# Drivers of soil organic carbon recovery during secondary succession along a West African climate gradient

Ahou Blandine Koffi, Jerôme Ebagnerin Tondoh, Evans Ehouman, Vincent Freycon, Camille Piponiot, Bienvenu H. K. Amani, Raphaël Aussenac, Bob Kouakou Kouadio, Thierry Roland Kouamé, Bruno Hérault

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Ahou Blandine Koffi, Jerôme Ebagnerin Tondoh, Evans Ehouman, Vincent Freycon, Camille Piponiot, et al.. Drivers of soil organic carbon recovery during secondary succession along a West African climate gradient. Ecological Indicators, 2025, 178, pp.114073. 10.1016/j.ecolind.2025.114073. hal-05264152

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# Drivers of soil organic carbon recovery during secondary succession along a West African climate gradient

Ahou Blandine Koffi<sup>a</sup>, Ebagnerin Jérôme Tondoh<sup>a</sup>, Evans Ehouman<sup>b</sup>, Vincent Freycon<sup>c,d</sup>, Camille Piponiot<sup>c,d</sup>, Bienvenu H K Amani<sup>a</sup>, Raphaël Aussenac<sup>b,c,d</sup>, Bob Kouakou Kouadio<sup>e</sup>, Thierry Roland Kouamé<sup>e</sup>, Bruno Hérault<sup>c,d,*</sup>

## ARTICLE INFO

Keywords:

Soil organic carbon

Resilience

Resistance

Forest succession

Recovery

Bayesian modeling

## ABSTRACT

Quantifying how soils resist degradation and recover organic carbon following agricultural abandonment is critical for understanding land restoration trajectories in West Africa. This study applies a Bayesian hierarchical modeling framework to assess resistance (the proportion of soil organic carbon [SOC] retained after land use) and resilience (the rate of SOC recovery) during forest succession across eight chronosequences in Côte d'Ivoire. SOC was measured across three depths (0–30 cm) under fallow, secondary, and old-growth forests. Modeling revealed that soil resistance was about 50 % of their original SOC, with lower resistance under cotton (42 %) and perennial crops (47 %), and higher under annual crops (57 %). Resistance also varied by topography, with lower-slope sites averaging 42 %, versus over 52 % at other positions. SOC resilience was highly influenced by environmental and management factors. Remnant tree density had the strongest positive effect: areas with over 50 remnant trees per hectare had recovery rates twice as high as sites without. Conversely, increasing precipitation, temperature, and their seasonality slowed SOC recovery, highlighting climate-related limits to resilience. SOC recovery also varied by soil type, with Acrisols showing the highest resilience. Overall, the model indicates that up to 90 % of SOC can be recovered within 10 years in favorable conditions. The developed Bayesian approach provides probabilistic estimates of SOC trajectories, offering nuanced insight into ecological and management drivers. Our findings emphasize the importance of preserving remnant trees as ecological anchors in post-agricultural landscapes, reinforcing passive regeneration as a cost-effective strategy to restore soil carbon in West Africa.

## 1. Introduction

Soils, as major reservoirs of organic carbon, play a central role in biogeochemical cycles through close interactions with the atmosphere, climate, and vegetation. These interactions regulate carbon fluxes by influencing the exchange, transformation, and storage of organic matter, particularly during land-use transitions such as forest succession (Jobbágy and Jackson, 2000). When agricultural land is abandoned, secondary forest succession is initiated, gradually transforming vegetation density, composition, and structure (Grebner et al., 2022). These aboveground changes are closely linked to shifts in belowground

processes, including increases in soil biological activity and organic matter content. Continuous litter inputs during succession enhance the accumulation and stabilization of organic carbon, contributing to its long-term storage in soils (Shi et al., 2023). Soil Organic Carbon (SOC) is fundamental to ecosystem functions and services, including nutrient cycling, water retention, and productivity (Bünemann et al., 2018). Maintaining SOC levels within a critical threshold—typically 1.5 to 2.0 % in the root zone—is essential for sustaining soil health and resilience in the face of intensive land use and climate variability (Lal, 2014). However, it is not only the quantity and quality of SOC that matter, but also how SOC evolves over time following disturbance. Conceptually,

<footnote><sup>a</sup> Université Nangui Abrogoua, UFR des Sciences de la Nature, Abidjan, Côte d'Ivoire</footnote>

<footnote><sup>b</sup> CIRAD, Yamoussoukro, Côte d'Ivoire</footnote>

<footnote><sup>c</sup> CIRAD, UPR Forëts et Sociétés, F-34398 Montpellier, France</footnote>

<footnote><sup>d</sup> Forëts et Sociétés, Université de Montpellier, CIRAD, Montpellier, France</footnote>

<footnote><sup>e</sup> UMRI SAPT, INPHB, Yamoussoukro, Côte d'Ivoire</footnote>

<footnote>* Corresponding author at: CIRAD, Forëts et Sociétés, F-34398 Montpellier, France.
E-mail address: bruno.herault@cirad.fr (B. Hérault).</footnote>
the trajectory of SOC during succession can be captured by two key
mechanisms: resistance, which reflects the impact of agricultural
disturbance on SOC concentrations, and resilience, which describes the
rate of SOC recovery toward pre-agriculture (i.e., forest) levels (Van der
Sande et al., 2023).

Resistance refers to the soil's ability to retain its key proper-
ties—particularly soil organic carbon (SOC)—when subjected to
external disturbances such as agricultural use (Hodgson et al., 2015). It
is commonly measured as the percentage of SOC remaining after
disturbance, relative to a reference ecosystem such as old-growth forest
(Yi and Jackson, 2021). Thus, the difference in SOC between old-growth
reference values and those at the time of land abandonment can serve as
an indicator of soil resistance (Van der Sande et al., 2023). Resistance is
largely shaped by historical land use and topographic position (Zhou
et al., 2023). In general, perennial cropping systems tend to preserve
SOC more effectively than annual systems due to reduced soil distur-
bance, greater root biomass, and more consistent organic inputs (Ledo
et al., 2020; Shang et al., 2024). In contrast, annual crops often involve
intensive tillage and shorter growing cycles, which can accelerate car-
bon losses and soil degradation (Loges et al., 2018). For example,
transitioning from annual to perennial systems has been shown to result
in a 20 % increase in SOC within the top 0–30 cm over two decades
(Ledo et al., 2020). Topography also plays a significant role in SOC
conservation. Steeper slopes are more susceptible to erosion, runoff, and
vegetation loss, all of which can reduce SOC retention. In contrast,
gentler slopes and flat areas support better water infiltration, soil sta-
bility, and the accumulation of organic matter (Zhou et al., 2023).
Therefore, both cropping history and landscape position are critical
factors influencing a soil's resistance to degradation.

