# Mid- to Late-Holocene branched GDGT-based air temperatures from a crater lake in Cameroon (Central Africa)

Guillemette Ménot, Salomé Ansanay-Alex, Valérie F Schwab, Gilbert Todou, Olivier Séné, Jean-Michel Onana, Gerd Gleixner, Dirk Sachse, Yannick Garcin

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Guillemette Ménot, Salomé Ansanay-Alex, Valérie F Schwab, Gilbert Todou, Olivier Séné, et al. Mid-to Late-Holocene branched GDGT-based air temperatures from a crater lake in Cameroon (Central Africa). Organic Geochemistry, 2025, 204, pp.104982. 10.1016/j.orggeochem.2025.104982. hal-05451013

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# Mid- to Late-Holocene branched GDGT-based air temperatures from a crater lake in Cameroon (Central Africa)

Guillemette Ménot<sup>a, *, †</sup>, Salomé Ansanay-Alex<sup>a</sup>, Valérie F. Schwab<sup>b</sup>, Gilbert Todou<sup>c, d</sup>, Olivier Séné<sup>e</sup>, Jean-Michel Onana<sup>f, g</sup>, Gerd Gleixner<sup>b, ‡</sup>, Dirk Sachse<sup>h</sup>, Yannick Garcin<sup>i ‡</sup>

## ARTICLE INFO

Associate editor – Stefan Schouten

### Keywords:

Holocene

GDGTs

Central Africa

Temperature proxies

Lakes

## ABSTRACT

African low-latitude regions tend to be underrepresented in global continental temperature reconstructions, limiting both our understanding and the reliability of predictions of past and future changes in temperature and precipitation in those parts of the world. The lack of continuous sedimentary archives and quantitative temperature proxies further complicates this issue, especially outside Eastern Africa. Here, we use data collected in Cameroon lakes to assess branched glycerol dialkyl glycerol tetraether (brGDGT)-based temperature proxies and examine potential confounding variables impacting temperature reconstructions. By analyzing GDGT distributions in soil watershed, water column, and surface sediment samples from a total of 11 Cameroon lakes, we could verify that the degree of methylation of brGDGTs (through the MBT<sup>5</sup><sub>5Me</sub>) can serve as a reliable indicator for reconstructing mean annual air temperatures. Additionally, we could confirm that surface water conductivity represents a controlling factor for the brGDGT assemblage in surface sediments of crater lakes. Moreover, we provide the first reconstruction of Mid- to Late-Holocene GDGT-based air temperatures for a crater lake in Cameroon (Central Africa), revealing a temperature decrease of 2.5 °C over the last 7000 years, which agrees with recently published records for East Africa but exceeds current model predictions. These discrepancies highlight the need for additional studies to focus on this geographically underrepresented area.

## 1 Introduction

While extensive research has been conducted on the precipitation history of the African continent over the past 15,000 calendar years before present (ka cal BP) (Tierney et al., 2011; Costa et al., 2014; Shanahan et al., 2015; Garcin et al., 2018; Trauth et al., 2018; Wagner et al., 2018; Jaeschke et al., 2020; Bittner et al., 2022b; Baxter et al., 2023), only very few studies have attempted to reconstruct temperature changes. All of these have focused on East Africa and none on continental West or Central Africa (Tierney et al., 2008, 2010a, 2016; Berke et al., 2012b; Loomis et al., 2012, 2015, 2017; Morrissey et al., 2018). A

marine sediment core recovered near the Congo River outflow represents the only continental temperature record for this region and represents a watershed extending over several millions km² (Weijers et al., 2007a). This lack of research makes it challenging to assess continental-scale temperature patterns. All global Holocene temperature databases released to date (Cartapanis et al., 2022; Kaufman et al., 2020; Marcott et al., 2013; Osman et al., 2021) either underrepresent or completely omit the African continental low latitudes, which limits our understanding and hinders our ability to predict possible links between precipitation amount and temperature (Lupien, 2023). The absence of robust quantitative temperature proxies and continuous sedimentary

<footnote><sup>a</sup> ENS de Lyon, UCBL, CNRS, LGL-TPE, 46 allée d'Italie, 69364 Lyon cedex 07, France</footnote>

<footnote><sup>b</sup> Max-Planck-Institut für Biogeochemie, Jena, Germany</footnote>

<footnote><sup>c</sup> Université de Maroua, Faculté des Sciences, Laboratoire de Botanique-Ecologie, Maroua, Cameroon</footnote>

<footnote><sup>d</sup> Université de Garoua, Faculté des Sciences, Garoua, Cameroon</footnote>

<footnote><sup>e</sup> USDA, US Forest Service, International Programs, Yaoundé, Cameroon</footnote>

<footnote><sup>f</sup> Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon</footnote>

<footnote><sup>g</sup> National Herbarium of Cameroon, IRAD, Yaoundé, Cameroon</footnote>

<footnote><sup>h</sup> GFZ German Research Centre for Geosciences, Potsdam, Germany</footnote>

<footnote><sup>i</sup> Aix Marseille University, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France</footnote>

<footnote>* Corresponding author.
E-mail address: guillemette.menot@ens-lyon.fr (G. Ménot).</footnote>
archives means that this important climate parameter remains poorly constrained. Several temperature reconstruction methods have been developed over the past 15 years based on glycerol dialkyl glycerol tetraethers (GDGTs), archaean and bacterial membrane lipids (see Schouten et al., 2013b, for a review). These approaches rely on the structural diversity of these lipids, including both isoprenoid and branched forms (iso- and brGDGTs, respectively). In soils and lake sediments, methylations and cyclization in brGDGTs have been found to correlate with both temperature and pH (e.g. De Jonge et al., 2014; Dearing Crampton-Flood et al., 2020; Martínez-Sosa et al., 2021; Naafs et al., 2017; Raberg et al., 2021; Weijers et al., 2007; Zhao et al., 2023). Aside from pH, a variety of environmental parameters can affect the temperature/brGDGT (T/brGDGT) relationship in lakes, including conductivity, nutrient availability, water depth, and dissolved oxygen concentration (Wang et al., 2012; Weber et al., 2018; van Bree et al., 2020; Li et al., 2023).

