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Impacts of climate change on the geographic distribution of African oak tree (Afzelia africana Sm.) in Burkina Faso, West Africa

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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 variables 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.
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 dIvoire
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 eldwork 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
identication 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 efcient tools likely to guide con-
servation decisions. Indeed, SDMs allow the identication 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 signicance.
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
0902015050N and the longitudes 02020E05030W. 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
300600 mm. year
1
in the Sahelian zone to 9001200 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 600900 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 mahoganyor African oakis
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 8001800
mm for mean annual rainfall, 2035 C for mean annual temperature,
and 2001200 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
ehas 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 rst phase extensive eld 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 eld 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 (19502000) 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 Denition 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 Ofce 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 ofce 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 ltered 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 le 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 variablesin 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 coefcient. Five least correlated predictors and ecologi-
cally meaningful for the studied species were selected at the pairwise
correlation coefcient 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 rareed 590 presence records (CSV
format) and the layers of the ve bioclimatic variables (ASCII format)
were used as input data to run the model (Supplementary information,
Appendix 2).
2.5. Model tting 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 t, 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
(specicity). 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 classied as suitable habitatsor unsuitable habitatsusing
the 10percentile training presence logistic threshold. To calculate the
current and future extents of suitable/unsuitable habitats, the raster les
were polygonised. Maps of the species suitable areas were nally 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) conrms that the model performs
better than random with a good predictive ability.
The ve 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 9.4323.99% 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 denition 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 specic 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
variablescontribution, the ndings 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 ndings are in
line with Guidigan et al. (2018) who reported the maximum temperature
of warmest month among the signicant climatic variables driving the
distribution of Kigelia africana (Lam.) in Benin.
The ndings 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 2035
C. Previous studies on species distribution modelling in West Africa
reported the precipitation as the major factor inuencing 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 8001200 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 fth (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 Sudanosahelian zone. This nding 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-
neoCongolean 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 rst 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 classied 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 16.4423.13% of
habitat loss. Our results corroborate previous studies which predicted a
climatedriven 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
signicantly 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 signicant 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 ndings 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 ndings, 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 ndings 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 rstly
depend upon the environmental variables selected as predictors (Guisan
and Zimmermann, 2000;Pearson et al., 2007). Signicant 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
ndings corroborate the fact that the choice of climate models in species
distribution modelling inuences 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 ndings, 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 rst 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 inuence 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 ndings 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, efcient 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 signicance
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 conict 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 signicantly improved the manuscript.
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... aegilops) in Turkey and Iraq respectively. In addition, Balima et al. (2022) expected that range changes of African oak trees toward the south and a sharp decline in their suitable environments (Afzelia africana Sm.) might be accounted for by considerable temperature fluctuations under future climate estimates. In addition, an expanding body of empirical data supports the idea that shifting climatic conditions will significantly influence the geographic distributions of vascular plant species and lead to range shifts and habitat losses in tree species around the globe (Balima et al., 2022;Dyderski et al., 2018;Larsen et al., 2011). ...
... In addition, Balima et al. (2022) expected that range changes of African oak trees toward the south and a sharp decline in their suitable environments (Afzelia africana Sm.) might be accounted for by considerable temperature fluctuations under future climate estimates. In addition, an expanding body of empirical data supports the idea that shifting climatic conditions will significantly influence the geographic distributions of vascular plant species and lead to range shifts and habitat losses in tree species around the globe (Balima et al., 2022;Dyderski et al., 2018;Larsen et al., 2011). The environmental suitability of plant species and ecosystems will decline due to an increase in temperature in semiarid and arid places in the future (Hassan and Nile, 2021). ...