Resilience is broadly defined as the capacity of an ecosystem to re-
turn to its original state and functions following disturbance (Hodgson
et al., 2015). In the context of soil organic carbon (SOC), resilience is
typically assessed by the time required for SOC levels to recover after
disturbance, with shorter recovery times indicating greater resilience
(Lehman and Tilman, 2000; Yang et al., 2023). As such, SOC resilience
can serve as a practical indicator of long-term soil sustainability. The
time required for SOC recovery during secondary succession varies
widely from 10 to 120 years depending on multiple factors (Rhoades
et al., 2000; Poeplau et al., 2011; Poorter et al., 2021). In tropical sec-
ondary forests, three key drivers have been identified: soil texture,
climate, and the presence of remnant trees. First, clay-rich soils retain
more carbon than sandy soils due to the formation of organo-mineral
complexes, which reduce microbial access, slow decomposition, and
promote long-term stabilization (Gregory et al., 2007). Second, climatic
factors exert contrasting effects: while increased rainfall can enhance
biomass productivity and litter input, higher temperatures accelerate
microbial decomposition, reducing SOC content (Jobbågy and Jackson,
2000; Wiesmeier et al., 2019). Third, remnant trees contribute to
ecosystem recovery by facilitating species recruitment and enhancing
productivity, with soils beneath them often showing higher SOC con-
centrations (Seidl et al., 2014; Sirois et al., 1998).

In addition, soil depth plays a critical role in SOC distribution and recovery. The upper 30 cm of soil typically receive the highest organic inputs and support greater microbial and root activity, leading to faster carbon incorporation and turnover. In contrast, deeper horizons accumulate carbon more slowly and are dominated by physico-chemical stabilization processes (Jobbågy and Jackson, 2000; Rumpel and Kögel-Knabner, 2011). The efficiency of carbon storage in each horizon is influenced by fresh organic matter input, disturbance, and moisture dynamics, which affect stabilization, mineralization, and vertical redistribution (Franzluebbers, 2021). SOC tends to accumulate first in the top 0–10 cm during early secondary succession and gradually re-distributes to deeper layers (Zhao et al., 2015). Consequently, the vertical SOC profile varies considerably depending on intrinsic soil characteristics and environmental conditions (Syswerda et al., 2011).

In West Africa, forest succession plays a key role in shaping

landscape dynamics, particularly in post-forest environments (Traoré
et al., 2024) where large areas of agricultural lands have been aban-
doned following cultivation (Amani et al., 2021). These fallow lands
represent a major opportunity for soil fertility restoration and ecosystem
regeneration (Yao et al., 2025). This is especially critical in a region
where agriculture is essential to food security and where sustainable
post-agricultural soil management strategies are needed to maintain
long-term productivity and mitigate climate change. Forest succession
thus emerges as a strategic pathway for restoring soil health and pre-
serving ecosystem services in degraded landscapes. While aboveground
carbon recovery during secondary succession is increasingly well
documented in West Africa (N'Guessan et al., 2019; Amani et al., 2022),
the recovery of SOC remains poorly understood. This gap limits our
ability to accurately assess the full impact of land-use changes on carbon
budgets and soil health – key challenges for both sustainable land
management and climate mitigation. Chronosequence-based studies in
the region have shown significant SOC depletion under tree-based
perennial cropping systems (Tondoh et al., 2015; 2019), yet a more
nuanced understanding of SOC dynamics during succession—particu-
larly through the lens of resistance and resilience—is still lacking.

The purpose of this research is to assess how soil organic carbon (SOC) recovers after agricultural land abandonment, with a specific focus on the mechanisms of resistance and resilience that shape this process during secondary forest succession in tropical environments. The study aims to quantify the rate at which SOC accumulates in topsoil horizons and to identify the main environmental and land-use factors that influence both the initial carbon loss (resistance) and the subsequent recovery trajectory (resilience). To achieve these objectives, the following hypotheses were formulated based on ecological resilience theory and forest succession dynamics:

1. SOC accumulates rapidly during the initial 10 years post-abandonment before stabilizing;

2. SOC concentrations in Nitisols (most clay soils) would be higher at all depths compared to Plinthosols, Acrisols and Alisols;

3. Resistance of forest-derived soil organic carbon (SOC) is higher under perennial cropping systems than under annual crops, particularly in lower slope positions;

4. Resilience of soil organic carbon (SOC) following agricultural land abandonment is enhanced by a high density of remnant trees, clay-rich soils, lower temperatures, and higher annual rainfall.

