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Research article
Impacts of climate change on the geographic distribution of African oak tree
(Afzelia africana Sm.) in Burkina Faso, West Africa
Larba Hubert Balima
a
,
b
,
*
, Blandine Marie Ivette Nacoulma
b
,Si
e Sylvestre Da
c
,
Amad
eOu
edraogo
b
, Dodiomon Soro
a
, Adjima Thiombiano
b
a
WASCAL Graduate Research Program on Climate Change and Biodiversity, University F
elix Houphou€
et-Boigny, 31 Po Box 165, Abidjan 31, C^
ote d’Ivoire
b
Laboratory of Plant Biology and Ecology, 03 Po Box 7021, Ouagadougou 03, Burkina Faso
c
WASCAL Competence Center, 06 Box 9507, Ouagadougou 06, Burkina Faso
ARTICLE INFO
Keywords:
Threatened species
Climate change
Distribution modelling
Habitat suitability
West African Sahel
ABSTRACT
Afzelia africana Sm –a multipurpose leguminous tree species –is threatened in West Africa –a climate change
hotspot region. Yet, although the impacts of land use on this species dynamics have been widely reported, there is
a little literature on the impacts of climate change on its spatial distribution. This study aimed to predict the
impacts of climate change on the geographic distribution of A. africana in Burkina Faso. A total of 4,066 records of
A. africana was compiled from personal fieldwork and vegetation database. Current and future bioclimatic var-
iables were obtained from WorldClim website. For future climatic projections, six global climate models (GCMs)
were selected under two emission scenarios (RCP 4.5 &RCP 8.5) and two horizons (2050 &2070). Presence data
and bioclimatic variables were processed in ArcGIS software and used in the algorithm MaxEnt (maximum of
entropy) to predict the species distribution. Findings showed that maximum temperature of warmest month and
mean temperature of coldest quarter mostly affect the habitat suitability of A. africana. About 25.54% of Burkina
Faso land surface was currently suitable for A. africana conservation. Under future climatic projections, all the
climate models predict climate-driven habitat loss of the species with a southward range shift. Across the two
emission scenarios, the spatial extent of suitable habitats was predicted to decline from 9.43 to 23.99% and from
12.29 to 25% by the horizons 2050 and 2070, respectively. Habitat loss and range shifts predicted in this study
underline the high vulnerability of A. africana to future climate change. Reforestation actions and the protection
of predicted suitable habitats are recommended to sustain the species conservation.
1. Introduction
Global climate change represents unprecedented challenges for
biodiversity conservation worldwide. In most climate scenarios, extreme
climatic events and high climate variability are expected to occur (IPCC,
2007;Busby et al., 2012;IPCC, 2014). Such changes will induce severe
climatic stress to biodiversity with negative repercussions on all levels of
biological organization. Several studies reported that ongoing climate
variability is affecting tree phenology and physiology (Walther et al.,
2002;Walther, 2003;Thuiller et al., 2005), plant diversity (Heubes et al.,
2013) and ecosystem functions (Walther et al., 2002;Root et al., 2003;
Thuiller, 2003). In many areas of the world, climate-driven range shifts
and extinction risks are predicted for some woody plants (Thuiller, 2003;
Walther, 2003;McClean et al., 2005;Thuiller et al., 2005;Sommer et al.,
2010). These effects of climate change have drastically increased in
recent years a growing need for predicting the impacts of climate change
on the geographic distribution of woody plants.
Understanding species distributional dynamics is important in ecol-
ogy, evolution and conservation (Elith et al., 2006). The assessment of
the effects of climate change on species distribution is based on the
identification of bioclimatic envelopes through distribution modelling
(Guisan and Zimmermann, 2000;Pearson and Dawson, 2003;Phillips
et al., 2006). Species Distribution Models (SDMs) are effective tools for
predicting species environmental suitability and potential changes in
their geographic range. According to Thuiller et al. (2005) and Phillips
et al. (2009), predictive models are efficient tools likely to guide con-
servation decisions. Indeed, SDMs allow the identification of bioclimatic
envelopes of species which represent their potential climatic refuges or
critical habitats (Thomas et al., 2004;Elith et al., 2006;Phillips et al.,
2006). These models also enable to predict changes in the suitable
* Corresponding author.
E-mail address: lhubertbalima@gmail.com (L.H. Balima).
Contents lists available at ScienceDirect
Heliyon
journal homepage: www.cell.com/heliyon
https://doi.org/10.1016/j.heliyon.2021.e08688
Received 2 September 2021; Received in revised form 6 November 2021; Accepted 24 December 2021
2405-8440/©2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Heliyon 8 (2022) e08688
habitats over time and to identify species which may be endangered,
vulnerable or adapted to changing environmental conditions (Guisan
et al., 2013).
West Africa represents a climate change hotspot region where
increased probability of hazards, high vulnerability and severe exposure
meet (Heubes et al., 2013;IPCC, 2014). In such a context, empirical data
on species environmental suitability and distributional dynamics are
essential for conservation planning. The lack of reliable data on the
spatial distribution of plant biodiversity hampers the effectiveness of
conservation actions (Schmidt et al., 2017). As West African plants
constitute important providers of provisioning, supporting and cultural
services that local people essentially and traditionally rely on (Schumann
et al., 2011;Zizka et al., 2015), forecasting the impacts of climate change
on the spatial distribution of tree species is essential for maintaining
ecosystem services. In this perspective, a particular attention should be
paid to the threatened plants with high socio-economic significance.
Previous studies reported severe impacts of anthropogenic pressures
on the dynamics and stand diversity of some West African valuable plants
(Nacoulma et al., 2011;Schumann et al., 2011). Similarly, other studies
assessed the suitable habitats for the conservation of multipurpose trees
such as Parkia biglobosa (Jacq.) R.Br. ex G.Don (Dotchamou et al., 2016),
Vitex donania Sweet (Hounkp
evi et al., 2016), Kigelia africana (Lam.)
Benth. (Guidigan et al., 2018) and Vitellaria paradoxa C.F. Gaertn.
(Dimobe et al., 2020). Despite this growing literature, SDMs are lacking
for threatened plants, hindering the development of effective conserva-
tion strategies for these species. Through this study, we aim to bridge
knowledge gaps on distribution modelling of threatened plants in West
Africa. The study is focused on Afzelia africana Sm, a threatened and
multipurpose leguminous tree, endemic to Africa. The general objective
of the study is to assess the geographic distribution of Afzelia africana in
response to current and future climatic conditions.