Article
Quercus infectoria and Quercus libani are two important species distributed across most of the Kurdistan Region of Iraq's mountain ranges (KRI). They have significant ecological, medicinal, and socioeconomic values. Recent studies have documented how plant distributions have been impacted by climate change. This study's goal is to establish the existing distributions of both species, measure the consequences of prospective environmental conditions on their distributions, predict possible habitat distributions, map the overlapped habitat ranges for the species in the KRI, and identify the key factors influencing their distributions. For these aims, distribution data points of the species, different environmental factors, including the existing climate, three emission predictions for the 2050s, 2070s, and 2090s of two general circulation models (GCMs), a machine learning approach, and geospatial techniques were used. Modeling revealed that the total magnitude of the habitat increase for the species would be less than the overall magnitude of the habitat contraction. The yearly mean temperature, yearly precipitation, and minimum temperature during the coldest period mostly alter the target species' geographic dispersion. Across the three emission scenarios of the both models, Q. infectoria habitat would contract by 2760.9–2856.9 km² (5.36–5.55%), 2856.9–3357.2 km² (5.55–6.52%) and 2822.1–3400.2 km² (5.48–6.60%), whereas it would expand by 1153.3–1638.9 km² (2.24–3.18%), 761.0–1556.8 km² (1.48–3.02%), and 721.5–1547.1 km² (1.40–3.00%) for the 2050s, 2070s, and 2090s, respectively. A similar pattern was also noted for Q. libani. The two species' habitat ranges in KRI would be considerably reduced due to climate change. The species' estimated area would extend mostly to the east and southeast of the KRI at high altitudes. The mountain areas, notably those where the species overlap by 1767.2–1807.5 km² (3.43–3.51%) for the two GCMs, must be the primary objective of conservation efforts. This research presents new baseline data for future research on mountain forest ecosystems and the techniques of biodiversity conservation to reduce climate change's effects in Iraq.
... The temperature growth was attributed to anthropogenic greenhouse gasses emissions such as carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) (IPCC, 2018). In record climate scenarios, there is a very high degree of climate variability (Balima et al., 2022;Busby et al., 2012;IPCC, 2018). Nowadays forecasts indicate that over this 21st Century, the concentrations of the greenhouse gasses will result in a rise of 1.4-5.8 ...
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Bombax costatum is one of the multipurpose indigenous species in Mali, found in the Sudanian and Sudano-Guinean climatic zones with an important socioeconomic contribution. This study assessed the potential impact of climate change on the geographic distribution of B. costatum in Mali, using the 19 bioclimatic variables downloaded from Worldclim at a 2.5 arc-minute resolution and 2013 occurrence points across west-Africa gathered from GBIF and fieldwork. The future niche of species was predicted using three climate models (CanESM5, CNRM-CM6, MIROC6) and four climatic scenarios (Shared Socioeconomic Pathways: SSP126; SSP245; SSP370 and SSP585) at two time periods (2021–2040 and 2041–2060). The study shows that the current suitable habitats for B. costatum species represent 10.780% of Mali territory. According to the climatic scenarios, the species distribution range especially its highly suitable areas will increase by 2040 and 2060. Moreover, the species could be found in some parts of the Sudano-Sahelian zone in the future. Therefore, sustainable management measures are necessary for B. costatum and should be integrated in reforestation policies to ensure its availability for its multiple uses in the coming years in Mali.
... The temperature growth was attributed to anthropogenic greenhouse gasses emissions such as carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) (IPCC, 2018). In record climate scenarios, there is a very high degree of climate variability (Balima et al., 2022;Busby et al., 2012;IPCC, 2018). Nowadays forecasts indicate that over this 21st Century, the concentrations of the greenhouse gasses will result in a rise of 1.4-5.8 ...
Article
Full-text available
Bombax costatum is one of the multipurpose indigenous species in Mali, found in the Sudanian and Sudano-Guinean climatic zones with an important socio-economic contribution. This study assessed the potential impact of climate change on the geographic distribution of B. costatum in Mali, using the 19 bioclimatic variables downloaded from Worldclim at a 2.5 arc-minute resolution and 2013 occurrence points across west-Africa gathered from GBIF and fieldwork. The future niche of species was predicted using three climate models (CanESM5, CNRM-CM6, MIROC6) and four climatic scenarios (Shared Socioeconomic Pathways: SSP126; SSP245; SSP370 and SSP585) at two time periods (2021-2040 and 2041-2060). The study shows that the current suitable habitats for B. costatum species represent 10.780% of Mali territory. According to the climatic scenarios, the species distribution range especially its highly suitable areas will increase by 2040 and 2060. Moreover, the species could be found in some parts of the Sudano-Sahelian zone in the future. Therefore, sustainable management measures are necessary for B. costatum and should be integrated in reforestation policies to ensure its availability for its multiple uses in the coming years in Mali.