## 2. Materials and Methods

2.1. *Study area*

This study was conducted in Côte d'Ivoire (West Africa), between 4°30'–10°30' N and 2°30'–8°30' W, covering 322,462 km2 along the Gulf of Guinea. Mean annual temperature ranges from 25 °C to 26 °C, while annual precipitation varies between 1116 mm and 1642 mm (Table 1). Study design included eight sites of secondary and old-growth forest (Yaya, Irobo, Niégré, Téné, Agbo, Haut sassandra, Foumbou, and Badenou), distributed across four major phytogeographic sectors (Savané et al., 2011): ombrophilous, mesophilic, sudanese, and sub-sudanese to account for the potential climatic variability between the chronosequences (Fig. 1). At each site, twenty 0.2 ha plots were selected to represent different stages of forest succession following agricultural abandonment, using a chronosequence approach. Each chronosequence included plots distributed across four age classes since abandonment (0–10, 11–20, 21–30, and >30 years), resulting in a total of 32 plots per age class across the full study. In addition, 32 old-growth forest plots—never logged or burned in human memory and with no visible signs of disturbance—were included as reference ecosystems. Plot selection was informed by preliminary surveys and interviews with local landowners (current and former) as well as officials from SODEFOR, the national forest management agency. These interviews provided detailed
<table><caption>**Table 1**
Soil types and environmental characteristics of the sampled sites.</caption><thead><tr><th>Sites</th><th>Soil types</th><th>Altitude (m)</th><th>Total Annual Precipitation (mm)</th><th>Annual Mean Temperature (°C)</th><th>Geology</th></tr></thead><tbody><tr><td>Agbo</td><td>Plinthosol</td><td>116</td><td>1116</td><td>26.3</td><td>Birrimien (Flyschs)</td></tr><tr><td>Badenou</td><td>Acrisol</td><td>333</td><td>1169</td><td>26.8</td><td>Birrimien (Schist)</td></tr><tr><td>Foumbou</td><td>Plinthosol</td><td>378</td><td>1282</td><td>26.2</td><td>Granitoides (Granites and Migmatites)</td></tr><tr><td>Haut-Sassandra</td><td>Alisol</td><td>273</td><td>1481</td><td>25.8</td><td>Granitoides (Granites and Migmatites)</td></tr><tr><td>Irobo</td><td>Alisol</td><td>38</td><td>1493</td><td>25.9</td><td>Birrimien (Flyschs)</td></tr><tr><td>Niégré</td><td>Nitisol</td><td>81</td><td>1515</td><td>25.5</td><td>Granitoides (Granites and Migmatites)</td></tr><tr><td>Téné</td><td>Acrisol</td><td>183</td><td>1128</td><td>26.3</td><td>Granitoides (Granites and Migmatites)</td></tr><tr><td>Yaya</td><td>Alisol</td><td>91</td><td>1642</td><td>25.7</td><td>Birrimien (Flyschs)</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Chronosequences' Map. The black dots are the plots sampled in 8 chronosequences of varying ages and phytogeographical zones, i.e. a total of 159 plots, 0.2 ha each. The colours represent the phytogeographical zones of Côte d'Ivoire.</figcaption></figure>

information on the land-use history (type and duration of past cultivation), remnant tree presence, and the exact age of the secondary forest plots. This space-for-time substitution design allowed for the assessment of long-term changes in soil organic carbon and vegetation structure during forest succession under a range of environmental and management conditions (Teixeira et al. 2020).

Soils types include Acrisols, Alisols, Plinthosols and Nitisols. According to the IUSS Working Group WRB (2014), Acrisols and Alisols are acidic soils characterized by a subsurface accumulation of clay (i.e. argic horizon), a low base saturation (≤50 %) and a high aluminium saturation. Acrisols differ from Alisols in that they have low clay activity (CEC < 24 cmol·kg⁻¹ clay). Plinthosols are characterized by a layer indurated by iron (i.e. petroplinthite) or mottled materials that irreversibly hardens after repeated drying and wetting (i.e. plinthite). They have high content of iron and/or aluminium. In contrast, Nitisols are deep, well-drained and have a clay-rich subsurface horizon (clay ≥ 30 %). These soils develop on geological substrates such as flysch, granite, migmatite and schist formations (Vennetier et al., 1978).

## 2.2. Environmental variables

Local data were collected through structured interviews with landowners to gather information on sampling plots including age and agricultural history (Table 2): cotton, perennial crops (oil palm, cocoa, coffee, cashew) and staple or annual crops (banana, maize, peanut, rice, yam, and cassava). Topographical position (upper, middle, lower slope or plain) was recorded in the field and used to guide soil sampling. Remnant tree abundance was assessed on a tree-by-tree basis by asking their status at the time of land abandonment to landowners. Climate parameters were sourced from the CHELSA (Climatology at High Resolution for the Earth's Land Surface Areas) database at 1 km resolution (Karger et al., 2017), ensuring the capture of broad climate gradients while minimizing microclimate variations. Soil type data were retrieved from the soil map of Africa at the continental scale (Dewitte et al., 2013).

## 2.3. Soil sampling and laboratory analysis

Soil sampling was conducted between October 2022 and October
<table><caption>**Table 2**

Environmental covariables.</caption><thead><tr><th>Model Part</th><th>Variables</th><th>Description</th><th>Unit</th><th>Source</th></tr></thead><tbody><tr><td rowspan="2">Resistance</td><td>Topography</td><td>Topographic position of each plot</td><td>Lower Slope, Plain, Middle Slope, Upper Slope</td><td>Field</td></tr><tr><td>Land-Use</td><td>Previous land use</td><td>annual crops, cotton, perennial crops</td><td>Field</td></tr><tr><td rowspan="7">Resilience</td><td>Annual Precipitation</td><td>Precipitation (BIO 12)</td><td>mm</td><td>https://ch.elsa-cli.mate.org/</td></tr><tr><td>Precipitation Seasonality</td><td>Precipitation seasonality (BIO 04)</td><td>% (Coefficient of Variation)</td><td>https://ch.elsa-cli.mate.org/</td></tr><tr><td>Annual Temperature</td><td>Temperature (BIO 01)</td><td>°C</td><td>https://ch.elsa-cli.mate.org/</td></tr><tr><td>Temperature Seasonality</td><td>Temperature seasonality (BIO 15)</td><td>°C (standard deviation *100)</td><td>https://ch.elsa-cli.mate.org/</td></tr><tr><td>Remnants</td><td>Number of trees preserved during agriculture</td><td>stems/ha</td><td>Field</td></tr><tr><td>Soil Types</td><td>Soil groups for each plot from WRB classification</td><td>Plinthosol, Acrisol, Alisol, Nitisol</td><td>https://e.sdac.jrc.ec.europa.eu/</td></tr><tr><td>Soil Types</td><td>Soil groups for each plot from WRB classification</td><td>Plinthosol, Acrisol, Alisol, Nitisol</td><td>https://e.sdac.jrc.ec.europa.eu/</td></tr><tr><td>Depth</td><td></td><td></td><td></td><td></td></tr></tbody></table>

2023. At each soil sampling site, a 0.1 ha central plot (50 m × 20 m) was established within homogeneous vegetation and topography with the aim of representing the diversity of local vegetation conditions. The geographical position was recorded at the center of each plot with the help of a GPS. A composite standard soil sample per plot was collected with a soil auger from five points (four corners and one center of each plot) to reduce small-scale variability. Soils were sampled to a depth of 0–30 cm, stratified into three layers (0–10, 10–20, and 20–30 cm), in accordance with IPCC guidelines for assessing soil organic carbon in forest ecosystems (FAO, 2019a). Standard soil samples were air-dried, sieved to 2 mm, and analyzed at the Soil Health and Plant Nutrition Laboratory of the Institut National Polytechnique – Houphouët Boigny (INP-HB, Yamoussoukro, Côte d'Ivoire).

