# From shape to source: sedimentary charcoal morphology as a proxy for tropical burned biomass composition

Fiona Cornet, Vincent Montade, Stéphanie C Bodin, Justine Descloitre, Maxime Réjou-Méchain, Gaëlle Viennois, Julie C Aleman, Chimène Assi-Kaudjhis, William F Kenney, Richard Oslisly, et al.

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Fiona Cornet, Vincent Montade, Stéphanie C Bodin, Justine Descloitre, Maxime Réjou-Méchain, et al. From shape to source: sedimentary charcoal morphology as a proxy for tropical burned biomass composition. Journal of Quaternary Science, In press, 10.1002/jqs.70053. hal-05525345

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# From shape to source: sedimentary charcoal morphology as a proxy for tropical burned biomass composition

FIONA CORNET,¹* VINCENT MONTADE,¹ STÉPHANIE C. BODIN,² JUSTINE DESCLOITRE,³ MAXIME RÉJOU-MÉCHAIN,³ GAËLLE VIENNOIS,³ JULIE C. ALEMAN,⁴ CHIMÈNE ASSI-KAUDJHIS,⁵ WILLIAM F. KENNEY,⁶ RICHARD OSLISLY,⁷,⁸ MONIQUE G. TOSSOU,⁹ LAURENT BREMOND¹,¹⁰† and CHARLY FAVIER¹†

¹ISEM, Univ Montpellier, CNRS, IRD, EPHE, Montpellier, France

²IMBE, Aix Marseille Univ, Avignon Univ, CNRS, IRD, Aix-en-Provence, France

³AMAP, Univ Montpellier, CNRS, CIRAD, INRAE, IRD, Montpellier, France

⁴Aix Marseille Univ, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence, France

⁵Laboratory of Natural Environments and Biodiversity Conservation, Félix Houphouët-Boigny University, Abidjan, Ivory Coast

⁶Land Use and Environmental Change Institute, University of Florida, Gainesville, FL, USA

⁷PALOC, IRD, MNHN, CNRS, Paris, France

⁸Cellule Scientifique, Agence Nationale des Parcs Nationaux ANPN, Libreville, Gabon

⁹Laboratoire des Sciences du Végétal et Pharmacopée (LASVEP), Université d'Abomey-Calavi (UAC), Godomey, Bénin

¹⁰Ecole Pratique des Hautes Etudes (EPHE), PSL University, Paris, France

Received 7 November 2025; Revised 15 January 2026; Accepted 29 January 2026

**ABSTRACT:** Sedimentary charcoal elongation is increasingly being used in paleoecology to distinguish herbaceous from woody fuel in past fires. However, the relationship between charcoal morphotypes and plant types has never been formally tested in tropical environments, despite its potential to improve understanding of fire regimes and deforestation, and to analyze long-term ecosystem resilience. Separation between herbaceous and woody charcoal commonly relies on a fixed elongation threshold, but this arbitrary division overlooks the morphological continuum of charcoal and may bias interpretations. To address this, we developed a Bayesian model to classify charcoal fragments into herbaceous and woody subpopulations based on their elongation distributions. We applied it to 19 sediment charcoal records spanning the last 30 years across a broad geographic and vegetation gradient in West and Central Africa. Compared with contemporary vegetation cover derived from remote sensing, the estimated proportion of woody charcoal was significantly positively correlated with tree cover, validating our approach and confirming charcoal elongation as a reliable proxy for past fire-related biomass sources. Finally, we applied the model to a paleosequence from Lake Sélé (Benin) to reconstruct temporal changes in biomass and compared results with fossil pollen data, further assessing the proxy relevance for reconstructing past fire regimes and advancing tropical paleoecology. © 2026 The Author(s). *Journal of Quaternary Science* Published by John Wiley & Sons Ltd.

**KEYWORDS:** Bayesian model; charcoal elongation ratio; forest-savanna mosaic; paleoecology; West and Central Africa

## Introduction

Savannas, spanning from 25° N to 25° S, cover almost half of the African continent and represent one of the world's largest tropical biomes (Lehmann et al., 2011). These ecosystems often form a structurally heterogeneous mosaic landscape with tropical forests, creating a broad continuum from open grasslands to closed-canopy forests, shaped by interactions between climate, fire, and herbivory (Scholes and Hall, 1996; Bond, 2008). In Central and West Africa, fire dynamics vary according to ecosystems. In savannas, fuel consists primarily of dry grass, which is highly flammable and promotes frequent surface fires. The effects of fire on savanna trees depend largely on their stature: resprouts and young trees within the flame zone are the most vulnerable (Bond and Keeley, 2005), whereas taller trees are protected by their bark and generally experience little or no damage (Bond, 2008; Hoffmann et al., 2020). In contrast,

tropical forest fires typically involve leaf litter, fallen branches, and sometimes understory shrubs, leading to less frequent but often more intense fires (Poilecot and Loua, 2009; Aleman et al., 2017). Fires are rare in Central African tropical rainforests, and when they do occur, they are usually related to human activities and do not spread spontaneously (Goldammer, 2022; Wimberly et al., 2024). Such events are most often linked to human activities such as land clearing or slash-and-burn agriculture (Woods, 1989; Cochrane, 2003).

During a fire, vegetation undergoes incomplete combustion, resulting in the formation of charcoal fragments that can accumulate over time in favorable depositional environments such as lakes or peatlands. These accumulations ultimately form sedimentary charcoal records that are archives of past fire regimes. Such archives have revealed that, although fire has long shaped African vegetation, its frequency and intensity have increasingly been influenced by rising anthropogenic pressures, including agriculture and deforestation (Schulte to Bühne et al., 2023; Bremond et al., 2024). Reconstruction of past fire regimes, particularly their frequencies, intensities, fuel, and spatial extents, is essential for understanding

<footnote>*Correspondence: Fiona Cornet, as above.</footnote>

<footnote>Email: fiona.cornet@umontpellier.fr</footnote>

<footnote>†Laurent Bremond and Charly Favier are co-last authors.</footnote>
long-term ecosystem trajectories (Moritz et al., 2014). Because fire regimes respond to both climatic and anthropogenic changes, sedimentary charcoal records offer valuable insights into environmental changes and human impacts, and provide benchmarks for sustainable landscape management and risk prevention (Conedera et al., 2009).

Traditionally, reconstructions of fire regimes from sedimentary charcoal rely on charcoal particle concentrations or accumulation rates as proxies for fire frequency and burned biomass (Whitlock and Larsen, 2001; Power et al., 2008). This approach has been successfully applied in ecosystems where fires are relatively infrequent, but it remains much less explored in tropical environments, where fire–charcoal calibrations are still scarce. Moreover, these metrics offer limited information about the nature of the burned fuel. In tropical ecosystems, determination of the share of herbaceous and woody fuels is critical, as it indicates whether fires were linked to deforestation or significantly affected the woody component of savanna ecosystems. To address this, several studies used charcoal morphoanatomy to infer the specific fuel type represented by each particle (Enache and Cumming, 2006; Courtney Mustaphi and Pisaric, 2014; Rehn et al., 2019; Frank-DePue et al., 2023). This method involves examining the distinct characteristics of individual charcoal fragments, using progressively developed reference atlases derived from experimental work. This method provides information on specific fuel categories (e.g., grass, conifer needles, and broadleaves), but it is time-consuming. A complementary method, based on particle elongation, has therefore been explored to differentiate between woody and herbaceous sources (Enache and Cumming, 2006; Jensen et al., 2007; Aleman et al., 2013; Crawford and Belcher, 2014; Levs et al., 2017). Elongation is typically measured by the length-to-width or the width-to-length ratio (WL ratio, used in this study). Experimental and field data suggest that herbaceous plants tend to produce more elongated charcoal particles than woody tissues (Umbanhowar and McGrath, 1998; Crawford and Belcher, 2014; Hu et al., 2020; Vachula et al., 2021, 2024; Feurdean et al., 2023). This pattern is attributed to differences in lignification. Herbaceous plants, with their fibrous, weakly lignified structures, tend to fragment into long and thin particles during combustion (Feurdean, 2021). In contrast, lignified tissues form a dense and compact structure that generally burns as a block, and the wood cells create a three-dimensional tissue that is more resistant to fragmentation, resulting in the formation of compact charcoals (Komarek et al., 1973; Vaughan and Nichols, 1995).

While promising for distinguishing between woody and herbaceous fuel sources, the applications of the WL ratio approach to sedimentary records have remained limited by methodological inconsistencies, such as the reliance on mean values (Hu et al., 2020) and thresholds to quantify fuel types (Leys et al., 2015; Aleman et al., 2019; Ndiaye et al., 2022; Daniau et al., 2023). However, the mean WL ratio does not accurately reflect the diversity and proportion of fuels that can feed a fire. Similarly, use of an arbitrary threshold to separate charcoal fragments derived from herbaceous fuel (herbaceous charcoal) from those derived from woody fuels (woody charcoal) fails to consider the variability observed, as some woody plants produce elongated charcoal fragments and some herbaceous plants produce compact charcoal fragments (Feurdean et al., 2023). Addressing these limitations requires robust, field-based measurements that calibrate charcoal elongation in sedimentary deposits against high-resolution data on the actual proportions of woody and herbaceous vegetation, including tree cover (TC), in surrounding landscapes.

