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Khaya senegalensis Desr & Juss is an urban tree species with high quality wood, unfortunately disturbed by Hypsipyla robusta Moore. However, how vulnerable this species is with regard to climate change and to Hypsipyla robusta over time and space is unknown. This study aimed at assessing as well the climate change impacts on both species as the overlapping extent of their suitable areas over time and space. To this end, the MaxEnt approach for Ecological Niche Modelling was used to compute suitable areas for both species under current and future climates (Africlim RCP 4.5 and RCP 8.5). Spatio-temporal Analysis was performed using Geographic Information System. Upon 2055, climate change will impact negatively 15-16% of Benin while the positive impacts will account only for 2-3%, and the stable areas will represent 74-75%. As for Hypsipyla robusta, climate change will provide only habitat loss of about 66% of the country. So, many plantation sites are exposed to biological attack from the pest, but wouldn"t be more in future, giving hope for Khaya senegalensis' high quality wood production. Meanwhile, there will be an ecological imbalance due to the drastic potential habitat loss for the insect.
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Int. J. Biol. Chem. Sci. 12(1): 24-42, February 2018
ISSN 1997-342X (Online), ISSN 1991-8631 (Print)
© 2018 International Formulae Group. All rights reserved. 4030-IJBCS
DOI : https://dx.doi.org/10.4314/ijbcs.v12i1.3
Original Paper http://ajol.info/index.php/ijbcs http://indexmedicus.afro.who.int
Vulnerability of Khaya senegalensis Desr & Juss to climate change and to the
invasion of Hypsipyla robusta Moore in Benin (West Africa)
Akotchiffor Kévin Géoffroy DJOTAN*, Augustin Kossi Nounangnon AOUDJI,
Donald Romaric Yehouenou TESSI, Sunday Berlioz KAKPO,
Alain Jaurès GBÈTOHO, Koura KOUROUMA and Jean Cossi GANGLO
Laboratoire des Sciences Forestières, Faculté des Sciences Agronomiques,
Université d’Abomey-Calavi. BP: 1493 Calavi, Bénin.
*Corresponding author; E-mail: geoffroydjotan@yahoo.fr; Tel: 0022995475392
ACKNOWLEDGMENTS
This work is a contribution to the achievement of data use in the framework of the Biodiversity
Information for Development (BID) projects funded by the European Union and facilitated by the Global
Biodiversity Information Facility (GBIF).
ABSTRACT
Khaya senegalensis Desr & Juss is an urban tree species with high quality wood, unfortunately
disturbed by Hypsipyla robusta Moore. However, how vulnerable this species is with regard to climate change
and to Hypsipyla robusta over time and space is unknown. This study aimed at assessing as well the climate
change impacts on both species as the overlapping extent of their suitable areas over time and space. To this
end, the MaxEnt approach for Ecological Niche Modelling was used to compute suitable areas for both species
under current and future climates (Africlim RCP 4.5 and RCP 8.5). Spatio-temporal Analysis was performed
using Geographic Information System. Upon 2055, climate change will impact negatively 15-16% of Benin
while the positive impacts will account only for 2-3%, and the stable areas will represent 74-75%. As for
Hypsipyla robusta, climate change will provide only habitat loss of about 66% of the country. So, many
plantation sites are exposed to biological attack from the pest, but wouldn‟t be more in future, giving hope for
Khaya senegalensis’ high quality wood production. Meanwhile, there will be an ecological imbalance due to
the drastic potential habitat loss for the insect.
© 2018 International Formulae Group. All rights reserved.
Keywords: Khaya senegalensis, Forest pests, Wood quality, Ecological modelling.
INTRODUCTION
Life on Earth without natural resources
is quite impossible (Pamlin and Armstrong,
2015). However, threats on these natural
resources nowadays are numerous, huge, and
likely to threaten human‟s life itself (Myers et
al., 2009). The sources of the threats are
mainly twofold, anthropic causes and global
changes causes. These factors together are
combining to magnify threats on both
resources and human security (Myers et al.,
2009; Bello et al., 2017). Khaya senegalensis
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
25
Desr & Juss is one of the species exposed to
anthropic pressure (Houehanou et al., 2013)
because of its multi-uses that benefit people
(Sokpon and Ouinsavi, 2002).
The wood of Khaya senegalensis is of
high quality (Sokpon et al., 2004) and is
selectively harvested (Glèlè Kakaï and Sinsin,
2009). According to Botha et al. (2004), these
pressures on the species may expose it to
threats. Khaya senegalensis, a West African‟s
urban tree (Orwa et al. 2009), plays important
roles in the livelihoods of hundreds of
millions of rural and urban peoples across the
globe (Emanuel et al., 2005). So, the species
is greatly harvested by Fulani herders across
the country for fodder (Gaoue and Ticktin,
2016). Modelling of 5197 tree species using
the Hadley Center‟s third generation coupled
ocean-atmosphere General Circulation Model
predicted the diminution of the repartition
areas for about 81%-97% of the 5197 African
tree species (McClean et al., 2005). So, Khaya
senegalensis is also likely to be exposed to the
impacts of climate change on some ways. The
same species is expected to be exposed to
higher rates of insect attack and mortality
(Botha et al., 2004). Most of these attacks
come from Hypsipyla robusta Moore (Sokpon
and Ouinsavi, 2004). A recent study found
that insects develop resistance to pesticide and
that it is also difficult to access adequate
materials to handle and apply those pesticides
(Agboyi et al., 2015). It is therefore urgent to
seek for practical ways to conserve and
manage sustainably this resource.