We addressed the following research questions:
(i) Which bioclimatic variables do control the distribution of Afzelia
africana?
(ii) What are the current spatial extents of suitable habitats for the
species conservation?
(iii) What are the dynamics of the suitable habitats of A. africana?
(iv) Which factors affect the variations in species distribution models?
2. Material and methods
2.1. Study area
The study was conducted in Burkina Faso (Figure 1), a landlocked
West African Sahelian country located between the latitudes
09020–15050N and the longitudes 02020E–05030W. Burkina Faso is
situated at the centre of West Africa, covering the major bioclimatic
gradient of the region. Biogeographically, the country extends from the
Sudanian regional centre of endemism to the Sahelian transitional zone
(White, 1983). Spanning the tropical sub-arid and sub-humid zones,
Burkina Faso is subdivided into three climatic zones namely the Sahelian
zone, the Sudano-sahelian zone and the Sudanian zone (Figure 1). The
mean annual rainfall increases from the North to the South, varying from
300–600 mm. year
1
in the Sahelian zone to 900–1200 mm. year
1
in
the Sudanian zone. The mean annual temperature decreases from 35 C
in the Sahelian zone to 20 C in the Sudanian zone. The Sudano-sahelian
zone represents an intermediate area between the Sahel and the Suda-
nian zone. This area has mean annual rainfall between 600–900 mm.
year
1
and mean annual temperature varying from 25 to 30 C. The
broader climatic gradient of the country imposes a similar gradient in
plant diversity (Heubes et al., 2013) which increases from the Sahelian
zone to the Sudanian zone (Schmidt et al., 2013,2017). The vegetation is
dominated by a mosaic of savannas (shrub savannas and tree savannas)
with patches of forests (woodlands, dry forests and riparian forests).
Figure 1. Location of the study area in Burkina Faso, West Africa.
L.H. Balima et al. Heliyon 8 (2022) e08688
2
2.2. Study species
Afzelia africana Sm. also called ‘African mahogany’or ‘African oak’is
an African endemic leguminous timber species from the Fabaceae family.
This species is the most widely distributed among the seven African
Afzelia species. Its natural distribution range spans the Sudanian regional
centre of endemism, the Guineo-Congolea/Sudanian regional transition
zone and the Guineo-Congolean regional centre of endemism (Orwa
et al., 2009), covering 19 African countries (Donkpegan et al., 2014). The
natural geographic range of the species characterizes the transition zone
between wooded savannas and dry forests (Orwa et al., 2009;G
erard and
Louppe, 2011). In Burkina Faso, the distribution range of A. africana
extends from the Sudano-sahelian zone to the Sudanian zone. The bio-
physical limits of this species are reported to range between 800–1800
mm for mean annual rainfall, 20–35 C for mean annual temperature,
and 200–1200 m for altitude (Orwa et al., 2009;G
erard and Louppe,
2011). A. africana is an agroforestry tree species with high
socio-economic, industrial, cultural and ecological importance (Balima
et al., 2018). Its wood called ‘doussi
e’has a high economic value in the
international timber market because of its excellent properties as termite
resisting wood (G
erard and Louppe, 2011;Donkpegan et al., 2014). The
leaves have high fodder value and are used as forage for livestock (Balima
et al., 2018). The barks abound in various medicinal properties with
potential interests in the traditional medicine (Orwa et al., 2009).
2.3. Input data
2.3.1. Species occurrence records
Presence data (or occurrence records) of A. africana (Figure 2) was
compiled from two sources. A first phase extensive field survey was
carried out throughout the species distribution area in Burkina Faso. The
location of individual trees of the species was georeferenced using a GPS
(Global Positioning System, Garmin 64). The collected occurrence re-
cords were supplemented by data from the vegetation database (VegDa)
of the University Joseph Ki-Zerbo (Ouagadougou, Burkina Faso). A total
dataset of 4,066 occurrence records was obtained, of which 3,637 re-
cords (89.45%) were collected from field surveys and 429 records
(10.55%) from the vegetation database (Supplementary information,
Appendix 1).
2.3.2. Environmental data
Environmental variables were composed of both climatic and non-
climatic data. Current (1950–2000) bioclimatic data were downloaded
from WorldClim database version 1.4 (Hijmans et al., 2005,http:
//www.worldclim.org). This dataset includes 19 bioclimatic variables
derived from interpolated averages of minimum and maximum temper-
ature and rainfall (Hijmans et al., 2005). For future climate projections,
six global climate models (GCMs) (ACCESS1-0, CCSM4, CNRM-CM5,
HadGEM2-ES, MIROC5 and NorESM1-M) from the Coupled Model
Inter-comparison Project phase 5 (CMIP5) were selected (Table 1).
Among the set of selected GCMs, three models (HadGEM-ES, CNRM-CM5
and MIROC5) have been used in previous studies related to species dis-
tribution modelling in West Africa (Dotchamou et al., 2016;Hounkp
evi
et al., 2016;Guidigan et al., 2018;Dimobe et al., 2020). Climate models
were downloaded at a spatial resolution of 30 s (1 km 1 km) under the
Figure 2. Geographic distribution of collected occurrence records of A. africana in Burkina Faso.
Table 1. Global climate models used for running the species distribution model.
GCMs Definition Code
ACCESS1-0 Australian Community Climate and Earth-System Simulator ac
CCSM4 Community Climate System Model cc
CNRM-CM5 National Centre for Meteorological Coupled Model 5 cn
HadGEM2-
ES
Hadley Global Environment Model 2 Met Office Climate Model he
MIROC5 Model for Interdisciplinary Research on Climate mc
NorESM1-M Norwegian Climate's Center Earth System Model no
GCMs: global climate model.
L.H. Balima et al. Heliyon 8 (2022) e08688
3
representative concentration pathways (RCP) 4.5 and 8.5 at the horizons
2050 and 2070. The two emission scenarios (RCP 4.5 and RCP 8.5) were
considered to capture the range of emission uncertainties (Harris et al.,
2014). Indeed, the RCP 4.5 describes the lowest emission scenario,
whereas the RCP 8.5 describes the highest emission scenario. In addition
to the bioclimatic layers, soil data composed of soil types were obtained
from the national soil office of Burkina Faso.
2.4. Data processing and model calibration
The presence data and the bioclimatic variables were processed in
ArcGIS 10.5 software using the package SDMtoolbox 2.0 (Brown, 2014;
Brown et al., 2017). To reduce sampling bias, the occurrence records
were spatially filtered using the function ‘spatially rarefy occurrence data’
in SDMtoolbox. This process enables to remove all duplicate records
within each grid. A total of 590 presence records was kept after removing
the duplicated records, and then compiled into a single CSV file format.
