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Positive impacts of climate change on forest health, and species restoration in Benin: cases of Khaya senegalensis Desr. & Juss., and Garcinia kola Heckel

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Khaya senegalensis Desr & Juss and Garcinia kola Heckel are two medicinal forest trees species that provide as well lots of meaningful Non Timber Forest Products as Forest Timber Products. Those two species are undergoing many threats including climate change and forest pest attack. In order to provide forest resources managers and planted forest promoters with forest pest prevention means, forest species restoration and conservation strategies, this study was aimed at assessing the vulnerability of Khaya senegalensis to climate change and to the invasion of Hypsipyla robusta Moore in Benin over time and space; and analyzing how far climate changes can help to restore and conserve Garcinia kola, an extinct species in the wild in Benin. To this end, MaxEnt approach for Ecological Niche Modelling was used to compute suitable areas for target species under current, and future climates (RCP 4.5 and RCP 8.5 of AfriClim Ensemble mean,). Biodiversity presence data were gathered on the database of the Global Biodiversity Information Facility (GBIF). Gap analysis and Spatio-temporal Analysis were performed using Geographic Information System Tools. In the case of K. senegalensis, projections at horizon 2055 from AfriClim Ensemble mean showed that it can occur in the future with some areas left out and some gained. The loss was assessed at 15-16% of Benin superficies while the gain was 2-3% of the country’s total area. As for Hypsipyla robusta, climate change will provide only habitat loss of about 66% of the country’s total area. So, some plantation sites being currently exposed to biological attack from the pest could no more exist in the 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. It is worth that future investigations focus on the economics of attacks in plantations. As for Garcinia kola, results revealed that climate change proved to have only positive consequences on its distribution. Considering the High Confidence Projection Areas (HCPA), the percentage of municipalities predicted suitable for the species is far above the percentage of Protected Areas Network (PAN) predicted as such (7.44% versus 0.93%). RCP4.5 and RCP8.5 of AfriClim Ensemble mean indicated respectively 3.00% and 6.27% of PAN as positive climate change impact zones, predicted respectively 13.60% and 17.60% of the total municipalities’ areas as such. Therefore, it is worth relying not only on PAN but also and mainly on urban forestry and reforestation to restore and conserve the species. Further studies focusing on the introduction of Garcinia kola in urban areas, and its use for reforestation are compulsory. Key Words: Khaya senegalensis, Hypsipyla robusta, Garcinia kola, Ecological niche Modelling, Forest pest outbreak, Climate change.
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UNIVERSITY OF ABOMEY-CALAVI (UAC)
************
FACULTY OF AGRONOMIC SCIENCES (FSA)
*************
SCHOOL OF ENVIRONMENT MANAGEMENT
Thesis for Professional Master Degree in Agronomic Sciences
Major:
FORESTRY AND NATURAL RESOURCES MANAGEMENT
Submitted and defended by:
BSc. Akotchiffor Kévin Géoffroy DJOTAN
On July 8
th
, 2019
ACADEMIC YEAR 2017-2018
Positive impacts of climate change on forest health, and
species restoration in Benin: cases of Khaya senegalensis
Desr. & Juss., and Garcinia kola Heckel
Supervision
:
Dr. Jean C. GANGLO
Professor of Forest Sciences
Jury
:
Chairman: Prof. Dr. Ir. Philippe LALEYE