Methods for characterizing
environmental requirements of species have
been used over the past two decades, to
anticipate species‟ distributional potential in
novel regions or under scenarios of
environmental change (Owens et al., 2013).
Known globally as Biodiversity Informatics,
the domain of computation is helping
transversally many scientists from many fields
all over the world. Its use (Gbesso et al., 2013;
Saliou et al., 2014) provided huge importance
in conservation and sustainable management
of natural resources.
Through those methods and the
relation between Khaya senegalensis tree and
its harsh driller Hypsipyla robusta, this study
aimed at assessing how vulnerable Khaya
senegalensis is with regard to climate change
and to Hypsipyla robusta over time and space
in West Africa and particularly in Benin. It
then intended to assess as well the climate
change impacts on both species as the
overlapping extent of their suitable areas over
time and space.
MATERIALS AND METHODS
Study areas and species
K. senegalensis is one of the most
important tree species in the Meliaceae family
in West Africa. It grows up to 30 m high and
3 m girth, with a dense crown and short bole
covered with dark grey scaly bark (Burkill,
2004). The bark is bitter and gum can flow
from it when it is wounded. It is a semi
deciduous tree that doesnt tolerate shade
(Sokpon and Ouinsavi, 2002). K. senegalensis
is found in various vegetation types, including
gallery forest, dry dense forest, woodland
forest, and savannah and in both the Sudano-
Guinean and the Sudanian ecological regions
of Benin (Sokpon and Ouinsavi, 2002). Used
in urban planning in Benin, the stems of the
species are being devoid of their bark and
leaves. The species is greatly believed for its
numerous medicinal uses, and is known to be
used ethno medicinally as a therapy for
several human and animal disorders
(Nacoulma-Ouedraogo, 2008). Sokpon and
Ouinsavi (2002) identified 55 diseases that
can be treated by Khaya senegalensis,
showing them how great its importance is in
pharmacy or medicine for people‟s health, and
ethnobotany.
Hypsipyla robusta is an insect species
in the Pyralidae family. It is a harsh driller of
Khaya senegalensis wood (Sokpon and
Ouinsavi, 2004). Specifically, with shoot-
borer activity, it is apparently restricted in its
feeding to plants belonging to the family
Meliaceae (Griffiths, 2001). The distribution
of the mahogany shoot borer coincides with
that of its principal host plant species
(Griffiths 2001). The two most important
Hypsipyla species with respect to shoot borer
activity are H. grandella (Zeller) occurring in
the Americas, and H. robusta Moore,
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
26
occurring through areas of Africa and the
Asia/Pacific region (Griffiths, 2001).
Data collection
We collected occurrence data of Khaya
senegalensis and Hypsipyla robusta on GBIF
site (www.gbif.org). These downloads can
always be viewed on
http://www.gbif.org/occurrence/download/000
3585-160526112335914 and on
http://www.gbif.org/occurrence/download/000
3589-160526112335914. Present data for H.
robusta was completed with documentation.
We had also downloaded present bioclimatic
variables data (1950 à 2000) on
https://webfiles.york.ac.uk/KITE/AfriClim/Ge
oTIFF_150s/baseline_worldclim/ (Platts et al.,
2015) at the resolution 2.5 minutes (150s);
format GeoTIFF at the extent of Africa. For
projection in future, bioclimatic variables data
are downloaded from AfriClim
https://webfiles.york.ac.uk/KITE/AfriClim/Ge
oTIFF_150s/africlim_ensemble_v3_worldcli
m/ (Platts et al., 2015).
The file set used is the ensemble v3
worldclim. The Scenarios used are the
Representative Concentration Pathways 4.5
that is realistic (Meinshausen et al., 2011), and
the Representative Concentration Pathways
8.5 that is pessimistic (Meinshausen et al.,
2011), by 2055.
Model fitting
Data have been appropriately cleaned
using QGIS, Excel, Earth Explorer and
Google Earth. Occurrence data served to
define the geographical background as
recommended by Acevedo et al. (2012).
Environmental layers have been put in
appropriate format. The maximum entropy
species distribution model algorithm (MaxEnt,
version 3.3.3k” Princeton University,
Princeton, New Jersey, USA) was used to
calculate the maximum entropy species
distribution (Philips et al., 2006; Pearson et
al., 2007). The default parameters as
recommended by Dossou et al. (2016) have
been used.