Organic carbon concentration was determined by flash combustion using the Dumas dry method (FAO, 2019b) with a Thermo Fisher™ FLASH 2000 Autoanalyzer, Series 2012.F0166 (ThermoFisher 2013, 2023). Particle size distribution was assessed according to soil type and topography. It was determined at depths of 0–10 and 20–30 cm, using the Robinson pipette method (Robinson, 1933) in four classes: clay (<2 µm), fine silt (2–20 µm), coarse silt (20–50 µm), and sand (50 µm–2 mm). Bulk density was estimated from soil cores collected at 0–10, 10–20, and 20–30 cm depths using a sampling cylinder. The samples were oven-dried at 105 °C for 48 h, and bulk density was calculated based on dry mass and a core volume of 237.58 cm³.

## 2.4. Modeling strategy

The modeling framework used in this study is based on the approach explained in detail in Maurent et al. (2023). This framework was developed to analyze the recovery in time of a given forest attribute (e.g. carbon content), leveraging statistical and process-based modeling techniques to account for environmental and historical land-use influences (Hérault et al., 2018; Piponiot et al., 2016). This modeling framework was successfully applied in Poorter et al. (2021) to model the recovery of soil carbon in secondary forests. The model is a Bayesian

hierarchical framework designed to describe soil carbon concentration of plot *i* in site *s* as a function of forest age *t* at three depths *d*: 0–10 cm (d = 010), 10–20 cm and 20–30 cm in both secondary and old-growth forests. The SOC recovery rate (resilience, λ) is estimated within this Bayesian framework as the exponential rate at which soil carbon returns toward its old-growth asymptotic value (θ<sub>inf</sub>) following abandonment. It is not a direct empirical calculation but a modeled parameter, shaped by land use, soil properties, remnant tree density, and climate variables.

For old-growth forests OG, the predicted soil organic carbon content (μ<sub>OG</sub>) at each depth depends only on the asymptotic value (θ<sub>inf,s</sub>) and depth-specific effects (θ<sub>d,soil</sub>) which are soil types-dependent:

At 0–10 cm depth,

$$ \mu_{OG,010,s} = \theta_{inf,s} \quad (1) $$

At 10–20 cm depth (d = 1020) and 20–30 cm depth (d = 2030),

$$ \mu_{OG,d,soil,s} = \theta_{d,soil} \times \theta_{inf,s} \quad (2) $$

The asymptotic value is the carbon content reached at high ages (for old-growth forests OG).

For secondary forests SF, the expected soil organic carbon content μ<sub>SF,010,i</sub> at 0–10 cm depth is modelled as a function of forest age *t*, resistance, and resilience.

* Resistance (Ω) is the decrease in soil carbon content due to land use (LU) and modulated by topography (T) relative to the carbon content of an infinitely old-growth forest (θ<sub>inf,s</sub>).

* Resilience (λ) is the recovery rate of soil carbon along a secondary forest succession, and is influenced by soil type soil, number of remnants trees Rem, and the standardized climatic variables such as annual precipitation AP, annual temperature AT, seasonality of precipitation SP, and seasonality of temperature ST.

$$ \mu_{SF,010,i,s} = (\theta_{inf,s} - \Omega_{SF,i}) + \Omega_{SF,i} \times (1 - \exp(-\lambda_{SF,i} \times t_i)) \quad (3) $$

where θ<sub>inf,s</sub> is the asymptotic carbon content per site and with

$$ \Omega_{SF,i,s} = (\Omega_{LU,i} + \Omega_{T,i}) \times \theta_{inf,s} \quad (4) $$

where Ω<sub>LU</sub> is the effect of land use on resistance and Ω<sub>T</sub> is the effect of topography, and *s* is the site

and with

$$ \lambda_{SF,i} = (\lambda_{soil} + \theta_{Rem} \times \text{Rem}_i) \times \exp(\theta_{AP} \times AP_i + \theta_{AT} \times AT_i + \theta_{SP} \times SP_i + \theta_{ST} \times ST_i) \quad (5) $$

where θ<sub>Rem</sub> is the effect of remnant trees on recovery rates and θ<sub>AP</sub>, θ<sub>AT</sub>, θ<sub>SP</sub>, θ<sub>ST</sub> are the effects of, respectively, annual precipitation, temperature and seasonality of precipitation, temperature.

The expected soil organic carbon contents at 10–20 cm depth and 20–30 cm depth are then defined relative to the carbon content at depth 0–10 cm. The ratio between carbon content at d∈{1020, 2030} and at 0–10 cm depth is modelled as a function of forest age *t* and a time-adjusted accumulation rate (λ<sub>t</sub>) on the quantity to be filled θ<sub>fill</sub>.

$$ \mu_{SF,d,i} = (\theta_{d,soil} + \theta_{fill} \times \exp(-\lambda_t \times t_i)) \times \mu_{SF,010,i} \quad (6) $$

To infer the model, the observed soil carbon content *C* at each depth *d* is modeled as through a log-normal likelihood distributed around the median predictions (μ):

For old-growth forests,

$$ C_{OG,d,i} \sim LN(\log(\mu_{OG,d,soil,s}, \sigma_{OG})) \quad (7) $$

For secondary forests,

$$ C_{SF,d,i} \sim LN(\log(\mu_{SF,d,soil,s}, \sigma_{SF})) \quad (8) $$
The model estimates the following key parameters and their hyperdistributions:

1. Asymptotic Carbon Content

$$ \circ \theta_{inf,s} \sim LN(\log(\mu_{\thetainf}), \sigma_{\thetainf}) \quad (9) $$

2. Resistance (Ω):

o Land use-specific resistance Ω<sub>LU</sub> ~ LN(log(μ<sub>ΩLU</sub>), σ<sub>ΩLU</sub>) (10)

o Topography-specific resistance Ω<sub>T</sub> ~ LN(log(1), σ<sub>ΩT</sub>) (11)

4. Resilience (λ):

o Soil-type-specific resilience λ<sub>soil</sub> ~ LN(log(μ<sub>λsoil</sub>), σ<sub>λsoil</sub>) (12)

o Effects of covariates: θ<sub>Rem</sub> is the effect of remnant trees and θ<sub>AP</sub>, θ<sub>AT</sub>, θ<sub>SP</sub>, θ<sub>ST</sub> the effects of, respectively, annual precipitation, annual temperature and seasonality of precipitation, temperature.

