibrated against an external standard with C₁₅–C₃₅ homologues. External standard mixtures (A4 mix from Arndt Schimmelmann, University of Indiana) were run between the samples for multipoint linear normalisation. The ${\mathrm{H}}_{\mathrm{3}}^{+}$ factor was determined on each measurement day and was constant throughout the periods of the sample batches. Samples were analysed in duplicates, and results typically agreed within 4 % (average difference = 1.4 %). All δ²H values are expressed relative to the Vienna Standard Mean Ocean Water (V-SMOW).

2.3.3  δ¹⁸O_sugar

Hemicellulose sugars were extracted and purified using a slightly modified standard procedure (Amelung et al., 1996; Guggenberger et al., 1994; Zech and Glaser, 2009). Briefly, myo-inositol was added to the samples prior to extraction as the first internal standard. The sugars were released hydrolytically using 4 M trifluoroacetic acid for 4 h at 105 ^∘C, cleaned over glass fibre filters and further purified using XAD and Dowex columns. Before derivatisation with methylboronic acid (Knapp, 1979), the samples were frozen and freeze-dried, and 3-O-methylglucose in dry pyridine was added as the second internal standard. Compound-specific hemicellulose sugar ¹⁸O measurements were performed in the laboratory of the Soil Biogeochemistry group, Institute of Agronomy and Nutritional Sciences, Martin Luther University Halle-Wittenberg, using GC-¹⁸O-Py-IRMS (all devices from Thermo Fisher Scientific, Bremen, Germany). Standard deviations of the triplicate measurements were 1.4 ‰ (over 29 investigated samples) for arabinose and xylose. We focus on these two hemicellulose-derived neutral sugars arabinose and xylose as they strongly predominate over fucose in terrestrial plants, soils and sediments (Hepp et al., 2016, and references therein). Rhamnose concentrations were too low to obtain reliable δ¹⁸O results. All δ¹⁸O values are expressed relative to the Vienna Standard Mean Ocean Water (V-SMOW).

2.4 Theory and calculations

2.4.1 Calculations used for the GDGT-based reconstructions

The branched and isoprenoid tetraether (BIT) index is calculated according to Hopmans et al. (2004); for structures see Fig. S5:

$$\begin{array}{}\text{(1)}& \mathrm{BIT}={\displaystyle \frac{\mathrm{Ia}+\mathrm{IIa}+\mathrm{IIIa}}{\mathrm{Ia}+\mathrm{IIa}+\mathrm{IIIa}+\mathrm{crenarchaeol}}}.\end{array}$$

The cyclopentane moiety number of brGDGTs correlates negatively with soil pH (Weijers et al., 2007), which led to the development of the ratio of cyclisation of branched tetraethers (CBT). CBT and the CBT-based pH (pH_CBT) were calculated according to Peterse et al. (2012):

$$\begin{array}{}\text{(2)}& {\displaystyle }\mathrm{CBT}=\log {\displaystyle \frac{\mathrm{Ib}+\mathrm{IIb}}{\mathrm{Ia}+\mathrm{IIa}}},\text{(3)}& {\displaystyle }{\mathrm{pH}}_{\mathrm{CBT}}=\mathrm{7.9}-\mathrm{1.97}\times \mathrm{CBT}.\end{array}$$

The number of methyl groups in brGDGTs correlates negatively with T_MA and soil pH (Peterse et al., 2012; Weijers et al., 2007). Thus, the ratio of the methylation of branched tetraethers (MBT) and the CBT ratio can be used to reconstruct T_MA. We use the equation given by Peterse et al. (2012):

$$\begin{array}{}\text{(4)}& {\displaystyle }{\mathrm{MBT}}^{\prime }={\displaystyle \frac{\mathrm{Ia}+\mathrm{Ib}+\mathrm{Ic}}{\mathrm{Ia}+\mathrm{Ib}+\mathrm{Ic}+\mathrm{IIa}+\mathrm{IIb}+\mathrm{IIc}+\mathrm{IIIa}}},\text{(5)}& {\displaystyle }{T}_{\mathrm{MA}}=\mathrm{0.81}-\mathrm{5.67}\times \mathrm{CBT}+\mathrm{31.0}\times {\mathrm{MBT}}^{\prime }.\end{array}$$

2.4.2 Calculations and concepts used for the coupled δ²H–δ¹⁸O approach

The apparent fractionation is calculated according to Cernusak et al. (2016):

$$\begin{array}{}\text{(6)}& {\displaystyle }{\mathit{\epsilon }}_{n\text{-alkane/precipitation}}=\left({\displaystyle \frac{{{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}}_{n\text{-alkane}}-{\mathit{\delta }}^{\mathrm{2}}{\mathrm{H}}_{\mathrm{GIPR},\mathrm{OIPC}}}{\mathrm{1}+{\mathit{\delta }}^{\mathrm{2}}{\mathrm{H}}_{\mathrm{GIPR},\mathrm{OIPC}}/\mathrm{1000}}}\right),\text{(7)}& {\displaystyle }{\mathit{\epsilon }}_{\mathrm{sugar}/\mathrm{precipitation}}=\left({\displaystyle \frac{{\mathit{\delta }}^{\mathrm{18}}{\mathrm{O}}_{\mathrm{sugar}}-{\mathit{\delta }}^{\mathrm{18}}{\mathrm{O}}_{\mathrm{GIPR},\mathrm{OIPC}}}{\mathrm{1}+{\mathit{\delta }}^{\mathrm{18}}{\mathrm{O}}_{\mathrm{GIPR},\mathrm{OIPC}}/\mathrm{1000}}}\right).\end{array}$$

