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Trees, Forests and People 1 (2020) 100006
Contents lists available at ScienceDirect
Trees, Forests and People
journal homepage: www.elsevier.com/locate/tfp
Impact of climate on ecology and suitable habitat of G arcinia kola heckel in
Nigeria
Onyebuchi Patrick Agwu
a
,
∗
, Adama Bakayokoa
b
, Saka Oladunmi Jimoh
c
, Kangbéni Dimobe
d
,
e
,
g
,
Stefan Porembski
f
a
WASCAL Graduate Research Program on Climate Change and Biodiversity, Universite ´ Felix Houphoue t Boigny, BP 165, Abidjan 31, Côte d’Ivoire
b
Université Nangui Abrogoua, Abidjan, Côte d’Ivoire and Centre Suisse de Recherches Scientifiques en Côte d’Ivoire(CSRS )
c
Department of Social and Environmental Forestry, University of Ibadan, Nigeria
d
West African Science Service Centre for Climate Change and Adapted Land Use (WASCAL) Competence Center, Ouagadougou, Burkina Faso
e
Laboratoire de Biologie et Ecologie Végétales, UFR/SVT, Université Joseph Ki-Zerbo, 03 B.P. 7021 Ouagadougou 03, Burkina Faso
f
Institute for Biodiversity Research, University of Rostock, Germany
g
Institut des Sciences de l’Environnement et du Developpement Rural (ISEDR), Université de Dédougou, BP 176 Dédougou, Burkina Faso
Key words:
Garcinia kola
Multipurpose species
Climate scenarios
Climate model
Bioclimatic
Garcinia kola is an indigenous multipurpose tree species, with signicant cultural value and medical benets,
commonly found in the tropical rain forest zone of West and Central Africa. The species has been reported to be
over-used and are now classied as vulnerable species close to commercial extinction. Hence, requires immediate
conservation action. This study assessed the impact of climate on habitat for cultivation of G. kola in Nigeria.
Ecological niche modelling approach was used to estimate the current geographical range and predicts the future
distribution of G. kola in Nigeria, using the nineteen (19) bioclimatic environment layers at a 30 ′ secs resolution.
Two climate models were used (HadGEM2-ES and CNRM-CM5) with two Representative Concentration Pathways
(RCP), RCP 4.5 and RCP 8.5 scenarios as predictor variables for projections of the potential geographical range of
this species for 2050 horizon. Results revealed that about 397,094 km
2
area, corresponding to 43.6% of Nigeria
land surface, are currently suitable for cultivating G. kola . The future projections showed a signicant decrease
in area suitable for propagating G. kola under the RCP scenarios used in the two climate models. HadGEM2-ES
predicts 27.2 and 26.1% loss of suitable habitats under RCP 4.5 and 8.5, respectively, by 2050 while CNRM-
CM5 projects 26.7 and 35.8% decrease for the corresponding RCPs. Furthermore, the HadGEM2-ES predicts that
149,365 and 159,384 km
2
corresponding to 16.4% and 17.5% of total land area will be suitable for cultivation
of G. kola in Nigeria under RCP 4.5 and 8.5, respectively. The model results showed that climate change would
have signicant inuence on the future suitable habitat of G. kola in Nigeria and the species is more subservient
in moist, humid area and some part of derived savanna zone in Nigeria. The results underscore the signicant
inuences of climate change on the ecology of G. kola. Based on these results, immediate action should be initiated
to conserve this valued species and secure their inherent agro-ecosystems services
1. Introduction
Climate Change is an existential threat to human wellbeing because
of its potential adverse impacts on natural resources that support hu-
man livelihoods ( Enete, 2004 ). All facets of socio-economic activities
are equally vulnerable to climate change and variability. Some associ-
ated climate impacts include extreme weather events, heavy rainfall,
heat waves and severe droughts. These poses dire consequence on plant
productivity and thus, leading to inadequate food supply ( Agwu et al.,
2018 ). For instance, agroforestry species which are considered as one of
the most essential livelihood support services derived from the ecosys-
∗ Corresponding author.
