# Vulnerability to Climate Changes of Tropical Forests Across Africa

Mady Parfait Noé Madingou, Gilles Dauby, Adeline Fayolle, Anaïs-pasiphaé Gorel

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Mady Parfait Noé Madingou, Gilles Dauby, Adeline Fayolle, Anaïs-pasiphaé Gorel. Vulnerability to Climate Changes of Tropical Forests Across Africa. Diversity and Distributions, 2025, 31 (10), pp.e70104. 10.1111/ddi.70104. hal-05362401

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RESEARCH ARTICLE

OPEN ACCESS

# Vulnerability to Climate Changes of Tropical Forests Across Africa

Mady Parfait Noé Madingou¹ ID | Gilles Dauby² | Adeline Fayolle¹,³,⁴ | Anaïs-Pasiphaé Gorel¹

¹Forest is Life, Gembloux Agro-Bio Tech, Université de Liège, Gembloux, Belgium | ²AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, France | ³UPR Forêts et Sociétés, CIRAD, Campus International de Baillarguet, Montpellier Cedex 5, France | ⁴Forêts et Sociétés, CIRAD, Université Montpellier, Montpellier, France

**Correspondence:** Mady Parfait Noé Madingou (mmadingou@uliege.be)

**Received:** 11 February 2025 | **Revised:** 29 September 2025 | **Accepted:** 30 September 2025

**Editor:** Rob Cooke

**Funding:** This work was supported by the Fonds De La Recherche Scientifique-FNRS (O.0026.22).

**Keywords:** climate change | exposure | risk assessment | safety margin | tropical moist forest | vulnerability

## ABSTRACT

**Aim:** Global climate projections identify tropical regions as hotspots of climate change during the 21st century. The few ground data in tropical Africa confirm significant warming and drying over the last decades, but how plant communities will tolerate these new climate conditions remains vastly uncertain. In this study, we assess the climatic vulnerability of tropical moist forests across Africa.

**Location:** Tropical Africa.

**Methods:** We mapped climate change exposure across the tropical moist forest biome, focusing on mean annual temperature (MAT), mean annual precipitation (MAP) and climatological water deficit (CWD) using climate projections for 2085 from five regional models under RCP4.5 and RCP8.5. Using occurrence records for 3,536 tree and shrub species, we estimated species' climatic limits and safety margins, then averaged these margins at the community level. Finally, we combined exposure and safety margins to assess species- and community-level risk by 2085.

**Results:** Under RCP4.5, corresponding to an average warming of 2.4°C by 2085, 92% of species (3,256) could be at risk in at least one community where they occur. This rate increases to 96% (3,405 species) under RCP8.5, with an average warming of 4.3°C. In all scenarios, the most at-risk communities are concentrated in low-elevation regions, where species have few opportunities to migrate if their climatic limits are exceeded. The high risk across the forest biome results from the combination of significant and widespread temperature increases and the relatively narrow safety margins of the species. Specifically, 50% of species have an average safety margin below 1.6°C above baseline temperatures, suggesting they are already near their observed climatic limits.

**Main Conclusions:** Beyond refining our understanding of the vulnerability of tropical moist forests across Africa, our results have far-reaching implications for conservation, allowing us to target species and communities of interest for further monitoring and conservation efforts.

## 1 | Introduction

Tropical forests store more than 200 Pg C in aboveground live biomass (Baccini et al. 2012; Li et al. 2022), contribute over 32%

of global primary productivity and exert a significant influence on the hydrological cycle (Beer et al. 2010). Furthermore, they harbour more than half of the described species (Pillay et al. 2022), and provide essential resources to around 1.5 billion

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). Diversity and Distributions published by John Wiley & Sons Ltd.
people who depend on these forests for food, medicine, potable water, shelter and energy (Vira et al. 2015).

Global climate projections identify tropical regions as per-
sistent hotspots of climate change during the 21st century
(IPCC 2023), meaning that they will experience more intense
or rapid climate changes compared with other regions. The sit-
uation of tropical forests in Africa is particularly concerning,
as they are already found under drier and more seasonal cli-
mates than the other tropical regions (Parmentier et al. 2007;
Guan et al. 2015). Furthermore, the expected future climate
conditions in much of Africa have no current analogues, cre-
ating so-called ‘novel climates’ (Greve et al. 2013). In central
Africa, depending on the climate scenarios, temperatures are
projected to increase by 2°C–4°C by 2099 compared with the
mid-20th century baseline (Aloysius et al. 2016), a trend con-
firmed by rare ground data, which report warming rates of
+0.21 and 0.25°C per decade, respectively, at Yangambi in
the Democratic Republic of Congo for the period 1960–2020
(Kasongo Yakusu et al. 2023) and at Lopé in Gabon for the
period 1984–2018 (Bush et al. 2020). Unlike temperature,
precipitation projections lack consensus due to inter- and in-
tramodel variability (Dosio et al. 2019, 2022), with predicted
changes ranging from –9% to +27% (Aloysius et al. 2016),
which complicates risk projections for biodiversity (Greve
et al. 2013). However, inland regions in tropical Africa are
likely to be exposed to an intensification of the dry seasons
(Zhou et al. 2014), a trend confirmed by ground data in the
two localities of central Africa previously mentioned (Bush
et al. 2020; Kasongo Yakusu et al. 2023).

In the Amazon, it has been demonstrated that forests ex-
posed to a 100 mm increase in water deficit lost 5.3 Mg of
aboveground carbon per hectare (Phillips et al. 2009) due to
widespread mortality of large trees, which are less tolerant
to drought-induced water stress than smaller ones (Bennett
et al. 2015; Phillips et al. 2010). A significant and widespread
decline in carbon sinks has been observed across the Amazon
Basin, a pattern that has not been observed for tropical moist
forests of Africa (Hubau et al. 2020). However, subregional
studies have reported shifts in the taxonomic, functional and
phylogenetic composition of forest communities (Aguirre-
Gutiérrez et al. 2020). In Ghana, tree functional changes have
been identified, with an increase in the abundance of decidu-
ous and canopy species with intermediate light demand, and a
decrease in evergreen, subcanopy and shade-tolerant species,
and these changes have been associated with two decades
of prolonged drought (Fauset et al. 2012; Aguirre-Gutiérrez
et al. 2019). In the semideciduous forests of central Africa, a
combined analysis of plot data (1982–2008) and models re-
vealed that tree growth declines as drought intensifies, but
understory shade-tolerant species are less affected, suggesting
that increased drought intensity could shift forest composition
(Ouédraogo et al. 2013). Meanwhile, in Afromontane forests,
plot data (monitored between 2010 and 2022) highlighted
an increase in the abundance of thermophilic species from
warmer climates (Cuni-Sanchez et al. 2024). These composi-
tional shifts highlight how African tree species may respond
differently to climatic changes, driven by their unique combi-
nations of functional and physiological traits that shape their
environmental tolerance ranges and limits. The latter remain

largely misunderstood for tropical tree species, despite being
crucial for quantifying mortality risks at specific sites under
different climate change scenarios.

To quantify the mortality risk in a specific site, species' hy-
draulic and thermal tolerance are widely employed to iden-
tify critical thresholds of water availability or temperature
(Tavares et al. 2023). The hydraulic safety margin is calcu-
lated as the difference between the minimum water potential
experienced by a tree in a given site and the limit of hydraulic
tolerance of the species, i.e., the water potential at which the
xylem undergoes hydraulic failure due to embolism (Meinzer
et al. 2009). Similarly, the thermal safety margin is calculated
as the difference between maximum leaf surface temperature
in the field and the critical temperature at which metabolic
processes, such as photosynthesis, are disrupted (Marchin
et al. 2022). Measuring tree physiological safety margins can
yield detailed information about species' performance and
tolerance under climatic stress. However, the lack of such
data for thousands of species in the tropical moist forests of
Africa hinders our ability to understand how tree species will
respond to rapid climate change and to forecast which for-
est communities are most at risk from climate-related stress.
Functional traits of species (including ecophysiological traits),
together with biotic interactions and macroclimate, act as key
filters shaping their spatial distribution (Kirschbaum 2000;
Pennington et al. 2004). Consequently, information derived
from species distributions and realised climatic niche lim-
its can serve as a useful proxy in the absence of direct eco-
physiological data (Esperon-Rodriguez, Ordoñez, et al. 2022).
Climatic tolerance limits inferred from the observed niche of
species are routinely used in ecological and biogeographical
studies to estimate species' climatic safety margins and assess
the vulnerability of communities and ecosystems to climate
change (Gallagher et al. 2019; Esperon-Rodriguez, Tjoelker,
et al. 2022; Esperon-Rodriguez, Ordoñez, et al. 2022). In this
context, the climatic safety margin is defined as the difference
between the observed climatic niche limits of a given species
and the climate conditions at the locations where the species
currently occurs (Gallagher et al. 2019).

In this study, we aimed to assess the climatic vulnerability of
tropical moist forests across Africa. We applied the approach
proposed by Gallagher et al. (2019), which quantifies (1) the ex-
posure to climate change, (2) the climatic safety margin based
on the observed species' climatic limits and (3) the climate risk
which combines the exposure and the safety margin. Exposure
is the extent of climate change that a specific site is likely to
experience. The climatic safety margin is the extent of climate
change that a species can tolerate in a specific site before ex-
ceeding its climatic limit. Climatic risk for a species in a spe-
cific site arises when the exposure in that site exceeds the safety
margin of the species, and the risk for a community depends
on the safety margin of the constituent species. We specifically
addressed the following questions:

1. What is the exposure to climate change that will be ex-
perienced by tropical moist forests in Africa according to
two main scenarios of future climate changes (RCP4.5
and RCP8.5)? We focused on mean annual tempera-
ture (MAT), mean annual precipitation (MAP) and
climatological water deficit (CWD). Given the intermodel variability for the prediction of precipitation (Dosio and Panitz 2016), we explore a large ensemble of regional climate model (RCM) simulations, to assess the robustness of the climate change signal and to identify areas of high climate uncertainty.