The isotopic composition of leaf water (δ²H_leaf-water and δ¹⁸O_leaf-water) can be calculated using ε_bio for δ²H_n-alkane (−160 ‰; Sachse et al., 2012; Sessions et al., 1999) and δ¹⁸O_sugar (+27 ‰; Cernusak et al., 2003; Schmidt et al., 2001):

$$\begin{array}{}\text{(8)}& {\displaystyle }{{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}}_{\text{leaf-water}}=\left({\displaystyle \frac{\mathrm{1000}+{{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}}_{n\text{-alkane}}}{\mathrm{1000}+{\mathit{\epsilon }}_{\mathrm{bio}}(n\text{-alkane)}}}\right)\times {\mathrm{10}}^{\mathrm{3}}-\mathrm{1000},\text{(9)}& {\displaystyle }{{\mathit{\delta }}^{\mathrm{18}}\mathrm{O}}_{\text{leaf-water}}=\left({\displaystyle \frac{\mathrm{1000}+{{\mathit{\delta }}^{\mathrm{18}}\mathrm{O}}_{\mathrm{sugar}}}{\mathrm{1000}+{\mathit{\epsilon }}_{\mathrm{bio}}\left(\mathrm{sugar}\right)}}\right)\times {\mathrm{10}}^{\mathrm{3}}-\mathrm{1000}.\end{array}$$

Zech et al. (2013) introduced the conceptual model for the coupled δ²H_n-alkane–δ¹⁸O_sugar approach in detail. Briefly, the coupled approach is based on the following assumptions (illustrated in Fig. 8). (i) The isotopic composition of precipitation, which is set to be equal to the plant source water, typically plots along the global meteoric water line (GMWL; ${\mathit{\delta }}^{\mathrm{2}}\mathrm{H}=\mathrm{8}\times {\mathit{\delta }}^{\mathrm{18}}\mathrm{O}+\mathrm{10}$) in a δ¹⁸O vs. δ²H space (Craig, 1961). (ii) Source water uptake by plants does not lead to any fractionation (e.g. Dawson et al., 2002), and significant evaporation of soil water can be excluded. (iii) Evapotranspiration leads to enrichment of the remaining leaf water along the local evaporation line (LEL; Allison et al., 1985; Bariac et al., 1994; Walker and Brunel, 1990), compared to the source water taken up by the plant. (iv) The biosynthetic fractionation is assumed to be constant. In addition, isotopic equilibrium between plant source water (∼ weighted mean annual precipitation) and the local atmospheric water vapour is assumed. Further assumption concerns the isotope steady state in the evaporating leaf water reservoir. The coupled approach allows for reconstruction of the isotopic composition of plant source water (δ²H_source-water and δ¹⁸O_source-water) from the reconstructed leaf water, by calculating the intercepts of the LELs with the GMWL (Zech et al., 2013). The slope of the LEL (S_LEL) can be assessed by the following equation (Gat, 1971):

$$\begin{array}{}\text{(10)}& S_{\mathrm{LEL}}={\displaystyle \frac{{\mathit{\epsilon }}_{\mathrm{2}}^{\ast }+C_{\mathrm{k}}^{\mathrm{2}}}{{\mathit{\epsilon }}_{\mathrm{18}}^{\ast }+C_{\mathrm{k}}^{\mathrm{18}}}},\end{array}$$

where ε^* represents equilibrium isotope fractionation factors and C_k represents kinetic fractionation factors. The latter equals 25.1 ‰ and 28.5 ‰ for $C_{\mathrm{k}}^{\mathrm{2}}$ and $C_{\mathrm{k}}^{\mathrm{18}}$, respectively (Merlivat, 1978). The equilibrium fractionation factors can be derived from empirical equations (Horita and Wesolowski, 1994) by using T_MDV values. For two Danish sites T_MDV values are not available; instead T_MV is used here (Sect. 2.2 and Table S2).