Examiner 1: Dr. Ir. Gaston AKOUEHOU
Examiner 2: Dr. Ir. Augustin AOUDJI
Reporter: Prof. Dr. Ir. Jean C. GANGLO
UNIVERSITE D’ABOMEY-CALAVI (UAC)
************
FACULTE DES SCIENCES AGRONOMIQUES (FSA)
*************
ECOLE D’AMENAGEMENT ET DE GESTION DE L’ENVIRONNEMENT
Mémoire de Master Professionnel en Sciences Agronomiques
Spécialité:
FORESTERIE ET GESTION DES RESSOURCES NATURELLES
Soumis et défendu par:
BSc. Akotchiffor Kévin Géoffroy DJOTAN
Le 8 Juillet 2019
ANNEE ACADENIQUE 2017-2018
Impacts positifs du changement climatique sur la
santé des forêts et la restauration d’espèces au
Bénin : cas de Khaya senegalensis Desr. & Juss. et
Garcinia kola Heckel
Supervision
:
Dr. Jean C. GANGLO
Professeur de Sciences Forestières
Jury
:
Président: Prof. Dr. Ir. Philippe LALEYE
Examinateur 1: Dr. Ir. Gaston AKOUEHOU
Examinateur 2: Dr. Ir. Augustin AOUDJI
Rapporteur: Prof. Dr. Ir. Jean C. GANGLO
iv
ABSTRACT
Khaya senegalensis Desr & Juss and Garcinia kola Heckel are two medicinal forest trees
species that provide as well lots of meaningful Non Timber Forest Products as Forest Timber
Products. Those two species are undergoing many threats including climate change and forest
pest attack. In order to provide forest resources managers and planted forest promoters with
forest pest prevention means, forest species restoration and conservation strategies, this study
was aimed at assessing the vulnerability of Khaya senegalensis to climate change and to the
invasion of Hypsipyla robusta Moore in Benin over time and space; and analyzing how far
climate changes can help to restore and conserve Garcinia kola, an extinct species in the wild
in Benin. To this end, MaxEnt approach for Ecological Niche Modelling was used to compute
suitable areas for target species under current, and future climates (RCP 4.5 and RCP 8.5 of
AfriClim Ensemble mean,). Biodiversity presence data were gathered on the database of the
Global Biodiversity Information Facility (GBIF). Gap analysis and Spatio-temporal Analysis
were performed using Geographic Information System Tools. In the case of K. senegalensis,
projections at horizon 2055 from AfriClim Ensemble mean showed that it can occur in the
future with some areas left out and some gained. The loss was assessed at 15-16% of Benin
superficies while the gain was 2-3% of the country’s total area. As for Hypsipyla robusta,
climate change will provide only habitat loss of about 66% of the country’s total area. So, some
plantation sites being currently exposed to biological attack from the pest could no more exist
in the 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. It is
worth that future investigations focus on the economics of attacks in plantations. As for
Garcinia kola, results revealed that climate change proved to have only positive consequences
on its distribution. Considering the High Confidence Projection Areas (HCPA), the percentage
of municipalities predicted suitable for the species is far above the percentage of Protected
Areas Network (PAN) predicted as such (7.44% versus 0.93%). RCP4.5 and RCP8.5 of
AfriClim Ensemble mean indicated respectively 3.00% and 6.27% of PAN as positive climate
change impact zones, predicted respectively 13.60% and 17.60% of the total municipalities’
areas as such. Therefore, it is worth relying not only on PAN but also and mainly on urban
forestry and reforestation to restore and conserve the species. Further studies focusing on the
introduction of Garcinia kola in urban areas, and its use for reforestation are compulsory.
Key Words: Khaya senegalensis, Hypsipyla robusta, Garcinia kola, Ecological niche
Modelling, Forest pest outbreak, Climate change.