2. What are the safety margins of tropical moist forest communities and of their constituent species? We will specifically examine how the climatic niche breadth and spatial extent of the species influence their climatic tolerance as demonstrated elsewhere (Lörz et al. 2022; Thuiller et al. 2005), but never tested for tropical forest species in Africa.

3. What is the climate risk encompassed by tropical moist forests across Africa and which forest communities are expected to be more resilient or at greater risks?

## 2 | Methods

### 2.1 | Climatic Data

The climate data were extracted from AFRICLIM version 3.0 (https://webfiles.york.ac.uk/KITE/AfriClim/), a high-resolution climate projection designed for ecological applications in Africa (Platts et al. 2015). By applying dynamical downscaling of global circulation models (GCMs) through regional climate models (RCMs), AFRICLIM captures fine-scale climatic gradients—such as orographic and coastal effects—and corrects GCM-related biases, thereby producing internally consistent projections. Although RCM-based products like AFRICLIM add value by resolving fine-scale climatic processes, this advantage is not universal (Feser et al. 2011), and statistically downscaled GCMs can sometimes provide comparable improvements.

To align modelled outputs with observed climatologies, AFRICLIM applied change-factor methods (Tabor and Williams 2010), superimposing modelled anomalies onto observational data sets (CHIRPS for precipitation and WorldClim for temperature). These observational products were therefore not used independently but integrated into the AFRICLIM downscaling process (Platts et al. 2015).

Climate projections were derived from the five RCMs available in AFRICLIM v3.0, each driven by one or more CMIP5 GCMs: CCCma-CanRCM4 (CanESM2), CLMcom-CCLM4-8-17, DMI-HIRHAM5, KNMI-RACMO22T and SMHI-RCA4 (driven by eight GCMs in total). Future climates were simulated under two IPCC AR5 representative concentration pathways (RCP4.5 and RCP8.5), corresponding to global temperature anomalies of +2.4°C and +4.9°C by 2100, respectively (Rogelj et al. 2012). These scenarios were selected because AFRICLIM v3.0 does not yet include CMIP6-based AR6 scenarios (SSPs). Including both provides a relevant range of potential futures for conservation planning, helping identify areas at risk under both moderate and extreme climate change conditions.

For this study, we used climate data at a 10 arc-minute (~18.5 km) resolution, consistent with the spatial accuracy of the

occurrence records. The baseline climate corresponds to the period 1961–1990, and future projections to 2071–2100, centred on 2085. Our analyses relied on the multimodel median across the five GCM-RCM combinations provided in AFRICLIM v3.0. To account for projection uncertainty, we also considered the full range of outputs from the individual models in addition to the median values (Almazroui et al. 2020).

We focused on three bioclimatic variables known to influence the distribution and composition of tropical tree species in Africa: mean annual temperature (MAT), mean annual precipitation (MAP) and climatological water deficit (CWD) (Fayolle et al. 2014; Gorel et al. 2022). CWD was calculated as the sum of monthly differences where potential evapotranspiration (PET), estimated from minimum and maximum temperatures (Droogers and Allen 2002), exceeds precipitation:

$$CWD = \sum_{i}^{12} \max(0, PET_{i} - P_{i}) \quad (1)$$

### 2.2 | Floristic Data

We extracted species occurrences from the RAINBIO database (https://gdauby.github.io/rainbio/download_page.html), which includes 673,372 georeferenced records across tropical and subtropical Africa. These data come from herbarium collections, personal databases and floristic inventories carried out between 1785 and 2022. The RAINBIO database contains 25,956 species of vascular plants native to Africa and representing around 89% of the known flora of the African continent (Dauby et al. 2016). The database has been updated since the original publication, including, among others, the occurrences from species lists compiled for tropical forests (Fayolle et al. 2014) and savannas (Fayolle et al. 2019) across Africa, and that contains 44,485 occurrences. For this study, we focused on tree and shrub species, resulting in a total of 360,065 records for 8,582 species. Next, we removed occurrences with poor georeferencing accuracy and species with fewer than 10 occurrences. This preprocessing resulted in 321,727 valid georeferenced occurrence records for 4,808 tree and shrub species across the African continent.

### 2.3 | Delimiting African Tropical Moist Forests and Defining Forest Communities

To define the spatial extent of tropical moist forests across Africa, we used the biome index map developed by Aleman et al. (2020), which quantifies the relative dominance of forest- versus savanna-specialist species in each 0.5° pixel across tropical and subtropical Africa. The index ranges from -1 (pure savanna) to +1 (pure forest). We applied a threshold of +0.2 (a conservative threshold), which corresponds to a minimum of 20% dominance by forest-specialist species in the floristic composition of each pixel. This threshold effectively excludes savanna-dominated and transitional mosaic areas (i.e., pixels with a biome index near 0), ensuring that only areas with a sufficiently strong forest signal according to the floristics are retained to represent meaningful tropical forest communities. We intersected the cleaned species occurrence data set (321,727 valid georeferenced occurrence records for 4,808 tree and shrub
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Conceptual example of climate risk assessment for tree and shrub species (adapted from Gallagher et al. 2019). The framework is illustrated using mean annual temperature (MAT) in a virtual forest community (j) composed of five species: *Aucoumea klaineana*, *Entandrophragma cylindricum*, *Pentaclethra macrophylla*, *Petersianthus macrocarpus* and *Terminalia superba*. Step 1: Floristic and climatic data. We extracted georeferenced occurrence records for the species of the community at the continental scale from the RAINBIO database and overlaid them with baseline climate data from AFRICLIM v3.0 to retrieve the MAT values associated with each occurrence. Step 2: Observed climatic limits of species. For each species, we quantified thermal upper limits by extracting the 95th percentile of MAT values across their known occurrence records (e.g., 26.4°C for *P. macrocarpus*). Step 3: Climate change vulnerability metrics. For a given species (i) within a specific community (j), exposure was calculated as the difference between the average projected future (2085) MAT and the baseline MAT (1961–1990), using only the pixels actually occupied by the species (e.g., under RCP8.5, 28°C vs. 24°C, corresponding to an exposure of +4°C). The safety margin was calculated as the difference between the species’ upper limit and the average baseline MAT across those same pixels (e.g., 26.4°C vs. 24°C, resulting in a safety margin of +2.4°C). Climate risk was then defined as the difference between exposure and safety margin (e.g., +1.6°C for *P. macrocarpus*). A positive value indicates that future climate conditions are expected to exceed the species’ tolerance threshold, suggesting a high risk of thermal stress. Step 4: Estimating community risk and mapping spatial patterns. By repeating this calculation for all species within each community, we quantified the proportion of species at risk. Applying this process across all communities allowed us to map climate risk patterns throughout African tropical moist forests. This approach was similarly applied to mean annual precipitation (MAP) and climatic water deficit (CWD).</figcaption></figure>

species) with this forest mask and retained only species falling within tropical moist forest pixels.

To investigate spatial patterns of species vulnerability, we subdivided the extent of tropical moist forests into distinct forest communities (tree and shrub species coexisting within pixels, Figure 1), which served as analytical units in subsequent assessments. To deal with the heterogeneity of botanical collections across tropical Africa and to balance sampling sizes across communities (Droissart et al. 2018), we used the interactive web application ‘Infomap Bioregion’ (Edler et al. 2016) to define pixels of varying size according to the density of occurrence records. Forest communities ranged from 0.125° (minimum size) to 1° (maximum size) and contained at least five species occurrences. Communities with more than 500 occurrences were iteratively subdivided into smaller units, down to the minimum permitted size of 0.125°. This procedure yielded 1,009 communities, including 199,787 occurrence records, representing 3,536 tree and

shrub species belonging to 145 botanical families (mean = 113, median = 85 species per community; range = 5–712).

Finally, we gathered information on species conservation status from the IUCN Red List and related sources (IUCN 2024). Among the 3,536 tree and shrub species included in this study, 0.4% were classified as critically endangered (CR), 2.5% as endangered (EN), 7.6% as vulnerable (VU), 3.4% as near threatened (NT), 44.0% as least concern (LC), 0.1% as data deficient (DD), and 41.8% as not evaluated (NE).

## 2.4 | Estimation of the Observed Climatic Limits of Species

Assessing species vulnerability to climate change requires knowledge of their ecophysiological tolerance thresholds. However, such physiological tolerances are unknown for tree
and shrub species in African tropical forests. In this context, realised climatic niche limits, inferred from species' geographic distributions, can serve as useful proxies for estimating climate tolerance (Esperon-Rodriguez, Rymer, et al. 2022).

For the 3,536 species identified as occurring within the extent of African tropical moist forests (as previously defined), we compiled all available cleaned occurrence records at the continental scale—including those from both inside and outside the tropical moist forest biome in Africa—to extract values of key climatic variables under baseline climate conditions (1961–1990).