In a δ¹⁸O–δ²H diagram, the distance of the leaf water from the GMWL defines the deuterium excess of leaf water ($d_{\text{leaf-water}}={{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}}_{\text{leaf-water}}-\mathrm{8}\times {{\mathit{\delta }}^{\mathrm{18}}\mathrm{O}}_{\text{leaf-water}}$, according Dansgaard (1964); Fig. 8). To convert d_leaf-water into mean RH during daytime and the vegetation period (RH_MDV), a simplified Craig–Gordon model can be applied (Zech et al., 2013):

$$\begin{array}{}\text{(11)}& \mathrm{RH}=\mathrm{1}-{\displaystyle \frac{\mathrm{\Delta }d}{{\mathit{\epsilon }}_{\mathrm{2}}^{\ast }-\mathrm{8}\times {\mathit{\epsilon }}_{\mathrm{18}}^{\ast }+C_{\mathrm{k}}^{\mathrm{2}}-\mathrm{8}\times C_{\mathrm{k}}^{\mathrm{18}}}},\end{array}$$

where Δd is the difference in d_leaf-water and the deuterium excess of source water (d_source-water).

2.5 Statistics

In the statistical analysis we checked sample distributions for normality (Shapiro and Wilk, 1965) and for equal variance (Levene, 1960). If normality and equal variances are given, we perform an analysis of variance (ANOVA). If that is not the case, we conduct the non-parametric Kruskal–Wallis test. ANOVA or Kruskal–Wallis are used to find significant differences (a=0.05) between the vegetation types (deciduous, conifer and grass).

In order to describe the relation along a 1 : 1 line, the coefficient of correlation (R²) was calculated as $R^{\mathrm{2}}=\mathrm{1}-\sum {\left(\mathrm{modeled}-\mathrm{measured}\right)}^{\mathrm{2}}/\sum {\left(\mathrm{measured}-\mathrm{measured}\mathrm{mean}\right)}^{\mathrm{2}}$. The small r² is taken as coefficient of correlation of a linear regression between a dependent (y) and explanatory variable(s). The root-mean-square error (RMSE) of the relationships was calculated as $\mathrm{RMSE}=\sqrt{\left(\frac{\mathrm{1}}{n}\cdot \sum {\left(\mathrm{modeled}-\mathrm{measured}\right)}^{\mathrm{2}}\right)}$. All data plotting and statistical analysis was realised in R (version 3.2.2; R Core Team, 2015).

3 Results and discussion

3.1 GDGT concentrations

GDGT Ia has the highest concentration under all vegetation types, followed by GDGT IIa and GDGT IIIa (Fig. 2). GDGT Ib, IIb and Ic occur in minor amounts and GDGT IIc and IIIb only in trace amounts. GDGT IIIc was below the detection limit in most of the samples (Table S3). Although other studies document an influence of the vegetation cover on soil temperature and soil water content, which control the microbial community composition in soils (Awe et al., 2015; Liu et al., 2014; Mueller-Niggemann et al., 2016), we find no statistically different pattern of the individual brGDGTs.

Figure 2 Mean concentrations of individual brGDGTs as percentage of all brGDGTs for the three investigated types. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=14); grass: grassland sites (n=6).

Total concentrations of brGDGTs range from 0.32 to 9.17 µg g⁻¹ dry weight and tend to be highest for the coniferous samples and lowest for the grasses (Fig. 3a, Table S3). Bulk brGDGT concentrations lie within the range of other studies examining soils of mid-latitude regions (Huguet et al., 2010a, b; Weijers et al., 2011). Similar concentrations in coniferous and deciduous samples imply that brGDGT production does not strongly vary in soils below different forest types. The grass samples show lower brGDGT concentrations compared to the forest samples, but this is probably mainly due to ploughing of the grass sites in former times and hence admixing of mineral subsoil material. The differences in brGDGT concentrations are not significant (p value = 0.06).

3.2 BIT index

Most of the samples have a BIT index higher than 0.9 (Fig. 3b and Table S3). The BIT values are typical for soils in humid and temperate climate regions (Weijers et al., 2006). However, outliers exist. The most likely source of iGDGTs in soils are Thaumarchaeota, i.e. aerobe ammonia oxidising archaea producing Crenarchaeol and its regioisomer (Schouten et al., 2013 and references therein), when the precipitation amount drops below 700–800 mm (Dang et al., 2016; Dirghangi et al., 2013). The P_MA data of our sampling sites mostly show precipitation >550 mm (Fig. 1b), but one has to be aware that these data are based on the climate station closest to the respective sampling locations and that microclimate effects, such as sunlight exposure, canopy cover or exposition, might have a pronounced influence on the brGDGT vs. iGDGT distribution. Mueller-Niggemann et al. (2016) found higher BIT indices in upland soils compared to paddy soils and stated that the management type also influences BIT values in soils. Along our transect, grass sites tend to have slightly lower BIT values than forest sites, probably due to the absence of a litter layer and hence no isolation mechanism preventing evaporation of soil water. Differences between vegetation types are not significant (p value = 0.32).

Figure 3 (a) Total concentrations of brGDGTs in microgrammes per gramme of dry weight, as well as (b) BIT, (c) CBT and (d) MBT^′. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=14); grass: grassland sites (n=6). Box plots show the median (red line), interquartile range (IQR) with the upper (75 %) and lower (25 %) quartiles, lowest whisker still within 1.5 IQR of the lower quartile, and highest whisker still within 1.5 IQR of the upper quartile; dots mark outliers.