... Climate change has an important impact on food security, water security, ecological security, traffic security, energy security, national defense security, air quality, etc. (Adeagbo et al. 2021;Balima et al. 2022;Ban et al. 2022;Guo et al. 2020;Hammond et al. 2022;He et al. 2022;Khairulbahri 2021;Oti et al. 2020;Stringer et al. 2021;Xue et al. 2022). In addition, climate change has an adverse impact on the evolution of human civilization, and also intensifies the cross species transmission of viruses and diseases, threatening social and economic development and people's health (Carlson et al. 2022;Naderi Beni et al. 2021). ...
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Climate change affects air quality and people’s health. Therefore, accurate prediction of future climate change is of great significance for human beings to better adapt and mitigate climate change. Using the projection simulation dataset of the CMIP6 multi-model ensemble, the future climate change in the Sahara region under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) is analyzed. The results show that annual and seasonal average surface air temperature in the Sahara region will continue to rise throughout the twenty-first century relative to the baseline period 1995–2014 if greenhouse gas (GHG) concentrations continue increasing. Under the four SSPs scenarios, the warming in the Sahara region will be more pronounced than in the whole world through the twenty-first century. The annual maximum temperature (TX), the annual minimum temperature (TN), the annual count of days with maximum temperature above 35 °C (TX 35), and the annual count of days with maximum temperature above 40 °C (TX 40) in the Sahara region will continue to increase until the end of the twenty-first century under the four scenarios. The results of climate change prediction can provide scientific reference for climate policy-making.
... However, it is also becoming apparent that some species may shift their distributions towards other directions (Tagliari et al., 2021;Balima et al., 2022). This was also the case in this study with some models like those of V. frutescens, V. heterophylla, V. racemosa, V. reticulata, and V. unguiculata var. ...
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Sustainable conservation of crop wild relatives is one of the pathways to securing global food security amid climate change threats to biodiversity. However, their conservation is partly limited by spatio-temporal distribution knowledge gaps mostly because they are not morphologically charismatic species to attract conservation attention. Therefore, to contribute to the conservation planning of crop wild relatives, this study assessed the present-day distribution and predicted the potential effect of climate change on the distribution of 15 Vigna crop wild relative taxa in Benin under two future climate change scenarios (RCP 4.5 and RCP 8.5) at the 2055-time horizon. MaxEnt model, species occurrence records, and a combination of climate- and soil-related variables were used. The model performed well (AUC, mean = 0.957; TSS, mean = 0.774). The model showed that (i) precipitation of the driest quarter and isothermality were the dominant environmental variables influencing the distribution of the 15 wild Vigna species in Benin; (ii) about half of the total land area of Benin was potentially a suitable habitat of the studied species under the present climate; (iii) nearly one-third of the species may shift their potentially suitable habitat ranges northwards and about half of the species may lose their suitable habitats by 5 to 40% by 2055 due to climate change; and (iv) the existing protected area network in Benin was ineffective in conserving wild Vigna under the current or future climatic conditions, as it covered only about 10% of the total potentially suitable habitat of the studied species. The study concludes that climate change will have both negative and positive effects on the habitat suitability distribution of Vigna crop wild relatives in Benin such that the use of the existing protected areas alone may not be the only best option to conserve the wild Vigna diversity. Integrating multiple in situ and ex situ conservation approaches taking into account “other effective area-based conservation measures” is recommended. This study provides a crucial step towards the development of sustainable conservation strategies for Vigna crop wild relatives in Benin and West Africa.
... However, it is also becoming apparent that some species may shift their distributions towards other directions (Tagliari et al., 2021;Balima et al., 2022). This was also the case in this study with some models like those of V. frutescens, V. heterophylla, V. racemosa, V. reticulata, and V. unguiculata var. ...