Statistical analysis
Statistics helped selecting variables
that might be included in the model. Those
statistics were twofold. First group of statistics
were those computed by the modelling
algorithm itself (MaxEnt). It includes the
Jackknife chart, the Area Under the Curve
(AUC) value, the response curves, and the
threshold table (Elith et al., 2006). Second
group of statistics include those we calculated
ourselves. There were the computation of
correlation table between variables using
ENMTools (Warren et al., 2010), the
computation of model evaluation criteria such
as the True Skill Statistics -TSS- (Allouche et
al., 2006), and the Partial ROC (Peterson et
al., 2008). The choice of the variables was
based on both groups of statistics with regard
to the ecology of the species. Classification
thresholds were selected in the table of
threshold of MaxEnt outputs based on the
objectives of our study.
Spatial analysis
The continuous maps of logistic
probability distributions generated by MaxEnt
were used to define the suitable areas, the
degree of this suitability across the country,
and the overlapping of studied species‟
suitable areas. Excel 2016 and QGIS Wien
2.16.3 were used to compute environmental
preferences of the species for the six most
important variables previously retained. The
process was done both for Khaya senegalensis
and Hypsipyla robusta. We overlaid the maps
of suitability areas. The raster computations
were based on the “10 percentile training
presence” used by Fandohan et al. (2015) and
the “Equal test sensitivity and specificity”
used by Ganglo et al. (2017).
RESULTS
Khaya senegalensis
Model evaluations indicated that
Model for Khaya senegalensis was robust and
yielded predictions statistically significant,
better than random. The training data AUC
and the test data AUC were, with the selected
variables respectively 0.930 and 0.974. The
mean test data AUC was 0.903 and the
standard deviation 0.048. The TSS was 0.76.
As for Partial ROC evaluation method, all
AUC ratios among 1000 replicates were well
above 1.0, (1.12 and 1.81 being respectively
the minimum and the maximum values).
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27
That‟s for AUC ratio equal to 1.12; the model
is significant with P-value less than 0.001.
Those statistics indicated that the model
showed excellent performance and was stable,
and the model prediction was accurate. The
distribution of the species in the landscape of
interest is shown on Figure 1, indicating that
both species showed good distribution pattern
across that landscape.
Table 1 and Figure 2 give estimates of
relative contributions of the environmental
variables to the MaxEnt model. The
isothermality (bio3 in 10 °C), the minimal
temperature of the coolest month (bio6 in 10
°C), the annual temperature range (bio7 in 10
°C), the mean temperature of the coolest
quarter (bio11 in 10 °C), the rainfall of the
driest month (bio14 in mm), and the rainfall of
the wettest quarter (bio16 in mm) were the
variables that contributed the most to the
models. Therefore, those variables control the
distribution of the species. High temperatures
in coolest quarter are unfavorable for Khaya
senegalensis (Figure 19). It tolerates extremes
values of isothermality and values between
57.5 °C and 67.5 °C are critical for the species
(Figure 20). Figures 3, 4, 5, 6, 7 and 8 show
respectively Partial ROC chart, spatial
distribution of the species at present, projected
distribution of the species at horizon 2055
under RCP4.5, projected distribution of the
species at horizon 2055 under RCP8.5, the
projected distribution of climate change
impacts on the species at the horizon 2055
under RCP4.5, and the one under RCP8.5
with regard to climate changes.
The contribution table and the
jackknife chart of regularized training gain
(Table 1 and Figure 2) show the contribution
of each of the six most contributing variables,
and it came out that the mean temperature of
the coolest quarter [Bio11] is very influent on
the distribution of Khaya senegalensis. The
receiver operating characteristic showed how
well the training and test data AUCs are above
0.90 (Figure 3). The continuous probability
maps range from 0 to 1, represented with
colors ranging from red (0) to green (1). So,
the more the area ranges toward green the
more is this area projected suitable for the
species (Figures 5, 6 and 7). For climate
change impacts on the species, Figures 7 and
8 show stable areas in green, positive impact
areas in violet, and negative impact areas in
red.
Hypsipyla robusta
Model for Hypsipyla robusta was
robust and generated predictions statistically
significant, better than random. The training
data AUC and the test data AUC are, with the
selected variables respectively 0.902 and
0.880. The mean test data AUC is 0.856 and
the standard deviation is 0.070. The TSS was
0.79. As for the Partial ROC, all AUC ratios
among 1000 replicates were well above 1.0,
(1.01 and 1.80 being respectively the
minimum and the maximum values). That‟s
for AUC ratio equal to 1.01; the model was
significant with P-value less than 0.001.