3.3 CBT-derived pH

The CBT ratio shows a pronounced variation independent of vegetation type with values between 0.03 and 2.16 (Fig. 3c). The coniferous samples tend to be highest, but the differences between vegetation types are not significant (p value = 0.48). The CBT index can be related to pH in acidic and/or humid soils (e.g. Dirghangi et al., 2013; Mueller-Niggemann et al., 2016; Peterse et al., 2012; Weijers et al., 2007) but might be an indicator of soil water content and hence precipitation in more arid and alkaline soils (e.g. Dang et al., 2016). There is a pronounced correlation between CBT and soil pH (Fig. 4), which is in good agreement with other studies from mid-latitude regions where precipitation is relatively high (Anderson et al., 2014, and references therein). Moreover, the CBT-to-pH relationship in terms of slope and intersect in our dataset (CBT = $-\mathrm{0.47}\times \mathrm{pH}+\mathrm{3.5}$, r²=0.7, p value < 0.0001, n=29) is comparable to the correlation described for the global calibration dataset of Peterse et al. (2012) (CBT = $-\mathrm{0.36}\times \mathrm{pH}+\mathrm{3.1}$, r²=0.7, p value < 0.0001, n=176).

Figure 4 CBT-to-pH relationship in our dataset in comparison to the global calibration dataset from Peterse et al. (2012) (CBT = $-\mathrm{0.36}\times \mathrm{pH}+\mathrm{3.1}$, r²=0.7, p value < 0.0001, n=176, black line). Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=14); grass: grassland sites (n=6).

However, there are some outliers in the CBT–pH correlation, which need further examination (see locations grass L04, dec L10 and dec L12 as marked in Figs. 4 and 5). The outliers show lower BIT indices (<0.85, Table S3). Even though the data from the nearest climate station suggest no abnormal P_MA. Local effects such as differences in the amount of sunlight exposure, nutrient availability for brGDGT-producing organisms or most likely soil water content might influence the brGDGT production at these locations (Anderson et al., 2014; Dang et al., 2016). A lower BIT index as well as a lower CBT occur when soil water content decreases (Dang et al., 2016; Sun et al., 2016) or when aeration is high and less anoxic microhabitats for GDGT-producing microbes exist (e.g. Dirghangi et al., 2013).

Figure 5 (a) Correlation between measured pH and reconstructed soil pH (pH_CBT) from our transect data in comparison to the global calibration dataset from Peterse et al. (2012) (R²=0.7, RMSE = 0.75, n=176). The black line indicates the 1 : 1 relationship. (b) Box plots of ΔpH (refers to pH_CBT–pH). Box plots show the median (red line), interquartile range (IQR) with the upper (75 %) and lower (25 %) quartiles, lowest whisker still within 1.5 IQR of the lower quartile, and highest whisker still within 1.5 IQR of the upper quartile; dots mark outliers. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=14); grass: grassland sites (n=6).

As the CBT and pH are similarly correlated in our dataset and the global dataset of Peterse et al. (2012), the CBT-derived pH correlated well with the actual pH (Fig. 5a; R²=0.3). Expressed as ΔpH (CBT-derived pH – measured pH), there is a tendency that the GDGTs result in an overestimation of the real pH for the forest sites (Fig. 5b). However, a Kruskal–Wallis test shows no statistically significant difference between the vegetation types, with a p value of 0.13. The overall ΔpH of 0.6±0.6 shows that the reconstruction of soil pH using brGDGTs works well along this transect.

3.4 MBT^′–CBT-derived T_MA reconstructions

The MBT^′ shows high variability with values ranging from 0.17 to 0.67 and no statistical differences between vegetation types (p value = 0.54; Fig. 3d, Table S3). When comparing reconstructed (MBT^′–CBT-derived) T_MA with climate station T_MA, the data plot close to the 1 : 1 line and fit well into the global dataset of Peterse et al. (2012) (Fig. 6a). The ΔT_MA values reveal an overall offset of 0.5 ^∘C ± 2.4, and there is no statistical difference between vegetation types (Fig. 6b). The standard deviation in ΔT_MA of ±2.4 is well in line with the RMSE of 5.0 for the global calibration dataset (Peterse et al., 2012).

Figure 6 (a) Correlation between climate station T_MA and reconstructed (MBT^′–CBT-derived) T_MA. For comparison, the global calibration dataset from Peterse et al. (2012) is shown. The black line indicates the 1 : 1 relationship. (b) Box plots of ΔT_MA (refers to reconstructed T_MA−T_MA from climate stations) in the different vegetation types from our transect study. Box plots show the median (red line), interquartile range (IQR) with the upper (75 %) and lower (25 %) quartiles, lowest whisker still within 1.5 IQR of the lower quartile, and highest whisker still within 1.5 IQR of the upper quartile; dots mark outliers. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=14); grass: grassland sites (n=6).