For each species, we then estimated upper climatic limits for MAT and CWD, and a lower limit for MAP. Specifically, we calculated the 95th percentile of MAT and CWD values and the 5th percentile of MAP values across the occurrence records. These thresholds were interpreted as proxies for each species' climatic tolerance limits, corresponding to the hottest, most water-stressed and driest conditions under which the species currently occurs (Gallagher et al. 2019). An example using MAT is provided in Figure 1.

## 2.5 | Climate Change Vulnerability Metrics

To assess the climatic vulnerability of tropical moist forests in Africa, we adopted the approach initially proposed for the Australian flora (Gallagher et al. 2019) and then adapted for urban tree species selection in Canada (Esperon-Rodriguez, Tjoelker, et al. 2022). This approach considers three key dimensions of climate impact: (1) exposure, (2) safety margin and (3) risk. These metrics were calculated for three climatic variables (MAT, CWD and MAP) under two emissions scenarios (RCP4.5 and RCP8.5).

Exposure to climate change refers to the extent of climate change likely to be experienced by a species or location. It depends on the rate and magnitude of change in climatic variables (MAT, MAP and CWD) in regions occupied by the species (Dawson et al. 2011; Foden et al. 2013).

For a given species (i) in a specific community (j), exposure was calculated as the difference between the average projected future climate and the baseline climate, calculated only across the pixels actually occupied by the species within that community (Equation 2, see Figure 1 for an example with MAT). This approach accounts for spatial heterogeneity, particularly in mountainous regions where a single community may span contrasted climatic conditions. Averaging across the entire community could overestimate exposure for climate-specialist species by attributing to them environmental conditions they do not experience.

$$ \text{Exposure}_{(ij)} = \text{Future Climate}_{(ij)} - \text{Baseline Climate}_{(ij)} \quad (2) $$

where (i) denotes the species and (j) the community under consideration. Positive exposure values for MAT and CWD indicate that the species is expected to experience higher temperatures and increased water stress, respectively. Conversely, negative

exposure values for MAP indicate that the species is likely to face reduced precipitation within this community.

## 2.6 | Climatic Safety Margin

This metric captures a species' intrinsic sensitivity to climate change and provides an indication of its potential tolerance to shifting environmental conditions in natural ecosystems (Esperon-Rodriguez, Ordoñez, et al. 2022). This margin reflects how much warmer (for MAT), drier (for MAP) or more stressful due to water deficit (for CWD) the climate can become before exceeding observed climatic limits (Gallagher et al. 2019; Esperon-Rodriguez, Rymer, et al. 2022).

For a given species (i), within a specific community (j), and for a given climatic variable, the safety margin was calculated as the difference between the species' climatic limit and the average baseline climatic conditions across the set of pixels occupied by the species within that community (Equation 3 and Figure 1).

$$ \text{Safety Margin}_{(ij)} = \begin{cases} \text{Species (i) upper limit} - \text{Baseline Climate}_{(ij)}; & \text{for MAT and CWD} \\ \text{or} \\ \text{Baseline Climate}_{(ij)} - \text{Species (i) lower limit}; & \text{for MAP} \end{cases} \quad (3) $$

A negative safety margin (<0) indicates that the species has exceeded its tolerance limit in that specific community, suggesting that it is potentially facing stressful climate conditions. For each community and climatic variable, we mapped the average safety margin across all species (hereafter referred to as the *community average safety margin*) as well as the standard deviation within the community.

We then investigated the relationship between the community average safety margin, the climatic niche breadth and spatial extent of the species within the communities. The climatic niche was characterised by the minimum, optimum, maximum and amplitude values for mean annual temperature (MAT), mean annual precipitation (MAP) and climatic water deficit (CWD), extracted from the selected occurrences. The spatial extent was estimated using the extent of occurrence (EOO), calculated with the minimum convex hull method using the R *rgeos* package (Dunnington and Pebesma 2020). We then performed a principal component analysis (PCA) on the matrix containing information on the climatic niche and spatial extent of the species. The community average scores were calculated by averaging the scores on the first two principal components (PC1 and PC2) of all the species in each community. Finally, we analysed the correlations between the community average safety margins and these community average scores using linear regressions.

Climate risk refers to the potential for adverse impacts on biological systems resulting from climate change (Foden et al. 2019). For a given species (i) within a specific community (j), risk was calculated as the difference between the exposure and the safety margin (Equation 4, see Figure 1 for an example with MAT).
<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Projected exposure to climate changes across African tropical forests. Exposures (a–f) highlight regions in red where MAT or CWD are projected to increase, and MAP to decrease. Conversely, regions in blue indicate where MAT or CWD are projected to decrease, and MAP to increase. Inset maps illustrate the uncertainty associated with each variable, calculated as the range between the highest and lowest projections across five regional climate models (RCMs).</figcaption></figure>

$$ \text{Risk}_{(ij)} = \begin{cases} \text{Exposure}_{(ij)} - \text{Safety Margin}_{(ij)} & \text{for MAT and CWD} \\ \text{or} \\ \text{Safety Margin}_{(ij)} + \text{Exposure}_{(ij)} & \text{for MAP} \end{cases} \quad (4) $$

If exposure exceeds the safety margin, the resulting risk is positive for MAT and CWD, indicating that the species is likely to experience future climatic conditions (temperature or water stress) beyond its observed tolerance limits. Conversely, for MAP, a negative risk indicates that the species is likely to face drier conditions than it can tolerate. For each climatic variable and emissions scenario, we mapped the proportion of species potentially at risk within communities, that is, those for which projected climatic conditions exceed their observed climatic tolerance limits.

# 3 | Results

## 3.1 | Exposure to Climate Change

We first examined the exposure to climate changes, that is, the nature and extent of climate change according to MAT, MAP or CWD, that a species or community is likely to experience. A considerable increase in MAT will be observed for the whole tropical moist forest biome in Africa by the year 2085 (Figure 2a,d). The average exposure to a MAT increase is 2.4°C ± 0.2°C for the RCP4.5 scenario. The magnitude of exposure is relatively uniform (ranging between 1.9°C and 2.9°C), with approximately

74% of the tropical moist forest biome experiencing a MAT increase greater than 2°C. Under the RCP8.5 scenario, the average MAT increase reaches 4.3°C ± 0.4°C (ranging between 3.3°C and 5°C), with 85% of the biome area experiencing a MAT increase greater than 4°C. Importantly, the associated uncertainty for MAT exposure remains relatively low (mostly < 2.5°C; see inset maps in Figure 2a,d), indicating strong agreement across the five regional climate models (RCMs).

The MAP projections show no consistent trends and are characterized by high variability among models (Figure 2b,e). Although each RCM predicts substantial changes in precipitation (exceeding 200mm) under both RCP4.5 and RCP8.5, the direction of change—whether an increase or decrease—varies considerably across models (Figure S1). This inter-model disagreement results in pronounced spatial uncertainty, particularly under the RCP8.5 scenario.

The CWD projections indicate an exposure to more stressful conditions (Figure 2c,f) that are driven by MAT increase, but with high uncertainty, especially in the centre of the Congo Basin (Figure 1c,f) due to the huge uncertainty on MAP changes.

## 3.2 | Climatic Safety Margins and Determinants

The climatic safety margin represents the extent of climate change that a species can tolerate within a specific community before exceeding its climatic limit. Given the uncertainties associated with MAP and CWD projections (Figure 2), we focused solely on the MAT safety margin (Figure 3). However, the
<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | MAT safety margin of tree and shrub species in the forest communities of tropical Africa. (a) Map of community average safety margin (inset maps illustrate the community standard deviation of safety margin), calculated by averaging the safety margins of all species within each community. (b) Distribution of species average safety margins, calculated for each species by averaging the safety margins across all the communities in which it occurs. Negative values indicate species currently exceeding their tolerance limits for MAT in all or most of the communities where they have been observed. The dotted black line represents the median across all species.</figcaption></figure>

same approach was also applied to MAP and CWD (Figures S3 and S4).

The average safety margin per species for MAT (i.e., averaged across all the communities in which the species occurs) ranges from -3.3°C to 15.9°C (Figure 3b). We found that more than half of the study species (2,033 species, 57%) are already experiencing temperatures exceeding their observed climatic limits in at least one of the communities where they occur. Additionally, 29 species (1%) have already surpassed their climatic limits in all communities where they have been observed, indicating that these species are currently highly vulnerable (species with negative safety margins). However, only 10 of these species are listed in the IUCN Red List—five as EN, three as VU, and two as NT (Table S1).

We found that more than 50% of the moist forest communities across tropical Africa had an average safety margin (i.e., averaged across all species within the community) below 2°C, with low variability within communities (less than 1°C, Figure 3a). Spatial analysis revealed that regions with the smallest community average safety margins include most of West Africa, Atlantic central Africa (e.g., Lopé in Gabon), and most of the DRC. In these regions, more than a quarter of species in some communities already exceed their climatic limits for MAT (Figure S2). Notably, the coastal evergreen forests of Gabon, the southwest evergreen forests of the DRC, and forests in southern Nigeria and southeastern Ivory Coast stand out as currently vulnerable.

Communities with the largest average safety margins (> 2°C) are primarily located in high-altitude regions (> 1000 m a.s.l.), such as the Albertine Rift, Ethiopian highlands, the Cameroon volcanic line and the Adamaoua region of Cameroon, where safety margin variability is also high (> 1°C). They are also found in the southeast of Guinea in West Africa, the TRIDOM Dja-Odzala-Minkébé (transboundary forest complex across Cameroon, Congo and Gabon), the Batéké Plateau (the transboundary region between southern Gabon and western Congo), and along the northern and southern margins of the Congo Basin (east of Luki, Figure 3a).