To derive temperatures from brGDGT assemblages, empirical cali-
brations have been derived both at both global (Naafs et al., 2017;
Dearing Crampton-Flood et al., 2020; Martínez-Sosa et al., 2021; Raberg
et al., 2021) and regional scales, including East Africa (Tierney et al.,
2010b; Loomis et al., 2012, 2014; Russell et al., 2018). A recent study
specifically focused on lakes at tropical latitudes contributing new
datasets from South America and Southeast Asia to existing databases
(Zhao et al., 2023). Prior to any temperature reconstruction, it is
therefore necessary to conduct an evaluation of these existing
calibrations.

In the present study, we use samples collected from different lakes in Cameroon (Garcin et al., 2012) to assess the accuracy of paleotemper- ature proxies and to identify potential confounding factors in T/brGDGT relationships in continental archives. This results in the first late- Holocene temperature reconstruction for Central Africa based on sedi- ment data from Mbalang lake (Cameroon) (Vincens et al., 2010; Nguetsop et al., 2011).

2 Materials and methods

2.1 Cameroon lakes and sedimentary archive

The present study used samples collected in November 2009 along a
SW-NE transect covering 11 Cameroon lakes between latitudes 3.7°N to
11°N (Table 1, Fig. 1) (Garcin et al., 2012, 2014; Schwab et al., 2015).

The sampling procedure has been described in Garcin et al. (2012). Samples of soil, water column particulate matter and surface sediment were collected from each lake over the course of one or two days. The pH, lacustrine temperature and conductivity were measured using a multiprobe profiler. Mean annual air temperatures were estimated using the WorldClim 2.50 gridded dataset (Hijmans et al., 2005). Lake sediment surfaces (0–4 cm) were sampled using a gravity corer deployed in the deepest and most central parts of the lakes. In Barombi Mbo and Manengouba lakes, where the maximum depth exceeds 100 m in depth, the gravity corer was deployed in water depths of 65 and 40 m, respectively.

Of the eleven studied lakes, six are crater lakes, characterized by
limited catchment areas, steep inner slopes and greater maximum
depths (Table 1). The five remaining lakes included two shallow lakes,
one quarry lake and two artificial reservoirs, all with maximum water
depths of less than 6 m (Table 1, Fig. 1).

Lake Mbalang (7°19′N, 13°44′E; 1110 m.a.s.l., Fig. 1) is located within this transect on the Adamawa Plateau in Central Cameroon and was not sampled during the 2009 field campaign. Its average and maximum depths are 29 m and 55 m, respectively, and it covers an area of 42 ha on a steep watershed extending over 78 ha. A survey of lake water column properties conducted between mid-March 2019 and early-March 2020 by Abba et al. (2023) showed that surface temperatures varied between 20 and 27 °C, while pH and conductivity ranged from 6.2–7.9 and 165–212 µS/cm, respectively. The local mean annual air temperature (MAAT) and precipitation were 22 °C and 1500 mm, respectively, while the rainy season lasts 7 months from April to October. Here, we studied a 6-m-long sediment core (core M4) that was collected in 1998 from the deepest part of Lake Mbalang as part of the ECOFIT project (Vincens et al., 2010).

2.2. Mbalang age-depth model recalculation

Nine previously published radiocarbon dates (Vincens et al., 2010; Nguetsop et al., 2011; Ngos and Giresse, 2012) were used to reevaluate the chronological framework of core M4, based on the SHCal20 (Hogg et al., 2020) and postbomb NH3 (Hua et al., 2013) calibration curves, assembled using the R-software Bacon (v2.5, (Blaauw and Christen, 2011)). The obtained age model aligns with the three previously cited studies, and the sedimentary sequence yields a continuous coverage of

<caption>**Table 1**</caption>

Characteristics of the Cameroon lake sampling sites (Garcin et al., 2012). Lake surface temperature, pH and conductivity were measured at the lake surface in November 2009.