Article
Full-text available
Sustainable conservation of crop wild relatives is one of the pathways to securing global food security amid climate change threats to biodiversity. However, their conservation is partly limited by spatio-temporal distribution knowledge gaps mostly because they are not morphologically charismatic species to attract conservation attention. Therefore, to contribute to the conservation planning of crop wild relatives, this study assessed the present-day distribution and predicted the potential effect of climate change on the distribution of 15 Vigna crop wild relative taxa in Benin under two future climate change scenarios (RCP 4.5 and RCP 8.5) at the 2055-time horizon. MaxEnt model, species occurrence records, and a combination of climate- and soil-related variables were used. The model performed well (AUC, mean = 0.957; TSS, mean = 0.774). The model showed that (i) precipitation of the driest quarter and isothermality were the dominant environmental variables influencing the distribution of the 15 wild Vigna species in Benin; (ii) about half of the total land area of Benin was potentially a suitable habitat of the studied species under the present climate; (iii) nearly one-third of the species may shift their potentially suitable habitat ranges northwards and about half of the species may lose their suitable habitats by 5 to 40% by 2055 due to climate change; and (iv) the existing protected area network in Benin was ineffective in conserving wild Vigna under the current or future climatic conditions, as it covered only about 10% of the total potentially suitable habitat of the studied species. The study concludes that climate change will have both negative and positive effects on the habitat suitability distribution of Vigna crop wild relatives in Benin such that the use of the existing protected areas alone may not be the only best option to conserve the wild Vigna diversity. Integrating multiple in situ and ex situ conservation approaches taking into account “other effective area-based conservation measures” is recommended. This study provides a crucial step towards the development of sustainable conservation strategies for Vigna crop wild relatives in Benin and West Africa.
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Kigelia africana (Bignoniaceae), is an indigenous species widely recognised for its medicinal, magic uses and therapeutic virtue used throughout Africa and especially in Benin Republic. Distribution of the species coincides with that of the intermediate hosts as determined by environmental factors. This study aimed to model the present-day and future distribution of Kigelia africana in Benin. Maximum Entropy (MaxEnt) modelling technique was used to predict the distribution of suitable habitats of Kigelia africana using presence data combined with two future forescats: CNRM-CM5and HadGEM2-ES. Results showed that Annual Temperature range, precipitation seasonality, soil, temperature seasonality, maximum temperature of the warmest month were most significant variables. Which mean that the excellent of the model. Likewise, must of the distribution of the species will be find mostly stable. The different model used identified different areas as highest conservation priority although the highest priority areas keeping most of Kigelia africana species are located in the Guineo-Congolian and Sudano-Guinean region. Additional analyses could help to have more information about the distribution and population and cultivation of Kigelia africana species, which in future will help us to improve operative conservation strategies for this medicinal species. MaxEnt model is robust in Kigelia africana species habitat modelling.
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Background: The lack of literature on the interactions between indigenous people and the valuable agroforestry trees hinder the promotion of sustainable management of plant resources in West African Sahel. This study aimed at assessing local uses and management of Afzelia africana Sm. in Burkina Faso, as a prerequisite to address issues of domestication and sustainable conservation. Methods: One thousand forty-four peoples of seven dominant ethnic groups were questioned in 11 villages through 221 semi-structured focus group interviews. The surveys encompassed several rural communities living around six protected areas along the species distribution range. Questions refer mainly to vernacular names of A. africana, locals’ motivations to conserve the species, the uses, management practices and local ecological knowledge on the species. Citation frequency was calculated for each response item of each questionnaire section to obtain quantitative data. The quantitative data were then submitted to comparison tests and multivariate statistics in R program. Results: A. africana is a locally well-known tree described as a refuge of invisible spirits. Due to this mystery and its multipurpose uses, A. africana is conserved within the agroforestry systems. The species is widely and mostly used as fodder (87.55%), drugs (75.93%), fetish or sanctuary (70.95%), food (41.49%), and raw material for carpentry (36. 