In this context, our study aims to refine the use of charcoal elongation as a proxy for distinguishing between herbaceous and woody fuel sources in mixed sedimentary charcoal assemblages from tropical ecosystems. Our approach goes beyond simple metrics by leveraging the entire distribution of charcoal WL ratio values, thereby capturing more nuanced signals of fuel type within samples. To validate this approach, we counted and measured charcoal particles from recent sediments collected in natural lakes, reservoirs, and ponds distributed across a gradient of vegetation types in West and Central Africa, and we compared charcoal WL ratio distributions to satellite-derived land use/land cover (LULC) data. We first identified samples from locations where the vegetation, and thus the burned fuel, was predominantly herbaceous or woody. By comparing elongation distributions in these well-characterized contexts, we assessed how the WL ratio effectively discriminates between fuel types. In a second step, we developed a method to separate two subpopulations of charcoal particles within mixed assemblages: one characterized by a distribution with an elongated mode, supposedly indicative of herbaceous charcoal, and another with a compact mode, supposedly indicative of woody charcoal. We then estimated the proportion of compact-mode distributed particles in each sample and assessed how this estimation correlates with TC values derived from remote sensing data to validate them as woody charcoal. Finally, we applied this method to a sediment core from West Africa to reconstruct past changes in fuel composition and vegetation structure over the last 3000 years. Through this new calibration, we address the following questions: How does sedimentary charcoal particle elongation vary across different fuel sources? How accurately does the WL ratio quantify the relative contributions of woody versus herbaceous fuels?

## Materials and methods

### Environmental settings and study sites

Sediment cores were collected between 2010 and 2019 from 19 natural and artificial lakes located in savanna environments across five different West and Central African countries: Ivory Coast, Benin, the Central African Republic (hereafter CAR), Gabon, and the Republic of Congo (hereafter Congo) (Fig. 1). The objective was to extract the uppermost centimeters of lacustrine sediment. Two different coring systems were used: a *Kajak–Brinkhurst corer* for lakes in Gabon, Congo, CAR, and Benin, and a *Uwitec Gravity corer* for those in Ivory Coast.

The study sites span a broad latitudinal and climatic gradient and encompass diverse land-use and vegetation types. Climatic conditions in these regions are tropical, with a unimodal rainfall regime, except in Gabon, where the rainfall regime is bimodal. Mean annual temperatures and precipitation vary from 20°C to 35°C and from 900 mm/a to 2500 mm/a (Fick and Hijmans, 2017). According to the Köppen–Geiger classification, these correspond to the *Aw* (tropical monsoon climate) and *Aw* (tropical savanna climate) types (Geiger, 1954, 1961; Kottek et al., 2006).

The study sites also show a wide disparity in land use (Table 1), ranging from areas with minimal human impact to landscapes with natural vegetation interspersed with small-scale cultivation, and to regions that have undergone intensive agricultural conversion, reflecting the continent's environmental diversity (Masolele et al., 2024). The lakes sampled in Benin and Ivory Coast are mainly surrounded by savannas, wooded savanna (see FBA, Fig. 1), or forest–savanna mosaics, heavily impacted by human activities in recent decades (see GRO, Fig. 1). The lake
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Location of the study sites in West and Central Africa, with regional vegetation based on White (1984). The satellite images and photos of five sites, representing the variety of vegetation types and the degree of agricultural conversion, are framed in different colors to make them easier to find throughout this article. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

sampled in Congo is in a forest-savanna mosaic (see NGO, Fig. 1). In CAR, one lake (GBL) is situated in a savanna and surrounded by a riparian forest. In contrast, others are in forests that have undergone deforestation (see DKL, Fig. 1). All lakes sampled in Gabon are in a mosaic of tropical humid forest and herbaceous (see ELI, Fig. 1) or shrub savanna, with one site (DINA) surrounded by forest and the others by savanna.

## Age-depth models

Chronologies for 12 of the sediment cores were established using $^{210}$Pb dating, supplemented in a few cases with $^{14}$C dating (Table 1). The natural and radioactive isotope $^{210}$Pb (half-life 22.3 years) is produced in the atmosphere and has a strong affinity with aerosols, enabling its deposition into lakes (Appleby and Oldfieldz, 1983; MacKenzie et al., 2011; Le Roux, 2013; Swarzenski, 2014; Arias-Ortiz et al., 2018; Abril, 2020). Sediments

were sampled at different but regular core depths and analyzed for $^{210}$Pb dating activity using gamma spectrometry. Age-depth models were developed using the *Rplum* package (Aquino-López et al., 2018) within the *R* environment (R Core Team, 2024). Because samples for $^{210}$Pb measurements were not always contiguous along cores, the Bayesian model Plum allowed more flexibility and fewer hypotheses to estimate age models.

For the seven remaining cores (Table 1), where $^{210}$Pb measurements were unavailable, chronologies were inferred assuming a constant dry mass deposition rate, anchored either by a single radiocarbon date or by the known date of reservoir creation. Due to the absence of chronological control in the FBA pond core, only the uppermost 3 cm were analyzed in this study.

To allow comparison between our data and satellite imagery, we focused on the uppermost section of the sediment cores, corresponding to the past 30 years of sediment deposition. This interval extends from the surface down to a
<table><caption>**Table 1.** Characterization of the different study sites and their dating method.</caption><thead><tr><th>Site</th><th>ID</th><th>Country</th><th>Site type</th><th>Coordinates</th><th>Regional vegetation</th><th>Surrounding vegetation</th><th>Age model origin</th></tr></thead><tbody><tr><td>Guinagourou Reservoir</td><td>GRO</td><td>Benin</td><td>Artificial (1997)</td><td>9°35'10.824" N 2°55' 34.788" E</td><td>Sudanian woodland</td><td>Crops, wooded savanna</td><td>Assumption of constant accumulation of dry matter Top samples</td></tr><tr><td>FBA pond</td><td>FBA</td><td>Benin</td><td>Natural</td><td>9°13'14.326" N 2°15' 26.978" E</td><td>Mosaic of lowland rain forest secondary grasslands (Guineo- Congolian)</td><td>Wooded savannas</td><td><sup>210</sup>Pb</td></tr><tr><td>Lake Azili</td><td>AZL</td><td>Benin</td><td>Natural</td><td>7°15'58.31" N 2°27' 32.73" E</td><td></td><td>Riparian forest, shrub savanna, and crops Crops</td><td><sup>14</sup>C and assumption of constant accumulation of dry matter</td></tr><tr><td>Lake Sélé</td><td>SLE</td><td>Benin</td><td>Natural</td><td>7°9'11.25" N 2°26' 27.11" E</td><td></td><td>Degraded forest, crops, shrub savanna</td><td><sup>210</sup>Pb</td></tr><tr><td>Lake Djeto</td><td>DTO</td><td>Benin</td><td>Natural</td><td>6°36'54.05" N 1°43' 11.98" E</td><td>Sudanian woodland</td><td>Wooded savanna, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Nindio Reservoir</td><td>NIN</td><td>Ivory Coast</td><td>Artificial (1974)</td><td>9°31'46.149" N 5°39' 36.918" W</td><td></td><td>Wooded savanna, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Lofa Reservoir</td><td>NAP</td><td>Ivory Coast</td><td>Artificial (1973)</td><td>9°16'15.333" N 5°35' 14.687" W</td><td>Mosaic of lowland rain forest secondary grasslands (Guineo- Congolian)</td><td>Wooded savanna, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Niakara Reservoir</td><td>NIAK</td><td>Ivory Coast</td><td>Artificial (1954)</td><td>8°38'39.131" N 5°15' 27.533" W</td><td></td><td>Wooded savanna, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Nondogo Reservoir</td><td>NON</td><td>Ivory Coast</td><td>Artificial (1970)</td><td>8°11'35.801" N 4°24' 1.184" W</td><td></td><td>Wooded savanna, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Bocanda Pond</td><td>BOC</td><td>Ivory Coast</td><td>Natural</td><td>7°5'21.793" N 4°31' 2.485" W</td><td></td><td>Wooded savanna, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Lake Gbali</td><td>GBL</td><td>CAR</td><td>Natural</td><td>4°49'6.571" N 18°15' 47.581" E</td><td>Guineo-Congolian rain forest: drier types</td><td>Riparian forest, wooded savanna</td><td><sup>210</sup>Pb and <sup>14</sup>C</td></tr><tr><td>Lake Doukoulou</td><td>DKL</td><td>CAR</td><td>Natural</td><td>4°15'4.158" N 18°25' 27.026" E</td><td></td><td>Degraded rainforest, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Lake Nguengue</td><td>NGG</td><td>CAR</td><td>Natural</td><td>3°46'4.8" N 18°7' 17.31" E</td><td>Guineo-Congolian rain forest: drier types</td><td>Rainforest, crops</td><td><sup>210</sup>Pb</td></tr><tr><td>Lopé Reservoir [Lopé National Parc]</td><td>R1</td><td>Gabon</td><td>Artificiel (1987)</td><td>0°6'36.09" S 11°35' 49.585" E</td><td>Mosaic of lowland rain forest secondary grasslands (Guineo- Congolian)</td><td>Shrub savanna</td><td>Assumption of constant accumulation of dry matter</td></tr><tr><td>Lake Dina [Wonga Wongué Presidential Réserve]</td><td>DINA</td><td>Gabon</td><td>Natural</td><td>0°24'6.444" S 9°23' 25.62" E</td><td></td><td>Rainforest, grass savanna</td><td><sup>14</sup>C and assumption of constant accumulation of dry matter</td></tr><tr><td>Lake Ndaminze [Wonga Wongué Presidential Réserve]</td><td>NDA</td><td>Gabon</td><td>Natural</td><td>0°25'25.752" S 9°32' 39.12" E</td><td></td><td>Grass savanna, rainforest</td><td>Assumption of constant accumulation of dry matter</td></tr><tr><td>Lake Eseb [Wonga Wongué Presidential Réserve]</td><td>ESB</td><td>Gabon</td><td>Natural</td><td>0°30'37.584" S 9°33' 38.16" E</td><td></td><td>Grass savanna, rainforest</td><td><sup>14</sup>C and assumption of constant accumulation of dry matter</td></tr><tr><td>Lake Eliwawanyé [Wonga Wongué Presidential Réserve]</td><td>ELI</td><td>Gabon</td><td>Natural</td><td>0°31'57.972" S 9°28' 14.232" E</td><td></td><td>Grass savanna, rainforest</td><td><sup>210</sup>Pb</td></tr><tr><td>Lake Ngofouo</td><td>NGO</td><td>Republic of Congo</td><td>Natural</td><td>3°47'44" S 14°48' 16.999" E</td><td></td><td>Shrub savanna, riparian forest, rainforest</td><td><sup>210</sup>Pb</td></tr></tbody></table>