The 19 bioclimatic variables were extracted for the study area (Burkina
Faso) as GeoTIFF format and converted into ASCII format to be used in
the algorithm. To determine how each predictor contributes to the dis-
tribution of the species, the environmental variables (20 variables in
total) were submitted to autocorrelation tests using the function ‘remove
highly correlated variables’in SDMtoolbox (Brown, 2014;Brown et al.,
2017). From the 19 bioclimatic variables and soil data, different sets of
predictors were tested by accounting for different thresholds of the
autocorrelation coefficient. Five least correlated predictors and ecologi-
cally meaningful for the studied species were selected at the pairwise
correlation coefficient of 0.75. These variables were bio1 (annual mean
temperature), bio3 (isothermality), bio5 (maximum temperature of
warmest month), bio11 (mean temperature of coldest quarter) and bio14
(precipitation of driest month). The rarefied 590 presence records (CSV
format) and the layers of the five bioclimatic variables (ASCII format)
were used as input data to run the model (Supplementary information,
Appendix 2).
2.5. Model fitting and evaluation
The model was run using MaxEnt v3.3.3k (Phillips et al., 2006), a
machine learning algorithm that applies the principle of maximum en-
tropy to predict the species potential distribution from a presence-only
data and environmental predictors (Phillips et al., 2006). MaxEnt algo-
rithm is one of the most powerful and widely used software programs for
species distribution modelling (Elith et al., 2006;Pearson et al., 2007).
This software has been used in several studies on species distribution
modelling in West Africa (Fandohan et al., 2013;Gbesso et al., 2013;
Gb
etoho et al., 2017). Before running the model, the following regula-
rization parameters were set: 25 for random test percentage, 10 repli-
cates, subsample as replicated run type, and 5000 iterations. A Jackknife
test was performed on the environmental variables to determine the
contribution of each variable to the prediction of species distribution.
Regarding model evaluation, we used 25% of species occurrence re-
cords for model testing and 75% for model calibration. The predictive
ability of the model was assessed using the Area Under the receiver
operating characteristics Curve (AUC) (Phillips et al., 2006). The AUC is
the probability that a randomly chosen presence cell have a higher pre-
dicted value than an absence cell (Araújo et al., 2005;Elith et al., 2006).
This index measures the ability of a model to discriminate between sites
where a species is present from sites where it is absent (Elith et al., 2006).
The AUC values range from 0 to 1, where values close to one (AUC
0.75) indicates a good fit, 0.5 implies a predictive discrimination that is
no better than a random guess, and values less than 0.5 indicate per-
formance worse than random (Elith et al., 2006). Due to the recent
criticism about the limitations of AUC in assessing SDMs performance
(Lobo et al., 2007;Jimenez-Valverde et al., 2012), threshold dependent
test was used through the True Skill Statistics (TSS) for a better evalua-
tion of the model (Allouche et al., 2006). The TSS is the capacity of the
model to accurately detect true presences (sensitivity) and true absences
(specificity). Model with value of TSS 0 indicates a random prediction
(performance not better than random), while values close to 1 (TSS >0.5)
characterize a model with good predictive power (Allouche et al., 2006).
The TSS values were averaged for the 10 run replicates using the back-
ground predictions and the sample predictions of the MaxEnt outputs.
This index was computed using the following formula (Allouche et al.,
2006):
TSS ¼ad bc
ðaþcÞðbþdÞ¼Sensitivity þSpecificity 1 (1)
The model outputs were processed in the software ArcGIS 10.5. The
averaged outputs of MaxEnt obtained for each climate model under each
scenario at each horizon were converted from ASCII format to raster, and
afterwards classified as ‘suitable habitats’or ‘unsuitable habitats’using
the 10–percentile training presence logistic threshold. To calculate the
current and future extents of suitable/unsuitable habitats, the raster files
were polygonised. Maps of the species suitable areas were finally pro-
duced for current and future climatic conditions under the two scenarios
at the two horizons.
3. Results
3.1. Model performance and variable contribution
Both the AUC and the TSS showed a good quality of the predicted
model. The Area Under the receiver operating characteristic Curve
(Figure 3) showed higher value of AUC (AUC ¼0.902 0.012). This
indicates a good predictive ability of the predicted model. The threshold
dependent test also revealed high value of the True Skill Statistics (TSS ¼
0.732). Such value of TSS (TSS >0.5) confirms that the model performs
better than random with a good predictive ability.
The five least correlated variables were selected among the predictor
variables to run the model. Among the selected predictors, the maximum
temperature of warmest month (bio5) and mean temperature of coldest
quarter (bio11) contributed the most to the model, while mean annual
temperature (bio1) contributed the least (Table 2).
The results of the Jackknife tests (Figure 4) showed that bio5
(maximum temperature of warmest month) represents the environ-
mental variable that decreases the gain the most when it is omitted. This
variable also constitutes the environmental variable with highest gain
when used in isolation. The maximum temperature of warmest month
appears therefore to have both the most useful information by itself and
the most information that is not present in the other predictors.
3.2. Distribution of A. africana under current and future climate change
The potential current suitable habitats for the species represent
25.542% of Burkina Faso land surface (Table 3). These habitats cover
about 70,091.85 km
2
and span the Sudano-sahelian zone and the Suda-
nian zone of the country (Figure 5). About 74.46% of the national ter-
ritory is unsuitable for A. africana conservation under current climatic
conditions. At the horizon 2050, a decrease in the extents of the suitable
habitats of the species was predicted by all the climate models under the
two emission scenarios (Table 3,Figure 5). Under the RCP 4.5, the
suitable habitats represented 4.32% (11,849.42 km
2
) to 13.48%
(36,986.23 km
2
) of the total land surface of Burkina Faso, corresponding
to habitat loss of 12.06% and 21.22% by 2050. Similarly, about 1.55%
(4248.01 km
2
) to 16.12% (44,214.43 km
2
) of the country surface was
predicted to be suitable by 2050, under the RCP 8.5, corresponding to
about 9.43–23.99% loss in the suitable habitats of the species. At the
horizon 2070, the projected suitable habitats range from 6,618.99 km
2
(2.41%) to 363,666.04 km
2
(13.25%) under the RCP 4.5. The RCP 8.5
predicted drastic changes in the species spatial patterns by 2070, with
only 3.16% of suitable areas. Only predictions from the climate models
L.H. Balima et al. Heliyon 8 (2022) e08688
4
ACCESS10 and CCSM4 were presented (Figure 5). The results from the
four other climate models (CNRM-CM5, HadGEM2-ES, MIROC5 and
NorESM1-M) were provided as supplementary data (Supplementary in-
formation, Appendix 3 and Appendix 4).