Those statistics showed that the model
performed well, predicted accurately, and is
stable. Table 2 and Figure 9 give estimates of
relative contributions of the environmental
variables to the MaxEnt model. The most
contributing variables were the mean annual
temperature (bio1 in 10 °C), the minimal
temperature of the coolest month (bio6 in 10
°C), the mean temperature of the coolest
quarter (bio11 in 10 °C), the rainfall of the
driest month (bio14 in mm), the rainfall of the
wettest quarter (bio16 in mm), and the rainfall
of the driest quarter (bio17 in mm). Hypsipyla
robusta prefers high temperatures in coolest
months (Figure 21) and needs a minimum of
rainfall in driest quarter (Figure 22). Figures
10-15 show respectively Partial ROC chart,
continuous suitability map of the current
scenario, continuous suitability map for 2055
RCP4.5, continuous suitability map for 2055
RCP8.5, projected distribution of climate
change impacts on the species by 2055 under
RCP4, and projected distribution of climate
change impacts on the species by 2055 under
RCP4.5.
The contribution table and the
jackknife chart of regularized training gain
(Table 2 and Figure 9) show the contribution
of each of the six most contributing variables,
and it comes out that the mean temperature of
the coolest month (bio6) is very influent on
the distribution of Hypsipyla robusta. The
receiver operating characteristic showed how
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
28
well the training and test data AUCs are above
0.90 (Figure 10). The continuous probability
maps range from 0 to 1, represented with
colors ranging from red (0) to green (1). So,
the more the area ranges toward green the
more is this area projected suitable for the
species (Figures 11, 12 and 13). For climate
change impacts on the species, Figures 14 and
15 show stable areas in green, positive impact
areas in violet, and negative impact areas in
red.
Khaya senegalensis in interaction with
Hypsipyla robusta over the time in the space
Khaya senegalensis and Hypsipyla
robusta share four variables out of the six
most contributing ones retained. Those
variables are the minimal temperature of the
coolest month (bio6 in 10 °C), the mean
temperature of the coolest quarter (bio11 in 10
°C), the rainfall of the driest month (bio14 in
mm), and the rainfall of the wettest quarter
(bio16 in mm).
Taking into account the biotic factor
consisting in the fact that Hypsipyla robusta is
a harsh driller and shoot borer of Khaya
senegalensis, in addition to abiotic factors, we
got the maps of interaction results over the
time and the space. Figures 16-18 show
respectively the current overlaps between the
two species, overlaps in 2055 RCP4.5, and
overlaps in 2055 RCP8.5.
Overall models outputs analysis for Benin
Models revealed that Khaya
senegalensis can occur currently in Benin.
Projections in the 2055 showed that it can
occur in the future with some areas left out
and some gain. The loss was assessed at 15-
16% of Benin superficies while the gain was
2-3% of the country‟s total area, and the stable
areas were projected to be 74-75% of Benin‟s
total areas. As for Hypsipyla robusta, it was
shown to be likely to occur currently with
high prevalence in southern Benin, and
moderately in the other part of the country.
Projections into the 2055s showed that the
species ecological niche may be going to
disappear from the country. This loss was
estimated at 66% of the country‟s total area;
no stable areas were predicted. Added to the
environmental influence, biological
interactions between K. senegalensis an H.
robusta showed significant overlapping zones
in current situations and almost no
overlapping in future, where the driller and
borer may harm the tree by destroying the
quality of its wood.
Figure 1: Spatial distribution of Khaya senegalensis and Hypsipyla robusta in the landscape of
interest.
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
29
Table 1: Variables contribution.
Percent contribution
Permutation importance
31.1
26.9
20.8
21.7
18.5
9.3
10.7
14
9.8
8.1
9.2
20.1
Values are in percentages
Figure 2: Jackknife of regularized training gain (Khaya).
Figure 3: Receiver Operating Characteristic (Khaya).
Figure 4: Spatial distribution of Khaya senegalensis under current climate.
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
30
Figure 5: Projected distribution of the species by 2055 under RCP4.5 (Khaya).
Figure 6: Projected distribution of the species by 2055 under RCP8.5RCP8.5 (Khaya).
Figure 7: projected distribution of climate change impacts on the species by 2055
under RCP4.5 (Khaya).
Figure 8: projected distribution of climate change impacts on the species by 2055 under
RCP8.5 (Khaya).
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
31
Table 2. Variables contribution.
Variable
Percent contribution
Permutation importance
Bio6
40.2
16
Bio17
35
38.7
Bio11
15.5
17.5
Bio1
4.1
9.3
Bio14
2.7
8.1
Bio16
2.6
10.4
Values are in percentages
Figure 9: Jackknife of regularized training gain (Hypsipyla).
Figure 10: Receiver Operating Characteristic (Hypsipyla).
Figure 11: Distribution of H. robusta at present.
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
32
Figure 12: Projected distribution of H. robusta by 2055 under RCP4.5.
Figure 13: Projected distribution of H. robusta by 2055 under RCP8.5.
Figure 14: projected distribution of climate change impacts on the species by
2055 under RCP4.5 (Hypsipyla).
Figure 15: projected distribution of climate change impacts on the species by 2055 under
RCP8.5 (Hypsipyla).