v
RESUME
Khaya senegalensis Desr & Juss et Garcinia kola Heckel sont deux espèces médicinales
forestières qui fournissent aussi des Produits Forestiers Non Ligneux que Ligneux. Ces deux
espèces subissent certaines menaces dont les changements climatiques et les attaques par les
insectes. Pour fournir aux gestionnaires et promoteurs de ressources forestières des moyens de
prévention de pestes forestières, des stratégies de restauration et de conservation d’espèces
forestières, cette étude visait à évaluer la vulnérabilité de Khaya senegalensis aux changements
climatiques et à l’invasion de Hypsipyla robusta Moore au Bénin dans le temps et dans
l’espace ; puis analyser dans quelle mesure les changements climatiques peuvent aider à
restaurer et conserver Garcinia kola, une espèce éteinte à l’état sauvage au Bénin. À cette fin,
l’approche de MaxEnt pour la modélisation de la niche écologique a été utilisée pour identifier
et calculer les zones appropriées aux espèces cibles sous les climats actuels, et futurs issus des
RCP 4.5 et RCP 8.5 de la moyenne des scenarii d’AfriClim. Des données de présence de la
biodiversité ont été recueillies sur la base de données du « Global Biodiversity Information
Facility » (GBIF). Des analyses spatio-temporelles et des analyses gap ont été effectuées à
l’aide d’outils de système d’Information géographique. Dans le cas de K. senegalensis, les
projections issues des RCPs sur 2055 démontraient que l’espèce aura encore ses aires favorables
mais avec des pertes de certaines zones et des gains d’autres. La perte a été évaluée à 15-16 %
de la superficie totale du Bénin, tandis que le gain était de 2 à 3 % de cette superficie. En ce qui
concerne Hypsipyla robusta, les changements climatiques occasionneront seulement la perte
d’habitat d’environ 66 % de la superficie totale du pays. Ainsi, de nombreux sites de plantation
actuellement exposés aux attaques biologiques de l’insecte ne le seraient plus à l’avenir,
donnant de l’espoir pour la production de bois de Khaya senegalensis ayant une haute qualité.
Cependant, il y aura un déséquilibre écologique à la perte drastique d’habitat potentiel de
l’insecte. Il serait important d’élucider l’économie des attaques dans les plantations. Quant à
Garcinia kola, les résultats ont révélé que les changements climatiques se sont avérés n’avoir
que de conséquences positives sur sa répartition. En outre, considérant la Zone de Haute
Confiance de Prédiction (HCPA), le pourcentage des municipalités prédites favorables à
l’espèce est bien supérieur au pourcentage du Réseau d’Aires Protégées (PAN) ainsi prédites
(7,44 % contre 0,93 %). Les RCP4.5 et RCP8.5 de moyenne des scenarii ont indiqué
respectivement 3,00 % et 6,27 % du PAN comme zones positivement impactées par les
changements climatiques. Quant aux municipalités, ils en étaient respectivement de 13,60 % et
17,60 %. Par conséquent, il faut se baser non seulement sur les Aires Protégées, mais aussi et
surtout sur la foresterie urbaine et le reboisement pour restaurer et conserver l’espèce.
Cependant, d’autres études se focalisant sur l’introduction de Garcinia kola dans les zones
urbaines et son utilisation pour le reboisement sont obligatoires.
Mots clés : Khaya senegalensis, Hypsipyla robusta, Garcinia kola, Modélisation de Niche
Ecologique, invasion de pestes forestières, Changement climatique.
vi
ACKNOWLEDGEMENT
The completion of this study has been possible thanks to the help of the Supreme Being and
those of several people to whom I express my profound gratitude. I am mainly eager to mention:
Prof. Dr. Ir. Jean Cossi GANGLO, whose sense of understanding, goal oriented
actions, perfection, rigor, objectivity, and love for scientific research built up and shaped
not only my scientific research skills, but also my predisposition to collaborative and
social life throughout his supervision. Thank you for your invaluable help, you are
probably the best advisor a student could have.
Dr. Lizanne ROXBURGH, and Professor A. Townsend PETERSON, who inspired
me during my internships and training in Biodiversity Informatics, and Ecological
Niche Modelling at the Endangered Wildlife Trust (EWT, South Africa), and at the
Biodiversity Institute of the University of Kansas (USA). Thanks for the transfer of
knowledge that made this achievement possible.
My adoring parents, those fantastic people who gave me the value of life, Mr. Basile
DJOTAN, and Mrs. Marianne GLOTO DJOTAN. Thanks for your endeavor for
raising me up to where I couldn’t reach alone. You helped me grow up in a lovely
environment, educated me as no one else could. You gave me a chance to go beyond
your academic achievements. I am proud of you, and I promise you to reach the top
level of our dreams.
My lovely sisters, Edwige, Sabine, Anette, Lydie, and Bénidicte. You accepted my
absence, and my lack of attention in some circumstances where I might be there to guide
you, I am so happy that you understood the necessity of this absence, of being so far
away from you. This achievement is a tissue to wipe away your tears of not being there
for you.