5. Soil accumulation rate Effects:

o For 10–20 cm and 20–30 cm depths, θ<sub>d,soil</sub> ~ LN(log(μ<sub>θsoil</sub>), σ<sub>θsoil</sub>). (13)

The log-normal distributions are often chosen because soil carbon concentrations and model parameters are strictly positive and often exhibit skewed distributions. The log-normal likelihood ensures that carbon content data remains positive and captures the typical skew observed in environmental data. The model is written in Stan (Carpenter et al. 2017), and inferred using 5000 iterations, ensuring a good convergence rate. All analyses were conducted with R (4.4.2 version).

3. Results

3.1. Soil organic carbon recovery rate

SOC values predicted by the model closely matched observed values (Fig. 2), explaining 65 % of the variance in the data. At all three depths (0–10 cm, 10–20 cm, and 20–30 cm), the soil recovers 90 % of the lost organic carbon within 10 years (Fig. 3), with an average recovery rate of 0.23 % of C.year<sup>-1</sup>. The organic carbon content in all three layers increases rapidly at the beginning of forest succession and then stabilizes.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Recovering Trajectories. Predicted trajectories for recovery of the soil carbon content for the 3 depths studied (0–10 cm, 10–20 cm, 20–30 cm). Shown are the maximum likelihood trajectory (center line) and the 90 % confidence interval (colored areas). The trajectories are represented for average environmental conditions. The observed variability is solely due to the complete propagation of model uncertainties on the predictions.</figcaption></figure>

3.2. SOC and physical properties variation with depth and soil type during forest succession

SOC progressively decreased with depth, regardless of soil type. Nitisols had the highest SOC at all depths, followed by Plinthosols and Alisols, then by Acrisols. In contrast, mean bulk density increased with

<figure><img src="image_6.png" /><figcaption>Fig. 2. Model Performance.</figcaption></figure>
depth. All four soil types had a sandy loam texture. At 0–10 cm, Nitisols and Plinthosols were slightly higher in clay content than Acrisols and Alisols. At 20–30 cm, only Plinthosols had more clay than Acrisols, with Nitisols showing the lowest clay content (Table 3). The distribution of SOC varied with depth throughout forest succession. Over time, a slight decrease in SOC proportion was observed in deeper layers, with reductions of –2.63 % at 10–20 cm and –3.95 % at 20–30 cm. In contrast, the surface layer (0–10 cm) maintained a dominant proportion and exhibited a moderate increase of 6.57 % (Fig. 4A). In old-growth forests, the proportion of carbon in Alisols and Nitisols at 0–10 cm was slightly higher, accounting for more than 50 % of SOC content, than in Acrisols and Plinthosols (Fig. 4B).

### 3.3. Soil resistance

Based on modeling, SOC resistance values were approximately 50 % at the beginning of secondary succession, suggesting that around 50 % was lost since deforestation (Fig. 5). Regarding the effects of previous land uses, initial soil resistance at the onset of succession was about 42 % and 47 % for cotton and perennial crops respectively, compared to nearly 57 % for staple or annual crops. In terms of topography, significant uncertainties remain in parameter estimates. However, the initial resistance at the onset of succession was approximately 42 % at the lower slope, lower than the overall average of 50 %, while it exceeded 52 % for the three other topographic positions.

### 3.4. Soil resilience

Rising values of climate variables, including annual precipitation, annual temperature, and precipitation and temperature seasonality, negatively affected resilience by slowing down SOC recovery. In terms of soil type, Acrisols exhibited the highest resilience in terms of SOC. Conversely, SOC resilience was lower in other soil types (Plinthosols, Alisols, and especially Nitisols). Furthermore, a higher density of remnant trees enhanced SOC resilience, with the recovery rate doubling at 50 remnant trees per hectare compared to sites without remnant trees (Fig. 6).

## 4. Discussion

### 4.1. Soil organic carbon recovery rate

Our findings reveal that soil organic carbon (SOC) increases rapidly during the early stages of forest succession, with soils recovering approximately 90 % of their original carbon stocks, within just 10 years. This result supports our 1st hypothesis, which proposed that SOC

<table><caption>**Table 3**
Soils chemical and physical properties (mean ± SE) across depths and soil types.</caption><thead><tr><th>Soil types</th><th>Depth (cm)</th><th>C (%)</th><th>Bulk density (g cm<sup>-3</sup>)</th><th>Clay (%)</th><th>Silt (%)</th><th>Sand (%)</th></tr></thead><tbody><tr><td rowspan="2">Acrisol</td><td rowspan="2">0–10</td><td>1.64 ± 0.93</td><td>1.42 ± 0.27</td><td>9.1 ± 1.43</td><td>27.24 ± 6.42</td><td>63.66 ± 6.61</td></tr><tr><td>2.03 ± 0.6</td><td>1.21 ± 0.21</td><td>7.83 ± 2.32</td><td>24.29 ± 1.69</td><td>67.87 ± 3.42</td></tr><tr><td rowspan="2">Nitisol</td><td rowspan="2">0–10</td><td>2.61 ± 0.71</td><td>1.18 ± 0.18</td><td>14.75 ± 1.25</td><td>30.57 ± 9.44</td><td>54.68 ± 10.69</td></tr><tr><td>2.34 ± 1.03</td><td>1.32 ± 0.25</td><td>10.98 ± 2.42</td><td>26.54 ± 3.07</td><td>62.47 ± 4.69</td></tr><tr><td rowspan="2">Plinthosol</td><td rowspan="2">0–10</td><td>0.69 ± 0.26</td><td>1.63 ± 0.18</td><td>11.1 ± 2.52</td><td>31.46 ± 3.33</td><td>57.44 ± 4.84</td></tr><tr><td>0.74 ± 0.29</td><td>1.6 ± 0.18</td><td>9.78 ± 2.41</td><td>22.85 ± 1.46</td><td>67.37 ± 2.39</td></tr><tr><td rowspan="2">Alisol</td><td rowspan="2">20–30</td><td>1.04 ± 0.51</td><td>1.58 ± 0.21</td><td>5.75 ± 2.75</td><td>39.41 ± 2.14</td><td>54.84 ± 4.89</td></tr><tr><td>1.01 ± 0.43</td><td>1.71 ± 0.14</td><td>13.52 ± 1.89</td><td>25.18 ± 5.75</td><td>61.30 ± 5.30</td></tr></tbody></table>