We then investigated the correlates of the community average safety margins, and notably species climatic niche descriptors (optimum, amplitude, minimum and maximum) for MAT, MAP and CWD, as well as the extent of occurrence (EOO) of the species composing the community. We identified two dominant and independent axes of the climatic niche and spatial extent of species (Figure 4a), which together explained 69.1% of the total variance. The first axis of the PCA (PC1, 48.5% of variance) differentiated species living in drier and more seasonal climates—higher water deficit (CWD.max, CWD.opt), tolerating wide variations in temperature (higher MAT.amp)—from those restricted to wetter climates with low water deficit, lower temperature amplitude and limited range (EOO). The second axis (PC2, 20.6% of variance) differentiated species according to maximum (MAT.max) and optimum (MAT.opt) temperatures, both negatively correlated with the altitudinal gradient. Correlation analysis revealed significant relationships ($R^2$=0.52, *p* < 0.001) between community
<figure><img src="image_6.png" /><figcaption>**FIGURE 4** | Determinants of community safety margin, that is, averaged across species. (a) Correlation circle (PC1 and PC2) of the principal component analysis (PCA) based on the climatic niche descriptors and spatial extent of the species. This includes the minimum (min), optimum (opt) and maximum (max) values, as well as the amplitudes (amp) for mean annual temperature (MAT, in red), mean annual precipitation (MAP, in blue) and climatic water deficit (CWD, in yellow), along with the species' Extent of Occurrence (EOO, in black). (b) Community average scores on PC1 vs. PC2, representing the average scores per community of species on the first two principal components of the PCA. (c) Baseline MAT for each community. In blue, communities where temperatures are consistently below 25°C, and where the average safety margin exceeds 2°C (Figure 3b).</figcaption></figure>

average safety margins and community average scores on the first two principal components of the PCA (i.e., species average in each community), highlighting the influence of the species climatic niche and spatial extent on the community safety margin. Forest communities in west Africa are characterised by low average safety margins, driven by species that tolerate diverse moisture and seasonality conditions and are associated with warm climates (variable average scores on PC1 and high average scores on PC2, Figure 4b). However, in the Simandou Hills of southeastern Guinea, cooler temperatures (Figure 4c) increase the safety margins of these communities, despite being dominated by species with similar climatic niches. Similarly, in the Congo Basin, where average safety margins are also low, forest communities predominantly consist of species restricted to warm-moist climates (low average scores on PC1 and high average scores on PC2). Yet, in the Batéké Plateau, the Chaillu Massif provides cooler local temperatures (Figure 4c), enhancing the safety margins of these communities, despite their composition being dominated by species with similar climatic niches. Along the northern and southern margins of the Congo Basin, forest communities exhibit relatively high safety margins, supported by species with wide geographic ranges that tolerate broad gradients of moisture and seasonality (high average scores on both PC1 and PC2, Figure 4b). In high-altitude regions, where safety margins are generally higher, communities are composed, on average, of species adapted to drier and more seasonal climates, capable of thriving across a broad altitudinal gradient (high average scores on PC1 and low average scores on PC2).

## 3.3 | Climate Risk

The climate risk refers to the vulnerability of species to projected future climate change. We calculated the risk as the difference between the exposure and the safety margin of a given species in a given community. We highlighted the risk associated with MAT (Figure 5), but the same approach was applied for MAP and CWD (Figures S5 and S6). Widespread temperature increases predicted for the end of the century will pose a considerable risk to many forest species. Under the RCP4.5 scenario,

3,256 species (92%) are projected to face risk in at least one community where they occur, while 617 species (17%) are expected to be at risk in all communities where they have been observed (inset histogram, Figure 5a). By 2085, approximately 75% of the communities are projected to have 75%-100% of species at risk (Figure 5a). The proportion of species at risk is particularly high in west Africa, Atlantic central Africa (including Lopé), and the DRC. Communities with a moderately low proportion of species at risk (around 50%) are primarily located in the Albertine Rift, the southern margin of the Congo Basin (east of Luki), the Cameroon volcanic line, and the Adamaoua region of Cameroon. They are also found in the Batéké Plateau, the TRIDOM area, the northern margin of the Congo Basin, and the Simandou hills. Under the RCP8.5 scenario, the projected risks are even more severe. The number of species at risk rises to 3,405 (96%) in at least one community and 1,2747 species (49%) in all communities where they occur (inset histogram, Figure 5b). By 2085, only communities in the Albertine Rift, the Ethiopian Highlands and along the Cameroon Volcanic Line are expected to maintain a proportion of species at risk below 50% (Figure 5b).

## 4 | Discussion

In this study, we aimed to assess the climatic vulnerability of tropical moist forests across Africa by quantifying the exposure to climate change, the climatic safety margins and the climate risks of the species and communities. We focused on changes in three key climate variables—mean annual precipitation (MAP), mean annual temperature (MAT) and the climatological water deficit (CWD)—under two representative concentration pathways, RCP4.5 and RCP8.5, representing intermediate and high emission trajectories, respectively. We found that future climate projections predict a considerable and widespread increase in temperature in tropical moist forests across Africa that will create novel climate conditions (Williams and Jackson 2007). The projected MAT increase in African tropical moist forests is estimated, on average, at 2.4°C ± 0.2°C under RCP4.5 and 4.3°C ± 0.4°C under RCP8.5 by 2085. However, no consistent trend is observed for precipitations, with models sometimes
<figure><img src="image_7.png" /><figcaption>b- Risk to predicted changes in MAT in 2085 (RCP8.5)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**FIGURE 5** | Risk to forest species and communities from the predicted rise in temperatures. (a, b) Proportion of species projected to be at risk in each community by 2085 under the RCP4.5 and RCP8.5 scenarios, respectively. The inset histograms display the distribution of average species-level risk for MAT under RCP4.5 and RCP8.5 scenarios. For each species, the average risk was calculated by averaging the risk values across all communities where the species was observed. The dashed red line represents zero risk. Positive values indicate species likely to exceed their tolerance limits for MAT at all or most of the observed sites by 2085.</figcaption></figure>

showing opposing changes—reflecting uncertainties that have
already been highlighted for Africa (Dosio and Panitz 2016;
Dosio et al. 2019; Almazroui et al. 2020).

It should be noted that our estimates of exposure to climate
change are based on the AR5 scenarios (RCPs) and the CMIP5
simulations. However, the CMIP6 simulations associated
with the AR6 scenarios (SSPs) project more marked tem-
perature rises over a large part of Africa. For example, pro-
jected temperatures in the northern hemisphere of Africa are
up to 2.5°C higher in CMIP6 than in CMIP5, particularly in
the region around Sudan and Niger (outside our study area),
while the difference is around 1°C in the southern hemisphere
(Almazroui et al. 2020). This suggests that the levels of ther-
mal exposure we report may be underestimated if we consider
the CMIP6 projections. In contrast, the precipitation projec-
tions between CMIP5 and CMIP6 show more mixed signals
(Almazroui et al. 2020).

Our climatic safety margin analyses show that most of the 3,536 tree and shrub species studied have narrow safety margins for MAT (<1.6°C), indicating high sensitivity—and potentially limited tolerance—to rising temperatures based on their known distributions. Species in lowland forest communities are particularly vulnerable, with narrow safety margins often below predicted exposure levels. This narrow safety margin is likely linked to the relatively stable temperature conditions across lowland tropical forests, which are characterised by minimal spatial and seasonal variations (Jirinec et al. 2022). Under such conditions, species may not be able to cope with temperature fluctuations but may instead thrive near their maximum tolerance thresholds for MAT. Consequently, even modest temperature increases could have potentially damaging effects (Trew and Maclean 2021).

More than 50% of moist forest communities across tropical Africa exhibit relatively narrow thermal safety margins (<2°C in community average) in response to projected increases in MAT exceeding 2.4°C, even under the RCP4.5 scenario. These community-level safety margins are primarily shaped by local altitudinal gradients (Gallagher et al. 2019) and the climatic niche breadths of the constituent species (García-Robledo et al. 2016; Feeley et al. 2020). More alarming, our analyses identified the coastal evergreen forests of Gabon, the southwestern evergreen forests of the DRC and those in southern Nigeria and southeastern Ivory Coast as currently vulnerable. In these regions, more than a quarter of species are already experiencing temperatures beyond their observed upper limits of MAT. These findings are consistent with those of Réjou-Méchain et al. (2021), who also highlighted the sensitivity of the evergreen coastal forests of Gabon and central DRC to ongoing climate change.

In contrast, some communities show markedly broad thermal
safety margins. Forest communities with the largest safety
margins are mostly found in high-altitude regions, such as the
Albertine Rift, Ethiopian Highlands, Cameroon Volcanic Line
and the Adamaoua region of Cameroon. They are also present in
the Simandou Hills, the Batéké Plateau, the TRIDOM and along
the northern and southern forest margins of the Congo Basin.
The large safety margins observed in mountainous regions
are consistent with the broad thermal niche breadth (8.8°C) of
Afromontane tree species (Cuni-Sanchez et al. 2024). However,
such wide distributions may mask a mosaic of genetically dis-
tinct populations, each adapted to local climatic conditions
(Savolainen et al. 2007). Moreover, the resolution of climate
data may artificially inflate safety margins in mountainous
areas by grouping lowland and upland species within the same
community. Along the northern margin of the Congo Basin,
where topographic relief is low, we also observed relatively

high safety margins. These are primarily driven by generalist
species with wide geographic distributions and broad climatic
niches—characterised by wide CWD amplitudes and extensive
EOOs—and found in both forest and savanna ecosystems, such
as *Erythrophleum suaveolens* (Gorel et al. 2019).