<table>
  <thead>
    <tr>
      <th>Lake</th>
      <th>Site code</th>
      <th>Location</th>
      <th>Altitude (masl)</th>
      <th>Type</th>
      <th>Max depth (m)</th>
      <th>Lake area (ha)</th>
      <th>Catchment area (ha)</th>
      <th>Mean annual air temperature (°C)</th>
      <th>Lake surface temperature (°C)</th>
      <th>pH</th>
      <th>Conductivity (µS cm<sup>-1</sup>)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Ossa</td>
      <td>OSSA</td>
      <td>3°77′N, 10°00′E</td>
      <td>17</td>
      <td>Shallow lake</td>
      <td>6</td>
      <td>3700</td>
      <td>1650</td>
      <td>26.3</td>
      <td>29</td>
      <td>6</td>
      <td>22</td>
    </tr>
    <tr>
      <td>Debundscha</td>
      <td>DEBU</td>
      <td>4°10′N, 8°98′E</td>
      <td>70</td>
      <td>Crater lake</td>
      <td>16</td>
      <td>7</td>
      <td>5</td>
      <td>25.7</td>
      <td>29</td>
      <td>7</td>
      <td>38</td>
    </tr>
    <tr>
      <td>Barombi Mbo</td>
      <td>BARO</td>
      <td>4°65′N, 9°40′E</td>
      <td>304</td>
      <td>Crater lake</td>
      <td>110</td>
      <td>410</td>
      <td>800</td>
      <td>24.9</td>
      <td>29</td>
      <td>7.3</td>
      <td>40</td>
    </tr>
    <tr>
      <td>Manengouba</td>
      <td>MANE</td>
      <td>5°03′N, 9°83′E</td>
      <td>1911</td>
      <td>Crater lake</td>
      <td>165</td>
      <td>35</td>
      <td>105</td>
      <td>16</td>
      <td>20.5</td>
      <td>7</td>
      <td>32</td>
    </tr>
    <tr>
      <td>Baleng</td>
      <td>BALE</td>
      <td>5°55′N, 10°42′E</td>
      <td>1376</td>
      <td>Crater lake</td>
      <td>52</td>
      <td>11</td>
      <td>18</td>
      <td>20.5</td>
      <td>26</td>
      <td>7.1</td>
      <td>126</td>
    </tr>
    <tr>
      <td>Assom</td>
      <td>ASSO</td>
      <td>6°62′N, 12°98′E</td>
      <td>896</td>
      <td>Shallow lake</td>
      <td>3</td>
      <td>141</td>
      <td>4500</td>
      <td>23</td>
      <td>28</td>
      <td>6.5</td>
      <td>97</td>
    </tr>
    <tr>
      <td>Tabéré</td>
      <td>TABE</td>
      <td>7°13′N, 13°69′E</td>
      <td>1161</td>
      <td>Crater lake</td>
      <td>62</td>
      <td>10</td>
      <td>46</td>
      <td>21.7</td>
      <td>23.5</td>
      <td>7.5</td>
      <td>89</td>
    </tr>
    <tr>
      <td>Tizong</td>
      <td>TIZO</td>
      <td>7°25′N, 13°58′E</td>
      <td>1155</td>
      <td>Crater lake</td>
      <td>45</td>
      <td>7</td>
      <td>17</td>
      <td>21.9</td>
      <td>24.5</td>
      <td>8.5</td>
      <td>175</td>
    </tr>
    <tr>
      <td>Mamguiewa</td>
      <td>MAMG</td>
      <td>8°39′N, 13°71′E</td>
      <td>322</td>
      <td>Quarry lake</td>
      <td>5</td>
      <td>0.6</td>
      <td>2</td>
      <td>26.3</td>
      <td>25.5</td>
      <td>8</td>
      <td>155</td>
    </tr>
    <tr>
      <td>Rhumsiki</td>
      <td>RHUM</td>
      <td>10°08′N, 13°58′E</td>
      <td>1028</td>
      <td>Permanent reservoir</td>
      <td>3</td>
      <td>1.3</td>
      <td>140</td>
      <td>22.7</td>
      <td>23.5</td>
      <td>7.8</td>
      <td>145</td>
    </tr>
    <tr>
      <td>Mora</td>
      <td>MORA</td>
      <td>11°05′N, 14°15′E</td>
      <td>438</td>
      <td>Temporary reservoir</td>
      <td>1,5</td>
      <td>0.1</td>
      <td>7</td>
      <td>27.2</td>
      <td>31</td>
      <td>8.8</td>
      <td>116</td>
    </tr>
  </tbody>
</table>
<figure><img src="image_4.png" /><figcaption>Fig. 1. A. Locations of existing sampling sites of African lacustrine GDGT records. Dots represent the surface GDGT calibration datasets with blue colors identifying the Cameroon lake data included in the present study (Garcin et al., 2012), while gray dots represent previously published datasets (Zhao et al., 2023). The red triangle shows the location of GDGT downcore samples in lake Mbalang, while others refer to isoGDGTs (white) (Powers et al., 2005; Tierney et al., 2008; Berke et al., 2012a, 2012b) and brGDGTs (orange) (Sinninghe Damsté et al., 2012; Loomis et al., 2015, 2017; Bittner et al., 2022a; Garelick et al., 2022). B. Zoom on the eleven lakes in Cameroon. The six crater lakes are marked by grey labels, while white labels are used for the other lake types (Table 1). The different shades of green background illustrate the succession of vegetation from tropical forests in the south over transitional forest and ultimately savannah woodland further north (White, 1983). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

the last 7 ka cal BP (Fig. S1).

## 2.3. GDGT extraction and analysis

The lipid extraction and separation procedures for the modern lake samples have been described previously (Garcin et al., 2012). In the present study, we focused on polar fractions that were eluted from pre-combusted and deactivated Al₂O₃ using a mixture of dichloromethane/methanol (1:1 v/v).

Total lipid extracts were obtained from M4 down-core sediments using the accelerated solvent extraction method (ASE 200 system, Dionex, California, USA) at a temperature of 120 °C and pressure of 100 bars, using a mixture of dichloromethane/methanol (90:10 v/v). The GDGT fractions were purified using hexane/dichloromethane (1:1 v/v) and methanol/dichloromethane (1:1 v/v) through successive elution steps on Al₂O₃ cartridges.

Some extraction parameters in Garcin et al. (2012) applied to samples collected along the transect of 11 lakes in 2009 slightly differ from those used on the M4 down-core samples, such as the ASE extraction conditions of 100 °C and 1500 psi. A recent round-robin experiment comparing offsets in proxy values across various extraction and purification methods confirms that the observed offsets between samples and corresponding extracts are unrelated to differences in workup procedures (De Jonge et al., 2024), thus enabling the comparison of results between our two sample sets.