Under current climatic conditions, the suitable habitats span the
Sudano-sahelian and the Sudanian climatic zones (Figure 5). A south-
ward shift in the current suitable habitats is predicted to occur under
future climatic conditions (Figure 5). Across all the climate models, only
the Sudanian zone is predicted to be suitable for the species conservation
at the horizons 2050 and 2070.
3.3. Factors affecting the variations of species distribution models
The range of habitats loss predicted for the species differs between the
six climate models, the two emission scenarios and the two horizons.
Under the RCP 4.5 and the horizon 2050, the model MIROC5
(mc4.5bi50) predicts the highest habitat loss (21.22%) of the species,
while the model CNRM-CM5 (cn4.5bi50) predicts the lowest habitat loss
(12.06%). The model CCSM4 (cc8.5bi50) and the model HadGEM2-ES
(he8.5bi50) predict the highest habitat loss (23.98%) under the RCP
8.5 at the horizon 2050. The lowest habitat loss (9.43%) was predicted by
the model CNRM-CM5 under the RCP 8.5 for the horizon 2050. At the
horizon 2070 and under the RCP 8.5, all the climate models (except the
model CNRM-CM5) predict a declining environmental suitability for the
species with less than 1% of the suitable habitats. However, about
12.29% (CNRM-CM5) to 23.13% (CCSM4) of habitat loss was predicted
for the horizon 2070 under the RCP 4.5. Across both horizons, habitat
loss was more pronounced under the RCP 8.5 than the RCP 4.5. Similarly,
Figure 3. Average receiver operating characteristic curve and related AUC.
Table 2. Contribution of bioclimatic variables used for model running.
Variable Variable definition Percent
contribution (%)
Permutation
importance (%)
bio5 Max temperature of
warmest month
46.1 56.8
bio11 Mean temperature of
coldest quarter
25.2 21.2
bio3 Isothermality 16 10
bio14 Precipitation of driest
month
9.3 7.2
bio1 Annual mean temperature 3.3 4.7
Figure 4. Jackknife tests for the regularized training gain for A. africana. For a given predictor variable, the corresponding green bar (without variable) shows how
much the total gain is decreased if this specific variable is excluded from the model. The blue bar (with only variable) shows the obtained gain if the considered
variable is used in isolation and the others are excluded from the model.
L.H. Balima et al. Heliyon 8 (2022) e08688
5
drastic habitat loss was expected at the horizon 2070 compared to the
horizon 2050.
4. Discussion
4.1. Climatic variables controlling the distribution of Afzelia africana
Five less correlated predictors were used to predict the geographic
distribution of the species. From the Jackknife tests and the table of
variables’contribution, the findings showed that maximum temperature
of warmest month (bio5) and mean temperature of coldest quarter
(bio11) are the most important factors affecting the habitat suitability of
A. africana. Higher value of the maximum temperature of warmest month
decreases the habitat suitability, while lower value of the mean tem-
perature of coldest quarter decreases the suitability. Our findings are in
line with Guidigan et al. (2018) who reported the maximum temperature
of warmest month among the significant climatic variables driving the
distribution of Kigelia africana (Lam.) in Benin.
The findings highlight the ecology of A. africana which occurs in
Africa humid forests and dry savannas (Orwa et al., 2009), demarcating
the transition zone between wooded savannas and dense dry forests
(G
erard and Louppe, 2011). The ecological optimum of A. africana
regarding these climatic variables (bio5 and bio11) is within its tolerance
limits for temperature in Burkina Faso, reported to range between 20–35
C. Previous studies on species distribution modelling in West Africa
reported the precipitation as the major factor influencing vegetation
patterns and the distribution of woody plants (Sommer et al., 2010;
Heubes et al., 2011;Ganglo et al., 2017). The mean annual rainfall was
not used as predictor in the modelling because of its high correlation with
the other bioclimatic variables. The ecological tolerance of A. africana for
rainfall in Africa ranges between 800–1200 mm of mean annual rainfall
(Orwa et al., 2009;G
erard and Louppe, 2011). Due to this high ecological
amplitude of the species, rainfall may not constitute a limiting factor for
the species throughout the study area.
4.2. Distribution of Afzelia africana under current climatic conditions
The predicted current suitable habitats for A. africana conservation in
Burkina Faso represent one fifth (25.54%) of the country total area.
Habitats predicted suitable for the species conservation are located
within the Sudanian regional centre of endemism which spans the
Sudanian zone and the Sudano–sahelian zone. This finding is consistent
with the distribution range of the studied species in West Africa. Indeed,
the natural distribution range of A. africana extends from the Gui-
neo–Congolean regional centre of endemism to the Sahel Southern limit.
The current spatial extent of A. africana reported in this study is lower
than those reported on other high socio-economic plants of West Africa.
Indeed, a study by Hounkp
evi et al. (2016) reported that about 85% of
Benin area was suitable for the cultivation of Vitex doniana Sweet.
Similarly, about 52% and 53% of Benin territory was reported to be very
suitable for the conservation of Kigelia africana (Lam.) Benth. (Guidigan
et al., 2018) and Parkia biglobosa (Jacq.) R.Br. ex G.Don (Dotchamou
et al., 2016), respectively. The lower value of the current suitable habi-
tats predicted for A. africana highlights the conservation status of this
species in Burkina Faso. In fact, A. africana undergoes severe anthropo-
genic pressures across most West African countries where it is considered
Table 3. Current and future geographic distribution of A. africana in Burkina Faso.