The projected widespread warming, combined with the rela-
tively narrow thermal safety margins of species, is likely to pose
a significant risk of temperature-induced stress to most forest
communities by the end of the century. By 2085, under RCP4.5,
92% of species are projected to be at risk in at least one loca-
tion where they currently occur, and 96% under RCP8.5. Across
African tropical moist forests, climatic exposure varies little,
with projected increases of 2.4°C ± 0.2°C and 4.3°C ± 0.4°C
under RCP4.5 and RCP8.5, respectively. Species risk—and
thus the proportion of species at risk within communities—is
largely driven by species composition and thermal safety mar-
gins. Communities dominated by species with narrow margins,
such as lowland forests, are more vulnerable, whereas highland
forests, hosting species with broader thermal margins, may be
more resilient depending on the scenario.

This widespread vulnerability is consistent with growing em-
pirical evidence suggesting that tropical forests worldwide are
approaching critical thermal limits. In a pantropical study,
Doughty et al. (2023) combined leaf thermocouple measure-
ments, remote sensing (ECOSTRESS) and heating experiments
to show that canopy leaves already reach midday peak tempera-
tures close to—or in some cases exceeding—the critical thresh-
old for photosynthetic function (T<sub>(crit)</sub> ≈ 46.7°C). The convergence
between our projected increase in mean annual temperature
under RCP8.5 (+4.3°C ± 0.4°C) and the functional tipping point
identified by Doughty et al. (+3.9°C ± 0.5°C above current ambi-
ent temperatures) suggests that some African forests may face
widespread physiological disruption by the end of the 21st cen-
tury. Moreover, Trew et al. (2024) show that novel below-canopy
thermal regimes are already emerging across large parts of the
African tropics (56%), especially in the Congo Basin—highlight-
ing that many forest ecosystems are now operating under cli-
matic conditions with no recent analogue, which may challenge
species' adaptive capacity and ecosystem functioning.

Our findings therefore highlight the vulnerability of African
tropical forests to future warming. However, given the inher-
ent assumptions and limitations in our approach, our results
should be interpreted with caution. We rely on realised climate
niches to infer species' climatic limits—a common approach in
large-scale vulnerability assessments (Foden et al. 2013; Pacifici
et al. 2015; Foden et al. 2019; Gallagher et al. 2019). Yet, these
realised niches may underestimate the species' fundamental cli-
matic niche due to historical, geographical or biotic constraints
(Guisan et al. 2017; Scherrer et al. 2021), as well as the per-
sistence of marginal populations in microclimates or as climate
relicts in otherwise unsuitable areas (Hampe and Petit 2005;
Morelli et al. 2016; Esperon-Rodriguez, Tjoelker, et al. 2022). In
addition, using coarse-resolution climate data risks overlooking
both horizontal (topography-driven) and vertical (vegetation-
driven) microclimatic heterogeneities. Microclimates are
particularly pronounced in tropical moist forests, where the
three-dimensional canopy structure drives shading, air mix-
ing and evapotranspirative cooling (Zellweger et al. 2019). For
instance, understory species may experience cooler and more
humid conditions than those captured by standard climate data
sets. Accounting for forest microclimate dynamics is therefore
essential to better understand the responses of tree species to cli-
mate change (Zellweger et al. 2020). The choice of the baseline
period (1961–1990) may also introduce bias, as it likely no longer
reflects current climatic conditions (Bush et al. 2020; Kasongo
Yakusu et al. 2023).

A further methodological assumption concerns the use of a
space-for-time substitution approach, inferring temporal re-
sponses from spatial patterns (Adler et al. 2020). This intro-
duces additional uncertainty, as the assumption does not always
hold (Guisan et al. 2017). Projections become particularly un-
certain when species are not in equilibrium with their environ-
ment (Guisan et al. 2017), when transient processes dominate
or when future climates have no analogues (Veloz et al. 2012;
Worth et al. 2014; Perret et al. 2024). Furthermore, several
unaccounted-for processes—such as phenotypic plasticity
and rapid adaptation through high genetic diversity (Kremer
et al. 2012, 2014, 2025)—could help tropical trees endure future
climate stress.

In this study, we focused primarily on thermal risk, while risks
related to MAP and CWD were more uncertain. The effects of
rising temperatures on plants will strongly depend on future pre-
cipitation patterns and their interaction with warming (Teskey
et al. 2015), as higher temperatures can intensify drought
through increased evapotranspiration—even in regions where
total rainfall remains relatively stable (Gallagher et al. 2019).
Beyond these variables, unmodelled climate extremes—such
as maximum temperatures, multimonth water deficits and
heatwaves—may trigger abrupt physiological failures that are
not captured by long-term climate averages (Choat et al. 2018;
Anderegg et al. 2020). As such extremes are projected to inten-
sify across Africa (Dosio 2017; Dosio et al. 2022), their exclusion
from our analysis likely resulted in a conservative estimate of
climate risk. Integrating them would probably reveal even more
pronounced vulnerabilities, but local factors such as soil prop-
erties can also modulate species' responses to water availability
(Luo et al. 2024).

Finally, the vulnerability of Africa's tropical moist forests cannot
be fully understood through the prism of climate variables alone.
Climate risks need to be considered in conjunction with other
major anthropogenic pressures, such as overexploitation, habitat
conversion, overgrazing and fires—all of which are often inten-
sified by rapid population growth (Réjou-Méchain et al. 2021).
In central Africa, overexploitation remains the greatest threat to
tree species, followed by habitat conversion and climate change,
while other disturbances such as overgrazing and fires appear
less influential (Ceccarelli et al. 2022). Although not addressed
in this study, these pressures may nonetheless exacerbate spe-
cies' vulnerability (Brice et al. 2019; Zellweger et al. 2020) and
must be considered in conservation planning.

Forest conservation programs must now integrate climate con-
siderations into planning, policy development and implemen-
tation (Malakoutikhah et al. 2020). This includes prioritising
communities already identified as vulnerable—hosting unique
assemblages of tree species (Réjou-Méchain et al. 2021) and

mammals (Fonteyn et al. 2023)—such as the coastal evergreen
forests of Gabon, the evergreen forests of southwestern DRC and
those of southern Nigeria and southeastern Ivory Coast. In these
regions, conservation efforts should aim to reduce non-climatic
pressures, which may otherwise amplify the impacts of climate
change. In parallel, the establishment of new protected areas
under the 30×30 agenda should focus on ecologically and cli-
matically strategic zones, with their relevance validated through
field-based assessments. Ensuring large-scale ecological con-
nectivity—especially across transboundary landscapes (Miles
et al. 2004)—remains critical to regional and national strategies
(Carr et al. 2017; Lapola et al. 2020; Merenlender et al. 2022).
Expanding protected area networks, particularly in regions en-
compassing both lowland and montane forests, will help main-
tain ecological corridors and strengthen community resilience
(Miles et al. 2004). The TRIDOM region, which combines high
climatic safety margins with an existing protected area network,
stands out as a conservation priority. In this context, our maps
should be viewed as strategic tools—to guide future research,
prioritise monitoring and support conservation planning under
climate change.

Author Contributions

M.P.N.M., A.-P.G. and A.F. contributed to the conceptualisation of the study. G.D. assembled and provided the data. M.P.N.M. analysed the data with assistance from G.D. M.P.N.M. led the writing of the manuscript and all authors critically reviewed the manuscript and approved the final version.

Acknowledgements

This study is part of the CANOPi project (EOS number O.0026.22),
which has received funding from the FWO and F.R.S.-FNRS under the
Excellence of Science (EOS) program. We also extend our thanks to the
Centre for Biodiversity Synthesis and Analysis (CESAB) for providing ac-
cess to the occurrence data. We are specifically grateful to Yves Brostaux,
Éric Marcon and to the entire CANOPi team for stimulating discussions.

Disclosure

Permission to reproduce material from other sources: Occurrence data were extracted from the RAINBIO database available at https://gdauby.github.io/rainbio/download_page.html. The database has been updated since the original publication, including, among others, the occurrences from the species lists assembled by Fayolle et al. (2014) for forest sites and by Fayolle et al. (2019) for savanna sites across Africa. The distribution of the moist forest biome across Africa was derived from the biome index of Aleman et al. (2020) and the conservation status of tree and shrub species was extracted from the IUCN Red List. Baseline and future climatic data were obtained from the Africlim database (Platts et al. 2015).

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

All the data used in this study, including species occurrence records and
climate projections, are available on the Dryad repository: https://doi.
org/10.5061/dryad.7h44j105d.
## References

Adler, P. B., E. P. White, and M. H. Cortez. 2020. "Matching the Forecast Horizon With the Relevant Spatial and Temporal Processes and Data Sources." *Ecography* 43, no. 11: 1729-1739. https://doi.org/10.1111/ecog.05271.

Aguirre-Gutiérrez, J., Y. Malhi, S. L. Lewis, et al. 2020. "Long-Term Droughts May Drive Drier Tropical Forests Towards Increased Functional, Taxonomic and Phylogenetic Homogeneity." *Nature Communications* 11, no. 1: 1. https://doi.org/10.1038/s41467-020-16973-4.