GDGTs were analyzed using High Performance Liquid Chromatography Mass Spectrometry (HPLC-APCI-MS, Agilent 1200) in a hexane: iso-propanol (98.2:1.8) solvent system, following the methodology

described by (Hopmans et al., 2016). Acquisition was carried out via selective ion monitoring (SIM) of m/z 1050, 1048, 1046, 1036, 1034, 1032, 1022, 1020 and 1018 (Hopmans et al., 2016). Compound identification and quantification were performed using Chemstation software. Measurement accuracy was evaluated by regular analysis of samples processed during the intercalibration exercises conducted in 2009, 2011 and 2023 (Schouten et al., 2009, 2013a; De Jonge et al., 2024). Analytical reproducibility was assessed by replicating the analysis of sediment samples from the sediment core, with the same operator injecting each sample five times for a total of three samples.

## 2.4. GDGT proxy calculations

Initial calculation of the fractional abundances of isoprenoid (iso-) and branched (br-) GDGTs was based on the total GDGTs. Then, the fractional abundance of each compound was determined relative to the sum of each group. The proportions of tetra-, penta-, and hexamethylated brGDGTs (I, II, and III, respectively) were calculated using the fractional abundances of brGDGTs according to (Sinninghe Damsté, 2016), including the 5- and 6-methyl forms as follows:

$$ \%\text{tetra} = \frac{\text{Ia} + \text{Ib} + \text{Ic}}{\sum \text{brGDGT}} \qquad (1) $$

$$ \%\text{penta} = \frac{\sum_{i=5}^{6} \text{IIa}_i + \sum_{i=5}^{6} \text{IIb}_i + \sum_{i=5}^{6} \text{IIc}_i}{\sum \text{brGDGT}} \qquad (2) $$
$$ \text{and } \%\text{hexa} = \frac{\sum_{i=5}^{6} \text{IIIa}_i + \sum_{i=5}^{6} \text{IIIb}_i + \sum_{i=5}^{6} \text{IIIc}_i}{\sum \text{brGDGT}} \quad (3) $$

MBT⁵ₘₐ, a measure for the degree of methylation in 5-methyl brGDGT assemblages was calculated using (De Jonge et al., 2014):

$$ \text{MBT}'_{5\text{Me}} = \frac{\text{Ia} + \text{Ib} + \text{Ic}}{\text{Ia} + \text{Ib} + \text{Ic} + \text{IIa} + \text{IIb} + \text{IIc} + \text{IIIa}} \quad (4) $$

Lastly, to explore whether other parameters could introduce perturba-

tions into the temperature reconstructions, we calculated the isomeri- zation ratio IR₆ₘₐ (De Jonge et al., 2014), the Branched and Isoprenoid Tetraether (BIT) index (Hopmans et al., 2004), as well as the degrees of cyclization CBT' and CBT₅ₘₐ, used to derive pH changes (De Jonge et al., 2014):

<figure><img src="image_5.png" /><figcaption>Fig. 2. GDGT fractional abundance (in %) in soil, surface and downcore lake sediments from Cameroon. Fractional abundances of A. i- and brGDGTs, B. tetra-methylated, C. pentamethylated, and D. hexamethylated brGDGT forms. Brown, blue, and gray colors correspond, to soil, water column, and lake surface sediments, respectively from the eleven Cameroon lakes (n = 11). Black symbols refer to downcore sediments from the Mbalang Lake record (n = 31). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
$$IR_{6Me} = \frac{\Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c'}{(\Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c') + (\Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c')} \quad (5)$$

$$CBT' = 10^{\log} \left( \frac{Ic + \Pi a' + \Pi b' + \Pi c' + \Pi a' + \Pi b' + \Pi c'}{Ia + \Pi a + \Pi la} \right) \quad (6)$$

$$CBT_{5Me} = 10^{\log} \left( \frac{Ib + \Pi b}{Ia + \Pi a} \right) \quad (7)$$

### 3 Results

#### 3.1 GDGT distributions in modern samples and Lake Mbalang down-core sediments

brGDGTs dominated the GDGT assemblages in all sample types (Fig. 2A). Soils exhibited higher fractional abundances of brGDGTs than isoGDGTs, ranging from 77% to 98% with a median of 95%. We observed slightly higher brGDGT fractional abundances and variabilities in lake surface sediments (45%–98%, median: 88%) compared to the sedimentary sequence (57%–78%, median: 65%).

Among brGDGTs, the tetramethylated forms were the most abundant, followed by the penta- and hexa-methylated forms (Fig. 2B-D). In each of the three methylation groups, the acyclic forms dominated (mean values ranging from 50% to 68% for Ia, from 15% to 25% for IIa, and <10% for IIIa). All sample types showed similar fractional abundances of the mono and bicyclic brGDGT forms, with the acyclic forms, IIa and IIa', yielding fractional abundances >10%, while all the other brGDGTs having mean values <5% (Fig. 2C). IIIa' is the most abundant hexamethylated form, showing higher fractional abundances in the downcore sediment (Fig. 2D), where hexamethylated brGDGTs have systematically lower abundances compared to the tetra- and penta-methylated forms (Fig. 3A). While tetramethylated forms dominate the brGDGT assemblage, they exhibit a decreasing trend over time, declining from 70% at 7 ka cal BP to 40% at present (Fig. 3A).