3.5 Potential impact of the used liquid chromatography method on pH and T_MA reconstructions

The GDGT data presented in this study are not acquired with the up-to-date method (e.g. compare De Jonge et al., 2014 vs. R. Zech et al., 2012). De Jonge et al. (2014) presented a new liquid chromatography method which enables the separation for the brGDGTs with m∕z 1036, 1034, 1032, 1050, 1048 and 1046 into 6-methyl and 5-methyl stereoisomers. The old method did not allow such a separation (R. Zech et al., 2012); thus, in the calibration, often the sum of 6 and 5-methylated brGDGTs was used (see and compare De Jonge et al., 2014, vs. Peterse et al., 2012). This introduces scatter to the MBT^′–CBT-based T_MA reconstructions and can cause a correlation between pH and MBT^′ (for more details see De Jonge et al., 2014). De Jonge et al. (2014) moreover show that the 6-methyl brGDGTs are ubiquitously abundant in soils from all over the world, based on reanalysing the dataset of Peterse et al. (2012). However, they also compare reconstructed T_MA values based on MBT^′–CBT calibration (Peterse et al., 2012) and their newly developed T_MA calibration and state that they plot around a 1 : 1 line. They furthermore state that, especially for arid areas, larger deviations can be expected. Finally, they conclude that the use of the newly developed calibrations will improve the T_MA and pH reconstructions for areas with arid climate conditions. Because our study transect spans from southern Germany to southern Sweden, representing temperate and humid climate conditions, we argue that the usage of the older liquid chromatography method does not introduce a systematic error in our T_MA and pH reconstructions. Still, a higher variability/scatter could be associated with the calibration of Peterse et al. (2012) and therefore also present in our T_MA and pH reconstructions.

3.6 Apparent fractionation of δ²H and δ¹⁸O in the different vegetation types

δ²H values could be obtained for the n-alkanes C₂₇, C₂₉ and C₃₁ in all samples and additionally at two locations for n-C₂₅ and at six other locations for n-C₃₃. The δ²H_n-alkane values, calculated as the mean of n-C₂₅ to n-C₃₁ δ²H, range from −156 ‰ to −216 ‰. Pooled standard deviations show an overall average of 3.6 ‰. The δ¹⁸O_sugar values, calculated as the area-weighted means for arabinose and xylose, range from +27.7 ‰ to +39.4 ‰. The average weighted mean standard deviation is 1.4 ‰. The compound-specific isotope data are summarised along with the calculations in Table S4.

Apparent fractionation (ε_{n-alkane/precipitation}) is on the order of −120 ‰ to −150 ‰, i.e. a bit less than the biosynthetic fraction of −160 ‰. This implies that evapotranspirative enrichment is ∼10 ‰ to 40 ‰ (Fig. 7a). ε_{n-alkane/precipitation} is lower for grass sites compared to the forest sites. Differences are significant between deciduous and grass sites (p value = 0.005). This finding supports the results of other studies (Kahmen et al., 2013; Liu and Yang, 2008; McInerney et al., 2011) and can be named signal damping. Grasses do not only incorporate the evaporatively enriched leaf water but also unenriched xylem water in the growth and differentiation zone of grasses (Gamarra et al., 2016; Liu et al., 2017).

Figure 7 Apparent fractionation (a) ε_{n-alkane/precipitation} and (b) ε_{sugar∕precipitation}. Biosynthetic fractionation factors according to Sect. 2.4.2. Box plots show the median (red line), interquartile range (IQR) with the upper (75 %) and lower (25 %) quartiles, lowest whisker still within 1.5 IQR of the lower quartile, and highest whisker still within 1.5 IQR of the upper quartile; dots mark outliers. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=11 and 14 for n-alkanes and sugars, respectively); grass: grassland sites (n=4 and 6 for n-alkanes and sugars, respectively). The figure conceptually illustrates the effect of biosynthetic fractionation and evapotranspirative enrichment as well as signal damping.

The grass-derived hemicellulose sugar biomarkers do not fully record the evapotranspirative enrichment of the leaf water, either, as indicated by lower apparent fractionation (ε_{sugar∕precipitation}) in Fig. 7b. The differences are significant between forest and grass sites (p value < 0.005). This is in agreement with a study on cellulose extracted from grass blades (Helliker and Ehleringer, 2002), and again the signal damping can be explained with incorporation of enriched leaf water and non-enriched stem water.

Based on the comparison of evapotranspirative enrichment between forest and grass sites, the signal damping can be quantified to be ∼31 % for the hemicellulose sugars and ∼49 % for the n-alkanes. This is in agreement with other studies that reported a loss of 22 % of the leaf water enrichment for hemicellulose sugars (Helliker and Ehleringer, 2002) and 39 % to 62 % loss of the leaf water enrichment for n-alkanes (Gamarra et al., 2016).

3.7 δ²H_source-water and δ¹⁸O_source-water reconstructions

The δ²H versus δ¹⁸O diagram shown in Fig. 8 graphically illustrates the reconstruction of δ²H_leaf-water and δ¹⁸O_leaf-water (coloured dots) from δ²H_n-alkane and δ¹⁸O_sugar (crosses), as well as the reconstruction of δ²H_source-water and δ¹⁸O_source-water (black dots). For reconstructing δ²H_source-water and δ¹⁸O_source-water, LELs with an average slope of 2.8±0.1 (Eq. 10) can be generated through every leaf water point and the intercepts of these LELs with the GMWL.