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
33
Figure 16: Current overlap (Khaya X Hypsipyla).
Figure 17: Overlap in 2055 RCP4.5 (Khaya X Hypsipyla).
Figure 18: Overlap in 2055 RCP8.5 (Khaya X Hypsipyla).
For the overlaps maps, we defined following explained criteria in methods,
three different types of areas. First, we computed areas where only Khaya
senegalensis was projected to prosper (green color on the maps); secondly we
calculated areas where only Hypsipyla robusta was projected to find its
preference (red color on the maps), and finally areas where both species are
likely to occur (violet on the maps).
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
34
Figure 19: Response of K. senegalensis to the mean temperature of the coolest quarter.
Figure 20: Response of K. senegalensis to isothermality.
Figure 21: Response of H. robusta to mean temperature of the coolest month.
Figure 22: Response of H. robusta to the rainfall of the driest quarter.
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
35
DISCUSSION
Biodiversity informatics and applications
Many scientists (Pearson et al., 2006;
Peterson et al., 2011; Gbesso et al., 2013;
Saliou et al., 2014; Fadohan et al,, 2015;
Ganglo and Kakpo, 2016; Idohou et al., 2016;
Ganglo et al., 2017) used Geographical
Information System, and Biodiversity
Informatics to explore world resources issues.
This is exactly what we did in the present
study to present the impacts of climate
changes on Khaya senegalensis and Hypsipyla
robusta. Moreover, we considered the
biological interaction between the two species
in order to point out the impacts of the insect
Hypsipyla robusta, which is a wood driller
and shoot borer for Khaya senegalensis. Many
scientists did such studies but few of them
applied biodiversity informatics to the
understanding of interactions between species.
In instance, Usher (2010) modelled the
malaria transmission potential (MTP) to find
links between malaria transmission and
climate and to use further scenarios to see if
predicted climate change will affect the
frequency and spread of malaria in West
Africa and South Europe. That is to point out
the application of this field of study in health
and security related domains, and in biology.
In Forestry and natural resources
management, for example Fandohan et al.
(2015) used Ecological Niche Modelling
Tools to model vulnerability of protected
areas to invasion by Chromolaena odorata
under current and future climates. Such
studies raise awareness of people on the
dangers these resources are and will be
exposed to, and give a range of solutions
according to the obtained results, on the
conservation of concerned resources.
Similarly, our study was not only an
application of Biodiversity Informatics to
assess climate change impacts on biological
resources, but also an application to forest
pest management for the production of high
quality forest biomaterials.
Climate changes impacts on Khaya
senegalensis
The Beninese districts that do not meet
fully Khaya senegalensis environmental
preferences were Karimama, Banikoara,
Natitingou, Dangbo, So-ava, Semè-Kpodji,
Adjara, Ifangni, Aplahoué, and Djakotomey.
Some spots in Gogounou, Segbana and Djidja
were not suitable. However the species could
grow since the suitability value is not null, and
the tree is not too exigent, and is considered
dry areas savanna mahogany tree (Nikiema et
al., 2008). Kandi is shown highly suitable for
Khaya, this shows the evidence that the model
does well the job because there is a plantation
of Khaya in this urban district (Sokpon et al.,
2004). Natitingou holds plantations of Khaya
too, precisely in Birni, Kouaba, Kouandé, and
Tanguiéta (Sokpon et al., 2004) where the
suitability value is not null, and confirms the
fact that since the probability is not null added
to the tolerance of the species, the latter could
grow. It is important to add that the soil could
have been suitable for the establishment of
Khaya‟s plantations in the North part of the
country. Those planted in Atchérigbé and in
Toffo are in the highly favorable zone of the
species. Particular areas in southern Benin
where the species have low prevalence such as
urban districts of So-Ava, Dangbo, Adjara,
Seme-Kpodji, and Ifangni deserve further
investigation, but we assume that it is due to
the soil properties. Overall, the model
revealed what we can observe currently in
Benin where there are successful plantations
of the species from the south to the north in
Toffo, Atchérigbé, Birni, Kouaba, Kouandé,
Tanguiéta and Kandi (Sokpon et al., 2004).
Then, it is very important to point out that
Benin is a relatively good ecosystem for the
species, specifically from the North to the
South of the country. Moreover, Nikiema and
Pasternak (2008) in their study dedicated to
the species gave a range of a zone including
the whole Benin.
Climate changes are in favor of Khaya
senegalensis occurrence in some areas and
unfavorable for it in others. Idohou et al.