Regional vegetation is based on White (1984) and the surrounding vegetation from Diamouangana (2003), Maloba Mankanga (2011), Favier et al. (2012), and Aleman et al. (2013). The indications in brackets indicate whether the sites are part of protected areas.
depth of 56 cm. The DKL sequence was subdivided to distinguish the effects of a documented deforestation episode (Aleman et al., 2013): DKL designates the section deposited after the episode, whereas DKL(b) corresponds to the section contemporaneous with it.

## Sedimentary charcoal analysis

For charcoal analysis, cores were sampled contiguously with 1 cm³ of sediment collected from 1-cm-thick layers. Subsamples were soaked in a solution composed of potassium hydroxide (KOH), metaphosphate (NaPO₃), and sodium hypochlorite (NaClO) to deflocculate and bleach the sediment (Bamber, 1982). Samples were agitated for 24 h at 100 turns/min before being rinsed with water through a 160 µm mesh (Whitlock and Larsen, 2001; Carcaillet et al., 2001; Carcaillet, 2007). The selection of charcoal from these class sizes allows the recording of signals produced by local fires, within a few kilometers (Higuera et al., 2007; Duffin et al., 2008; Aleman et al., 2013). It also helps to reduce certain taphonomic biases, as previous studies have shown that small charcoal particles (i.e., <125 µm) tend to fragment further, creating a discrepancy with the elongation observed in macroscopic particles (Miao et al., 2020; Zhou et al., 2023; Inoue and Usuki, 2024). Each charcoal particle was examined under a stereomicroscope equipped with a camera and analyzed using *WinSEEDLE PRO 2019a* to obtain measurements of length, width, area, and perimeter. The WL ratio was subsequently calculated. Charcoal particle count and total number per sample were also recorded to quantify charcoal concentration (CharConc), as well as the charcoal influx (CharInflux).

## Model of charcoal elongation population analysis

### Normalization of the reference WL ratio distribution via Box-Cox logit transformation

Based on the hypothesis that herbaceous fuel produces primarily elongated charcoals, while woody fuel generates more compact or square-shaped charcoals (Enache and Cumming, 2006; Jensen et al., 2007; Aleman et al., 2013; Crawford and Belcher, 2014; Leys et al., 2017; Feurdean, 2021), the objective of the model was to separate charcoal particles from each site into two main groups (herbaceous vs. woody) based on their elongation (WL ratio). Because the WL ratio is bounded between 0 and 1 and its distribution is often skewed, a data transformation was required before fitting normal distributions. To model the distribution of charcoal elongation at each site as a mixture of two normal distributions (one representing mostly elongated charcoals from herbaceous fuel and the other representing mostly compact charcoals from woody fuels), we applied a Box-Cox logit transformation to the variable WL ratio (Guerrero and Johnson, 1982). The transformation consists of two steps:

* First, we computed the odds as:

$$ \frac{\text{WL ratio}}{1 - \text{WL ratio}} \qquad (1) $$

* Then, we applied a Box-Cox power transformation with parameter $\lambda$:

$$ y = \begin{cases} \frac{\left( \frac{\text{WL ratio}}{1 - \text{WL ratio}} \right)^{\lambda} - 1}{\lambda} & \text{if } \lambda \neq 0 \\ \log\left( \frac{\text{WL ratio}}{1 - \text{WL ratio}} \right) & \text{if } \lambda = 0 \end{cases} \qquad (2) $$

This Box-Cox logit transformation renders the variable unbounded and closer to normally distributed, allowing it to be modeled as a mixture of Gaussians. The variable $\lambda$ controls the shape of the transformation. When $\lambda = 0$, the transformation reduces to the standard logit; for other values of $\lambda$, the Box-Cox formulation allows flexibility to optimize the approximation to a Gaussian distribution. Notably, when the WL ratio is below 0.5, the transformed value $y$ is negative, and when it exceeds 0.5, $y$ becomes positive, providing an intuitive interpretation of elongation relative to this midpoint.

To determine the optimal value of $\lambda$, we used reference distributions derived from two known contexts: (i) charcoal assemblages from a grass savanna environment from DINA, ELI, ESEB, and NDA sediment cores; and (ii) a section DKL(b) of the DKL sediment core (11 cm to the bottom) corresponding to the 1990s–2000s deforestation phase for agricultural expansion (Aleman et al., 2013). A value $\lambda = -0.07$ (Fig. S1) was found to normalize both distributions effectively, but this can fluctuate and vary slightly depending on the site (from $\lambda = -0.34$ for NDA to $\lambda = -0.06$ for DKL(b)).

## Bayesian mixture modeling

We hypothesized that, in environments with mixed herbaceous and woody fuels, Box-Cox logit-transformed WL ratios can be separated into two Gaussian components: one associated with herbaceous charcoal and the other with woody charcoal. Formally, we consider that one $\lambda$ parameter of the Box-Cox logit transformation allows transformed data $y = (y_1, ..., y_N)$ to be modeled as arising from a mixture of two Gaussian distributions, with component-specific means ($\mu_H$ for herbaceous and $\mu_W$ for trees) and standard deviations ($\sigma_H$, $\sigma_W$) (Table 2). Letting $\theta_W$ represent the proportion of woody charcoal, the probability for each transformed variable $y_i$ is:

$$ p(y_i|\theta, \mu, \sigma) = (1 - \theta_W) \cdot \mathcal{N}(y_i|\mu_H, \sigma_H) + \theta_W \cdot \mathcal{N}(y_i|\mu_W, \sigma_W) \qquad (3) $$

with $\mathcal{N}(y_i|\mu, \sigma)$ being the probability density of observing $y_i$ under a Gaussian distribution with mean $\mu$ and standard deviation $\sigma$.

To prevent the two components from becoming too similar, and therefore unidentifiable, and to ensure that the model captures distinct distributions for herbaceous and woody charcoals, we applied the following constraints: $\mu_H < -0.4$ (corresponding to a median WL ratio < 0.4), $\mu_W > 0.2$ (corresponding to a median WL ratio > 0.550), and $0.4 < \sigma_H, \sigma_W < 1.1$. Traditional Gaussian mixture models methods, such as the expectation-maximization method, do not allow such constraints. Therefore, we implemented a Bayesian model in which we assigned the following priors:

<table><caption>**Table 2.** Description of the various symbols used in the model equations.</caption><thead><tr><th>Symbol</th><th>Statistical interpretation</th></tr></thead><tbody><tr><td>λ</td><td>Box-Cox logit transformation parameter</td></tr><tr><td>y<sub>i</sub></td><td>Box-Cox logit-transformed WL ratio of each charcoal particle</td></tr><tr><td>μ<sub>H</sub>μ<sub>W</sub></td><td>Means of herbaceous and woody Gaussian charcoal distributions</td></tr><tr><td>σ<sub>H</sub>, σ<sub>W</sub></td><td>Standard deviations of herbaceous and woody Gaussian charcoal distributions</td></tr><tr><td>θ<sub>W</sub></td><td>Woody charcoal proportion</td></tr></tbody></table>
- $\mu_H \sim \mathcal{N}(-1.7,0.3)$ and $\mu_W \sim \mathcal{N}(0.7,0.3)$, reflecting that the median for herbaceous and woody charcoal WL ratio is expected to be around 0.17 and 0.67, respectively.

- $\log \sigma_H$ and $\log \sigma_W \sim \mathcal{N}(2.0,0.7)$, reflecting typical standard deviation values of around 0.7.

- $\theta_W \sim \text{Dirichlet}(1,1)$, representing a uniform prior on the mixture weights.

- $\lambda \sim \mathcal{N}(-0.07,0.3)$, reflecting the range of optimal values observed in homogeneous fuel sites.

The model was implemented in the *Stan* language (v2.32.2; Stan Development Team, 2024) and interfaced with R via the *cmdstanr* package (Gabry et al., 2025).

Assuming that the means and standard deviations of herbaceous and woody charcoal distributions may vary between sites but remain constant over recent decades, we applied the Bayesian Gaussian mixture model to classify charcoals for each core top representing the last few decades (maximum 30 years).