#### 3.2 GDGT-based proxies in modern samples and Lake Mbalang down-core sediments

The mean analytical errors between repeated analyses of BIT, MBT'_{5Me}, and CBT' were 0.01 (0.004–0.011), 0.01 (0.003–0.01), and 0.02 (0.098–0.031), respectively. The reproducibility of BIT and MBT'_{5Me} indices is consistent with results from intercalibrations undertaken in 2009, 2013, and 2024 (Schouten et al., 2009, 2013a; De Jonge et al., 2024).

The BIT-index varied between 0.7 and 0.9 along the Mbalang sediment core, yielding some of the lowest values measured in any of the sampled Cameroon lakes (Fig. 3B). The CBT'-index increases from –0.5 to 0 along the core, falling within the range of –2.1 to 0.1 (mean: –0.7 ± 0.5, Fig. 3C) reported in the Cameroon sites included in this study. The IR_{6Me}-index varies between 0.59 and 0.70 (mean; 0.63 ± 0.03, Fig. 3D) along the core, which is near the upper end of the values reported for core tops from this transect (from 0.31 to 0.74, mean: 0.50 ± 0.14, Fig. 3D). Furthermore, the Mbalang sediment core shows a decrease of the MBT_{5Me} index over time from 0.86 to 0.74 (Fig. 3E). Well within the range of previously reported values for surface sediments from this transect of 0.65 to 0.92 (mean: 0.78 ± 0.08, Fig. 3E).

### 3.3. Statistical analysis of climate parameters and brGDGT indices in core top sediments from the Cameroon lakes

Considering that surface sediments and water column samples exhibited similar fractional abundances of the individual brGDGTs

across the studied lakes (Fig. 2 and Wilcoxon-tests in Table S1), we combined both datasets for the statistical analysis of climatic parameters and brGDGT indices (Fig. 4). Results show a statistically significant positive correlation (R = 0.92) between mean annual air temperature

<figure><img src="image_6.png" /><figcaption>Fig. 3. GDGT-based indices from soils and surface lake sediments (open and closed symbols, respectively). The shaded area contains data from 11 Cameroon lakes, while the part on the right shows the data from the lake Mbalang sediment core for A. Tetra-, penta-, and hexamethylated brGDGT fractional abundances, B. BIT, C. CBT', and D. IR_{6Me} and E. MBT'_{5Me}. The error envelope estimated from the analytical errors is represented by the shaded areas around the curve.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Correlation matrix for environmental parameters and brGDGT indices in water column particulate organic matter and surface sediments in the six Cameroon crater lakes. MAAT: Mean Annual Air Temperature; LST: Lake Surface Temperature; Cond.: Conductivity. Equations for calculating the brGDGT indices are provided in the Methods. Values in the upper right triangle represent correlation coefficients. Statistical significance is indicated as follows: \*\*\*: p ≤ 0.001, \*: p ≤ 0.01, and \*: 0.01 ≤ p ≤ 0.05. Numbers at the beginning/end of the row or column represent the range of variation of the corresponding parameter, which units provided in Table 1.</figcaption></figure>

(MAAT) and lake surface temperature (LST) across all six crater lakes included in this study (p-value < 0.001, Fig. 4). Similarly, conductivity and pH show a statistically significant positive correlation (p-value < 0.01, Fig. 4). We did not find any other statistically significant

correlations among the climate parameters.

MBT'_{5Me} shows a positive correlation with both MAAT and LST, both of similar magnitudes and with respective p-values below 0.01 and 0.001. We also found a significant association between conductivity and

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Comparison of temperature indices from core-top lake sediments. A. MBT'_{5Me}, B. stepwise forward selection (SFS) calibration after Loomis et al. (2012) (Eq. (8)), R² = 0.94; RMSE = 1.9 °C, RMSEP = 2.1 °C), and C. Multivariate linear regression (MLR) of individual brGDGT after Zhao et al. (2023) (Eq. (9)), RMSE = 2.13 °C, Adj. R² = 0.94). Eq. (10) corresponds to the calibration of Zhao et al. (2023) and Eq. (11) to the equation obtained by pooling the datapoints from all 11 Cameroon lakes included in our study. MAAT = 22.77 – 33.58\*III – 12.88\*II – 418.53\*IIc + 86.43\*Ib (8). MAAT = –4.11 + 31.63\*Ia + 64.50\*Ib + 32.28\*Ila' (9). MAAT = –1.78 + 31.01\*MBT'_{5Me} (10). MAAT = –1.90(± 0.52) + 32.5(± 0.9)\*MBT'_{5Me}, R² = 0.92, RMSE = 2.46; n = 116 (11). The Cameroon data is shown as gray circles (this study), data from East African lakes in dark blue (Martínez-Sosa et al., 2021), and data from the Bale Mountains in light blue (Bittner et al., 2022a). Red triangles correspond to Lake Mbalang core-top values with closed (open) symbols corresponding to the calculation with separated (combined) 5 and 6 methyl isomers. The dashed line in each panel represents the linear regression. Dark gray shading shows the 95% confidence interval and light gray the 95% prediction interval. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
CBT' and also between conductivity and CBT<sub>5Me</sub>, as well as minor associations between the pairs pH/CBT', pH/CBT<sub>5Me</sub> and pH/IR<sub>6Me</sub> (Fig. 4).

For comparison purpose, we performed a similar statistical analysis on the combined surface sediments and water column samples from all eleven lakes, which yielded similar results (Fig. S3).