(2016) found similar results of climate
changes on wild palms in West Africa when
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36
they stated that much of the distribution of the
wild palms will remain largely stable, albeit
with some expansion and retraction in some
species. Similarly to our findings, Gbesso et
al. (2013) found that climate change could be
an opportunity for a long term conservation of
a species, in their case Chrysophyllum
albidum G. Don. The realistic (RCP4.5) and
the pessimistic (RCP8.5) scenarios showed
approximately the same results. The
significantly positive impacts zone accounts
for 3% (RCP4.5) and 2% (RCP8.5) of Benin
area, while the significantly negative impacts
zone accounts for 15% (RCP4.5) and 16%
(RCP8.5). 75% and 74% of national area are
projected to be stable according respectively
to RCP4.5 and RCP8.5. These results
confirmed shifts and the negative impacts of
climates changes on natural resources many
authors wrote about (McClean et al., 2005;
Alig, 2011). As our results showed, shifts in
the distributions of species with climate
change have now been documented for many
species (Rosenzweig et al., 2008) and many
more are expected to shift with future climate
change. Unfortunately, few of them point out
that the climate changes could be benefic
somehow. It is important to transform the
climate threats into opportunity in favor of
natural resources. The only way to achieve
this is to stand up earlier and assess the
vulnerability of climate changes on these
resources over time and space. Anticipating
likely effects of climate changes on species
distribution (Peterson et al., 2011) and transfer
of model predictions to novel regions and/or
time periods (Pearson et al., 2006) will make
of changes, great opportunities to solve the
world natural resources issue. In instance,
through this study, we know where changes
are in favor of Khaya senegalensis growth, so
further actions toward the conservation of this
species should be directed to these areas, and
meanwhile, areas shown to be less suitable
than former could be used for another species.
As each species could be object of spatial
analysis for plants prosperity in the time,
biodiversity informatics through ecological
niche modelling using MaxEnt can provide
highly informative biogeographical
information and discrimination of suitable vs.
unsuitable areas for a species (Philips et al.
2006). Such information should be used
appropriately for decision making concerning
natural resources. Despite the well-recognized
conceptual ambiguities and uncertainties
about bioclimatic envelope modeling
(Schwartz, 2012), MaxEnt remains a practical
tool that allows assessment of the potential
impact of climate changes on the distribution
of suitable habitats of plants and animals
(Elith et al., 2010). However, we are eager to
remind that ecological niche modelling results
have to be interpreted carefully. That is, it can
happen to meet Khaya senegalensis at a place
not predicted to hold it.
Climate changes impacts on Hypsipyla
robusta occurrence over time and space
Hypsipyla robusta depends mostly on
the minimal temperature of the coolest month
and the rainfall of driest quarter (Figures 21
and 22). The southern part of Benin, from the
coastal limit to the latitude of Parakou is
highly exposed under the current scenario to
the occurrence of Hypsipyla robusta. The
species still has a chance to appear beyond
this latitude but the environmental conditions
will be limiting outside Tanguieta, Kobli,
Materi, Kerou, Kouande, Pehonco and
Bembèrèkè. According to Griffiths (2001),
Hypsipyla robusta is likely to occur in
Tanzania, Madagascar and in West Africa.
Our results confirm theirs on the points that
the prevalence of this species as shown by our
model is very high in Southern Benin and
moderate across the Northern Benin, and then
the country is supposed exposed to presence
of the species. This presence could be driven
by the suitability of the areas to its host plant
Khaya senegalensis since it is apparently
limited in feeding on Meliaceae trees. We are
also eager to recap that living form develop
many aptitudes to adapt new environmental
situations, mainly animals. For these reasons
we recommend that our results be used with
the greatest fitness, and therefore should be
considered with close attention.
Hypsipyla robusta will face upon
2055, a severe regression of its climatic
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
37
envelop. Here, even if this study considered
Hypsipyla as a pathogen for Khaya, on the
view of biodiversity conservation, it is
important to point out that Hypsipyla robusta
is going to disappear from Benin by 2055
according to our projections. It could create
biological and ecological disasters since the
insect is an element of the ecological supply
chain. Both scenarios gave approximately the
same results. There won‟t be any significantly
positive impact zone for the species in Benin
upon 2055s; the significantly negative impacts
zone will account for 66% regardless the
RCP. Any area does not save significant
positive impacts for the species. Climate
variability doesn‟t provide only negative
effects on natural resources (Rosenzweig et
al., 2008) but our findings on Hypsipyla
robusta in West Africa in general and
especially in Benin indicated that climate
change could have only negative impacts on a
species. Hounkpèvi et al. (2016) found that
climate changes will make Vitex doniana
distribution increases about 14 to 23% in the
Protected Area Network of Benin by 2050,
but the contrary shift is going to be observed
for Hypsipyla robusta by 2055. In fact,
Hypsipyla robusta, seen as natural resource
will suffer from these changes. However, a
forester who is willing to settle plantation of
Khaya senegalensis will consider that the
future climatic conditions provide best
conditions for his business.
Biological interactions between Khaya
senegalensis and Hypsipyla robusta
Here, it is not a competition between
the two species; instead it is a parasitism from
Hypsipyla. This parasitism destroys the
quality of the wood of Khaya senegalensis.