GCM Code Unsuitable habitats Suitable habitats
Extent (km
2
) % Extent (km
2
) % Trend (%)
Current
20,4312.752 74.458 70,087.248 25.542
Horizon 2050
ACCESS1-0 ac4.5b50 249,542.104 90.941 24,857.896 9.059 -16.483
ACCESS1-0 ac8.5b50 273,013.838 99.495 1386.162 0.505 -25.037
CCSM4 cc4.5b50 255,943.856 93.274 18,456.144 6.726 -18.816
CCSM4 cc8.5b50 270,124.848 98.442 4275.152 1.558 -23.984
CNRM-CM5 cn4.5b50 237,416.368 86.522 36,983.632 13.478 -12.064
CNRM-CM5 cn8.5b50 230,188.672 83.888 44,211.328 16.112 -9.429
HadGEM2-ES he4.5b50 255,455.424 93.096 18,944.576 6.904 -18.638
HadGEM2-ES he8.5b50 270,152.288 98.452 4247.712 1.548 -23.994
MIROC5 mc4.5b50 262,551.408 95.682 11,848.592 4.318 -21.224
MIROC5 mc8.5b50 258,232.352 94.108 16,167.648 5.892 -19.649
norESM1-M no4.5b50 250,947.032 91.453 23,452.968 8.547 -16.995
norESM1-M no8.5b50 261,012.024 95.121 13,387.976 4.879 -20.663
Horizon 2070
ACCESS1-0 ac4.5b70 249,418.624 90.896 24,981.376 9.104 -16.438
ACCESS1-0 ac8.5b70 ** ** ** **
CCSM4 cc4.5b70 267,781.472 97.558 6618.528 2.412 -23.129
CCSM4 cc8.5b70 ** ** ** **
CNRM-CM5 cn4.5b70 238,036.512 86.748 36,363.488 13.252 -12.289
CNRM-CM5 cn8.5b70 265,739.936 96.844 8660.064 3.156 -22.386
HadGEM2-ES he4.5b70 264.488.672 96.388 9,911.328 3.612 -21.929
HadGEM2-ES he8.5b70 ** ** ** **
MIROC5 mc4.5b70 258,836.032 94.328 15,563.968 5.672 -19.869
MIROC5 mc8.5b70 ** ** ** **
norESM1-M no4.5b70 257,041.456 93.674 17,358.544 6.326 -19.216
norESM1-M no8.5b70 266,836.957 97.244 7563.043 2756 -22.786
The first two letters in column "code" (ac, cc, cn, he, mc and no) refer to global climate models; 4.5: RCP4.5; 8.5: RCP8.5; b: bioclimatic variables; 50: horizon 2050; 70:
horizon 2070; *Unpredicted (1%); negative sign (-) indicates habitat loss.
L.H. Balima et al. Heliyon 8 (2022) e08688
6
as a threatened (Nacoulma et al., 2011) or endangered species (Sinsin
et al., 2004). These pressures reduce the occurrence and the geographic
range of the species. A. africana is also classified as a vulnerable species in
the IUCN Red List of threatened species.
4.3. Distribution of A. africana under future climatic projections
Afzelia africana has been reported to have a strong adaptation to
various climatological conditions (Orwa et al., 2009;G
erard and Louppe,
2011). However, through this study, we found that future climate change
will negatively affect the spatial patterns of this species in Burkina Faso.
Across all climate models, a decline in the environmental suitability with
a southward range shift trend was expected at both horizons. At the
horizon 2050, Afzelia africana is predicted to lose between 12.06 and
21.22% of its current suitable habitats under the RCP 4.5. Under the RCP
8.5, between 9.43 to 23.99% of the suitable habitats will be lost. More
drastic changes are expected at the horizon 2070, with 16.44–23.13% of
habitat loss. Our results corroborate previous studies which predicted a
climate–driven habitat loss for some valuable West African plants (Fan-
dohan et al., 2013;Gb
etoho et al., 2017). In fact, a growing body of
empirical evidence supported that changing climatic conditions will
cause range shifts and habitats loss for many species across the world
(IPCC, 2007;Busby et al., 2012). Species range contraction and extinc-
tion risks have been also predicted in West Africa (Sommer et al., 2010)
and elsewhere (Thomas et al., 2004;Thuiller et al., 2005). In Burkina
Faso, climate change induced habitat loss was reported for Vitellaria
paradoxa C.F. Gaertn. (Dimobe et al., 2020). Similarly, Heubes et al.
(2013) reported that future climate change and land use change will
significantly reduce plant diversity in Burkina Faso, with the impacts of
climate change being more important than that of land use change. The
predicted southward range shifts under future climate projections could
be explained by significant changes in temperature. This may indicate
that an increase in temperature will likely occur in the semi-arid areas of
West Africa, thereby, reducing the environmental suitability of plant
biodiversity and ecosystems. These findings imply high conservation
challenges for A. africana in Burkina Faso and call for reforestation ac-
tions within the Sudanian region to reduce species extinction risks.
In contrast to our findings, climate-induced range expansion was re-
ported for some West African plants (Fandohan et al., 2013;Gbesso et al.,
2013;Hounkp
evi et al., 2016;Kirchmair, 2017). Indeed, an average
habitat increase of 70% was projected for 17 woody plants in Burkina
Faso (Kirchmair, 2017), with higher projected increase for Vitex chrys-
ocarpa Planch. ex Benth. (218%), Anogeissus leiocarpa (DC.) Guill. and
Perr. (133%) and Diospyros mespiliformis Hochst. ex A. DC. (80%). Simi-
larly, climate-induced habitat gain was predicted for Tamarindus indica L.
(Fandohan et al., 2013), Chrysophyllum albidum G. Don (Gbesso et al.,
2013), Vitex donania Sweet (Hounkp
evi et al., 2016) and Anogeissus
leiocarpa (DC.) Guill. and Perr. (Gb
etoho et al., 2017) in Benin. A study by
Heubes et al. (2011) reported a northward increase in species diversity
across the Sahelian zone of Burkina Faso. Such predicted climate effects
on the diversity and distribution of West African woody plants concur
with the Sahel greening hypothesis which supports the replacement of
savannas by deciduous and evergreen forest biomes (Heubes et al.,
2011).
4.4. Factors affecting species distribution modelling
The study indicates that the geographic distribution of A. africana
under future climate change varied within and between the six climate
models (GCMs) across the two emission scenarios (RCP 4.5 and RCP 8.5)
and the two horizons (2050 and 2070). This corroborates findings from
previous studies (Thuiller et al., 2005;Fandohan et al., 2013;Heubes
et al., 2013) and highlights the fact that distribution modelling outputs
varied according to many factors. In fact, the model outputs firstly
depend upon the environmental variables selected as predictors (Guisan
and Zimmermann, 2000;Pearson et al., 2007). Significant variations in
these predictors induce changes in the future potential distributions of
the species. For instance, climate-induced range expansion as predicted
for some plants in West Africa (Fandohan et al., 2013;Gbesso et al., 2013;
Hounkp
evi et al., 2016;Kirchmair, 2017) may underscore the predicted
Figure 5. Geographic distribution of A. africana under the models ACCESS10 (ac) and CCSM4 (cc).