19%) and construction (7.05%). While the uses as fodder, food and construction involved one organ, the leaves and wood respectively, the medicinal use was the most diversified. All tree organs were traditionally used in 10 medical prescriptions to cure about 20 diseases. The species use values differed between ethnic groups with lower values within the Dagara and Fulani. The findings reveal a total absence of specific management practices such as assisted natural regeneration, seeding, or transplantation of A. africana sapling. However, trees were permanently pruned and debarked by local people. Harvesting of barks mostly contributed to the decline of the species populations. Local people acknowledged declining populations of A. africana with lower densities within the agroecosystems. They also perceived between individuals, variations in the traits of barks, leaves, fruits and seeds. Significant differences were found between ethnic groups and gender regarding the species uses. Local knowledge on the species distribution differed between ethnic groups. Conclusion: This study showed the multipurpose uses of A. africana throughout Burkina Faso. The results provide relevant social and ecological indicators to all stakeholders and constitute a springboard towards the species domestication and the elaboration of efficient sustainable conservation plans. Keywords: African mahogany, Local knowledge, Sustainability, Sahel, West Africa,
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SDMtoolbox 2.0 is a software package for spatial studies of ecology, evolution, and genetics. The release of SDMtoolbox 2.0 allows researchers to use the most current ArcGIS software and MaxEnt software, and reduces the amount of time that would be spent developing common solutions. The central aim of this software is to automate complicated and repetitive spatial analyses in an intuitive graphical user interface. One core tenant facilitates careful parameterization of species distribution models (SDMs) to maximize each model’s discriminatory ability and minimize overfitting. This includes carefully processing of occurrence data, environmental data, and model parameterization. This program directly interfaces with MaxEnt, one of the most powerful and widely used species distribution modeling software programs, although SDMtoolbox 2.0 is not limited to species distribution modeling or restricted to modeling in MaxEnt. Many of the SDM pre- and post-processing tools have ‘universal’ analogs for use with any modeling software. The current version contains a total of 79 scripts that harness the power of ArcGIS for macroecology, landscape genetics, and evolutionary studies. For example, these tools allow for biodiversity quantification (such as species richness or corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of the significance of spatial randomizations, and enforcement of dispersal limitations of SDMs projected into future climates—to only name a few functions contained in SDMtoolbox 2.0. Lastly, dozens of generalized tools exists for batch processing and conversion of GIS data types or formats, which are broadly useful to any ArcMap user.
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In this study, species distribution modelling (SDM) was applied to the management of secondary forests in Benin. This study aims at identifying suitable areas where the use of candidate pioneer species, such as Lonchocarpus sericeus and Anogeissus leiocarpa, could be targeted to ensure at low cost, currently and in the context of global climate change, fast reconstitution of secondary forests and disturbed ecosystems and the recovery of their biodiversity. Using occurrence records from the Global Biodiversity Information Facility (GBIF) website and current environmental data, the factors that affected the distribution of the species were assessed in West Africa. The models developed in MaxEnt and R software for West Africa only, for both species, showed good predictive power with AUC > 0.80 and AUC ratios well above 1.5. The results were projected in future climate at the horizon 2055, using AfriClim data under rcp4.5 and rcp8.5 and suggested a little reduction in the range of L. sericeus and any variation for A. leiocarpa. The potential distribution of the two species indicated that they could be used for vegetation restoration activities both now and in the mid-21st century. Improvement are needed through the use of complementary data, the extension to others species and the assessment of uncertainties related to these predictions.
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West Africa is a floristically understudied region that is facing severe environmental changes in the 21st century. Basic distribution data and information on the conservation status for most plant species of the region are scarce, and good information only exists for small areas of interest or for key species. This lack of knowledge seriously hampers urgently needed regional conservation efforts. Here we present comprehensive distribution information and preliminary, automated species conservation assessments for the flora of Burkina Faso, a country in tropical West Africa with a flora and vegetation typical for the savanna belt of the region. We documented and analysed the distribution of 1,568 species or 80% of the flora of Burkina Faso based on an expert curated dataset comprising ca. 150,000 occurrence records from herbarium specimens and vegetation surveys. We used this dataset and environmental niche models to calculate three indicator variables for a preliminary, automated conservation assessment. We classified 350 species (18% of the flora, excluding introduced species) as potentially "Critically Endangered", "Endangered", "Vulnerable" or "Near-Threatened" on the national level. The analyses confirmed species-rich areas in the south-west and south-east of the country, and showed a particular concentration of potentially Endangered species in the south. Furthermore, the proportion of potentially Endangered species differed between plant families, growth forms and habitats. Our results set the base for further plant geographical and ecological studies and are a data-driven baseline for further conservation assessments and large scale conservation strategies of the West African flora.