## 4 Discussion

### 4.1 Validation of brGDGT mean annual air temperature proxies in Cameroon crater lakes

The limited sample size (n = 11 lakes) and observed temperature range (from 16 to 27 °C, Table 1) prevent us from developing a new brGDGT/temperature calibration equation specific to West Africa; however, they allow us to evaluate the reliability of various brGDGT-based temperature proxies in the context of Cameroon crater lakes. Our dataset showed a strong positive correlation between MAAT and LST (Fig. 4), which supports the use of MAAT for correlating with the brGDGT indices, as noted in existing literature.

The observed MAAT/MBT<sub>5Me</sub> relationships are consistent with previously published values from the low latitude lacustrine database (Bittner et al., 2022a; Zhao et al., 2023) (Fig. 5A). In fact, the MAAT/MBT<sub>5Me</sub> relationship we obtained by pooling the 11 datapoints of the modern lake dataset is identical to that of Zhao et al. (2023) (Eqs. (11) and (10), grey and blue symbols in Fig. 5A). All polled datapoints fall within the 95% prediction interval for the MAAT/MBT<sub>5Me</sub> regression curves (Fig. 5A). The inclusion of the new West African lakes affects the high-temperature end of the calibration and may introduce minor differences (Fig. 5A). We therefore applied a slightly modified version (Eq. (11)) of the Zhao et al. (2023) equation (Eq. (10)) to the Mbalang record.

To compare the temperature reconstructions from Mbalang with previous studies, we analyzed data from Bittner et al. (2022a), Zhao et al. (2023), and our new dataset using the stepwise forward selection (SFS) method from Loomis et al. (2012). This method was developed before the optimization of chromatographic methods enabled the separation of the 5- and 6-methyl isomers of brGDGTs (Loomis et al., 2012) (Fig. 5B). Additionally, a comparable statistical technique based on optimal subset selection was recently proposed, which employs multivariate linear regression (MLR) of individual brGDGT abundances on

MAAT and includes all brGDGTs (Zhao et al., 2023). This calibration was specifically tested for low-latitude lakes (Zhao et al., 2023), and we evaluated its performance on the modern surface samples from the Cameroon lakes (Fig. 5C). Both the top sediment in the downcore record and modern lacustrine samples fall within the 95% prediction interval for the MAAT/SFS MAAT and MAAT/MLR MAAT regression curves. The few outliers correspond to surface samples. The higher temperatures, although included in the confidence interval, plot slightly below the 1:1 MAAT/SFS MAAT line (Fig. 5B and C). Part of the MLR calibration dispersion could be due to the inclusion of the 6-methyl isomer brGDGTs and their possible link to pH (De Jonge et al., 2014; Russell et al., 2018; Raberg et al., 2021).

Recent studies have proposed two techniques for temperature reconstructions from brGDGTs in lakes: a Bayesian technique (Martínez-Sosa et al., 2021) and one that groups brGDGTs by isomer, cyclization, or methylation (Raberg et al., 2021). While both approaches seem to work well on mid-to high latitude lakes, they were found to produce biased temperature estimates in tropical regions as both approaches were developed using data from sites with strong seasonal variations in temperature (Zhao et al., 2023). Given the low altitudes and latitudes of our sites in Cameroon, we chose not to discuss these alternative approaches here but focus on calibrations specifically developed for tropical lakes (Fig. 5).

### 4.2. Mean annual air temperature reconstructions at Mbalang

We evaluated the performance of temperature proxies and their associated calibration equations for the Mbalang core-top (Fig. 5). While GDGT values remain within the 95% prediction interval, the type of calibration used has a significant impact on the reconstructed values. Given the uncertainties in intercept and slope estimations, the brGDGT calibrations based on MBT<sub>5Me</sub> (Eq. (11)) yield values that are consistent with present day mean annual temperatures at Mbalang (Fig. 6). SFS and MLR reconstructions yielded respective shifts of 5 °C towards higher and 2 °C towards lower temperatures are obtained using the, respectively. However, all three brGDGT-based reconstructions consistently indicate a steady temperature decrease over the past 7000 years (Fig. 6). For the MBT<sub>5Me</sub> reconstruction, the amplitude of this decrease is about 2.5 °C, whereas MLR and SFS yielded respective decreases of 4 °C and 5 °C

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Comparing brGDGT-based temperature reconstructions over time from the Mbalang sediment sequence using MBT<sub>5Me</sub> (Eq. (11), Fig. 5A), stepwise forward selection (SFS) (Eq. (8)) and multivariate linear regression (MLR) of individual brGDGT (Eq. (9)). The error envelope derived from the calibration and analytical errors of MBT<sub>5Me</sub> is represented by the shaded region surrounding the curve. Dashed lines indicate the analytical uncertainties of the SFS and MLR reconstructions derived from analytical uncertainties. The gray line represents the mean annual temperature of 22 °C at the Mbalang site.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Late Holocene paleoenvironmental reconstructions in Central West Africa. A. δD<sub>n-alkC31</sub> corrected for vegetation changes in Lake Barombi (Garcin et al., 2018), B. Simplified pollen data from Lake Mbalang (Vincens et al., 2010), C. CBT' values and D. brGDGT-based reconstruction of MAAT from the Mbalang sedimentary sequence. The error envelopes are shown as shaded areas and were estimated based on MBT<sub>5Me</sub> calibration and analytical errors. E. Amplitude of the mid-Holocene temperature anomaly for intertropical latitudes in African lakes based on brGDGT-based temperature proxies. Our data for the Mbalang lake is shown as a red triangle, results from previous studies are shown as filled (Bittner et al., 2022a; Garelick et al., 2022) and open circles (Loomis et al., 2017, 2015; Sinninghe Damsté et al., 2012). In each published record, temperature anomalies were calculated by subtracting the mean value of the three samples at the top of the sequence from those close to 6 ka cal BP. Error bars show the corresponding standard deviations. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