## Remote sensing site characterization

To examine the relationship between the proportions of herbaceous and woody charcoal and environmental variables, we focused on a 5-km radius around each study site. Although herbaceous charcoal can be transported over longer distances by wind compared to woody charcoal (Vachula and Rehn, 2023), the relevant source area for mesoscale charcoal in grasslands and savannas typically extends only a few kilometers (Vachula, 2021). We also formulated the hypothesis of the existence of a first-order relationship between land cover and the morphology of plant charcoal, observed both in the past and in the present, unlike in temperate environments, where this relationship can be strongly modified by agricultural practices (crops and/or herbivore feeding) (Feurdean et al., 2025).

To characterize land cover, we used 1 C-level *Sentinel-2* satellite images (10 spectral bands harmonized to 10 m resolution through a pan-sharpening approach in Google Earth Engine, Table S2) targeting 6 years (2015–2021) during the dry season to identify cloud-free images. Training data were defined from 523 regions of interest (ROI) delineated on very high-resolution imagery (from 15 cm to 15 m) available on the Google Earth platform to distinguish vegetation types and land-use classes. Principal component analyses (PCA) were performed in each of the 19 sites on random samples of 20 000 pixels per image (R packages *raster* and *ade4*) to reduce spectral redundancy and project data into the first three axes (Fig. S3); the blue and green bands were excluded for sites in Congo and Gabon due to haze. Supervised classification was then conducted in *Google Earth Engine* using the *Random Forest algorithm*, and performance was assessed with confusion matrices and the Kappa coefficient using 75% of the observations for training and 25% for validation. Based on these analyses, 11 LULC categories were classified, covering natural vegetation (grass savannas, shrub savannas, wooded savannas, *terra firme* forests, inundated forest, and floodplain), agricultural land (fields and tree crops), and other land cover types (water, bare ground, and urban area) (Table S4). The surface area of each LULC class was computed for each site at different radii (1 km to 5 km) (Fig. S5). To assess the extent of fires in the study areas, we used the 10 m resolution Global Annual Burned Area Map (GABAM) within a 5 km radius around each study site, for the period 1985–2021 (Long et al., 2019; Qi et al., 2024). We computed the average frequency of fires for each vegetation class identified earlier

and derived the imprint of fire-susceptible areas by aggregating all available annual fire scars over 1985–2021.

High-resolution TC data were obtained from two sources: the 10 m resolution Global forest cover data set for the year 2020 (v2) provided by the European Commission Joint Research Centre (Bourgoin et al., 2025) and the 1 m TC data set for the year 2019 based on *PlanetScope imagery* from Reiner et al. (2023). We intersected both TC data sources with the land-use classification and the burned area data to compute the average TC for each vegetation class and for the fire imprint within five concentric radii around each site (from 1 km to 5 km in 1-km increments). This process integrated remote sensing data from different sources, each with distinct spatial resolution and potential geolocation mismatches. To reduce the influence of misalignments between remote sensing products and minimize noise associated with ecotones, a 20 m buffer mask was systematically applied to the boundaries of both the fire imprint and each vegetation class.

## Comparative analysis of charcoal elongation and tree cover around each site

For each sediment core, the proportion of charcoal identified as woody, that is, belonging to the distribution with the compact mode, was compared with satellite-derived TC data across multiple spatial subsets using linear regression (lm function in R), with the regression line forced through the origin. Although TC does not perfectly reflect fuel availability, it provides a useful approximation of biomass structure. We hypothesize that in open landscapes with low TC, grass would constitute the nearly exclusive fuel source, whereas the proportion of woody fuel would increase with higher TC.

We tested correlations with both TC remote sensing data sets to assess whether potential data set introduced bias and within radii of 1 km to 5 km around each site to evaluate whether fuel availability is better captured at specific spatial scales. TC estimates were averaged considering either savannas alone, savannas combined with agricultural land, and areas burned at least once between 1985 and 2024, weighted either equally or by the number of detected fires. To reduce collinearity, we first assessed correlations among TC estimates from these 40 combinations using hierarchical clustering (*hclust* function, *stats* package in R) and retained one representative variable from each cluster for further analysis.

## Application to the Lake Sélé paleosequence

### Model for a paleosequence

The reconstruction of woody charcoal over the paleosequence relies on the relationship between the proportion of compact charcoal $p > 0.5$ (i.e., particles with WL ratio > 0.5, corresponding to the Box–Cox logit-transformed variable $y > 0$), and the proportion of woody charcoal $\theta_W$. Unlike a threshold-based approach that directly interprets the proportion of compact particles (WL ratio > 0.5), this probabilistic model explicitly accounts for the overlap between the WL ratio distributions of herbaceous and woody charcoals, thus providing a more robust and continuous estimate of $\theta_W$. This relationship depends on the parameters $\mu_H$, $\mu_W$, $\sigma_G$, $\sigma_W$ and is expressed as:

$$p > .5 = 1 - (1 - \theta_W) \Phi\left(-\frac{\mu_H}{\sigma_H}\right) + \theta_W \Phi\left(-\frac{\mu_W}{\sigma_W}\right) \quad (4)$$

where $\Phi$ denotes the cumulative distribution function of the standard normal distribution. Inverting this equation yields
$$ \theta_W = \frac{p_{>5} + \Phi\left(-\frac{\mu_H}{\sigma_H}\right) - 1}{\Phi\left(-\frac{\mu_H}{\sigma_H}\right) - \Phi\left(-\frac{\mu_W}{\sigma_W}\right)} \quad (5) $$

To reconstruct $\theta_W(t)$ along the sediment core, we first estimate the temporal evolution of the parameters $\mu_H(t)$, $\mu_W(t)$, $\sigma_H(t)$, and $\sigma_W(t)$ as follows:

1. Charcoal particles are grouped into depth intervals along the core.

2. The Bayesian model is applied independently to each group to extract 1000 posterior samples of $\mu_H$, $\mu_W$, $\sigma_H$, and $\sigma_W$.

3. These parameter series are interpolated using a loess smoother applied to the age estimated at the center of each depth interval.

Each charcoal particle is then assigned a binary value 1 if WL ratio >0.5, and 0 otherwise. The age of each particle is determined according to the age-depth model. This binary series is interpolated into a continuous time series $p_{>0.5}(t)$ using local regression (locfit) in R (Loader, 2006).

Locfit is a locally weighted regression method that fits smooth curves by combining adaptive bandwidth selection with locally defined polynomial fits, ensuring flexible, data-driven smoothing. Two parameters jointly control the locality of the regression: the minimum bandwidth $h$ determines the size of the time window around each point where neighbors are searched, while the span $s$ defines the minimum fraction of nearby data points to include in each local fit and increases the window adaptively when necessary to meet this threshold.

Finally, for each of the 1000 posterior parameter samples, the proportion of woody charcoal $\theta_W(t)$ is computed and clipped to the interval [0,1] as:

$$ \theta_W(t) = \Pi_{[0,1]} \left( \frac{p_{>5}(t) + \Phi\left(-\frac{\mu_G(t)}{\sigma_G(t)}\right) - 1}{\Phi\left(-\frac{\mu_G(t)}{\sigma_G(t)}\right) - \Phi\left(-\frac{\mu_W(t)}{\sigma_W(t)}\right)} \right) \quad (6) $$

where $\Pi_{[0,1]}$ denotes projection onto [0,1].

The median and quantiles of $\theta_W(t)$ are then estimated at each time step to provide uncertainty bounds on the reconstructed proportion of woody charcoal.

### Application to the Lake Sélé sequence

We applied this model to charcoal data from a sediment core from Lake Sélé (Benin), currently surrounded by agricultural fields (Table 1). A total of 8761 charcoals were measured and quantified from 311 samples of 1 cm³ taken at 1 cm intervals above the hiatus in sedimentation occurring around 3200 BP, as evidenced by Bremond et al. (2017). Charcoals were grouped into 10 cm intervals for the Bayesian model run. Results were interpolated at 10-year intervals. For locfit interpolation, the span was set at 3% and the minimum bandwidth was set at 50 years.

We then compared the results generated by our model with pollen analyses by Salzmann and Hoelzmann (2005) from a previous sediment core from the same lake. In this core, sedimentation resumed slightly earlier (ca. 3200 a BP), and 18 pollen samples were available for this interval (Table S6). We examined the temporal evolution of total charcoal concentration, as well as with the inferred herbaceous and woody charcoal concentrations, alongside three groups of pollen taxa: (i) Poaceae, commonly used as a proxy for herbaceous savanna cover; (ii) Arboreal pollen, calculated as the sum of proportions of taxa identified as trees in the literature; and

(iii) Pioneer tree pollen, calculated as the sum of proportions of taxa corresponding to light-demanding, colonizing forest trees, according to literature sources. The corresponding lists of taxa are detailed in Table S6.