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Parkia biglobosa (locust bean) is an indigenous species which, traditionally contributes to the resilience of the agricultural production system in terms of food security, source of income, poverty reduction and agro-ecosystem stability. Therefore, it is important to improve knowledge on its density, current and future spatial distribution. The main objective of this study is to evaluate the tree density, the climate change effects on the spatial distribution of the species in the future for better conservation. The modeling of the current and future geographical distribution of the species was based on the principle of Maximum Entropy (MaxEnt) using a total of 286 occurrence points from field work and the Global Biodiversity Information Facility GBIF-Data Portal. Two climatic models (HadGEM2_ES and Csiro_mk3_6_0) were used under two scenarios RCP 2.6 and RCP 8.5 for the projection of the species distribution at the horizon 2050. Correlation analyses and Jackknife test helped to identify seven variables which are less correlated (r < 0.80) with highest contribution to the model. Soil, annual precipitation and temperature (diurnal average Deviation) are the variables which have mostly contributed to the models. Currently, 53% of national territory of Benin, spread from north to south is very suitable for P . biglobosa. At the temporal horizon 2050, the scenarios have projected loss of habitats, which are currently very suitable for P . biglobosa. 51% and 57% are the highest proportions of this habitat lost, which has been registered with HadGEM2_ES model under two scenarios. In order to limit damages such as decreased in productivity and extirpation, some appropriate solutions must be found. It is important to plan the introduction of Parkia biglobosa in reforestation programs and the protection of its potential habitat at national level by the forestry administration which is an asset for a better conservation of this significant NTFP
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Sustainable management actions are needed for several indigenous agro forestry plant species like the black plum (Vitex doniana Sweet) because they are facing increasing pressures due to the rapid human growth and threats such as climate change. By combining species distribution modelling using the Maximum Entropy Algorithm (MaxEnt) and representation gap analysis, this study accessed the impacts of current and future (2050) climates on the potential distribution of Vitex doniana in Benin with insight on the protected areas network (PAN). The model showed a high goodness-of-fit (AUC = 0.92 ± 0.02) and a very good predictive power (TSS = 0.72 ± 0.01). Our findings indicated annual mean rainfall, annual mean diurnal range of temperature and mean temperature of the driest quarter as the most important predictors driving the distribution of V. doniana. Under current climate, about 85 % of Benin area is potentially suitable for its cultivation. This potential suitable area is projected to increase by 3 to 12 % under future climatic conditions. A large proportion (76.28 %) of the national PAN was reported as potentially suitable for the conservation of the species under current climate with increase projections of 14 to 23 % under future climate. The study showed that V. doniana can be cultivated in several areas of Benin and that the PAN is potentially suitable for its conservation. These findings highlighted some of the opportunities of integrating V. doniana in the formal production systems of Benin and also its potentialities in ecosystems restoration under the changing climate.
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Vitellaria paradoxa, the shea tree, an economically important fruit-tree species native to savanna regions is threatened in Burkina Faso due to overexploitation and changing land-use. Furthermore, it remains unclear how climate change will influence its frequency and distribution. We investigated the impact of climate change on the projected spatial distribution of favorable habitats for V. paradoxa. Species distribution modeling techniques implemented in MaxEnt combined with GIS were used to forecast the current and future distribution of V. paradoxa. We selected two climatic scenarios (RCP4.5 and RCP8.5) and two global climate models (MPI-ESM-MR and HadGEM2-ES) to encompass the full range of variation in the models. Presence records of the species were collected and linked to bioclimatic and edaphic variables. The most characteristic and least correlated variables were selected for modeling after a collinearity test. Under current climatic conditions, ~51% of the national area was found to be favorable for cultivation and conservation of the species. Under future climate projections, our models predict that favorable habitats of this species will decline by 12% (RCP4.5) and 13% (RCP8.5) by 2070. The predictive modeling approach presented here may be applied to other economically important tree species.