Aguirre-Gutiérrez, J., I. Oliveras, S. Rifai, et al. 2019. "Drier Tropical Forests Are Susceptible to Functional Changes in Response to a Long-Term Drought." *Ecology Letters* 22, no. 5: 855-865. https://doi.org/10.1111/ele.13243.

Aleman, J. C., A. Fayolle, C. Favier, et al. 2020. "Floristic Evidence for Alternative Biome States in Tropical Africa." *Proceedings of the National Academy of Sciences of the United States of America* 117, no. 45: 28183-28190. https://doi.org/10.1073/pnas.2011515117.

Almazroui, M., F. Saeed, S. Saeed, et al. 2020. "Projected Change in Temperature and Precipitation Over Africa From CMIP6." *Earth Systems and Environment* 4, no. 3: 455-475. https://doi.org/10.1007/s41748-020-00161-x.

Aloysius, N. R., J. Sheffield, J. E. Saiers, H. Li, and E. F. Wood. 2016. "Evaluation of Historical and Future Simulations of Precipitation and Temperature in Central Africa From CMIP5 Climate Models." *Journal of Geophysical Research: Atmospheres* 121, no. 1: 130-152. https://doi.org/10.1002/2015JD023656.

Anderegg, W. R. L., A. T. Trugman, G. Badgley, A. G. Konings, and J. Shaw. 2020. "Divergent Forest Sensitivity to Repeated Extreme Droughts." *Nature Climate Change* 10, no. 12: 12. https://doi.org/10.1038/s41558-020-00919-1.

Baccini, A., S. J. Goetz, W. S. Walker, et al. 2012. "Estimated Carbon Dioxide Emissions From Tropical Deforestation Improved by Carbon-Density Maps." *Nature Climate Change* 2, no. 3: 182-185. https://doi.org/10.1038/nclimate1354.

Beer, C., M. Reichstein, E. Tomelleri, et al. 2010. "Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation With Climate." *Science* 329, no. 5993: 834-838. https://doi.org/10.1126/science.1184984.

Bennett, A. C., N. G. McDowell, C. D. Allen, and K. J. Anderson-Teixeira. 2015. "Larger Trees Suffer Most During Drought in Forests Worldwide." *Nature Plants* 1, no. 10: 10. https://doi.org/10.1038/nplants.2015.139.

Brice, M.-H., K. Cazelles, P. Legendre, and M.-J. Fortin. 2019. "Disturbances Amplify Tree Community Responses to Climate Change in the Temperate-Boreal Ecotone." *Global Ecology and Biogeography* 28, no. 11: 1668-1681. https://doi.org/10.1111/geb.12971.

Bush, E. R., K. Jeffery, N. Bunnefeld, et al. 2020. "Rare Ground Data Confirm Significant Warming and Drying in Western Equatorial Africa." *PeerJ* 8, no. avril: e8732. https://doi.org/10.7717/peerj.8732.

Carr, M. H., S. P. Robinson, C. Wahle, et al. 2017. "The Central Importance of Ecological Spatial Connectivity to Effective Coastal Marine Protected Areas and to Meeting the Challenges of Climate Change in the Marine Environment." *Aquatic Conservation: Marine and Freshwater Ecosystems* 27, no. S1: 6-29. https://doi.org/10.1002/aqc.2800.

Ceccarelli, V., M. Ekué, T. Fremout, et al. 2022. "Vulnerability Mapping of 100 Priority Tree Species in Central Africa to Guide Conservation and Restoration Efforts." *Biological Conservation* 270, no. juin: 109554. https://doi.org/10.1016/j.biocon.2022.109554.

Choa, B., T. J. Brodribb, C. R. Brodersen, R. A. Duursma, R. López, and B. E. Medlyn. 2018. "Triggers of Tree Mortality Under Drought." *Nature* 558, no. 7711: 531-539. https://doi.org/10.1038/s41586-018-0240-x.

Cuni-Sanchez, A., E. H. Martin, E. Uzabaho, et al. 2024. "Evidence of Thermophilization in Afromontane Forests." *Nature Communications* 15, no. 1: 5554. https://doi.org/10.1038/s41467-024-48520-w.

Dauby, G., R. Zaiss, A. Blach-Overgaard, et al. 2016. "RAINBIO: A Mega-Database of Tropical African Vascular Plants Distributions." *Phytokeys* 74, no. novembre: 1-18. https://doi.org/10.3897/phytokeys.74.9723.

Dawson, T. P., S. T. Jackson, J. I. House, I. C. Prentice, and G. M. Mace. 2011. "Beyond Predictions: Biodiversity Conservation in a Changing Climate." *Science* 332, no. 6025: 53-58. https://doi.org/10.1126/science.1200303.

Dosio, A. 2017. "Projection of Temperature and Heat Waves for Africa With an Ensemble of CORDEX Regional Climate Models." *Climate Dynamics* 49, no. 1-2: 493-519. https://doi.org/10.1007/s00382-016-3355-5.

Dosio, A., R. G. Jones, C. Jack, C. Lennard, G. Nikulin, and B. Hewitson. 2019. "What Can We Know About Future Precipitation in Africa? Robustness, Significance and Added Value of Projections From a Large Ensemble of Regional Climate Models." *Climate Dynamics* 53, no. 9: 5833-5858. https://doi.org/10.1007/s00382-019-04900-3.

Dosio, A., C. Lennard, and J. Spinoni. 2022. "Projections of Indices of Daily Temperature and Precipitation Based on Bias-Adjusted CORDEX-Africa Regional Climate Model Simulations." *Climatic Change* 170, no. 1-2: 13. https://doi.org/10.1007/s10584-022-03307-0.

Dosio, A., and H.-J. Panitz. 2016. "Climate Change Projections for CORDEX-Africa With COSMO-CLM Regional Climate Model and Differences With the Driving Global Climate Models." *Climate Dynamics* 46, no. 5-6: 1599-1625. https://doi.org/10.1007/s00382-015-2664-4.

Doughty, C. E., J. M. Keany, B. C. Wiebe, et al. 2023. "Tropical Forests Are Approaching Critical Temperature Thresholds." *Nature* 621, no. 7977: 105-111. https://doi.org/10.1038/s41586-023-06391-z.

Droissart, V., G. Dauby, O. J. Hardy, et al. 2018. "Beyond Trees: Biogeographical Regionalization of Tropical Africa." *Journal of Biogeography* 45, no. 5: 1153-1167. https://doi.org/10.1111/jbi.13190.

Droogers, P., and R. G. Allen. 2002. *Estimating Reference Evapotranspiration Under Inaccurate Data Conditions*. Kluwer Academic Publishers.

Dunnington, D., and E. Pebesma. 2020. "Geos: Open Source Geometry Engine ('GEOS') R API." https://doi.org/10.32614/CRAN/package.geos.

Edler, D., T. Guedes, A. Zizka, M. Rosvall, and A. Antonelli. 2016. "Infomap Bioregions: Interactive Mapping of Biogeographical Regions From Species Distributions." *Systematic Biology* 66, no. 2: 197-204. https://doi.org/10.1093/sysbio/syw087.

Esperon-Rodriguez, M., C. Ordoñez, N. S. van Doorn, A. Hirons, and C. Messier. 2022. "Using Climate Analogues and Vulnerability Metrics to Inform Urban Tree Species Selection in a Changing Climate: The Case for Canadian Cities." *Landscape and Urban Planning* 228, no. décembre: 104578. https://doi.org/10.1016/j.landurbplan.2022.104578.

Esperon-Rodriguez, M., P. D. Rymer, S. A. Power, et al. 2022. "Assessing Climate Risk to Support Urban Forests in a Changing Climate." *Plants, People, Planet* 4, no. 3: 201-213. https://doi.org/10.1002/ppp3.10240.

Esperon-Rodriguez, M., M. G. Tjoelker, J. Lenoir, et al. 2022. "Climate Change Increases Global Risk to Urban Forests." *Nature Climate Change* 12, no. 10: 10. https://doi.org/10.1038/s41558-022-01465-8.

Fauset, S., T. R. Baker, S. L. Lewis, et al. 2012. "Drought-Induced Shifts in the Floristic and Functional Composition of Tropical Forests in Ghana." *Ecology Letters* 15, no. 10: 1120-1129. https://doi.org/10.1111/j.1461-0248.2012.01834.x.

Fayolle, A., M. D. Swaine, J. Aleman, et al. 2019. "A Sharp Floristic Discontinuity Revealed by the Biogeographic Regionalization of African Savannas." *Journal of Biogeography* 46, no. 2: 454-465. https://doi.org/10.1111/jbi.13475.
Fayolle, A., M. D. Swaine, J.-F. Bastin, et al. 2014. "Patterns of Tree Species Composition Across Tropical African Forests." *Journal of Biogeography* 41, no. 12: 2320-2331. https://doi.org/10.1111/jbi.12382.

Feeley, K., J. Martinez-Villa, T. Perez, A. S. Duque, D. T. Gonzalez, and A. Duque. 2020. "The Thermal Tolerances, Distributions, and Performances of Tropical Montane Tree Species." *Frontiers in Forests and Global Change* 3: e00025. https://doi.org/10.3389/ffgc.2020.00025.

Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn. 2011. "Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples." *Bulletin of the American Meteorological Society* 92: 1181-1192. https://doi.org/10.1175/2011BAMS3061.1.