L.H. Balima et al. Heliyon 8 (2022) e08688
7
increase in mean annual rainfall in the region (Heubes et al., 2011,2013;
Platts et al., 2014). Although all models predicted a general trend in the
geographic distribution of the species, some variations were observed
regarding the range of habitat loss. Predicted habitat loss varied between
climate models within each horizon and each emission scenario. These
findings corroborate the fact that the choice of climate models in species
distribution modelling influences the predicted models (Thuiller et al.,
2005;Fandohan et al., 2013;Heubes et al., 2013). Across all climate
models, the lowest impact of climate change was predicted by the model
CNRM-CM5 under the two emission scenarios at both horizons. How-
ever, the models MIROC5, CCSM4 and HadGEM2-ES predicted the
highest impacts of climate change. Such variations across climate models
highlight the differences in the global climate models, and therefore
introduce the issues of model uncertainties (Harris et al., 2014).
Accordingly, a given species can be predicted extinct by a set of climate
models, while under habitat loss or range expansion by other climate
models. Therefore, the choice of climate models represents an important
challenge in species distribution modelling (Heubes et al., 2013). The
regional climate models (RCM) are reported to provide more statistically
improved climate data which are suitable for ecological modelling in
Africa (Platts et al., 2014). However, most studies related to species
distribution modelling in Africa have relied on the use of global climate
models (Fandohan et al., 2013;Gbesso et al., 2013;Dotchamou et al.,
2016;Guidigan et al., 2018) rather than the use of regional climate
models (Ganglo et al., 2017). Such inconsistent use of climate models
may not enable to forecast the real impacts of climate change on woody
plants. Accordingly, there is an urgent need to harmonize the use of
climate models to reduce divergences of African climate forecasts
(Heubes et al., 2011,2013).
In accordance with our findings, the impact of future climate
change on the geographic distribution of A. africana also varies be-
tween emission scenarios (Thuiller et al., 2005;Ganglo et al., 2017;
Gb
etoho et al., 2017). This result is consistent with Harris et al. (2014)
who supported that emission scenarios represent the first source of
model uncertainties. High spatial extent in the potential unsuitable
areas was found under the highest emission scenario (RCP 8.5)
compared to the lowest emission scenario (RCP 4.5). This suggests that
in the absence of mitigation actions as assumed by the RCP 8.5, climate
change will severely affect the distribution range of the species.
Conversely, climate change impact could be reduced in the case of
mitigation assumption under the RCP 4.5. The study further showed
that modelling outputs also varied across periods with more drastic
changes expected by 2070.
The predicted habitat loss (Sommer et al., 2010;Dimobe et al., 2020;
Gb
etoho et al., 2017) and range expansion (Gbesso et al., 2013;
Hounkp
evi et al., 2016;Kirchmair, 2017) as expected for woody plants
in response to future climate change, highlight the uncertainties of
future climate in West African region. Indeed, if warmer conditions
(increase in temperature) are expected for West African Sahel under
most climate projections (Sommer et al., 2010;Heubes et al., 2011;
Fandohan et al., 2013), it is unclear whether precipitations will increase
or decrease. Nevertheless, an increase in mean annual rainfall is pro-
jected in Western and Eastern parts of Africa (Platts et al., 2014).
Similarly, increased rainfall is predicted across West African countries
under most climate projections (Heubes et al., 2011,2013;Platts et al.,
2014). Conversely, a decrease in precipitations in West Africa has been
also reported by some authors (Fandohan et al., 2013). The high vari-
ability of climate projections over West Africa (IPCC, 2007,2014)
constitutes an important challenge for regional ecological stimulations
and compromises correct inference about the impact of future climate
change on plant biodiversity and ecosystems. It is uncertain whether
climate change will cause habitat loss or range expansion, species
turnover, Sahel greening or drying out. Inversely, it is very obvious that
some species will experience more impacts of climate change than some
other species which may adapt, expand their spatial extent or shift their
geographic range.
5. Conclusion
This study used six groups of global climate models to investigate the
impacts of climate change on the geographic distribution of the African
oak tree, a multipurpose and threatened woody plant in West Africa. We
found that maximum temperature of warmest month and mean tem-
perature of coldest quarter mostly influence the geographic distribution
of A. africana in Burkina Faso. Climate change will negatively affect the
spatial distribution of the species, resulting in a southward range shifts
and a drastic loss in the suitable habitats by the horizons 2050 and 2070.
The current suitable habitats of the species representing 25.5% of the
country total area is predicted to drastically decline under future climatic
conditions. The findings also showed that the spatial extents of the
suitable habitats varied between climate models, emissions scenarios and
horizons. To prevent biodiversity loss and ecological degradation in West
African region, efficient and adapted management approaches are ur-
gently needed. In this perspective, it is important to enforce forestry
policies on the threatened plants with high socio-economic significance
and to reinforce the conservation of protected areas which represent their
last refuge. To prevent species habitat loss, studies on ecological niche
modelling must be extended to the other valuable West African timber
species. The use of different sets of climate models and the incorporation
of the other environmental variables may contribute to generate more
improved distribution models.
Declarations
Author contribution statement
Larba Hubert Balima: Conceived and designed the experiments; Per-
formed the experiments; Analyzed and interpreted the data; Wrote the
paper.
Blandine Marie Ivette Nacoulma &Amad
eOu
edraogo: Contributed
reagents, materials, analysis tools or data.
Si
e Sylvestre Da: Analyzed and interpreted the data; Contributed re-
agents, materials, analysis tools or data.
Dodiomon Soro: Conceived and designed the experiments.
Adjima Thiombiano: Conceived and designed the experiments;
Contributed reagents, materials, analysis tools or data.
Funding statement
This work was supported by German Federal Ministry of Education
and Research (BMBF) (WASCAL_GRP/CCB2).
Data availability statement
Data will be made available on request.
Declaration of interests statement
The authors declare no conflict of interest.
Additional information
Supplementary content related to this article has been published
online at https://doi.org/10.1016/j.heliyon.2021.e08688.
Ethics approval
Not applicable.
Acknowledgements
The authors show their gratitude to the German Federal Ministry of
Education and Research (BMBF) and the West African Science Service
L.H. Balima et al. Heliyon 8 (2022) e08688
8
Center on Climate Change and Adapted Land Use (WASCAL). The authors
are also grateful to Dr. Dimobe Kangb
eni for his help on the processing of
bioclimatic layers. Special thanks to the two anonymous reviewers for
their relevant comments which significantly improved the manuscript.
References
Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution
models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43,
1223–1232.