(Fig. 6). A distinction is that the MLR and SFS reconstructions also take into account 6-methyl brGDGT, particularly IIa' (Fig. 6). While we found no correlation is evident between the IIa' fractional abundance (in water column particulate matter and lake sediment samples) and pH—likely due to the limited range of pH values—a positive correlation is observed with conductivity (Fig. S4). Furthermore, the gradual increases in both brGDGT-methyl isomers and CBT' indicate an increase in pH of approximately 0.5 (Figs. S2 and 4) (De Jonge et al., 2014)). Consequently, these slightly larger temperature increases are likely attributable to temporal variations in 6-methyl isomers associated with conductivity and/or pH changes.

The second significant confounding factor for temperature reconstructions in lakes is the temporal variation of organic matter sources (De Jonge et al., 2014). Despite significant differences in vegetation cover across the Mbalang watershed (Fig. 7B) (Vincens et al., 2010), brGDGT patterns and BIT-index do not show any changes of comparable timing and magnitude (Figs. S3, 3, and 7C). This suggests that brGDGT contributions from erosion do not drastically change over time and does

not affect the temperature reconstructions. Therefore, and also considering the good agreement of our core-top temperature reconstruction with modern values, we favor the use of the equation based on MBT<sub>5Me</sub> equation (Eq. (11)) for the Mbalang record and we report a temperature decline of 2.5 °C over the past 7000 years.

### 4.3. Lake chemistry changes from the Mbalang record

The sedimentary record from Mbalang shows that 6-methyl isomers can be more abundant than their 5-methyl counterparts (Figs. 2C-D and S3B-G). Furthermore, 6-methyl isomer abundances exhibit a steady increase over time (Fig. 3), likely due to paleoenvironmental changes, although the exact causes remain elusive. Previous research demonstrated that 6-methyl isomers are abundant in lakes and linked to water column oxygenation (Weber et al., 2015). Similarly, pH or alkalinity levels were shown to influence 6-methyl isomers in soils (De Jonge et al., 2021) and lakes (Russell et al., 2018; Raberg et al., 2021). A sharp increase in II'a+IIIa' compounds was observed in lake Challa (Kenya/
Tanzania) at the onset of stratification (van Bree et al., 2020) or fluctuations related to temperature (Wu et al., 2021).

Throughout the crater lakes in Cameroon, CBT' values exhibited a significant increase with increasing conductivity in surface waters and, to a lesser extent, with pH (Fig. 4). This even holds true when all 11 lakes and their contrasted settings are considered (Fig. S2). Previous studies reported only a weak correlation between CBT and East African lake pH, despite relatively large pH ranges (Russell et al., 2018). Surprisingly, the isomer ratio (IR<sub>6ME</sub>, Eq. (5)), commonly used to evaluate the relative abundances of 5- and 6-methyl isomers (De Jonge et al., 2014; Yang et al., 2015), shows no correlation with pH and only a weak correlation with conductivity. This finding contrasts with previous reports of a strong correlation between IR<sub>6ME</sub> and pH reported in soils (De Jonge et al., 2014) and a more recent mention of this relationship in lake environments (Raberg et al., 2021). This inconsistency in the IR<sub>6ME</sub> response to pH might be due to the limited range of pH values in our dataset and/or a reduced sensitivity of this index to lake pH (Table 1, Fig. 5).

The increasing CBT' values over time in the Mbalang sequence suggest a possible increase in conductivity or in pH over the past 7000 years, especially since the BIT-index (see above) does not indicate any simultaneous changes in organic matter source, which could have also caused the observed increase in CBT' (Fig. 3B). Furthermore, in non-anthropized lacustrine systems, conductivity, water salinity, or their proxy, chloride concentrations, are directly linked to the lake's water balance (Langbein, 1961). For lakes with small watersheds, like lake Mbalang, the water budget is often expressed by the evaporation/inflow ratio (E/I), which can be reduced to rainfall. An increase in this ratio is associated with an increase in precipitation or a decrease in evaporation (Branchu and Bergonzini, 2004; Bessems et al., 2008; Russell et al., 2009; Bouchez et al., 2016; Abba et al., 2023). Based on the relationships between brGDGT-indices and environmental parameters in the Cameroon lakes (Fig. 4), an increase in CBT' in the Mbalang record is therefore likely to correspond to a slight increase in conductivity over the past 7000 years. The decrease in precipitation, following the insolation, as evidenced in the leaf wax δD Barombi Mbo record (Garcin et al., 2018) could elucidate the observed changes in conductivity and pH in Mbalang Lake.