accumulates rapidly during the initial 10 years post-abandonment before stabilizing. While this recovery is remarkable, it is slower than the pace observed by Poorter et al. (2021), who documented SOC recovery within five years across 77 sites in the Neotropics and West Africa — likely due to differences in land-use intensity. In their study, sites began with SOC levels around 80 %, whereas our study area started at just 50 %, pointing to more substantial depletion and therefore longer recovery trajectories. SOC restoration timelines vary widely across regions, shaped by local climate and soil conditions. For instance, Rhoades et al. (2000), working in warm and wet tropical areas (17–26 °C; 3200 mm/year), estimated a 20-year recovery period. In contrast, Li et al. (2012), in a global meta-analysis, found that meaningful SOC increases often occurred only after 30 years. In temperate zones, recovery can take far longer: Poelplau et al. (2011) estimated more than 120 years for full SOC restoration in mineral soils under cooler (<18 °C) and drier (900 mm/year) conditions. These disparities underscore the importance of environmental context. Warmer, more humid regions—like those studied by Rhoades et al. (2000)—support faster biomass accumulation and organic matter turnover, while colder, drier areas, such as those in Poelplau’s work, slow down these processes. Supporting this view, Kassi et al. (2017) reported rapid SOC recovery in central Côte d’Ivoire, with post-abandonment soils surpassing nearby reference forests within 5 to 10 years. Altogether, our results reinforce that secondary succession and natural reforestation can effectively rebuild SOC stocks in tropical regions. In Côte d’Ivoire, where conditions favor vegetation regrowth, abandoned agricultural lands show strong potential for carbon recovery within a decade.

### 4.2. SOC variation with depth and soil type during forest succession

Nitisols exhibited the highest soil organic carbon (SOC) concentrations across all depths, followed by Plinthosols and Alisols, while Acrisols consistently showed the lowest values in secondary forests —confirming our 2nd hypothesis, which posited that SOC concentrations in Nitisols (the most clay-rich soils) would exceed those in other soil type. These findings align with Kögel-Knabner and Amelung (2021), who highlight the superior SOC storage potential of Nitisols compared to Plinthosols and Acrisols. According to WRB (2014), Nitisols are among the most favorable tropical soils for long-term SOC sequestration due to their deep, porous, and well-structured solum, which supports deep rooting, effective water retention, and erosion resistance. Combined with moderate to high chemical fertility and a high content of weatherable minerals, these characteristics foster organic matter accumulation and stabilization under forest cover. At the surface layer (0–10 cm), all soil types maintained SOC levels within the resilient threshold range of 1.5–2.0 % defined by Lal (2014), indicating suitability for sustainable agriculture.

SOC levels increased with forest succession at all depths, but the most dynamic changes occurred near the surface. The 0–10 cm layer exhibited a moderate increase of 6.57 %. In contrast, the relative proportion of total SOC found in the deeper layers (10–20 cm and 20–30 cm) slightly declined, by –2.63 % and –3.95 % respectively, indicating a vertical redistribution of carbon toward surface horizons. This pattern does not reflect an absolute decrease in SOC content at depth, but rather a greater concentration of SOC accumulation in the topsoil. This depth-related trend supports Zhao et al. (2015), who showed that SOC accumulation is more prominent in surface layers during early forest recovery. Following land abandonment, litter incorporation through humification accelerates SOC accumulation near the surface. This buildup is primarily driven by inputs from aboveground biomass—such as decaying litter and deadwood—while microbial degradation processes gradually slow (Smith, 2008). Although our study focuses on SOC, these dynamics parallel aboveground biomass recovery patterns described by N'Guessan et al. (2019), reinforcing the link between vegetation regrowth and belowground carbon dynamics.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Drivers of Soil Filling. Drivers of soil filling. The variability of soil filling in depth is studied in link to succession time (left panel) and soil types (right panel). Excluding the studied variable, the values are presented for average environmental conditions at maximum likelihood.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Drivers of Soil Resistance. Drivers of soil resistance to land-use change. Effects of topography (blue) and major crop types (brown) are expressed as a percentage of resistance. A value of 100% indicates highly resistant soil (no change in carbon percentage relative to old-growth forest), and the lower the value, the less resistant the soil. Excluding the target studied variable, the values are represented for average environmental conditions. The observed variability is solely due to the complete propagation of model uncertainties on the resistance values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

### 4.3. Soil organic carbon resistance

Soil carbon resistance averaged 50 % at the time of abandonment, with higher values observed under annual cropping systems compared to cotton and perennial crops. This finding does not support our 3rd hypothesis, which proposed that forest-derived SOC would be more effectively retained under perennial cropping systems, particularly in lower slope positions. Perennial systems are typically associated with greater SOC accumulation than annual crops due to sustained biomass productivity and reduced disturbance (Ledo et al., 2020). Their longer growing cycles minimize soil disruption, thereby preserving structure, limiting erosion, and enhancing organic matter stabilization. However, our findings suggest a more complex reality. While perennial crops generally promote carbon storage, they are also linked to elevated microbial activity (Tiemann and Grandy, 2015), which may accelerate organic matter decomposition and reduce carbon resistance. Conversely, annual crops can contribute substantial organic inputs via crop residues, improving SOC retention (Jakab et al., 2023). Moreover, Shang et al. (2024) reported that perennial crops do not consistently outperform annuals in enhancing SOC, especially where soil fertility is

low or biomass inputs are limited. In our study context – characterized by low mechanization – annual systems often involve reduced tillage, which helps preserve SOC by curbing CO₂ emissions and supporting long-term sequestration (Lal, 2004). Importantly, not all annuals have the same impact. Cotton, for instance, produces fewer residues than crops like maize (Causarano et al., 2006), which likely explains the lower SOC resistance observed in cotton fields.