Foden, W. B., S. H. M. Butchart, S. N. Stuart, et al. 2013. "Identifying the World's Most Climate Change Vulnerable Species: A Systematic Trait-Based Assessment of All Birds, Amphibians and Corals." *PLoS One* 8, no. 6: e65427. https://doi.org/10.1371/journal.pone.0065427.

Foden, W. B., B. E. Young, H. R. Akçakaya, et al. 2019. "Climate Change Vulnerability Assessment of Species." *Wiley Interdisciplinary Reviews: Climate Change* 10, no. 1: e551. https://doi.org/10.1002/wcc.551.

Fonteyn, D., C. Vermeulen, A.-P. Gorel, P. L. de Silva Miranda, S. Lhoest, and A. Fayolle. 2023. "Biogeography of Central African Forests: Determinants, Ongoing Threats and Conservation Priorities of Mammal Assemblages." *Diversity and Distributions* 29, no. 6: 698-712. https://doi.org/10.1111/ddi.13677.

Gallagher, R. V., S. Allen, and I. J. Wright. 2019. "Safety Margins and Adaptive Capacity of Vegetation to Climate Change." *Scientific Reports* 9, no. 1: 8241. https://doi.org/10.1038/s41598-019-44483-x.

García-Robledo, C., E. K. Kuprewicz, C. L. Staines, T. L. Erwin, and W. J. Kress. 2016. "Limited Tolerance by Insects to High Temperatures Across Tropical Elevation Gradients and the Implications of Global Warming for Extinction." *Proceedings of the National Academy of Sciences of the United States of America* 113, no. 3: 680-685. https://doi.org/10.1073/pnas.1507681113.

Gorel, A.-P., J. Duminil, J.-L. Doucet, and A. Fayolle. 2019. "Ecological Niche Divergence Associated With Species and Populations Differentiation in Erythrophleum (Fabaceae, Caesalpinioideae)." *Plant Ecology and Evolution* 152, no. 1: 1. https://doi.org/10.5091/plecevo.2019.1543.

Gorel, A.-P., O. J. Hardy, G. Dauby, et al. 2022. "Climatic Niche Lability but Growth Form Conservatism in the African Woody Flora." *Ecology Letters* 25, no. 5: 1164-1176. https://doi.org/10.1111/ele.13985.

Greve, M., B. Reyers, A. M. Lykke, and J.-C. Svenning. 2013. "Spatial Optimization of Carbon-Stocking Projects Across Africa Integrating Stocking Potential With co-Benefits and Feasibility." *Nature Communications* 4, no. 1: 2975. https://doi.org/10.1038/ncomms3975.

Guan, K., M. Pan, H. Li, et al. 2015. "Mal." *Nature Geoscience* 8, no. 4: 4. https://doi.org/10.1038/ngeo2382.

Guisan, A., W. Thuiller, and N. E. Zimmermann. 2017. *Habitat Suitability and Distribution Models: With Applications in R. Ecology, Biodiversity and Conservation*. Cambridge University Press.

Hampe, A., and R. J. Petit. 2005. "Conserving Biodiversity Under Climate Change: The Rear Edge Matters." *Ecology Letters* 8, no. 5: 461-467. https://doi.org/10.1111/j.1461-0248.2005.00739.x.

Hubau, W., S. L. Lewis, O. L. Phillips, et al. 2020. "Asynchronous Carbon Sink Saturation in African and Amazonian Tropical Forests." *Nature* 579, no. 7797: 80-87. https://doi.org/10.1038/s41586-020-2035-0.

IPCC. 2023. *Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*. AR6 SYR. Intergovernmental Panel on Climate Change (IPCC). https://hdl.handle.net/10568/138481.

IUCN. 2024. "The IUCN Red List of Threatened Species." Version 2024-1. https://www.iucnredlist.org.

Jirinec, V., P. F. Rodrigues, B. R. Amaral, and P. C. Stouffer. 2022. "Light and Thermal Niches of Ground-Foraging Amazonian Insectivorous Birds." *Ecology* 103, no. 4: e3645. https://doi.org/10.1002/ecy.3645.

Kasongo Yakusu, E., J. Van Acker, H. Van De Vyver, et al. 2023. "Ground-Based Climate Data Show Evidence of Warming and Intensification of the Seasonal Rainfall Cycle During the 1960-2020 Period in Yangambi, Central Congo Basin." *Climatic Change* 176, no. 10: 142. https://doi.org/10.1007/s10584-023-03606-0.

Kirschbaum, M. U. F. 2000. "Forest Growth and Species Distribution in a Changing Climate." *Tree Physiology* 20, no. 5-6: 309-322. https://doi.org/10.1093/treephys/20.5-6.309.

Kremer, A., J. Chen, and M. Lascoux. 2025. "'Chimes of Resilience': What Makes Forest Trees Genetically Resilient?" *New Phytologist* 246, no. 5: 1934-1951. https://doi.org/10.1111/nph.70108.

Kremer, A., B. M. Potts, and S. Delzon. 2014. "Genetic Divergence in Forest Trees: Understanding the Consequences of Climate Change." *Functional Ecology* 28, no. 1: 22-36. https://doi.org/10.1111/1365-2435.12169.

Kremer, A., O. Ronce, J. J. Robledo-Arnuncio, et al. 2012. "Long-Distance Gene Flow and Adaptation of Forest Trees to Rapid Climate Change." *Ecology Letters* 15, no. 4: 378-392. https://doi.org/10.1111/j.1461-0248.2012.01746.x.

Lapola, D. M., J. M. C. da Silva, D. R. Braga, et al. 2020. "A Climate-Change Vulnerability and Adaptation Assessment for Brazil's Protected Areas." *Conservation Biology* 34, no. 2: 427-437. https://doi.org/10.1111/cobi.13405.

Li, Y., P. M. Brando, D. C. Morton, D. M. Lawrence, H. Yang, and J. T. Randerson. 2022. "Deforestation-Induced Climate Change Reduces Carbon Storage in Remaining Tropical Forests." *Nature Communications* 13, no. 1: 1964. https://doi.org/10.1038/s41467-022-29601-0.

Lörz, A.-N., J. Oldeland, and S. Kaiser. 2022. "Niche Breadth and Biodiversity Change Derived From Marine Amphipoda Species Off Iceland." *Ecology and Evolution* 12, no. 4: e8802. https://doi.org/10.1002/ece3.8802.

Luo, Z., J. Fan, M'a. Shao, Q. Yang, X. Yang, and S. Zhang. 2024. "Evaluating Soil Water Dynamics and Vegetation Growth Characteristics Under Different Soil Depths in Semiarid Loess Areas." *Geoderma* 442: 116791. https://doi.org/10.1016/j.geoderma.2024.116791.

Malakoutikhah, S., S. Fakheran, M.-R. Hemami, M. Tarkesh, and J. Senn. 2020. "Assessing Future Distribution, Suitability of Corridors and Efficiency of Protected Areas to Conserve Vulnerable Ungulates Under Climate Change." *Diversity and Distributions* 26, no. 10: 1383-1396. https://doi.org/10.1111/ddi.13117.

Marchin, R. M., D. Backes, A. Ossola, M. R. Leishman, M. G. Tjoelker, and D. S. Ellsworth. 2022. "Extreme Heat Increases Stomatal Conductance and Drought-Induced Mortality Risk in Vulnerable Plant Species." *Global Change Biology* 28, no. 3: 1133-1146. https://doi.org/10.1111/gcb.15976.

Meinzer, F. C., D. M. Johnson, B. Lachenbruch, K. A. McCulloh, and D. R. Woodruff. 2009. "Xylem Hydraulic Safety Margins in Woody Plants: Coordination of Stomatal Control of Xylem Tension With Hydraulic Capacitance." *Functional Ecology* 23, no. 5: 922-930. https://doi.org/10.1111/j.1365-2435.2009.01577.x.

Merenlender, A. M., A. T. H. Keeley, and J. A. Hilty. 2022. "Ecological Corridors for Which Species?" *Therya* 13, no. 1: 45-55. https://doi.org/10.12933/therya-22-1162.

Miles, L., A. Grainger, and O. Phillips. 2004. "The Impact of Global Climate Change on Tropical Forest Biodiversity in Amazonia." *Global Ecology and Biogeography* 13, no. 6: 553-565. https://doi.org/10.1111/j.1466-822X.2004.00105.x.

Morelli, T. L., C. Daly, S. Z. Dobrowski, et al. 2016. "Managing Climate Change Refugia for Climate Adaptation." *PLoS One* 11, no. 8: e0159909. https://doi.org/10.1371/journal.pone.0159909.
Ouédraogo, D.-Y., F. Mortier, S. Gourlet-Fleury, V. Freycon, and N. Picard. 2013. "Slow-Growing Species Cope Best With Drought: Evidence From Long-Term Measurements in a Tropical Semi-Deciduous Moist Forest of Central Africa." *Journal of Ecology* 101, no. 6: 1459-1470.
https://doi.org/10.1111/1365-2745.12165.

Pacifici, M., W. B. Foden, P. Visconti, et al. 2015. "Assessing Species Vulnerability to." *Climate Change (London, United States)* 5, no. 3: 215-224.
https://doi.org/10.1038/nclimate2448.

Parmentier, I., Y. Malhi, B. Senterre, et al. 2007. "The Odd Man Out? Might Climate Explain the Lower Tree α-Diversity of African Rain Forests Relative to Amazonian Rain Forests?" *Journal of Ecology* 95, no. 5: 1058-1071.
https://doi.org/10.1111/j.1365-2745.2007.01273.x.