Each species has its suitability areas according
to models. It emerges from the overlapping
that at present, a large part of Benin is suitable
for both species. Those areas may not
guaranty a production of high quality wood of
Khaya senegalensis. Meanwhile, some urban
districts contain some areas where only Khaya
senegalensis was projected to prosper. Those
areas are within Malanville, Segbana, Kandi,
Gogounou, Kalalé, Nikki, Prèrè, Djougou,
Kopargo, Ouaké and Kérou. Conservation and
production of high quality wood of Khaya
senegalensis could succeed on these sites. A
projection in the 2055s showed for both
scenarios (RCP4.5 and RCP8.5) that almost
no more areas will be available for Hypsipyla
robusta, and then we can produce Khaya
senegalensis easily in suitable areas as
projected through the two RCPs. However, we
need to be precautious because of the
complexity of living forms. The production of
Khaya senegalensis with high quality wood
will be difficult if the extent of its pathogen‟s
geographical preferences is widespread.
Sokpon et al. (2004) stated that Hypsipyla
robusta is a harsh driller of wood of K.
senegalensis, in the plantations of this species.
Several authors claimed how redoubtable
Hypsipyla robusta is for Khaya senegalensis
in the sub-region in Togo, Ghana, Ivory-
Coast, Nigeria, Burkina-Faso (Opuni-
Frimpong, 2012; Sokpon and Ouinsavi, 2004).
Some recent studies reported similar problems
between pests and tree species other than
Hypsipyla robusta and Khaya senegalensis.
Agboyi et al. (2015) reported the resistance of
pests to pesticides in Togo and the higher cost
in handling and application of the pesticides.
Those authors recommended integrated pest
management to face similar issues. Our
findings are likely to ease the implementation
of their recommendations. Results of this
study gave us hope that the future will be
better for Khaya senegalensis.
Perspectives for Khaya senegalensis
conservation
Climate changes and Hypsipyla
robusta environmental requirements are
acting together against the prosperity of
Khaya senegalensis. This tree is the quickest
growing urban tree among local species of
great value (Onefeli et al., 2014). It is very
important to conserve the species through
specific approaches according to the threats
that are threefold: human pressure, climate
changes, and Hypsipyla attack. There are little
actions to avoid human pressure on Khaya
senegalensis. So, what we suggest is to
promote the trees plantation across the
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
38
country. These plantations have to be settled
in protected areas projected to be favorable
despite the biological attack by the 2055s.
Protected areas located in these suitable areas
may be chosen for upgrading with the species.
As K. senegalensis is an urban tree, the
building plans in the country may make use of
this aspect of urbanization. As stated, we
suggest that the Government orders road
builders to plant the species along the roads. It
could also be a goal for mayors in their
districts. Many showed signaled negative
impacts of H. robusta in the plantation of K.
senegalensis, and many approaches and
technics of production have been used to
reduce these impacts. Under some conditions
such as growing in natural forests at low
densities, in association with other species, or
in open space the likelihood of Hypsipyla
robusta attack is decreased (Howard et
Michael, 2014). It is then worth on one side
considering our spatial analysis results for
plantations of Meliaceae in addition to the use
of wide spacing, partial shading and control of
competing vegetation in mixtures with non-
susceptible species in groups or lines with less
than 100 trees per hectare. On the other hand,
it is interesting that research continues in
finding silvicultural technics toward
considerable reduction of Khaya attack from
Hypsipyla (Grogan et al., 2002).
Attempts to control pests using
conventional insecticides are common, and
chemical control may become useful in
extreme circumstances although its scope for
large-scale application under operational
conditions is limited (Agboyi et al., 2015). A
pest may have natural enemies, and many of
the naturally occurring parasites can limit the
pest population to some extent (Agboyi et al.,
2015). However, it has been reported by Nair
(2001) that innate biological attributes of the
insects associated to the trees and
monoculture are some of factors that rise
insect pest outbreaks. Moreover, Wylie (2001)
reported that there is no single reliable, cost-
effective, and environmentally sound
chemical pesticide available to control
Hypsypila robusta. So, despite a great number
of natural enemy that a pest could has,
biological control could neither be enough,
nor could chemical control be, and
silvicultural control to stop its attack on its
host. However, Blach-Overgaad et al. (2010)
and Bowe and Haq (2010) recognized that
recommendation for management of
threatened species, agroforestry species, pests,
and invasive species can be based on
Ecological Niche Modelling. Then, integrated
approaches where spatial analyses are
performed will certainly be better.
Conclusion
As demonstrated by the present study,
biodiversity informatics has broad application
and is very helpful in decision making about
conservation and natural resources
management. We pointed out above that
Khaya senegalensis is very exposed to climate
changes, to biological attack from Hypsipyla
robusta, its harsh driller, and shoot borer in
West Africa in general and particularly in
Benin. We also showed that Hypsipyla
robusta is going to disappear by 2055 due to
climate changes. Details can be obtained from
our results on the urban districts, even cities
where future situations are unfavorable for the
two species including where the species can
be actually grown successfully, and provide
eventually areas where H. robusta may be a
problem for K. senegalensis. Meaningfully we
provided information on high quality wood
production. Because Khaya senegalensis is a
threatened species due to its uses, we
recommend further research on it to
participate actively to its sustainable
management. Description of damages pattern
and the economics of forest pest invasion are
some future research questions.