E-mail address: agwu.o@edu.wascal.org (O.P. Agwu).
tem in tropical regions is reported to have been negatively inuenced
by climate change ( Enete, 2004 ).
Decline in rainfall regimes have been reported in West Africa
( Dinar et al., 2006; Kotir, 2011 ). Rainfall is an important climatic param-
eter that determines the temporal and spatial locations of plant species
( Phillips et al. , 2009 ). An understanding of the inuence of these envi-
ronmental factors and the manner in which they aect plant distribu-
tion in Nigeria is critical to advance the knowledge required to conserve
these species. Therefore, there is an urgent need to increase multipur-
pose tree species population by identifying key ecological factors that
determine their current and future availability. Also, there is the need to
https://doi.org/10.1016/j.tfp.2020.100006
Received 24 February 2020; Received in revised form 7 June 2020; Accepted 8 June 2020
Available online 12 June 2020
2666-7193/© 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
O.P. Agwu, A. Bakayokoa and S.O. Jimoh et al. Trees, Forests and People 1 (2020) 100006
investigate the impacts of past and future climate scenarios on the distri-
bution of such species in order to ensure and encourage their continuous
existence for utilization for both rural and urban dwellers. The popula-
tion of many indigenous species are drastically shrinking; the produc-
tion rates and fruiting phenology are seriously changing over the years
which could be attributed to changes in climatic condition.
Garcinia kola is an important indigenous economic fruit trees in West
Africa. The species is a member of the Guttiferae plant family, commonly
known as bitter kola because of its bitter taste. It is a curative species for
dierent ailments from coughs to fever. Hence, it is a highly valuable
economic tree species ( Ko et al., 2015 ).
Despite the socio-economic importance of G. kola available liter-
atures are yet to extensively describe their ecology and distribution
( Adebisi 2004 ). G. kola is amongst many indigenous trees in West Africa
that are becoming rare and endangered as a result of continuous pres-
sure on their use ( Adebisi, 2004 ; IUCN 2004 , 2019 ), thereby reducing
the biodiversity in the area. Poor regeneration further heightens the rate
of extinction of the species ( Abbiw, 1990; Gyimah, 2000; Hawthorne,
1997 ; IUCN 2004 ). This has led to series of advocacies for the conser-
vation of the species and calls for further investigation into its suitable
ecology and distribution ( Sacande´ et al., 2004 ).
The remaining population of the species are left over stands from
farm plots and in the wild because no contiguous plantation for G. kola
in Nigeria. ( Adebisi, 2004 ). Knowledge on individual trees ecology is
vital in ensuring successful establishment of the plantations, encour-
age conservation options and their inherent agro-ecosystems services.
To prevent genetic erosion, appropriate strategies should be developed
to promote its conservation. There is therefore the need to undertake
studies aimed at providing information about suitable ecology necessary
for conservation of the species on a large-scale. However, the suitable
ecological conditions for propagation of G. kola are presently changing
which pose great danger for the continued availability of the species
( Agwu et al., 2018 ). Hence, the need for this study to assess the impact
of climate on suitable habitat for cultivation of G. kola in Nigeria. The
specic objectives are to (1) investigate the current and future spatial
distributions of the species using selected climate models and (2) to iden-
tify the important environmental variables controlling the distribution
of the species in Nigeria.
2. Material and method
2.1. Species occurrence data
Forty-two occurrence points of G. kola were collected from eldwork
across Nigeria and 79 additional occurrence points were obtained within
West Africa from the Global Biodiversity Information Facility portal
( GBIF, 2018 ). The occurrence data were lter carefully by sorting and
removing duplicate records with environmental niche models (ENMs)
tools known as Perl script. After the careful sorting the data reduced to
71 occurrence coordinates ( Warren et al., 2010) .