## Results

### Age-depth models, charcoal concentration, influx, and WL ratio time series for the 19 sites

The age-depth models for all sites are fairly linear (Fig. S7(A)). Basal ages of the sediment cores range from 1200 CE (NGO, the core with the oldest sequence) to 1997 CE (GRO, cores with the most recent basal age) (Fig. S7(A); Table S8). When focusing on the 1990–2020 time window, the corresponding core sections range in length from 1 cm to 56 cm. Charcoal concentration (CharConc) varies from 0 to approximately 400 particles/cm³ in individual samples (Fig. S7(B)). Over the last 30 years of study, the average CharConc per site is 47 particles/cm³ (±25) (Table S9). Gabon (mainly grass savanna and rainforest sites) was the country recording the least charcoal during this period, with CharConc average ~27 particles/cm³ (±13), compared to a slightly higher average in Ivory Coast (mainly wooded savannas and crops sites) of 75 particles/cm³ (±29). The sites FBA, BOC, and, to a lesser extent, NGO (in wooded or shrub savanna) show particularly high CharConc values for the period studied, with averages of 278 particles/cm³ (single sample), 276 particles/cm³ (±69), and 71 particles/cm³ (±33), respectively. These values contrast with generally lower CharConc values at the other sites, averaging around 18 particles/cm³ (±14) over the period considered. Several sediment cores show pronounced temporal variations in CharConc (Fig. S7(B); Table S9), particularly those from DKL (CAR, degraded rainforest and crops), NGO (Congo, in shrub savanna–rainforest mosaic), and reservoirs in Ivory Coast. These cores also reveal substantial spatial heterogeneity, as closely located sites can show markedly different CharConc, for instance, high at NIAK and NON, but low at nearby NIN and NAP, whereas all sites lie within wooded savanna and crops. Regardless of the differences observed between countries, our results highlight a variation in the number of charcoal particles recorded depending on the type of reservoir, with ponds showing higher concentrations than those observed in lakes or reservoirs (Fig. S10). The same trend is observed with the charcoal influx.

WL ratios from individual charcoal particles range from 0.01 to 0.99, and the minimal WL ratio per sample from each of the sites ranges from 0.01 to 0.05 (Fig. S7(C)). Based on the average WL ratio per sample, three types of sites can be distinguished. Eight sites in various countries show consistently low WL ratio over time (<0.4), such as AZL, DINA, DBO, ELI, ESB, NDA, NGO, and SLE (savanna or crops). The majority of sites, 10 in total, display intermediate average WL ratios (0.4 < WL ratio < 0.6), including all sites in Ivory Coast as well as FBA, GBL, GRO, NGG, and R1 (wooded savanna or rainforest). Only the DKL site shows a high average WL ratio (>0.7) with a decreasing trend in time, a site where deforestation has been documented (Aleman et al., 2013).

### Estimation of the Box–Cox logit transformation parameter from the reference charcoal distribution

First, we wanted to test the model on homogeneous environments, whether they are composed mainly of herbs or trees, so we used five sequences (ELI, ESB, NDA, and DINA for herbaceous fuel and DKL(b) for woody fuel) (Fig. 2). The WL ratio values of the charcoal populations differ markedly
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Probability density of (A) WL ratio values and (B) Box-Cox logit-transformed WL ratio. Distributions are distinguished according to herbaceous-dominated (orange gradient with a thin curve) and woody-dominated (with a thick green curve) fuels. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

between sequences recording herbaceous charcoal from grassland fires and woody charcoal from a deforestation episode (Fig. 1). In the former, herbaceous charcoal WL ratios ranged from 0.01 to 0.5, with a median of 0.15 and interquartile values of Q1 = 0.10 and Q3 = 0.22, showing a right-skewed distribution. In contrast, woody charcoal WL ratios range from 0.1 to 0.9, with a median of 0.7 and interquartile values of Q1 = 0.55 and Q3 = 0.82, and a left-skewed distribution (Table S11). A Box-Cox logit transformation with parameter $\lambda$ between -0.34 and -0.06, depending on the site (Fig. S1), appears to effectively normalize both distributions, with mean and standard deviations equal to $\mu \sim -2$ and $\sigma \sim 0.71$ for herbaceous charcoals and to $\mu \sim 1$ and $\sigma \sim 1$ for woody charcoals. These values were used to constrain both the limits and the priors in the Bayesian Gaussian mixture model applied to each of the sites (Fig. S7(D), (E)).

## Estimated proportions of herbaceous and woody charcoals by site

For each of the 19 sites studied, the model results are presented in the form of probability densities for the proportion of herbaceous (elongated-mode component) and woody (compact-mode component) charcoals over the last 30 years (Fig. 3(A)). A wide disparity in this proportion is observed between sites: some show a near absence of compact-mode distributed charcoals (~0%, e.g., ELI, ESB, DINA, NDA, SLE, and AZL), while two sites, in addition to DKL(b), contain more than 45% (NIN and FBA). The other sites fall between these two extremes, with proportions ranging from 10% to 40%.

The biplot of the modes of the inferred herbaceous and woody charcoal distributions is shown in Fig. 3(B), while Fig. S12 presents biplots of their standard deviations and of standard deviations and the modes of each fuel type and the variations of $\lambda$. A greater disparity is observed in herbaceous charcoal modes (0.12–0.32) than in woody charcoal modes (0.59–0.71) (Fig. 3(B)). No evident pattern emerges from standard deviations (Fig. S12). Overall, the uncertainty in woody charcoal mode values largely overlaps along the vertical axis, with comparable variability between sites (approximately

<figure><img src="image_4.png" /><figcaption>**Figure 3.** (A) Proportion of estimated woody (green) and herbaceous (yellow) charcoal across study sites. (B) WL Ratio modes, median, and interquartile ranges of the woody and herbaceous charcoal distributions at each site. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

$\pm 0.05$). In contrast, the modes of elongated herbaceous charcoals show lower uncertainty, ranging from approximately $\pm 0.01$ to $\pm 0.05$ (Fig. 3(B)). This variability does not overlap between sites, with the interquartile ranges of herbaceous charcoal modes remaining distinct. Three main groups of sites can be distinguished. The first group comprises sites with highly elongated herbaceous charcoals (mode ~0.15) associated with relatively compact woody charcoals (mode 0.65–0.70). Except for DKL(b), this first group has a high proportion of charcoals from herbaceous fuel, so the mode of the WL ratio of woody charcoals varies more than that of herbaceous charcoals. This trend is somewhat similar for the second group, which includes sites with intermediate herbaceous charcoal modes (0.20–0.25) and a broader range of woody charcoal modes (0.60–0.70). The third group consists of sites with the least elongated herbaceous charcoals (mode ~0.30) and less compact woody charcoals (mode ~0.60). The mode of herbaceous charcoal varies here a
little more than the mode of woody charcoal due to the lower
proportion of herbaceous charcoal at the sites (Fig. 3(B)).

When compared across sites to the proportion of compact
charcoal (WL ratio > 0.5), the estimated proportion of
ligneous-derived charcoal increasingly exceeds the propor-
tion of compact charcoal as values increase above 0.25
(Fig. S13(A)). This pattern arises because only a small fraction
of herbaceous-derived charcoals has WL ratios >0.5,
whereas woody fuels consistently produce a larger share of
charcoals with WL ratios <0.5. Even at lower proportions,
herbaceous modes that are less elongated contribute to a
widening discrepancy between the two metrics. A similar
pattern is observed when comparing the estimated propor-
tion of ligneous-derived charcoal to the average WL ratio
(Fig. S13(B)), reflecting the consistent relationship between
the proportion of compact charcoals and the mean WL ratio
(Fig. S13(C)).

Remote sensing site characterization

Vegetation classification

The results of the PCA conducted on all available spectral
bands (B2, B3, B4, B5, B6, B7, B8, B8A, B11, and B12) of
*Sentinel-2 imagery* show that the first principal component
accounts for the majority of the variance (95.4%), while
the second and third components explain 3.3% and 1% of the
variance, respectively (Fig. S3). Despite their lower contribu-
tion, components 2 and 3 provided meaningful insights and
were therefore included in the analysis. All spectral bands are
positively correlated with the first principal component,
indicating strong inter-band correlations. Axis 1 effectively
captures the overall variation in spectral values. On Axis 2,
bands B2, B3, B6, B7, B8, and B8A show positive correlations,
while bands B4, B5, B11, and B12 show weak negative
correlations. Axis 3 presents very weak correlations overall,
positive for bands B2, B3, and B4, and negative for the
remaining bands. These findings are consistent with standard
interpretations of PCA applied to satellite data: the first axis can
be interpreted as a brightness index, representing overall
surface reflectance (Mather and Tso, 2016); the second axis is
related to structural vegetation characteristics, for example,
tree cover (Couteron et al., 2001); and the third axis is
associated with surface moisture (Kauth and Thomas, 1976). In
our case, the surfaces show high reflectance across all bands,
resulting in strong values on the brightness axis and a visible-
infrared contrast on the moisture axis.

Vegetation classification accuracy was generally high, with
a mean Kappa coefficient of 0.86, indicating strong agreement
between the classification results and the reference data.
Although a few sites showed lower values (Kappa < 0.70)
(Table S14), the overall classification performance remains
robust. Two sites showed low Kappa values. Around Lake
NIAK (Kappa = 0.61), misclassifications mainly arose from
confusion between plantations and natural vegetation types
(wooded savanna and forest) in both directions. Around Lake
BOC (Kappa = 0.51), classification errors were mainly due to
confusion between crops, wooded savanna, and inundated
areas.

Occurrence of fires

Finally, the occurrence of fires was studied according to the
distribution of vegetation classes (Fig. S7(F)). The results show
that forests, inundated areas, and riparian forests consistently
show little to no fire occurrence, representing less than 1% of
the burned land. In contrast, savannas are subject to regular

burning, representing more than 50% of the burned land.
Croplands and plantations, which represent nearly 20% of the
burned land (Fig. S15), are also regularly impacted by fire. In
addition, the sites with the highest average charcoal concen-
trations (in Congo, Ivory Coast, and Gabon) are predominantly
surrounded by savannas (herbaceous, shrub, or wooded) and
cultivated lands. The other sites are characterized by increased
heterogeneity, resulting from the presence of forest formations
or plantation areas, in addition to savannas (Fig. S7(G)).