Araújo, M.B., Pearson, R.G., Thuiller, W., Erhard, M., 2005. Validation of species-climate
impact models under climate change. Glob. Change Biol. 11 (9), 1504–1513.
Balima, L.H., Nacoulma, B.M.I., Eku
e, M.R.M., Kouam
e, N.F., Thiombiano, A., 2018. Use
patterns, use values and management of Afzelia africana Sm. in Burkina Faso:
implication for domestication and sustainable conservation. J. Ethnobiol. Ethnomed.
14 (23).
Brown, J.L., 2014. SDMtoolbox: a python-based GIS toolkit for landscape genetic,
biogeographic and species distribution model analyses. Method Ecol. Evolut. 5 (7),
694–700.
Brown, J.L., Bennett, J.R., French, C.M., 2017. SDMtoolbox 2.0: the next generation
Python-based GIS toolkit for landscape genetic, biogeographic and species
distribution model analyses. PeerJ 5 (e4095).
Busby, J.W., Smith, T.G., White, K.L., Strange, S.M., 2012. Locating climate insecurity:
where are the most vulnerable places in Africa?. In: Climate Change, Human Security
and Violent Conflict. Springer Berlin Heidelberg, pp. 463–511.
Dimobe, K., Ou
edraogo, A., Ou
edraogo, K., Goetze, D., Stein, K., Schmidt, M.,
Nacoulma, B.M.I., Gnoumou, A., Traor
e, L., Porembski, S., Thiombiano, A., 2020.
Climate change reduces the distribution area of the shea tree (Vitellaria paradoxa C.F.
Gaertn.) in Burkina Faso. J. Arid Environ. 181, 104237.
Donkpegan, A.S.L., Hardy, O.J., Lejeune, P., Oumorou, M., Daïnou, K., Doucet, J.L., 2014.
Un complexe d’esp
eces d’Afzelia des for^
ets africaines d’int
er^
et
economique et
ecologique (synth
ese bibliographique). Biotechnol. Agron. Soc. Environ. 18 (2),
233–246.
Dotchamou, F.T., Atindogbe, G., Sode, A.I., Fonton, H.N., 2016. Density and spatial
distribution of Parkia biglobosa pattern in Benin under climate change. J. Agri.
Environ. Int. Develop. 110 (1), 173–194.
Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J.,
Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A.,
Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T.,
Phillips, S.J., Richardson, K., Scachetti-Pereira, R., Schapire, R.E., Soberon, J.,
Williams, S., Wisz, M.S., Zimmermann, N.E., 2006. Novel methods
improve prediction of species’distributions from occurrence data. Ecography 29 (2),
129–151.
Fandohan, B., Gouwakinnou, G.N., Fonton, N.H., Sinsin, B., Liu, J., 2013. Impact des
changements climatiques sur la r
epartition g
eographique des aires favorables
ala
culture et
a la conservation des fruitiers sous-utilis
es : cas du tamarinier au B
enin.
Biotechnol. Agron. Soc. Environ. 17 (3), 450–462.
Ganglo, J.C., Djotan, G.K., Gb
etoho, J.A., Kakpo, S.B., Aoudji, A.K.N., Kourouma,
Koura, K., Tessi, D.R.Y., 2017. Ecological niche modelling and strategies for the
conservation of Dialium guineense Willd. (Black velvet) in West Africa. Int. J.
Biodivers. Conserv. 9 (12), 373–388.
Gb
etoho, A.J., Aoudji, K.N.A., Roxburgh, L., Ganglo, C.J., 2017. Assessing pioneer
species’suitabilities for Benin’s secondary forest restoration in the context of global
climate change. Bois Forets Tropiques 332 (2), 43–55.
Gbesso, F.H.G., Tente, B.H.A., Gouwakinnou, N.G., Sinsin, B., 2013. Influence des
changements climatiques sur la distribution g
eographique de Chrysophyllum albidum
G. Don (Sapotaceae) au Benin. Int. J. Brain Cognit. Sci. 7 (5), 2007–2018.
G
erard, J., Louppe, D., 2011. Afzelia africana Sm. ex Pers. In: Lemmens, R.H.M.J.,
Louppe, D., Oteng-Amoako, A.A. (Eds.), Prota, Timbers/Bois d’œuvre 2. Wageningen,
Netherlands, 7 (2).
Guidigan, M.L.G., Azihou, F., Idohou, R., Okhimamhe, A.A., Fandohan, A.B., Sinsin, B.,
Adet, L., 2018. Modelling the current and future distribution of Kigelia africana
under climate change in Benin, West Africa. Model. Earth Sys. Environ. 4 (3),
1225–1238.
Guisan, A., Tingley, R., Baumgartner, J.B., Naujokaitis-Lewis, I., Sutcliffe, P.R.,
Tulloch, A.I.T., Regan, T.J., Brotons, L., McDonald-Madden, E., Mantyka-Pringle, C.,
Martin, T.G., Rhodes, J.R., Maggini, R., Samantha, A., Setterfield, S.A., Elith, J.,
Schwartz, M.W., Wintle, B.A., Broennimann, O., Austin, M., Ferrier, S.,
Kearney, M.R., Possingham, H.P., Buckley, Y.M., 2013. Predicting species
distributions for conservation decisions. Ecol. Lett. 16 (2), 1424–1435.
Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology.
Ecol. Model. 135, 147–186.
Harris, R.M.B., Grose, M.R., Lee, G., Bindoff, N.L., Porfiri, L.L., Fox-Hughes, P., 2014.
Climate projections for ecologists. Wiley Interdiscipl. Rev.: Clim. Change 5 (5),
621–637.
Heubes, J., Schmidt, M., Stuch, B., García M
arquez, J.R., Wittig, R., Zizka, G.,
Thiombiano, A., Sinsin, B., Schaldach, R., Hahn, K., 2013. The projected impact of
climate and land use change on plant diversity: an example from West Africa. J. Arid
Environ. 96, 48–54.
Heubes, J., Kühn, I., K€
onig, K., Wittig, R., Zizka, G., Hahn, K., 2011. Modelling biome
shifts and tree cover change for 2050 in West Africa. J. Biogeogr. 38 (12),
2248–2258.
Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high
resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25
(15), 1965–1978.
Hounkp
evi, A., Tosso, F., Gb
emavo, D.S.J.C., Kouassi, E.K., Kon
e, D., Gl
el
e-Kakaï, R.,
2016. Climate and potential habitat suitability for cultivation and in situ conservation
of the black plum (Vitex doniana Sweet) in Benin, West Africa. Int. J. Appl. Agric.