Topography also played a significant role in shaping SOC resistance at the onset of succession. Contrary to our hypothesis, lower slopes exhibited the weakest resistance compared to mid and upper slopes. This finding is consistent with Nozari and Borùvka (2023), who reported a positive correlation between slope steepness and SOC, suggesting that steeper slopes—often with reduced water retention—can retain more carbon. On the other hand, Hu et al. (2021) found greater SOC in lower slopes, attributed to erosion-driven deposition of soil and litter. In our study, excess moisture accumulation in lower slope areas may have intensified microbial activity, increasing CO₂ emissions and reducing SOC stability. In contrast, well-drained soils on mid slopes and hilltops likely experienced lower moisture levels, suppressing microbial respiration and promoting SOC retention (Wang et al., 2017). These results also contrast with findings from Epron et al. (2006) in French Guiana, where hilltops exhibited higher soil respiration rates than lower areas. This discrepancy may be due to climatic differences: Epron et al. studied a humid tropical forest with 3000 mm of annual rainfall, while Wang et al. (2017) worked in a semi-arid setting with only 560 mm/year. In very wet environments, excessive soil moisture can limit oxygen diffusion and suppress microbial activity, altering SOC dynamics. These nuances highlight the importance of contextual factors—especially climate and topography—in shaping soil carbon resistance following land-use change.

### 4.4. Soil resilience

Our hypothesis—that SOC restoration following agricultural land abandonment is modulated by a high number of remnant trees, clay-rich soils, low temperatures, and abundant rainfall—was only partially confirmed, as each of these drivers showed differing levels of influence.

Climatic variables such as annual temperature, precipitation, and seasonality were found to negatively affect SOC recovery. Our results are consistent with studies by Pries et al. (2017) and Yan et al. (2017), which reported a strong negative correlation between SOC and temperature. Warmer conditions stimulate microbial activity, accelerating
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Drivers of Soil Resilience. Drivers of soil resilience to land-use change. The effects of climate (blue), major soil types (brown), and remnant tree density (green) are expressed in terms of intrinsic recovery rate (resilience values). A value of 0 indicates a situation where soil carbon content does not recover, and the higher the value, the faster the recovery. For climatic variables, the provided resilience values correspond to a value of 1 for the standardized variable. For soil types, the resilience directly corresponds to the parameter value in the model. For residual tree density, the resilience values for 3 tree densities (0, 20, 50) are reported. Excluding the targeted variable, the values are presented for average environmental conditions. The orange vertical line represents the average resilience value, i.e. for averaged environmental conditions. The observed variability is solely due to the complete propagation of model uncertainties on the resilience values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

decomposition and SOC mineralization. This process releases carbon as CO₂, depleting soil carbon stocks and reducing the soil's ability to retain or rebuild SOC (Jobbégy and Jackson, 2000; Li et al., 2012). Higher annual precipitation did not enhance SOC recovery, contradicting both our hypothesis and the findings from Hobley et al. (2015). Although moisture generally favour plant productivity and litter input, excessive rainfall can disrupt soil aggregates, mobilize previously stabilized carbon (Jiang et al., 2021), and promote leaching – particularly in highly weathered tropical soils – contributing to surface SOC losses. Additionally, increased seasonality in both temperature and precipitation exacerbates moisture fluctuations, stimulating microbial turnover and intensifying SOC mineralization (Xu et al., 2020; Wang et al., 2023). These results highlight the complexity of climate effects, suggesting that favorable climatic conditions alone are insufficient to ensure SOC resilience, particularly in fragile tropical soils.

Among edaphic drivers, the hypothesis that clay-rich soils (e.g., Nitisols) would enhance SOC resilience was not supported. Acrisols unexpectedly demonstrated the highest resilience for SOC recovery. In contrast, Alisols, Plinthosols, and especially Nitisols exhibited slower recovery. Despite their higher SOC storage potential, Nitisols' low pH and strong organic matter fixation in subsoil horizons may make them more vulnerable to carbon losses after cultivation (Hernandez-Jimenez et al., 2017). Our findings echo those of Strey et al. (2016), who found that Acrisols outperformed Ferralsols in recovering herbaceous-derived SOC. Similarly, Desjardins et al. (2004) reported greater pasture-derived SOC in Acrisols than in Ferralsols. Several mechanisms may explain the enhanced resilience observed in Acrisols. One key factor is root distribution: due to low pH and aluminum toxicity, plant roots in Acrisols tend to concentrate in upper soil layers (Legger, 1991), increasing surface carbon input through root turnover. Additionally, their high bulk density slows organic matter decomposition, and waterlogging in the B horizon restricts oxygen diffusion, suppressing microbial activity and helping conserve SOC (Quesada et al., 2011). These physical constraints may act synergistically to reduce carbon losses and improve retention during succession.

In contrast, our hypothesis was strongly supported regarding the role of remnant trees. Higher densities of remnant trees significantly

accelerated SOC recovery rates. This supports Sirois et al. (1998), who observed higher SOC levels under remnant trees in Guinea. These trees likely improve soil function through multiple pathways: promoting deeper rooting, enhancing soil structure, and fostering microbial habitats. Their presence supports aggregate formation and organic matter stabilization (Seidl et al., 2014), while also facilitating the re-establishment of late-successional species and accelerating forest recovery more broadly (Amani et al., 2021; 2022). Taken together, our findings point to a multifactorial control of SOC recovery, shaped by interactions among climate, soil type, land-use legacy, and biological structure. Notably, passive restoration—especially where remnant trees are preserved—can be highly effective in promoting SOC resilience. This underscores the importance of land management practices that enhance biomass production, root development, and organic matter recycling. Encouraging natural regeneration (Doua-Bi et al., 2021; Kouassi et al., 2023; 2025), together with strategic conservation of remnant trees, offers a practical and low-cost pathway to restoring soil carbon.

## 5. Conclusion

These results emphasize that the restoration of soil organic carbon is not merely a function of time, but a dynamic process governed by the interaction of soil type, climate variability, historical land use, and biological legacies such as remnant trees (Kouamé et al. 2025).

Hypothesis 1 was fully supported: we found that soils can regain up to 90 % of their original carbon within a decade, demonstrating that SOC recovery can occur rapidly during early succession.

Hypothesis 2 was also confirmed: Nitisols, the most clay-rich soils in our study, consistently exhibited the highest SOC concentrations across all depths. However, contrary to Hypothesis 4, these clay-rich soils did not show the fastest recovery rates – Acrisols outperformed them in terms of resilience.