COMPETING INTERESTS
The authors declare that they have no
competing interests.
AUTHORS’ CONTRIBUTIONS
AKGD and JCG were first, in charge
of data collection, their processing and
analysis. Secondly, they wrote the manuscript.
The other authors contributed in the redaction
and the revision of the manuscript.
A. K. G. DJOTAN et al. / Int. J. Biol. Chem. Sci. 12(1): 24-42, 2018
39
ACKNOWLEDGMENTS
We address our sincere gratitude to
Professor Town A. Peterson of the University
of Kansas and his team. We also thank
Lizanne Roxburgh of Endangered Wildlife
Trust who trained us in ecological niche
modelling.
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... Is an evergreen tree, but in dry climates it can be deciduous. Its wood is considered to be hardwood with excellent commercial value [2,3]. And numerous traditional medicinal uses, such as anti-sickling, antimicrobial, anthelmintic, and malaria treatment. ...
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This study aim to use environmentally friendly material (Linseed and citrus oils) to reduce the harmful effect of water deficit on Khaya senegalensis plant due to its economic importance as a source of wood in addition to many uses of alternative medicine. Therefore, Khaya senegalensis plants were grown in green house with three irrigation intervals (5, 7 and 9 days) and oils foliar spray as linseed oil (Lin) at (0, 5, 7%) and citrus oils at (0, 1, 2%). The results indicated that the 9 days irrigation interval gave the lowest values of the most studied growth parameters, water relations (relative water content (RWC%), water retention capacity (WRC) and membrane stability index(MSI%), photosynthesis pigments, chlorophyll stability index (CSI%), minerals content, total free amino acid while increased values of electrolyte leakage (EL%), total sugar, total phenol, and antioxidant enzymes activities(Catalase (CAT), Peroxidase (POX), Superoxide dismutase (SOD)). Both oils treated especially linseed oil, at 7% increased values of all parameters and chemical composition compared with untreated plants plus different irrigation intervals, except EL% was decreased. The data provided evidence that linseed and citrus oils treatment reduces the adverse effect of water deficit on Khaya senegalensis plants and can play a role in providing stress tolerance.
... Attacks occur predominantly in monoculture plantations (Nair, 2001), lead to high mortality rates (Botha et al., 2004), alter the engineering qualities of the wood (Griffiths, 2001;Nair, 2001;2007;Sokpon and Ouinsavi, 2002), and cause significant economic damage (Griffiths, 2001). To help avoid or reduce injuries induced by pests, mainly H. robusta, Djotan et al. (2018) modeled the ecological niche of the host (K. senegalensis) and pest (Hypsipyla robusta) to identify vulnerable zones in West Africa. ...
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Most significantly, climatic disruption threatens the adequacy of the core "building blocks" of health for large populations around the globe: sufficient food and nutrition, safe water for drinking and sanitation, fresh air to breathe, and secure homes to live in. As climate change dismantles these central elements of healthy societies, people with fewer resources will be forced to migrate in large numbers to lands where they may not be welcome. A likely result of all of these processes will be increased civic instability and strife. Even if the global climate were stable, humans would still be converting more land, water, and ecosystem services for their own now endanger health and wellbeing globally and on scales never experienced in human history. These threats include: exposure to infectious disease, air pollution, water scarcity, food scarcity, natural disasters, and population displacement. Taken together, they represent the greatest public health challenge of the 21st century. We need to wake up to the danger and act with urgency to reduce ecological disruption as much as possible while simultaneously strengthening the resilience of populations to withstand the impacts of unavoidable environmental change. Populations vary dramatically in their vulnerabilities to these emerging health threats, in part because the environmental changes that are triggering these threats are not uniform. Rapid glacial melting on the Tibetan plateau threatens the dry-season water supply for more than 1 billion people living and growing irrigated crops in Asia's great river basins. In sub- Saharan Africa, droughts and increased temperatures caused by climate change will interact with existing soil degradation, nutrient loss, and water scarcity to further reduce crop yields and constrain food supplies. 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Because the impacts will depend in large part on a population's location and socioeconomic status, it is critical that all countries conduct rigorous, location-specific risk assessments to identify which populations are at highest risk for which threats. Governments and other stakeholders will also need to mobilize substantial financial resources, technical capacity (both in assessment and appropriate technologies), and new partnerships that can help build capacity over the long term. The health impacts of climate change present an opportunity as well as a challenge. The international community increasingly recognizes the moral imperative to help the poor reduce their vulnerability to climate change- a threat that developing countries have had little role in generating. At the same time, there is renewed emphasis on achieving the United Nations'Millennium Development Goals, as well as increasing attention in the United States to reforming foreign assistance. 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