Res. 8 (4), 67–80.
IPCC, 2014. Climate Change 2013: the Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental.
IPCC, 2007. Climate Change: Synthesis Report. Intergovernmental Panel on Climate
Change (IPCC), New York, USA.
Jimenez-Valverde, A., Acevedo, P., Barbosa, A.M., Lobo, J.M., Real, R., 2012.
Discrimination capacity in species distribution models depends on the
representativeness of the environmental domain. Glob. Ecol. Biogeogr. 22 (4),
508–516.
Kirchmair, I., 2017. Biogeography of West African Gallery Forests. PhD Thesis. vom
Fachbereich 15 der Goethe-Universit€
at Franckfurt, 213p.
Lobo, J.M., Jimenez-Valverde, A., Real, R., 2007. AUC: a misleading measure of the
performance of predictive distribution models. Glob. Ecol. Biogeogr. 17 (2), 145–151.
McClean, C.J., Lovett, J.C., Kuper, W., Hannah, L., Sommer, J.H., Barthlott, W.,
Termansen, M., Smith, G.E., Tokamine, S., Taplin, J.R.D., 2005. African plant
diversity and climate change. Ann. Mo. Bot. Gard. 92 (2), 139–152. http://www.jstor
.org/stable/3298511.
Nacoulma, B.M.I., Traor
e, S., Hahn, K., Thiombiano, A., 2011. Impact of land use types on
population structure and extent of bark and foliage harvest of Afzelia africana and
Pterocarpus erinaceus in Eastern Burkina Faso. Int. J. Biodivers. Conserv. 3 (3), 62–72.
Orwa, C., Mutua, A., Kindt, R., Jamnadass, R., Anthony, R., 2009. Agroforestree Database
4.0. http://worldagroforestry.org/output/agroforestree-database.
Pearson, R.G., Raxworthy, C.J., Nakamura, M., Peterson, A.T., 2007. Predicting species
distributions from small numbers of occurrence records: a test case using cryptic
geckos in Madagascar. J. Biogeogr. 34 (1), 102–117.
Pearson, R.G., Dawson, T.P., 2003. Predicting the impacts of climate change on the
distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr.
12 (5), 361–371.
Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species
geographic distributions. Ecol. Model. 190 (3-4), 231–259.
Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S.,
2009. Sample selection bias and presence-only distribution models: implications for
background and pseudo-absence data. Ecol. Appl. 190 (1), 181–197.
Platts, J.P., Peter, A., Omeny, P.A., Marchant, R., 2014. Africlim: high resolution climate
projections for ecological applications in Africa. Afr. J. Ecol. 53 (1), 103–108.
Root, T.L., Price, J.T., Hall, K.R., Schneider, S.H., Rosenzweig, C., Pounds, A., 2003.
Fingerprints of global warming on wild animals and plants. Nature 421 (6918),
57–60.
Sinsin, B., Eyog-Matig, O., Assogbadjo, A.E., Gaou
e, O.G., Sinadouwirou, T., 2004.
Dendrometric characteristics as indicators of human pressure on Afzelia africana Sm.
dynamic changes in trees found in different climatic zones of Benin. Biodivers.
Conserv. 13, 1555–1570.
Schmidt, M., Traor
e, S., Ou
edraogo, A., Mbayngone, E., Ou
edraogo, O., Zizka, A.,
Kirchmair, I., Kabor
e, E., Tindano, E., Thiombiano, A., Hahn, K., Zizka, G., 2013.
Geographical patterns of woody plants’functional traits in Burkina Faso. Candollea
68 (2), 197–207.
Schmidt, M., Zizka, A., Traor
e, S., Ataholo, M., Chatelain, C., Daget, P., Dressler, S.,
Hahn, K., Kirchmair, I., Krohmer, J., Mbayngone, E., Müller, J.V., Nacoulma, B.M.I.,
Ou
edraogo, A., Ou
edraogo, O., Sambar
e, O., Schumann, K., Wieringa, J., Zizka, G.,
Thiombiano, A., 2017. Diversity, distribution and conservation status of the flora of
Burkina Faso. Phytotaxa 304 (1), 1–215.
Schumann, K., Wittig, R., Thiombiano, A., Becker, U., Hahn, K., 2011. Impact of land use
type and harvesting on population structure of a non-timber forest product providing
tree in a semi-arid savanna, West Africa. Biol. Conserv. 144 (9), 2369–2376.
Sommer, J.H., Kreft, H., Kier, G., Jetz, W., Mutke, J., Barthlott, W., 2010. Projected
impacts of climate change on regional capacities for global plant species richness.
Proc. Royal Soc. Biol. Sci. 277 (1692), 2271–2280.
Thuiller, W., Lavorel, S., Araujo, M.B., Sykes, M.T., Prentice, I.C., 2005. Climate change
threats to plant diversity in Europe. Proc. Natl. Acad. Sci. U. S. A. 102 (23),
8245–8250.
Thuiller, W., 2003. Biomod –optimizing predictions of species distributions and projecting
potential future shifts under global change. Glob. Change Biol. 9 (10), 1353–1362.
Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Col-LIngham, Y.C.,
Erasmus, B.F.N., De Siqueira, M.F., Grainger, A., Hannah, L., Hughes, L., Huntley, B.,
Van Jaarsveld, A.S., Midgley, G.F., Miles, L., Or-Tega-Huerta, M.A., Peterson, A.T.,
Phillips, O.L., Williams, S.E., 2004. Extinction risk from climate change. Nature 427
(6970), 145–148.
Walther, G.R., 2003. Plants in a warmer world. Perspect. Plant Ecol. Evol. Systemat. 6 (3),
169–185.
Walther, G.-R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C.,
Fromentin, J.-M., Guldberg, O.H., Bairlein, F., 2002. Ecological responses to recent
climate change. Nature 416 (6879), 389–395.
White, F., 1983. The vegetation of Africa: a descriptive memoir to accompany the
Unesco/AETFAT/UNSO. In: Vegetation Map of Africa. UNESCO.
Zizka, A., Thiombiano, A., Dressler, S., Nacoulma, B.M.I., Ou
edraogo, A., Ou
edraogo, I.,
Ou
edraogo, O., Zizka, G., Hahn, K., Schmidt, M., 2015. The vascular plant diversity of
Burkina Faso (West Africa), a quantitative analysis and implications for conservation.
Candollea 70 (1), 9–20.
L.H. Balima et al. Heliyon 8 (2022) e08688
9