Tree cover

TC values vary markedly among vegetation classes. In sites
located in old-growth or secondary forests, high TC values are
recorded, particularly at sites in the Central African Republic
(GBL and NGG) or in some sites in Gabon (DINA and NON).
Conversely, low TC values are associated with more open
vegetation types, such as savannas or forest-savanna mosaics,
which include most sites in Benin, Congo, and Gabon. Sites
with the lowest TC values (NAP, NGO, NIN, R1, and SLE) are
mainly located in savanna associated with human-derived
environments. A comparison of TC values estimated from the
two data sources for the same sites reveals a consistent deviation
from the 1:1 line, indicating a systematic disparity between
them (Fig. S16). Although the estimations are significantly
correlated, TC values derived from the Reiner et al. (2023) 1 m
product (TC_1m) are 30% lower than those derived from the
JRC (2024) 10 m resolution product (TC_10m). At the dense
savanna class, for example, TC_1m = 0.75 compared to
TC_10m = 1, and it is the same for the savanna class, where
TC_1m is, for some of them, close to 0.3 versus TC_10m = 0.60.

Comparison between the estimated woody
charcoal proportion and TC

To evaluate the robustness of the model separating herbaceous
and woody charcoal populations, we tested whether the
proportion of compact-mode distributed charcoals (interpreted
as woody) correlates with the surrounding woody fuel. For this
purpose, the hierarchical clustering of the variables enabled us
to identify correlated variables and retain only the group of
variables from among the six final groupings (Fig. S17). The
relationship between JRC TC at 10 m resolution in the
savannas within a 5 km buffer (Fig. 4) shows the strongest R²
(R² = 0.69, p-value = 9.62e-06) among the six regressions
performed (Table 3; Fig. S17).

The grass savanna sites from Gabon, used to qualify our
Bayesian model parameters, appear in the lower left-hand
corner (Fig. 4), characterized by both a very low proportion of
woody charcoals and a zero TC. The most densely wooded
savanna sites (NIN and FBA), which also had a slightly higher
and/or fluctuating WL ratio, are on the right-hand side of the
figure, with values for the proportion of woody charcoals close
to 0.6 and those of TC around 0.6 to 0.9. The shrub savanna
sites (R1 and NGO) have an under-represented TC (Fig. 4).

Application of the model to the paleosequence of
Lake Sélé

The proportion of woody charcoal inferred by the model
applied to the paleosequence from Lake Sélé is consistently
higher than the proportion of compact charcoal (Fig. 5(B), (C)).
This offset between the proportion of modeled woody charcoal
and the proportion of compact charcoal is also observed in the
recent core sequences (Fig. S13). The modeling approach
smooths out sample-to-sample variability, particularly when
<figure><img src="image_5.png" /><figcaption>**Figure 4.** The 2019 savanna 10 m resolution TC with a 5 km radius, plotted against the proportion of woody charcoal in the last 30 years estimated by the Bayesian model. Each point represents the mean proportion of woody charcoal for a single site, with error bars indicating uncertainty. Colors distinguish the five study sites shown in Fig. 1. Sites without savannas are not represented. [Color figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<table><caption>**Table 3.** Summary of the relationships between tree cover (TC) variables and the proportion of woody charcoal from the model, for each site.</caption><thead><tr><th>Studied comparison</th><th>R<sup>2</sup></th><th>p-value</th></tr></thead><tbody><tr><td>TC with 1 m resolution of the weighted average of burned areas, within a radius of 5 km</td><td>0.3624</td><td>3.79e-03</td></tr><tr><td>TC with 1 m resolution of savannas, within a radius of 5 km</td><td>0.6371</td><td>4.36e-05</td></tr><tr><td>TC with 1 m resolution of savannas + agricultural land, within a radius of 1 km</td><td>0.5909</td><td>7.28e-05</td></tr><tr><td>TC with 10 m resolution of savannas, within a radius of 5 km</td><td>0.6982</td><td>9.62e-06</td></tr><tr><td>TC with 10 m resolution of savannas + agricultural land, within a radius of 5 km</td><td>0.3825</td><td>2.17e-03</td></tr><tr><td>TC with 10 m resolution of the weighted average of burned areas, within a radius of 5 km</td><td>0.5001</td><td>4.27e-03</td></tr></tbody></table>

charcoal concentrations are low, and variations between samples are influenced by random effects.

Consideration of the temporal trend of the estimated proportion of woody charcoal and the derived concentrations of herbaceous and woody charcoals over the past 3200 years, combined with pollen percentages of different vegetation types, provides insights into dominant vegetation and fuel sources during periods of fire (Fig. 5). Between 3200 and 2800 cal a BP, charcoal concentrations were high (Fig. 5(A)), while the average WL ratio of charcoal particles declined from approximately 0.75 to 0.25, and the proportion of woody charcoal decreased from ~95% to near 0% around 2800 cal a BP (Fig. 5(B), (C)). During this interval, arboreal pollen increased from ~20% to ~40% (Fig. 5(F)), pioneer trees increased from ~1% to 10%, whereas Poaceae pollen decreased but remained dominant (~60%; Fig. 5(H)). Between 2800 and 1100 cal a BP, charcoal concentrations declined, and fuel sources were predominantly herbaceous, as reflected by average WL ratio

values generally below 0.5 and a low proportion of woody charcoal (~15%). Around 1100 cal a BP, a shift in tree-grass pollen dominance occurred, with arboreal pollen slightly exceeding Poaceae pollen at the start of this phase. Between 1100 and 500 cal a BP, this trend reversed. Charcoal concentrations increased dramatically, but the proportion of woody charcoal remained low (0%–10%). Poaceae pollen increased markedly, while arboreal pollen reached its minimum (~20%). From 500 cal a BP to the present, charcoal concentrations remained relatively stable, but the average WL ratio increased, and the proportion of woody charcoal reached ~50%. This indicates a shift in fuel type without a corresponding change in vegetation composition, suggesting that the nature of burned material changed even though the overall vegetation structure remained largely similar.

## Discussion

The main objective of this study was to assess, using field data, whether the WL ratio of sedimentary charcoal fragments can reliably indicate the type of vegetation burned (herbaceous vs. woody fuels) in tropical environments, by comparing recent charcoal records from multiple lakes with the surrounding vegetation (i.e., the actual fuel sources) derived from remote sensing data. Charcoal concentrations and influx vary among these sites and within the same site over time, with some sites consistently showing low levels while others show high and temporally variable concentrations (Fig. S7(B); Table S9). These variations can largely reflect the surrounding vegetation type and local rainfall regimes (Sankaran et al., 2005; Archibald et al., 2013; Soro et al., 2021). However, this variability does not affect the WL ratio itself, which can thus be used independently of charcoal concentration, making it a reliable proxy even across sites with markedly different charcoal dynamics. Beyond this empirical validation, our study introduces, for the first time, a method that moves from a qualitative interpretation of the charcoal morphometric signal toward a quantitative estimation of the relative contributions of herbaceous and woody fuels. To this end, we have developed an original, robust, and operational design readily applicable by the scientific community in paleoecological research.

## Separation of herbaceous and woody charcoal using the WL ratio distribution and a Bayesian model

Calibration in homogeneous fuel environments (grasslands vs. deforested landscapes) shows that herbaceous fuels predominantly produce elongated charcoal (WL ratio mode ≈ 0.2), whereas woody fuels generate more compact fragments (WL ratio mode ≈ 0.6). These contrasts, previously documented in laboratory combustion experiments (Crawford and Belcher, 2014; Feurdean, 2021; Feurdean et al., 2023), are preserved in sedimentary records, supporting the use of WL ratio distributions as indicators of fuel type. However, substantial overlap between herbaceous and woody WL ratio distributions precludes the use of a strict cut-off value. The wide dispersion of WL ratios observed even under homogeneous vegetation reflects multiple processes: (i) minor fuel heterogeneity, such as understory herbaceous inputs in forests (Djego, 2006); (ii) intrinsic anatomical differences in plant structure and combustion behavior among different organs of the plants notably in woody plants (Enache and Cumming, 2006; Crawford and Belcher, 2014; Crawford et al., 2018) resulting in a greater variability in woody than herbaceous charcoal morphologies
**Figure 5.** Temporal trends of charcoal proxies over the past 3200 years and pollen records from Lake Sélé over the past 3500 years. (A) Total charcoal concentration (in number of particles/cm³). (B) WL ratio of individual charcoal fragments (red dots), with gray shading representing the density of the WL ratio along the core (darker shading indicates higher density), and a loess fit of WL ratio values (blue line) showing the temporal trend of the average WL ratio. (C) Proportion of herbaceous (yellow) and woody (green) charcoals estimated from the Bayesian model. (D) Percentage of Poaceae pollen. (E) Estimated concentration of herbaceous charcoal (in number of particles/cm³). (F) Percentage of arboreal pollen. (G) Percentage of pioneer tree pollen. (H) Estimated concentration of woody charcoal (in number of particles/cm³). [Color figure can be viewed at wileyonlinelibrary.com]

<figure><img src="image_6.png" /></figure>

(Feurdean, 2021); and (iii) post-depositional or laboratory-related fragmentation and abrasion leading to more compact forms (Crawford and Belcher, 2014; Hu et al., 2020; Inoue and Usuki, 2024). Consequently, a simple binary classification based on a threshold of around WL = 0.5, as applied in previous studies (Leys et al., 2017; Vachula et al., 2021; Feurdean, 2021), cannot reliably capture the relative contributions of herbaceous and woody fuels. Rather than from the single-particle WL ratio, the diagnostic signal to reflect the dominant fuel type and burning regime emerges from the statistical distribution of values within a sample.