Figure 8 δ²H vs. δ¹⁸O diagram illustrating the coupled δ²H_n-alkane–δ¹⁸O_sugar approach: measured δ²H_n-alkane and δ¹⁸O_sugar values, reconstructed δ²H_leaf-water and δ¹⁸O_leaf-water (according Eqs. 8 and 9), and reconstructed δ²H_source-water and δ¹⁸O_source-water in comparison to GIPR and OIPC-based δ²H_{precipitation} and δ¹⁸O_{precipitation}. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=11); grass: grassland sites (n=4).

The reconstructed δ²H_source-water and δ¹⁸O_source-water results can be compared with the δ²H_GIPR,OIPC and δ¹⁸O_GIPR,OIPC data (Fig. 9). This comparison reveals that the coupled δ²H_n-alkane–δ¹⁸O_sugar approach yields more accurate δ²H_source-water and δ¹⁸O_source-water compared to single δ²H_n-alkane approaches. However, the range of the reconstructed δ²H_source-water and δ¹⁸O_source-water values is clearly larger than in δ²H_GIPR,OIPC and δ¹⁸O_GIPR,OIPC values. δ²H is systematically underestimated by ∼21 ‰ ± 22 ‰ (Fig. 9b) and δ¹⁸O by ∼2.9 ‰ ± 2.8 ‰ (Fig. 9d). The type of vegetation seems to be not particularly relevant (p value = 0.18 for Δδ²H and p value = 0.34 for Δδ¹⁸O). Nevertheless, the systematic offsets tend to be lowest for the deciduous sites (Δδ²H and Δδ¹⁸O are closer to zero with $\sim -\mathrm{5}$ ‰ ± 15 ‰ and $\sim -\mathrm{1.1}$ ‰ ± 2.1 ‰), followed by grass sites ($\sim -\mathrm{14}$ ‰ ± 20 ‰ and $\sim -\mathrm{2.1}$ ‰ ± 2.6 ‰). In comparison, the coniferous sites show the largest offsets ($\sim -\mathrm{23}$ ‰ ± 26 ‰ for Δδ²H and $\sim -\mathrm{3.0}$ ‰ ± 3.3 ‰ for Δδ¹⁸O). Differences are, however, not statistically significant. The systematic offset and the large variability might have more specific reasons, and we suggest that this is related to the type of vegetation. Deciduous trees produce lots of leaf waxes and sugars (e.g. Prietzel et al., 2013; M. Zech et al., 2012a), and all biomarkers reflect and record the evapotranspirative enrichment of the leaf water (e.g. Kahmen et al., 2013; Tuthorn et al., 2014). By contrast, coniferous trees produce quite low amounts of n-alkanes (Diefendorf and Freimuth, 2016; M. Zech et al., 2012a), while sugar concentrations are as high as in other vascular plants (e.g. Hepp et al., 2016; Prietzel et al., 2013). For the coniferous soil samples this means that the n-alkanes stem most likely from the understorey whereas the sugars originate from grasses and coniferous needles. When the understorey is dominated by grass species, the n-alkane biomarkers do not record the full leaf water enrichment signal, whereas the sugars from the needles do. The reconstructed leaf water for the coniferous sites is therefore too negative concerning δ²H, and reconstructed δ²H_source-water and δ¹⁸O_source-water values thus also become too negative (Fig. 8). Concerning the grass sites, the following explanation can be found. Correcting for signal damping makes the reconstructed leaf water points more positive and shifts them in Fig. 8 up and to the right. As the signal damping is stronger for δ²H than for δ¹⁸O, the corrected leaf water points would now plot above the uncorrected ones. The corrected leaf water points lead to more positive reconstructed δ²H_source-water and δ¹⁸O_source-water values for the grass sites. However, Gao et al. (2014) and Liu et al. (2016) showed that the ε_bio (regarding ²H between n-alkanes and leaf water) of monocotyledon plants could be larger than those of dicotyledonous ones. This would therefore also cause a more negative ε_{n-alkane/precipitation} for grasses compared to trees. We observe that the ε_{n-alkane/precipitation} is indeed more negative for the grass sites compared to the forest sites (Fig. 7 and Sect. 3.6). Therefore, effects of signal damping vs. variable ε_bio along with vegetation types are indistinguishable here. As an outlook for a future study, we therefore strongly recommend a comparison between the here measured δ²H_n-alkane values with modelled ones using, for example, the new available model approach from Konecky et al. (2019), which could provide insights if such vegetation effects on ε_bio of ²H in n-alkanes are describable.