2.2. Environmental data
Climatic data in this study comprised data of environmental lay-
ers, which is considered as one of the important factors inuencing
species occurrences niche over broad extents ( Parviainen et al., 2008 ;
Pearson and Dawson 2003 ). Nineteen (19) ‘bioclimatic’, Environment
layers at a 30 ′ sec resolution obtained from Worldclim were examined
( Dan et al. 2011 ; Idohou et al., 2016 ). We inspect carefully the strength
of linear relationship amongst all variables using pearson correlation
coecient in order to eliminate closely related variables. The subset
variables with pearson correlation coecients below 0.8 were selected
and used ( Elith et al., 2010 ). To assess possible inuences of climate
change on the habitat and distribution of the species, we convey the
current model rule sets to the two selected future projections from two
climate models (HadGEM2-ES and CNRM-CM5) ( Idohou et al., 2016 ;
Good et al., 2013 ). The two selected climate models are based on two
greenhouse gas concentration scenarios, that is, representative concen-
tration pathways (RCP) 4.5 and 8.5 for the mid 21st Century (2050)
outlook of expected rainfall and temperature. RCPs are third generation
scenarios and are preferred to the Special Report on Emissions Scenar-
ios (SRES) because they allow more exibility and prescription of GHG
concentration in climate model thereby reducing computational costs
in terms of modelling physical processes ( van Vuuren et al. 2011 ). Ac-
cording to IPCC (2013) , with RCP 4.5 the atmospheric CO
2
could reach
up to 500 ppm, causing projected surface temperatures to rise above
industrial levels by at least 1.5 °C in West Africa. With more extreme
RCP 8.5 projections, temperatures are estimated to rise by 2.8 °C and
atmospheric CO
2
to be over 550 ppm ( IPCC 2013 ).
In the scenarios, there was incorporation of demographic and land
use in the model ( Moss et al., 2010 ). Selected species distribution mod-
elling in Africa has been done with the RCP 4.5 and 8.5 ( Kakpo et al.,
2020 : Idohou et al., 2016 ).
2.3. Modelling the distribution of the species
The maximum entropy approach (Maxent) has been used for mod-
elling species distribution modelling. The software is based on probabil-
ity density estimation and uses species occurrence coordinates location
and bioclimatic variables to predict temporal and spatial species distri-
bution. The selected variables in the distribution model were subjected
to Jackknife test to ascertain the percentage contributions.
We examined and corrected data for geographic sampling bias by us-
ing background data which was reported to substantially improve model
performance ( Syfert et al., 2013 ). We generated background data which
is also known as ‘‘pseudoabsences’ which has an identical geographical
sampling bias to that of the presence data. For this study, 70% of the
species occurrence points were used as training data, and the remaining
(30%) subset were used to evaluate and test the model’s performance.
We executed 10 replicates using cross-validation/repeated split samples
to measure the amount of variability in the model and then averaged
the results. Threshold for maps were reduced to binary to avoid eects
of over tting ( Peterson et al., 2007 ).
2.4. Model evaluation
The modelled data were divided into two subsets for training or
building of the models (70%) and testing (30%) of the models’ perfor-
mance. Receiver Operating Characteristics (ROC) was used to appraise
the performance of MaxEnt ability to predict the distribution of Garcia
kola in Nigeria. Sensitivity and specicity of the model was assessed us-
ing area under curve (AUC). When, the AUC value of a model is (AUC ≥
0.75), such models are said to accurately predict the spatial and tempo-
ral occurrence of species ( Idohou et al., 2016 ; Hounkpèvi et al., 2016 ).