Our study addresses this limitation by decomposing the WL ratio distributions into two sub-populations, corresponding to herbaceous and woody charcoal. The relative weight of each component in the mixture provides a direct estimate of the relative proportion of each fuel type. While this approach establishes an initial statistical framework, its application raises several methodological challenges. Indeed, conventional

Gaussian decomposition methods are poorly suited to this problem, due to their high sensitivity to data fluctuations and their dependence on a balanced representation of the two types of fuel (Svensén and Bishop, 2005; Cole et al., 2017), a condition that is rarely met in the field. Moreover, WL ratio frequency distributions are generally unimodal, suggesting strong overlap between the two populations. This reflects the limited separation between the two modes relative to their dispersion (Fig. 3). To overcome these limitations, we adopted a Bayesian framework, which integrates prior knowledge and allows constraints on distribution parameters, resulting in more robust posterior inferences (Caillot and Martin, 1972; Svensén and Bishop, 2005). In our case, the analyses of sites with homogeneous fuel types provided insights into WL ratio distributions for charcoals from almost pure herbaceous or woody fuels. These homogeneous sites also confirm the partial overlap between the WL ratio distributions of the two populations. The model can therefore incorporate these
validated constraints, ensuring both flexibility and structural rigor (Ellison, 2004). The Bayesian approach also explicitly handles uncertainty by providing full probability distributions for each parameter, rather than single-point estimates (Svensén and Bishop, 2005; Lu, 2021). Such probabilistic outputs enhance interpretability and are particularly valuable when data are sparse or noisy, allowing more robust inference in mixed vegetation contexts, and facilitate their integration into scientific decision-making processes, especially when binary classifications of fuel are required.

The morphological distributions of herbaceous and woody charcoals, including their modes, spreads, and skewness, vary among sites. As a result, the relationship between the proportion of compact particles with WL ratio >0.5 and the actual proportion of woody charcoal differs from one site to another. These differences may arise from three main sources of heterogeneity across sites: (i) species-specific physiological traits within both herbaceous and woody taxa (Feurdean et al., 2023); (ii) fire characteristics such as temperature and combustion intensity, which differentially affect charcoal morphology; and (iii) taphonomic processes, and especially fragmentation during secondary transport. By accounting for these sources of heterogeneity, our model provides a flexible yet constrained framework for inferring relative fuel contributions from sedimentary charcoal assemblages.

## Comparison of estimated woody charcoal proportions with Tree Cover data

Our model can be used to estimate the relative proportion of herbaceous and woody charcoals, and, under the assumption that this proportion accurately reflects the composition of the burned biomass, the relative proportion of herbaceous and woody fuels. This hypothesis was validated in homogeneous environments, where model outputs converged toward expected extremes (Fig. 2). In mixed environments, model results were evaluated by comparison with two sources of TC estimates, which, although quantitatively different, showed consistent spatial variations across sites and vegetation classes, in combination with LULC analyses around each site. Despite differences in temporal coverage between sedimentary charcoal (30-year integration) and satellite data, the comparison provides strong confidence in the model's ability to capture the relative contributions of herbaceous and woody fuels.

At the site level, each location comprises a mosaic of vegetation types, each associated with distinct fire regimes. Confrontation of LULC analyses and remote sensing burned areas data confirms that, in the study sites, fires predominantly occur in savannas and agricultural areas (Fig. S15). Consequently, charcoal inputs from rainforests are expected only during periods of intentional deforestation or major climatic shifts, and consist predominantly of woody charcoal. By contrast, savannas experience frequent and widespread fires (Bond and Keeley, 2005; Archibald et al., 2010, 2013; Kugbe et al., 2012) (Fig. S15), which are typically surface or sub-canopy fires primarily consuming seasonally drying grass cover (Govender et al., 2006; Smit et al., 2010; Fisher et al., 2014), while also affecting woody material within the flame zone, mainly resprouts and twigs (Kugbe et al., 2012; N'Dri et al., 2018). Combustion efficiency differs between fuel types (Shea et al., 1996; Prasad et al., 2001; Hély et al., 2003), with herbaceous biomass generally burning more completely than woody biomass, which may also show greater fire resistance due to bark properties, stem architecture, or growth rates (Gignoux et al., 1997; N'Dri et al., 2018). This difference can be partly offset by the higher biomass per unit of trees compared to grass (Bille and Poupon, 1972). In this context,

vegetation composition and land-use practices exert strong control on local fire regimes and charcoal production (Soro et al., 2021).

In the forest domain, three sites are primarily represented: DKL,DTO, and NGG. BothDTO and NGG lie entirely within the forest domain, without adjacent savanna, and are therefore not shown in Fig. 4. However, they differ markedly in their levels of human disturbance. NGG, embedded in a mosaic of dense rainforest and slash-and-burn fields, shows higher proportions of woody charcoal thanDTO, which is situated in a fully human-modified landscape dominated by croplands and plantations, where fires mainly target herbaceous weeds (Figs. S7-5, S7-13(G), (H)). DKL, plotted in Fig. 4, shows a much lower proportion of compact charcoal than would be expected from its relatively high tree cover, possibly reflecting the timing or legacy of deforestation at the site.

Within the wooded savanna domain, most of the sites occupy intermediate positions along the regression, consistent with mixed tree-grass fuel regimes (Fig. 4). Located in relatively dense shrub savannas, these sites are characterized by moderate TC values (~55%) combined with low proportions of woody charcoal (~12%). In contrast, some sites display more extreme values: FBA and NIN plot in the upper right of the regression, indicating high tree cover and a high proportion of compact charcoal (Figs. S7-8, S7-16(C), 4), whereas R1 and NGO are positioned in the lower left of the regression, showing very low tree cover but still detectable amounts of compact charcoal (Figs. S7-18, S7-14(C), 4). These discrepancies between tree cover and compact charcoal proportions suggest that high-resolution remote sensing imagery may inadequately detect low-stature woody vegetation (Lang et al., 2023; Brandt et al., 2025), leading to an underestimation of woody fuel contributions in structurally heterogeneous savanna landscapes.

Sites located in a forest-savanna mosaic display more heterogeneous patterns, reflecting the vegetation types predominantly affected by fire. At DINA, ELI, ESB, NDA, and NGO, despite substantial forest cover, charcoal signals indicate predominantly herbaceous fuels, consistent with fires, mainly affecting savannas. Conversely, GBL shows high savanna TC but low woody charcoal proportions, potentially reflecting a mismatch between TC and burned fuel type, as well as site-specific charcoal transport processes. The presence of dense riverine forest around Lake Gbali likely limits charcoal input by runoff, favoring wind transport from nearby open areas and possibly enhancing differential transport between fine herbaceous and heavier woody particles (Aleman et al., 2013, 2014).

In croplands and plantations, fires are commonly used to remove crop residues and herbaceous cover, resulting in a decoupling between TC and actual fuel composition (Kanmegne, 2004; Njume and Krah, 2020). As a result, areas with low TC may produce relatively high proportions of woody charcoal when resprouting is abundant, whereas densely planted areas may yield limited woody charcoal if fires mainly affect the herbaceous layer, as observed at GRO and NON (Fig. 4). This highlights the need to consider vegetation structure and land-use history when interpreting charcoal assemblages.

Overall, the decoupling between TC and actual fuel source composition may explain the weaker correlations between the proportion of woody charcoal and TC when considering all burned areas or agricultural land specifically (Table 3). Surface fires in a wooded environment can be detected by remote sensing, even when most woody biomass remains unburned (Albini and Reinhardt, 1997; Hollis et al., 2011; Harmon et al., 2022), meaning that TC does not necessarily reflect the
fuels actually consumed. Consequently, TC in fields and plantations is a poor predictor of woody fuel. However, in the absence of direct fuel biomass measurements, savanna TC remains a relevant proxy for assessing the balance between herbaceous and woody fuels across fire-affected open landscapes, including agricultural areas. As in savannas, the main source of woody fuel in fields and plantations is often recently regrown vegetation within the fire zone (N'Dri et al., 2018). High savanna TC would therefore reflect the vigor of woody regeneration in surrounding open landscapes (Staver and Levin, 2012; Devine et al., 2017). Although this relationship weakens in mixed or heavily human-impacted savanna–cropland mosaics (e.g., AZL, SLE, NIAK, NAP, NON, and GRO), the overall correlation between woody charcoal proportions and savanna TC within a 5 km radius supports the use of savanna TC as a proxy for fuel composition.

## Application of the model as a function of time, along a sedimentary sequence

To illustrate the value of estimating the respective proportions of herbaceous and woody sedimentary charcoal over time for a better characterization of fire regimes in paleoecological studies, the model was applied to the sedimentary sequence of Lake Sélé (Benin) (Fig. 5). This approach balances temporal resolution with computational feasibility, given the volume of data. The temporal trend of the WL ratio distribution modes and standard deviations was first estimated at 10 cm intervals and then interpolated. From this interpolation, an age-dependent transfer function was derived, linking the proportion of particles with WL ratio >0.5 to the relative shares of herbaceous and woody charcoal. This transfer function was then applied to a high-resolution *locfit* regression of a binary time series, in which each charcoal particle was assigned a value of 1 if WL ratio >0.5 and 0 otherwise, to reconstruct the proportion of woody charcoal through time.