Figure 9 Correlation of reconstructed δ²H_source-water and δ¹⁸O_source-water vs. precipitation δ²H_GIPR,OIPC and δ¹⁸O_GIPR,OIPC (a, c). Black lines indicate the 1 : 1 relationship. Differences between reconstructed source water and precipitation ($\mathrm{\Delta }{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}={{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}}_{\text{source-water}}-{{\mathit{\delta }}^{\mathrm{2}}\mathrm{H}}_{\mathrm{GIPR},\mathrm{OIPC}}$, $\mathrm{\Delta }{\mathit{\delta }}^{\mathrm{18}}\mathrm{O}={{\mathit{\delta }}^{\mathrm{18}}\mathrm{O}}_{\text{source-water}}-{{\mathit{\delta }}^{\mathrm{18}}\mathrm{O}}_{\mathrm{GIPR},\mathrm{OIPC}}$) for the three different vegetation types (b, d). Box plots show the median (red line), interquartile range (IQR) with upper (75 %) and lower (25 %) quartiles, lowest whisker still within 1.5 IQR of the lower quartile, and highest whisker still within 1.5 IQR of the upper quartile. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=11); grass: grassland sites (n=4).

Rooting depths specific to vegetation type could partly cause the overall high variability in reconstructed δ²H_source-water and δ¹⁸O_source-water. Deep-rooting species most likely use the water from deeper soil horizons and/or shallow groundwater, which is equal to the (weighted) mean annual precipitation (e.g. Herrmann et al., 1987). Shallow-rooting plants take up water from upper soil horizons, which is influenced by seasonal variations in δ²H_precipiation and δ¹⁸O_precipiation and by soil water enrichment (Dubbert et al., 2013). Thus, the overall assumption that the source water of the plants reflects the local (weighted) mean precipitation might be not fully valid for all sites. Moreover, a partial contribution of root-derived rather than leaf-derived sugar biomarkers in our topsoil samples is very likely. This does, by contrast, not apply for n-alkanes, which are hardly produced in roots (M. Zech et al., 2012b, and the discussion therein).

Moreover, the high variability within the vegetation types could be caused by variability in ε_bio of ²H in n-alkanes, as well as ¹⁸O in sugars. There is an ongoing discussion about the correct ε_bio for ¹⁸O in hemicellulose sugars (Sternberg, 2014 vs. Zech et al., 2014), and ε_bio is probably not constant over all vegetation types. This translates into errors concerning leaf water reconstruction and thus reconstructing δ²H_source-water and δ¹⁸O_source-water values (Eq. 9 and Fig. 8). Likewise, the ε_bio values reported in the literature for ²H of n-alkanes can be off from −160 ‰ by tens of per mille (Feakins and Sessions, 2010; Tipple et al., 2015; Feakins et al., 2016; Freimuth et al., 2017). The degree to which hydrogen originates from NADPH rather than leaf water is important, because NADPH is more negative (Schmidt et al., 2003). The wide range in biosynthetic ²H fractionation factors, which can be even larger, is therefore also related to the carbon and energy metabolism state of plants (Cormier et al., 2018).

3.8 RH reconstruction

Reconstructed RH_MDV ranges from 34 % to 74 %, while RH_MDV values from climate station data range from 61 % to 78 % (Fig. 10a). Biomarker-based values thus systematically underestimate the station data (ΔRH${}_{\mathrm{MDV}}=-\mathrm{17}$ % ± 12 %). However, the offsets are much less for deciduous tree and grass sites (ΔRH${}_{\mathrm{MDV}}=-\mathrm{10}$ % ± 12 % and −7 % ± 9 %, respectively; Fig. 10b). The offsets for the coniferous sites are −30 % ± 11 % and significantly larger than for the deciduous and grass sites (p values < 0.05).

Figure 10 (a) Comparison of reconstructed (biomarker-based) RH_MDV values and climate station RH_MDV data. The black line indicates the 1 : 1 relationship. (b) Differences between reconstructed and climate station RH_MDV values (ΔRH_MDV = reconstructed − climate station RH_MDV) for the three different vegetation types along the transect. Abbreviations: con: coniferous forest sites (n=9); dec: deciduous forest sites (n=11); grass: grassland sites (n=4).

Too low reconstructed RH_MDV values for the coniferous sites make sense in view of the previously discussed option that soils contain n-alkanes from the understorey (which is dominated by grass species), while sugars stem from needles and grasses. As explained earlier already, the signal damping leads to too negative reconstructed δ²H_leaf-water (whereas δ¹⁸O is affected less by the signal damping), and too negative δ²H_leaf-water translates into overestimated d-excess and underestimated RH values. In Fig. 8, a correction for this requires moving the coniferous leaf water data points upwards towards more positive δ²H values; thus the distance between the leaf water and the source water is reduced. It should be noted that here variable ε_bio (regarding ²H between n-alkanes and leaf water) along with vegetation types could also not be distinguished from signal damping effects.

The underestimation of RH for the deciduous and grass sites could be partly associated with the use of the GMWL as a baseline for the coupled δ²H_n-alkane–δ¹⁸O_sugar approach. The deuterium excess of local meteoric water lines (LMWLs) is generally lower than the +10 ‰ of the GMWL, while the slopes of the LMWLs are comparable to the GMWL (Stumpp et al., 2014). In addition, if soil water evaporation occurred before water uptake by the plants, this would lead to an underestimation of biomarker-based RH_MDV values, too. It can be furthermore assumed that plant metabolism is highest during times with direct sunshine and high irradiation, i.e. during noon on sunny days. The relevant RH could therefore be lower than the climate-station-derived RH_MDV. Indeed, already climate station RH_MDV is considerably lower than RH_MA and RH_MV (Table S1 in the Supplement).