Hypothesis 3 was not supported, as SOC resistance was higher under annual crops than under perennial systems, and was lowest in lower slope positions. These unexpected patterns point to the complexity of interactions between past land use, topography, and soil recovery.
Hypothesis 4 was partially supported. We found that low temperatures were indeed associated with faster SOC recovery, confirming part of the hypothesis. However, higher rainfall was linked to slower recovery, likely due to enhanced microbial turnover and leaching. In strong support of the hypothesis, remnant tree density emerged as a key driver of SOC resilience, significantly accelerating recovery rates and highlighting the critical role of biological legacies in soil restoration.

This study identifies practical drivers of soil organic carbon (SOC) recovery in post-agricultural tropical landscapes. Remnant trees significantly enhance SOC resilience by improving soil structure, promoting root activity, and supporting microbial processes that stabilize organic matter. SOC recovery is also strongly shaped by local conditions, including soil type, land-use history, and climate variability, which influence both the loss (resistance) and recovery (resilience) of carbon stocks. These findings are particularly relevant for West Africa, where widespread land abandonment and soil degradation threaten agricultural productivity and ecosystem stability. Prioritizing areas with favorable conditions and maintaining remnant trees can substantially improve SOC restoration outcomes. Natural regeneration, when guided by context-specific management, offers an effective and low-cost approach to restoring SOC. Further research should focus on how root traits and climate variability interact to control carbon accumulation, to better inform targeted restoration strategies.

## Funding sources

This study was conducted with the financial support from the "Fonds Français pour l'Environnement Mondial" (FFEM) under the "Agence

Française de Développement" (AFD) grant Agreement No. CCI 1711.01 D, as part of the TERRI4SOL project.

## CRediT authorship contribution statement

Ahou Blandine Koffi: Writing – original draft, Visualization, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Ebagnerin Jérôme Tondoh: Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Conceptualization. Evans Ehouman: Writing – review & editing, Project administration, Methodology, Investigation, Data curation. Freycon Vincent: Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization. Camille Piponiot: Writing – review & editing, Validation, Supervision, Methodology, Investigation, Conceptualization. Bienvenu Amani: Writing – review & editing, Supervision, Methodology, Investigation, Data curation, Conceptualization. Raphaël Aussenac: Writing – review & editing, Data curation. Bob Kouadio: Writing – review & editing, Investigation, Data curation. Thierry Roland Kouamé: Writing – review & editing, Investigation, Data curation. Bruno Hérault: Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration. Methodology, Investigation, Funding acquisition, Formal analysis, 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.

## Appendix

<table><caption>Appendice A – Distribution of model parameters values.</caption><thead><tr><th></th><th>median</th><th>2.50 %</th><th>25 %</th><th>50 %</th><th>75 %</th><th>97.50 %</th></tr></thead><tbody><tr><td colspan="7"><strong>Old-Growth Forests</strong></td></tr><tr><td>μ<sub>0inf</sub></td><td>2.31</td><td>1.81</td><td>2.13</td><td>2.29</td><td>2.46</td><td>2.9</td></tr><tr><td>σ<sub>0inf</sub></td><td>0.28</td><td>0.15</td><td>0.2</td><td>0.26</td><td>0.33</td><td>0.56</td></tr><tr><td colspan="7"><strong>Soil accumulation rate</strong></td></tr><tr><td>μ<sub>01020</sub></td><td>0.56</td><td>0.43</td><td>0.52</td><td>0.55</td><td>0.58</td><td>0.75</td></tr><tr><td>σ<sub>01020</sub></td><td>0.21</td><td>0.02</td><td>0.09</td><td>0.15</td><td>0.24</td><td>0.75</td></tr><tr><td>μ<sub>02030</sub></td><td>0.41</td><td>0.29</td><td>0.37</td><td>0.4</td><td>0.43</td><td>0.63</td></tr><tr><td>σ<sub>02030</sub></td><td>0.28</td><td>0.05</td><td>0.13</td><td>0.2</td><td>0.32</td><td>0.95</td></tr><tr><td>λ<sub>t</sub></td><td>0.42</td><td>0.1</td><td>0.24</td><td>0.35</td><td>0.58</td><td>0.96</td></tr><tr><td colspan="7"><strong>Resistance</strong></td></tr><tr><td>μ<sub>01LU</sub></td><td>0.52</td><td>0.29</td><td>0.45</td><td>0.51</td><td>0.58</td><td>0.81</td></tr><tr><td>σ<sub>01LU</sub></td><td>0.51</td><td>0.07</td><td>0.18</td><td>0.3</td><td>0.55</td><td>2.21</td></tr><tr><td>σ<sub>02LU</sub></td><td>0.1</td><td>0.02</td><td>0.05</td><td>0.08</td><td>0.11</td><td>0.27</td></tr><tr><td colspan="7"><strong>Resilience</strong></td></tr><tr><td>μ<sub>0soil</sub></td><td>0.08</td><td>0.01</td><td>0.03</td><td>0.07</td><td>0.13</td><td>0.19</td></tr><tr><td>σ<sub>0soil</sub></td><td>6.16</td><td>1.43</td><td>2.86</td><td>4.3</td><td>6.93</td><td>20.86</td></tr><tr><td>θ<sub>Rem</sub></td><td>0.22</td><td>0.05</td><td>0.12</td><td>0.18</td><td>0.27</td><td>0.61</td></tr><tr><td>θ<sub>AP</sub></td><td>-0.07</td><td>-0.76</td><td>-0.34</td><td>-0.09</td><td>0.16</td><td>0.77</td></tr><tr><td>θ<sub>AT</sub></td><td>-0.82</td><td>-1</td><td>-0.94</td><td>-0.87</td><td>-0.75</td><td>-0.41</td></tr><tr><td>θ<sub>SP</sub></td><td>-0.67</td><td>-0.99</td><td>-0.89</td><td>-0.74</td><td>-0.51</td><td>0.06</td></tr><tr><td>θ<sub>ST</sub></td><td>-0.54</td><td>-0.98</td><td>-0.8</td><td>-0.58</td><td>-0.33</td><td>0.11</td></tr></tbody></table>

## Data availability

Data will be deposited in a public repository upon acceptance.

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