The capacity of the models to predict true presence and true absence
was further validated with true skill statistic (TSS) ( Elith et al., 2006 ;
Pearson et al., 2007 ). The TSS has the capacity to accurately detect true
presences (sensitivity) and true absences (specicity). The accuracy of
the TSS in prediction model is evaluated with TSS of ≤ 0 indicating a
random prediction, while a model with a TSS close to 1 (TSS > 0.5) has
a good predictive power ( Allouche et al., 2006 ). The logistic probabil-
ity distributions generated by MaxEnt using the 10th percentile training
presence logistic threshold were used to assess the potential habitat suit-
ability of the species in Nigeria. Suitability of occurrence was based on
areas above the threshold, whereas areas below the threshold were con-
sidered not suitable ( Hounkpèvi et al., 2016 ; Scheldeman and van Zon-
neveld, 2010 ). Map production for habitat suitability and unsuitability
was done in ArcGIS 10.3 ( ESRI, 2014 ).
O.P. Agwu, A. Bakayokoa and S.O. Jimoh et al. Trees, Forests and People 1 (2020) 100006
Fig. 1. Occurrence points and sampling of G. kola across the West African Landscape.
Table 1
Contribution of bioclimatic variables for G. kola species model.
Variables kola Model
Bio19 Precipitation of warmest quarter 1.3
Bio17 Precipitation of driest quarter 82.3
Bio15 Mean temperature of driest qua rter 1.6
Bio13 Precipitation of wettest month 1.8
Bio 5 Max temperature of wa rmest month 1.3
Bio7 Temperature annual range 6
Bio1 Annual mean temperature 5.7
3. Results
3.1. Analysis of bioclimatic variables and model validation
The correlations analysis and Jackknife AUC test identied seven
bioclimatic variables as most contributing variables to the model across
the study region for G. kola. The results showed that seven out of the 19
bioclimatic variables are important environmental variation inuencing
the occurrence and suitability area of G. kola in Nigeria. ( Table 1 ).
These variables include precipitation of warmest quarter, precipi-
tation of driest quarter, mean temperature of driest quarter, precipita-
tion of wettest month, max temperature of warmest month, temperature
annual range and annual mean temperature ( Table 1 ). These variables
have signicant eect on the gain when used in isolation or removed
from the modelling process ( Fig. 1 ).
Precipitation of driest quarter are amongst the most contributing fac-
tor that drives the spatial distribution of G. kola species, contributing
about 82.3% predictability of the species distribution. Temperature an-
nual range and annual mean temperature are contributing 6 and 5.7%,
respectively, while precipitation of warmest quarter and max temper-
ature of warmest month are amongst the least contributing predictive
factors that determines the distribution of the species of G. kola in Nige-
ria. Precipitation of driest quarter is the most unique informative pre-
dictor for the species distribution of G. kola because its presence or ab-
sence in the model considerably aects the gain; the contribution and
permutation importance were over 50%. The model evaluations indi-
cated that the model was robust and had a goodness-of-t value with
a cross-validated average under curve (AUC) and standard deviation of
0.97 ± 0.02 ( Fig. 2 ). This low value of the standard deviation indicates
a limited dispersion of AUC values amongst the replicates. The values
of the True Skill Statistic (TSS) is 0.87, the values showed very good
predictive power, excellent performance and yielded statistically signif-
icant predictions. Areas with occurrence probability above this thresh-
old were then considered as suitable for the species, the remaining are
considered as unsuitable areas.
3.2. Impact of climate change on the spatial distribution of G. kola in
nigeria
The model revealed that about an area of 397,094 km
2
, correspond-
ing to 43.6% of Nigeria land surface area are currently suitable for cul-
tivating G. kola ( Table 2 ). G. kola is a tropical rain forest species and
is more subservient in moist, humid area and some part of derived sa-
vanna zone in Nigeria ( Fig. 3 ). The future projections from the two RCP
O.P. Agwu, A. Bakayokoa and S.O. Jimoh et al. Trees, Forests and People 1 (2020) 100006
Fig. 2. Partial Area Under Curve (AUC) distri-
bution.
Table 2
The suitable areas for G. kola in the present and the two future climate models.