## 4.4 Late Holocene temperature and precipitation reconstructions in Mbalang Lake

Continental air temperature reconstructions for Africa, based on GDGTs, are relatively scarce and typically limited to Eastern Africa (Fig. 1). The absence of a thermal maximum during the Holocene, based on a reconstruction using brGDGTs from a marine core on the Congo Fan (Weijers et al., 2007a), could be related to the sheer size of the continental watershed of several million km², spanning a range of latitudes and longitudes as well as altitudes up to 2000 m.a.s.l., which may average out regional temperature differences. Temperature reconstructions from East African lakes based on isoGDGTs show a temperature maximum around 5–6 ka cal BP with a lower amplitude of about 1–2 °C (Powers et al., 2005; Tierney et al., 2008; Berke et al., 2012a, 2012b). In contrast, studies based on brGDGTs that do not discriminate between 5 and 6-methyl forms show a very different picture with Mid-Holocene temperature anomalies of 1–7 °C (Sinninghe Damsté et al., 2012; Loomis et al., 2015, 2017). Our results from the Cameroon lakes and down-core Mbalang Lake suggest that larger temperature anomalies could be partly attributed to changes in lake water conductivity or pH, i.e., higher-amplitude temperature anomalies for the SFS reconstruction and lower-amplitude anomalies with MBT'<sub>5Me</sub>-based reconstructions (Fig. 6). Altitude and the tropical lapse rate cannot account for the observed differences in temperature anomalies. For instance, Lake Tana, although at an intermediate altitude of 1830 m.a.s.l., has a temperature anomaly of ΔT = 7 °C, which greatly exceeds those by both lower and higher altitude lakes such as Lake Challa (880 m.a.s.l.,

ΔT = 2 °C) and Lake Rutundu (3081 m.a.s.l., ΔT = 1 °C) (Fig. 7E). Future research should investigate whether these differences could be related to differences in latitudes and/or longitudes. Recent studies that used a similar analytical approach (Garelick et al., 2022) or local calibration (Bittner et al., 2022a) yielded mid-Holocene temperature gradients of similar amplitudes for East Africa (Fig. 7E), in agreement with other reconstructions based on pollen records from similar latitudinal ranges (Bartlein et al., 2011). The 2.5 °C mid-Holocene temperature anomaly recorded in Mbalang, the only site in Central Africa, suggests that latitude does not play a significant role in temperature anomalies.

Model simulations, conducted as part of the Paleoclimate Modeling Intercomparison Projects, are known to underestimate observation-based reconstructions of mid-Holocene temperatures (Bader et al., 2020; Brierley et al., 2020; Liu et al., 2014). A negative mid-Holocene temperature anomaly of −0.82 ± 0.23 °C has been predicted for latitudes ranging from 30°N to the Equator (Brierley et al., 2020). However, recent simulations that account for changes in vegetation (Thompson et al., 2022) or dust (Pausata et al., 2016) or that consider feedbacks between soil, vegetation and lakes (Chandan and Peltier, 2020) produced estimates more similar to those obtained based on proxy-based reconstructions. Similar corrections have not been implemented in model simulations of low-latitude environments that experience more narrow temperature ranges. As a result, these models still underestimate mid-Holocene temperatures in tropical regions (Brierley et al., 2020), although to a lesser degree if vegetation changes are taken into consideration (Thompson et al., 2022). Mid-Holocene temperature anomalies in the region from 30 °N to the Equator range from −0.94 °C (−1.21 °C to −0.65 °C) to 0.24 °C (−0.04 °C to 0.48 °C), without and with considering vegetation changes, respectively (Thompson et al., 2022). These findings are confirmed by a recent study which suggests that the mid-Holocene colder conditions simulated by the PMIP3 and PMIP4 models for low latitudes in Africa could be partly attributed to uncertainties in feedback mechanisms affecting precipitation, shifts in the monsoon regime in the Sahel, as well as changes in vegetation (Marshall et al., 2024).

## 5 Conclusions

In this study, we compared GDGT distributions in soil, freshwater, and surface sediment samples collected from different lake basins across Cameroon with those obtained from global and East African datasets. We investigated whether branched GDGTs could serve as proxy for air temperature. This resulted in the first mid- to late-Holocene air temperature reconstruction for Central Africa. Our results confirm that 6-methyl forms are strongly controlled by water conductivity and pH. Furthermore, the brGDGT record suggests that temperatures have been steadily decreasing by 2.5 ± 1 °C over the past 7000 years, consistent with records from East African lakes.

Low latitudes tend to be underrepresented in global continental temperature reconstructions (Kaufman et al., 2020; Cartapanis et al., 2022; Erb et al., 2022). Due to this lack of available records (with the exception of East Africa and precipitation), it had been impossible until now to produce a geographic pattern of temperature changes during the Holocene for this region. Our study has highlighted how GDGT-based records can be used for reconstructions in these data-poor areas, providing valuable information on the “missing latitudinal band” in global reconstructions. In light of recent advances in GDGT-thermometry, existing records must be re-examined using optimal chromatographic procedures and comprehensive comparisons of isoGDGTs and brGDGTs-proxies.

## CRediT authorship contribution statement

**Guillemette Ménot:** Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Salomé**
**Ansanay-Alex:** Writing – original draft, Methodology, Formal analysis, Data curation. **Valérie F. Schwab:** Writing – original draft, Data curation. **Gilbert Todou:** Writing – original draft, Data curation. **Olivier Séné:** Writing – original draft, Data curation. **Jean-Michel Onana:** Writing – original draft, Data curation. **Gerd Gleixner:** Writing – original draft, Data curation. **Dirk Sachse:** Writing – original draft, Data curation. **Yannick Garcin:** Writing – original draft, Methodology, Data curation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

GM received funding from the ANR project TAPIOCA (ANR-18-CE01-0005). We are grateful to Annie Vincens for providing samples from the M4 Mbalang core. Soil, freshwater, and surface sediment samples were collected as part of the German Science Foundation (DFG) project number 160487544, awarded to YG. We gratefully acknowledge P. Deschamps for insightful discussions regarding conductivity and hydrological budgets in tropical lakes. We thank the journal editor and two anonymous reviewers for their helpful reviews and comments.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.orggeochem.2025.104982.

Data availability

Data will be made available on request.

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