The uncertainty of reconstructed RH_MDV values is large for all three investigated vegetation types, and again these uncertainties are probably also related to ε_bio, which is most likely not constant as assumed for our calculations. Moreover, microclimate variability is underestimated in our approach. As mentioned in Sects. 2.4.2 and 3.7, in the coupled approach not only the source water of the plants is equated with (weighted) mean annual precipitation, but also an isotopic equilibrium between the source water and the (local) atmospheric water vapour is assumed. However, in areas with distinct seasonality this might be not fully valid. To account for this lack of equilibrium between precipitation and local atmospheric water vapour, apparent ε values can be calculated with data from Jacob and Sonntag (1991). As shown by Hepp et al. (2019) those values can be used to achieve alternative RH reconstructions based on the coupled δ²H_n-alkane–δ¹⁸O_sugar approach. Such calculated RH_MDV values are on average 1.5 % more negative than the original values. However, this difference in RH is far below the analytical uncertainties of the compound-specific biomarker isotope analysis.

Finally, the integration time of the investigated topsoils has to be discussed. Unfortunately, no ¹⁴C dates are available for the soil samples. However, most likely the organic matter has been built up over a longer timescale than the available climate data, which are used for comparison. In combination with vegetation changes/management changes throughout that period, this could surely lead to a less tight relationship of the reconstructions compared to the climate station data. Root input of arabinose and xylose seems to be of minor relevance in our topsoil samples. Otherwise, the reconstructed δ¹⁸O_sugar values would be too negative, resulting in RH_MDV overestimations, which are not observed.

4 Conclusions

We were able to show the following.

• i.

  The vegetation type does not significantly influence the brGDGT concentrations and proxies, yet the coniferous sites tend to have higher brGDGT concentrations, BIT indices and CBT-MBT^′ ratios, while grass sites tend to be lowest.

• ii.

  CBT faithfully records soil pH with a median ΔpH of 0.6±0.6. The CBT overestimates the real pH in particular at the forest sites.

• iii.

  CBT–MBT^′-derived T_MA values reflect the climate-station-derived T_MA values with a median ΔT_MA of 0.5 ^∘C ± 2.4, but again slightly too high reconstructions for the forest sites were observed.

• iv.

  Differences in the apparent fractionation between the investigated vegetation types can be explained with signal damping.

• v.

  The reconstructed δ²H_source-water and δ¹⁸O_source-water reflect the δ²H_GIPR,OIPC and δ¹⁸O_GIPR,OIPC with a systematic offset for δ²H of $\sim -\mathrm{21}$ ‰ ± 22 ‰ and for δ¹⁸O of $\sim -\mathrm{2.9}$ ‰ ± 2.8 ‰ (based on overall medians of Δδ²H, Δδ¹⁸O). This is caused by too negative reconstructions for coniferous and grass sites. For coniferous sites, this can be explained with n-alkanes originating from understorey grasses. As for the grass sites, the signal damping or variable ε_bio along with vegetation types affect δ²H more than δ¹⁸O. This leads to too negative reconstructed δ²H_leaf-water values and thus to too negative δ²H_source-water and δ¹⁸O_source-water reconstructions.

• vi.

  Reconstructed (biomarker-based) RH_MDV values tend to underestimate climate-station-derived RH_MDV values ($\mathrm{\Delta }{\mathrm{RH}}_{\mathrm{MDV}}=\sim -\mathrm{17}$ % ± 12 %). For coniferous sites the underestimations are strongest, which can be explained with understorey grasses being the main source of n-alkanes for the investigated soils under coniferous forests.

Overall, our study highlights the great potential of brGDGTs and the coupled δ²H_n-alkane–δ¹⁸O_sugar approach for more quantitative paleoclimate reconstructions. Taking into account effects of different vegetation types improves correlations and reconstructions. This holds particularly true for the coupled δ²H_n-alkane–δ¹⁸O_sugar approach, which is affected by signal damping of the grass vegetation or variable ε_bio (regarding ²H between n-alkanes and leaf water) along with vegetation types. By contrast, vegetation-related effects do not strongly influence the brGDGT-derived reconstructions. Assuming constant ε_bio is likely a considerable source of uncertainty and should be further addressed in future field and/or modelling studies. Climate chamber experiments are most promising to further evaluate and refine the coupled δ²H_n-alkane–δ¹⁸O_sugar approach, because uncertainties related to microclimate variability can be reduced. Field experiments like ours suffer from the fact that biomarker pools in the sampled topsoils may have been affected by past vegetation and climate changes and by the rather small range covered by the sampled transect. Both make the comparison between reconstructions and observations more difficult compared to large datasets and well-defined conditions.