Unsuitable Suitable
Present Area (km
2
) 512,768 397,094
Trend (%) 56.3 43.6
HadGEM2-
ES
RCP 4.5 760,491 149,365
Area (km
2
)
Trend (%) 83.5 16.4
RCP 8.5
Area (km
2
) 760,491 159,384
Trend (%) 82.5 17.5
CNRM-
CM5
RCP 4.5 Area (km
2
) 756,848 153,919
Trend (%) 83.1 16.9
RCP 8.5 Area (km
2
) 839,728 701,291
Trend (%) 92.2 7.74
(i.e. RCP4.5 and RCP8.5) scenarios shows a signicant decrease in areas
suitable for G. kola . The HadGEM2-ES model predicts 27.2 and 26.1%
loss under RCP 4.5 and 8.5, respectively, for 2050. For CNRM-CM5 the
loss in suitable habitats is modelled to be 26.7 and 35.8% for the cor-
responding RCPs by 2050 ( Table 2 , Fig. 4 ). Furthermore, HadGEM2-ES
predicts about 149,365 and 159,384 km
2
of suitable land for cultivat-
ing G. kola during the mid 21st Century, which corresponds to about
16.4% and 17.5% of total land area of the country ( Fig. 5 ). This implies
that only some parts of humid area and little parts of derived savanna
zone will remain suitable as a result of the prescribed emission pathways
used.
4. Discussion
Modelling species distribution have been used extensively to ascer-
tain suitable habitat and large scales cultivation, to produce maps that
will be useful for identifying areas where conservation eorts can be suc-
cessful. Previous works ( Hounkpèvi et al., 2016 ; Sommer et al., 2010 ;
Walther, 2003 ) had reported the potential role of biotic and abiotic fac-
tors for species distribution modelling and habitat suitability patterns.
IPCC (2007) gave evidences that change in climatic conditions will sig-
nicantly inuences the distribution of several species.
Phillips et al. (2006) had armed the suitability of species distri-
bution modelling in predicting spatial, current and predicted climatic
context. The models usually indicate the suitable niche for a given
species. Hence, information derived from such maps are useful in species
conservation interventions. Modelling ecological niche has been re-
ported, considered and known as a signicant strong tool for estimating
species distribution and ecological range of species ( Syfert et al., 2013 ;
Idohou et al., 2016 ). This study ascertained the present ecological range
and predicts the future ecological range for the G. kola . The models pro-
duce a signicant result that can be used for decision making processes
that seeks to conserve the species. The selection of variables for this
study was carried out as described by Adjahossou et al. (2016) , we per-
form the correlations between all the 19 bioclimatic variables. In the
model run, variables were subjected to equal chances of contribution.
The distribution of G. kola was enhanced with precipitation of driest
quarte, which is found to be the most contributing variable in terms
of determining the distribution of G. kola . It is the total precipitation
derived from the three driest months of the year, which can be useful
for examining how such climate variation may aect species distribution
over space and time. The result is very appropriate because G. kola is
a moisture-dependant species. These variables are very important and
contribute signicantly in the distribution of G. kola.
The studied species is indigenous evergreen tropical rain forest tree
species found in West and Central Africa forests ( Isawumi, 1993 ), while
the modelled suitable areas for both species was in consonance with
their known ecological distribution ( Isawumi, 1993 ). It strives well
within the moist (coastal areas) and lowland rain forests and derived
savannah in West Africa. Our study found that precipitation and temper-
ature are important variables in the occurrence of G. kola in West Africa.
The results conrmed an earlier report by Idohou et al. (2016) and
Purseglove (1972) that the balanced inuence of environmental factors
(precipitation and temperature) are signicantly important factors con-
trolling species existence in the humid zones.