When comparing the proportion of compact charcoal obtained using the threshold method with the proportion of woody charcoal estimated from our model, we observe broadly similar trends (Fig. 5(B), (C)). However, using a threshold of 0.5, we could conclude that there are few compact charcoal fragments between approximately 3200 and 2500 cal a BP. Conversely, our model highlights marked peaks in the abundance of woody charcoal, particularly between 3200 and 2900 cal a BP, reaching nearly 100%. Similarly, during more recent intervals around 500 cal a BP, while the 0.5 threshold method suggests an increase in the WL ratio, our model reproduces these trends but estimates the proportion of woody charcoal with greater accuracy, generally yielding higher proportions of woody charcoal. For instance, 50% charcoals with a WL ratio greater than 0.5 correspond, in our model, to approximately 60%-70% woody charcoals, indicating that the threshold method systematically underestimates the actual proportion of woody charcoals, as shown (Fig. S13). The underestimation is less pronounced when herbaceous charcoals are dominant. For example, between 2800 and 1100 cal a BP, when the WL ratio is close to 0.25, the reconstructed proportion of herbaceous charcoal is also approximately 25% (Fig. S13). The underestimation evidenced above could be explained by the fact that the WL ratio values are averaged, thus attenuating the extreme values. By ignoring the natural variability in the morphology of sedimentary charcoal particles, the fixed threshold therefore loses its biological significance. Since the WL ratio distribution does not naturally separate around 0.5, it appears that this arbitrary threshold introduces a bias in morphological interpretation. Nevertheless, this approach remains functional, as the bias is

constant between samples. It underestimates the absolute proportion of woody charcoals, but seems to reproduce the general trends accurately.

Comparison of the temporal trend of woody and herbaceous charcoal concentrations estimated from our model with past vegetation changes reveals that, before 2800 cal a BP, high concentrations of woody charcoal coincided with an increase in tree pollen. According to pollen data, Salzmann and Hoelzmann (2005) suggested that this reflected the development of a semi-evergreen forest under increased humidity, still surrounded by savanna, as indicated by the high relative Poaceae pollen percentages (>60%, Fig. 5(D)). Combined with the charcoal results (Fig. 5(C)–(I)), these data indicate that the forest was able to expand over the savanna, reflected by the increase of pioneer species and the decrease in Poaceae (Fig. 5(F)–(G)), but that this expansion was slowed by fires affecting trees. Between 2800 and 1100 cal a BP, the landscape was dominated by a forest-savanna mosaic with a low charcoal concentration. Relatively stable Poaceae and forest tree pollen, along with reduced pioneer species, suggest a stabilization of vegetation dynamics. Fires during this period were likely confined to open savannas, as indicated by the high proportion of herbaceous fuel reconstructed from charcoal data (Fig. 5(C)). From 1100 to 500 cal a BP, charcoal concentrations abruptly increased, reaching their maxima around 900 cal a BP, with fuels dominated by herbaceous plants (Fig. 5(A)–(E)). This shift followed a change in the balance between Poaceae and forest tree pollen, reflecting an expansion of grasslands and suggesting more arid climatic conditions that dried vegetation and increased the flammability of savanna grasses. However, the lack of a corresponding increase in woody charcoal, despite a decline in forest cover, may indicate human impact, such as selective tree cutting for metallurgy, ceramics, or domestic fuelwood, reducing the availability of woody fuel. Finally, since 500 cal a BP, the proportion of woody fuel charcoal has increased (Fig. 5(I)), while Poaceae and tree pollen signals have stabilized, possibly reflecting a change in human land-use practices, reduced tree cutting, and a maintenance of vegetation *statu quo* mediated by fires.

Compared to simpler metrics such as the mean WL ratio or the proportion of compact charcoals with a fixed WL ratio threshold, this probabilistic framework represents a significant methodological advance (Fig. S13). Modeling of the entire distribution of particle elongations enables a more detailed understanding of the role of fire in vegetation dynamics, specifying the types of vegetation affected over time. Earlier threshold-based approaches (Umbanhowar and McGrath, 1998; Aleman et al., 2013; Crawford and Belcher, 2014; Leys et al., 2017) provided useful but limited information. By enabling robust estimation of herbaceous- and woody-derived fire fuels recorded in sediments, and by making these estimates directly comparable across sites through the consideration of spatial and temporal variations in elongation distributions, the approach proposed in this study marks an important methodological advance for the understanding of fire regimes (Higuera et al., 2005; Loudermilk et al., 2022) and their link with past human activity and climate (Bird, 1997; Whitlock et al., 2010). Comparison between the types of vegetation burned—inferred from charcoal morphotypes—and the dominant vegetation, reconstructed from pollen assemblages, helps to specify the contribution of the fuel types. Hence, one can identify the ecosystems or plant formations that are most vulnerable to fire, even when they do not dominate the landscape, or, on the contrary, confirm recurring associations between certain types of vegetation and fire. This knowledge of the relative proportions of charcoal of woody and
herbaceous origin over time is therefore essential to better characterize the nature of the fuel and refine the interpretation of fire regimes. In Lake Sélé, fire regimes appear to be a determining factor in the opening of the landscape.

## Conclusion

This study provides a robust and innovative empirical and methodological framework for identifying fuel type based on the morphology of sedimentary charcoals in tropical ecosystems. By combining field observations from sites with different characteristics, remote sensing data on vegetation, and a Bayesian modeling approach, we demonstrate that the WL ratio of charcoal fragments reliably reflects the relative contribution of herbaceous and woody fuels. Unlike traditional threshold-based methods, our model explicitly incorporates uncertainty and variability, allowing for more accurate and probabilistic reconstructions of past fire regimes.

Beyond methodological innovation, our results highlight consistent spatial-temporal patterns in charcoal assemblages, which accurately reflect local vegetation structure and fire dynamics. High concentrations of herbaceous charcoal in open savannas and agricultural landscapes confirm the predominance of surface fires fueled by herbaceous biomass, while forest sites or more wooded savannas record fewer and more heterogeneous fire events, generating more compact charcoals. Use of the model to sedimentary sequences, such as those from Lake Sélé (Benin) over the last 3200 years, illustrates its potential for tracking long-term interactions between vegetation and fires/fuels and contributes to disentangling the respective roles of climatic and/or anthropogenic forcing in shaping tropical landscapes.

From an ecological perspective, quantification of the proportion of woody fuels relative to herbaceous fuels provides new insights and perspectives on the efficiency of biomass combustion, carbon emissions, and the resilience of these ecosystems to fires. As woody vegetation stores more carbon than herbs, a more accurate estimate of the proportion of trees burned will directly contribute to refining carbon balance reconstructions over the past millennia. In addition, identification of the type of fuel based on the morphology of the charcoal can serve as an indirect indicator of land-use pressure or the presence of herbivores, thus broadening the scope of interpretation of paleoecological archives.

However, this proxy could be further improved, and future research should focus on refining the calibration of the WL ratio for a wider range of vegetation types and combustion conditions, incorporating geochemical analyses, for example, to fully confirm their herbaceous or woody origin. These new analyses could be coupled with higher resolution remote sensing analyses, such as the work of Brandt et al. (2025) (very high-precision satellite data that can detect regrowth and very small bushes, etc.). Complementary studies in other regions and/or with different vegetation types would not only help to better calibrate the model and test its general validity but also contribute to a more comprehensive understanding of the feedbacks between fires, vegetation, and climate in tropical regions, where natural and anthropogenic processes have long shaped the landscape mosaic.

**Acknowledgements.** We thank Sandrine Canal and Sylvie Rouland for their logistical support in the lab. We are also grateful to lago Bonnici for his valuable insights and assistance in developing the Bayesian model, and to Fanny Lachery for her work counting charcoal in Lake Sélé. We thank Florian Reiner for providing TC data for our study sites at 1-m resolution. We thank the LMC14 staff (Laboratoire de Mesure du

Carbone-14), ARTEMIS national facility (LSCE (CNRS-CEA-UVSQ)-IRD-IRSN-MC), for the results obtained with the accelerator mass spectroscopy method. All scripts and materials necessary to reproduce the analyses presented in this article are publicly available at: https://github.com/charlyfavier/Charcoal-WLRatios-GMM. Thanks are due to the National Agency of National Parks of Gabon for logistical facilitation and for providing authorization to enter protected areas AE150001/PR/ANPN/SE/CS/AEPN as well as the National Center for Scientific and Technological Research for research authorization AR0004/15/MESR/CENAREST/CG/CST/CSAR. This work was supported by the National Research Agency DESSFOR (Grant No. ANR-20-CE32-0010), PAST-17 (Grant No. ANR-22-CE01-0027) awarded to Anne Alexandre, and the Labex CEMEB (Grant No. ANR-10-LABX-0004) project AFRIFIRE. F. Cornet was supported by the French National Research Agency project PAST-17 (Grant No. ANR-22-CE01-0027). Open access publication funding provided by COUPERIN CY26.

**Conflicts of interest**—The authors declare no conflicts of interest.

## Data availability statement

The data that support the findings of this study are openly available in Github at https://github.com/charlyfavier/Charcoal-WLRatios-GMM.

## Supporting information

Additional supporting information can be found in the online version of this article.

jqs70053-sup-0001-JQS_JQS70053.

jqs70053-sup-0002-Appendix_S7.

jqs70053-sup-0003-JQS_JQS70053.

jqs70053-sup-0004-Supmat.

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