Pennington, P. T., Q. C. B. Cronk, J. A. Richardson, F. I. Woodward, M. R. Lomas, and C. K. Kelly. 2004. "Global Climate and the Distribution of Plant Biomes." *Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences* 359, no. 1450: 1465-1476.
https://doi.org/10.1098/rstb.2004.1525.

Perret, D. L., M. E. K. Evans, and D. F. Sax. 2024. "A Species' Response to Spatial Climatic Variation Does Not Predict Its Response to Climate Change." *Proceedings of the National Academy of Sciences of the United States of America* 121, no. 1: e2304404120.
https://doi.org/10.1073/pnas.2304404120.

Phillips, O. L., L. E. O. C. Aragão, S. L. Lewis, et al. 2009. "Drought Sensitivity of the Amazon Rainforest." *Science* 323, no. 5919: 1344-1347.
https://doi.org/10.1126/science.1164033.

Phillips, O. L., G. van der Heijden, S. L. Lewis, et al. 2010. "Drought-Mortality Relationships for Tropical Forests." *New Phytologist* 187, no. 3: 631-646.
https://doi.org/10.1111/j.1469-8137.2010.03359.x.

Pillay, R., M. Venter, J. Aragon-Osejo, et al. 2022. "Tropical Forests Are Home to Over Half of the World's Vertebrate Species." *Frontiers in Ecology and the Environment* 20, no. 1: 10-15.
https://doi.org/10.1002/fee.2420.

Platts, P. J., P. A. Omeny, and R. Marchant. 2015. "AFRICLIM: High-Resolution Climate Projections for Ecological Applications in Africa." *African Journal of Ecology* 53, no. 1: 103-108.
https://doi.org/10.1111/aje.12180.

Réjou-Méchain, M., F. Mortier, J.-F. Bastin, et al. 2021. "Unveiling African Rainforest Composition and Vulnerability to Global Change." *Nature* 593, no. 7857: 90-94.
https://doi.org/10.1038/s41586-021-03483-6.

Rogelj, J., M. Meinshausen, and R. Knutti. 2012. "Global Warming Under Old and New Scenarios Using IPCC Climate Sensitivity Range Estimates." *Nature Climate Change* 2, no. 4: 248-253.
https://doi.org/10.1038/nclimate1385.

Savolainen, O., T. Pyhäjärvi, and T. Knürr. 2007. "Gene Flow and Local Adaptation in Trees." *Annual Review of Ecology, Evolution, and Systematics* 38, no. 1: 595-619.
https://doi.org/10.1146/annurev.ecolsys.38.091206.095646.

Scherrer, D., M. Esperon-Rodriguez, L. J. Beaumont, V. L. Barradas, and A. Guisan. 2021. "National Assessments of Species Vulnerability to Climate Change Strongly Depend on Selected Data Sources." *Diversity and Distributions* 27, no. 8: 1367-1382.
https://doi.org/10.1111/ddi.13275.

Tabor, K., and J. W. Williams. 2010. "Globally Downscaled Climate Projections for Assessing the Conservation Impacts of Climate Change." *Ecological Applications* 20, no. 2: 554-565.
https://doi.org/10.1890/09-0173.1.

Tavares, J. V., R. S. Oliveira, M. Mencuccini, et al. 2023. "Basin-Wide Variation in Tree Hydraulic Safety Margins Predicts the Carbon Balance of Amazon Forests." *Nature* 617, no. 7959: 111-117.
https://doi.org/10.1038/s41586-023-05971-3.

Teskey, R., T. Wertin, I. Bauweraerts, M. Ameye, M. A. Mcguire, and K. Steppe. 2015. "Responses of Tree Species to Heat Waves and Extreme

Heat Events." *Plant, Cell & Environment* 38, no. 9: 1699-1712.
https://doi.org/10.1111/pce.12417.

Thuiller, W., S. Lavorel, and M. B. Araújo. 2005. "Niche Properties and Geographical Extent as Predictors of Species Sensitivity to Climate Change." *Global Ecology and Biogeography* 14, no. 4: 347-357.
https://doi.org/10.1111/j.1466-822X.2005.00162.x.

Trew, B. T., D. P. Edwards, A. C. Lees, et al. 2024. "Novel Temperatures Are Already Widespread Beneath the World's Tropical Forest Canopies." *Nature Climate Change* 14, no. 7: 753-759.
https://doi.org/10.1038/s41558-024-02031-0.

Trew, B. T., and I. M. D. Maclean. 2021. "Vulnerability of Global Biodiversity Hotspots to Climate Change." *Global Ecology and Biogeography* 30, no. 4: 768-783.
https://doi.org/10.1111/geb.13272.

Veloz, S. D., J. W. Williams, J. L. Blois, F. He, B. Otto-Bliesner, and Z. Liu. 2012. "No-Analog Climates and Shifting Realized Niches During the Late Quaternary: Implications for 21st-Century Predictions by Species Distribution Models." *Global Change Biology* 18, no. 5: 1698-1713.
https://doi.org/10.1111/j.1365-2486.2011.02635.x.

Vira, B., C. Wildburger, S. Mansourian, and International Union of Forestry Research Organizations, eds. 2015. *Forests, Trees and Landscapes for Food Security and Nutrition: A Global Assessment Report*. IUFRO World Series 33. IUFRO.

Williams, J. W., and S. T. Jackson. 2007. "Novel Climates, No-Analog Communities, and Ecological Surprises." *Frontiers in Ecology and the Environment* 5, no. 9: 475-482.
https://doi.org/10.1890/070037.

Worth, J. R. P., G. J. Williamson, S. Sakaguchi, P. G. Nevill, and G. J. Jordan. 2014. "Environmental Niche Modelling Fails to Predict Last Glacial Maximum Refugia: Niche Shifts, Microrefugia or Incorrect Palaeoclimate Estimates." *Global Ecology and Biogeography* 23, no. 11: 1186-1197.
https://doi.org/10.1111/geb.12239.

Zellweger, F., D. Coomes, J. Lenoir, et al. 2019. "Seasonal Drivers of Understorey Temperature Buffering in Temperate Deciduous Forests Across Europe." *Global Ecology and Biogeography* 28, no. 12: 1774-1786.
https://doi.org/10.1111/geb.12991.

Zellweger, F., P. De Frenne, J. Lenoir, et al. 2020. "Forest Microclimate Dynamics Drive Plant Responses to Warming." *Science* 368, no. 6492: 772-775.
https://doi.org/10.1126/science.aba6880.

Zhou, L., Y. Tian, R. B. Myneni, et al. 2014. "Widespread Decline of Congo Rainforest Greenness in the Past Decade." *Nature* 509, no. 7498: 86-90.
https://doi.org/10.1038/nature13265.

## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Figure S1:** Projected changes in mean annual precipitation (MAP, mm) according to regional climate models (RCM) for the RCP4.5 and RCP8.5 scenarios: reduction zones in red. **Figure S2:** Proportion of species within communities exceeding their climatic limits for mean annual temperature (MAT). **Figure S3:** Mean annual precipitation (MAP) safety margin of tree and shrub species in the forest communities of tropical Africa. (a) Community average safety margin (and community standard deviation of safety margin in a), calculated by averaging the safety margins of all species within each community. (b) Species average safety margins, calculated for each species by averaging the safety margins across all the communities in which it occurs. Negative values indicate species currently exceeding their climatic limits for MAP in all or most of the communities where they have been observed. The dotted black line represents the median. (c) Proportion of species within communities exceeding their tolerance limits for MAP. **Figure S4:** Climatological water-deficit (CWD) safety margin of tree and shrub species in the forest communities of tropical Africa. (a) Community average safety margin (and community standard deviation of safety margin in a), calculated by averaging the safety margins of all species within each community. (b) Species average safety margins, calculated for each species by averaging the safety margins
across all the communities in which it occurs. Negative values indicate
species currently exceeding their climatic limits for CWD in all or most
of the communities where they have been observed. The dotted black
line represents the median. (c) Proportion of species within communi-
ties exceeding their tolerance limits for CWD. **Figure S5:** Risk to forest
communities from the predicted decrease in mean annual precipitation
(MAP). (a, b) Proportion of species projected to be at risk in each com-
munity by 2085 under the RCP4.5 and RCP8.5 scenarios, respectively.
**Figure S6:** Risk to forest communities from the predicted increase in
climatological water deficit (CWD). (a, b) Proportion of species pro-
jected to be at risk in each community by 2085 under the RCP4.5 and
RCP8.5 scenarios, respectively.

Biographies

**Mady Parfait Noé Madingou** is a Ph.D. candidate at Gembloux Agro-Bio Tech, University of Liège. His research focuses on the influence of climate on Africa's tropical forest biodiversity, specifically on the distribution of forest tree and shrub species.

**Gilles Dauby** is a researcher at IRD interested in plant assembly and distribution from community to biogeographical regions, with a strong focus on tree biodiversity and functioning of central African forests.

**Adeline Fayolle** is a researcher at Cirad whose research interests include tropical tree allometry, carbon stocks and sequestration, tree biodiversity and biogeography in Africa.

**Anaïs-Pasiphaé Gorel** is a researcher at Gembloux Agro-Bio Tech, University of Liège, interested in the biogeography, evolutionary history, functional traits and ecological strategies of tropical trees.