The climatic model showed similarity in the species distribution un-
der the two scenarios. The species maintain their occurrence range in
the current distribution area and they show variation in the potential fu-
ture habitat suitability. Variations have largely been reported in climate
modelling literatures which are largely due to variations in climate sce-
narios ( Araújo and New, 2007 ; Thuiller 2004 ). Djotan et al. (2018) re-
ported that changes in climate lead to expansion and contraction in the
distribution of G. kola . The study further posited that climate change
will have positive impact on suitability area in the protected area
to conserve and in restoring G. kola . In contrary, our study showed
only negative impacts on the distribution of this species. Though, our
study did not consider the potential of protected area in the suitabil-
O.P. Agwu, A. Bakayokoa and S.O. Jimoh et al. Trees, Forests and People 1 (2020) 100006
Fig. 3. Garcinia kola distribution model under the current climatic conditions in Nigeria.
Fig. 4. Garcinia kola distribution model in Nigeria mapped under RCP 4.5 and 8.5 future climatic conditions in CNRM-CM5 model.
O.P. Agwu, A. Bakayokoa and S.O. Jimoh et al. Trees, Forests and People 1 (2020) 100006
Fig. 5. Same as in Fig. 4 but for HadGEM2-ES model.
ity area of the species. Our results are not in agreement with that of
Hounkpèvi et al. (2016) who also reported that changes in climatic con-
dition will have positive impact on future habitat suitability of Vitex
doniana.
The results found in the current study in agreement with the patterns
of prediction documented in Thomas et al. (2004) that many species
could be threatened by human induced climate change. They opined
that without migration, a quantum of plant species will become vulner-
able by the year 2080 ( Thuiller 2014 ).
Thomas et al. (2004) noted that many species will be subjected to
local extinction with higher probability of total extinction as a result
of unfavourable climatic conditions. Decrease in range size implies that
smaller stochastic events aect a larger proportion of the species total
population, especially in fragmented landscapes ( IUCN 2011 ). The high
demand for the fruits and plants parts for traditional medicine has re-
sulted in their overexploitation thereby accelerating the extinction of
the species ( Hawthorne 1997 ; IUCN 2004 ). These ndings have incited
coordinated eorts towards identifying the suitable area for propagat-
ing G. Kola in Nigeria, and the species is amongst the priority species
for urgent conservation ( IUCN, 2019 ). Therefore, highlighting the need
to understand its suitable ecology and distribution for conservation plan
( Sacande´ et al., 2004 ).
5. Conclusion
This study was conducted to ascertain the impact of climate change
on ecology and suitable habitat for propagating Garcinia kola in Nigeria.
Climate Change has been identied as threats that will have signicant
inuence on ecosystems and on tropical species such as G. kola . The
species is amongst the highly rated socio-economic species with poten-
tial of ensuring food security, improving primary healthcare, promot-
ing farmers local income generation and improving their rural lives and
livelihood. The high demands for these species have resulted in their
over-exploitation and them being classied as vulnerable. The species
still receives little attention on the conservation plans and hence, it is
necessary to advocate for immediate conservation action to prevent its
erosion.
The present study has conrmed that species occurrences and dis-
tribution is principally governed by variability in the climatic parame-
ters, although the study was limited to only climatic information and
geographical coordinate on the occurrence point of the species for the
modelling process. This study has provided baseline information on the
environmental requirements and ecological range of G. kola species and
has identied possible suitable areas for the conservation. The results
showed that climate change will lead to truncation of the species in the
nearest future. To sustain the utilization of the species, it is therefore rec-
ommended that immediate action should be initiated to conserve these
valued species. Appropriate incentives should be given to encourage in-
dividual and rural communities in establishing plantation and possible
incorporation in agroforestry practices .
Declaration of Competing Interest
None
Acknowledgments
The authors wish to express their sincere gratitude to the West
African Climate Change and Adapted Land use funded by the German
Federal Ministry for Education and Research for providing nancial sup-
port to the corresponding author to carry-out this research as part of his
postgraduate studies. Thanks to the technical sta of Cocoa Research
Institute of Nigeria, Moist Research Station of Forestry Research Insti-
tute of Nigeria. Kangbéni Dimobe sincerely acknowledges the support
of the Alexander von Humboldt Foundation and the BMBF through the
African-German Network of Excellence in Science (AGNES) Grant for
Junior Researchers.
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