Those kind people, Jean-Marie NOBIME and Roméo GBAGUIDI, who always
showed their availability to hear my worries, and support me various ways.
The teaching staff of the Faculty of Agronomic Sciences for helping me enhance my
knowledge in forest sciences, and my friends for time we spent together.
I would like to express my profound gratitude to those international institutions to name GBIF
and BID, which supported my researches, in collaboration with the Laboratory of Forest
Science of the University of Abomey-Calavi.
vii
DEDICACE
I bestow this work to my lovely darling Mélodie AGOKOLI for her patience, her advices and
encouragements.
viii
TABLE OF CONTENT
CERTIFICATION ..................................................................................................................... iii
ABSTRACT .............................................................................................................................. iv
RESUME .................................................................................................................................... v
ACKNOWLEDGEMENT ........................................................................................................ vi
DEDICACE .............................................................................................................................. vii
TABLE OF CONTENT .......................................................................................................... viii
List of figures ............................................................................................................................. x
List of tables ............................................................................................................................. xii
CHAPTER ONE: INTRODUCTION ................................................................................... - 1 -
1.1. Context .................................................................................................................... - 1 -
1.2. Statement of problems and justification ................................................................. - 2 -
1.3. Objective ................................................................................................................. - 3 -
1.3.1. Overall objective ............................................................................................. - 3 -
1.3.2. Specific objectives ........................................................................................... - 3 -
1.3.3. Hypothesis ....................................................................................................... - 3 -
CHAPTER TWO: MATERIAL AND METHODS .............................................................. - 4 -
2.1. Study areas .............................................................................................................. - 4 -
2.2. Biological material ................................................................................................. - 5 -
2.2.1. Khaya senegalensis Desr & Juss ..................................................................... - 5 -
2.2.2. Hypsipyla robusta Moore ................................................................................ - 6 -
2.2.3. Garcinia kola Heckel ...................................................................................... - 7 -
2.3. Environmental and presence data ........................................................................... - 8 -
2.4. Model fitting ......................................................................................................... - 10 -
2.5. Statistical analysis ................................................................................................. - 10 -
2.6. Spatial analysis ..................................................................................................... - 10 -
2.6.1. Khaya senegalensis and Hypsipyla robusta .................................................. - 10 -
2.6.2. Garcinia kola ................................................................................................. - 11 -
CHAPTER THREE: RESULTS ......................................................................................... - 11 -
3.1. Khaya senegalensis and Hypsipyla robusta ......................................................... - 11 -
3.1.1. Khaya senegalensis ....................................................................................... - 11 -
3.1.2. Hypsipyla robusta .............................................................................................. 15
3.1.3. Khaya senegalensis in interaction with Hypsipyla robusta over time and space
18
ix
3.1.4. Overall models outputs analysis for Benin ........................................................ 21
3.2. Garcinia kola ............................................................................................................. 21
3.2.1. Model validation ................................................................................................ 21
3.2.2. Suitable areas for the species.............................................................................. 23
3.2.3. Climate change impacts and conservation of Garcinia kola .............................. 23
CHAPTER FOUR: DISCUSSION, RECOMMENDATIONS AND CONCLUSION ........... 27
4.1. Discussion .................................................................................................................. 27
4.1.1. Biodiversity informatics and applications .......................................................... 27
4.1.2. Climate changes impacts on Khaya senegalensis .............................................. 27
4.1.3. Climate changes impacts on Hypsipyla robusta occurrence over time and space
29
4.1.4. Biological interactions between Khaya senegalensis and Hypsipyla robusta ... 30
4.1.5. Relevance of modeling Garcinia kola ecological niche with regard to stated
problems ............................................................................................................................ 31
4.1.6. Climate change and conservation or restoration of Garcinia kola in Municipalities
or in Protected Areas Networks ........................................................................................ 32
4.2. Perspectives and Recommendations .......................................................................... 33
4.2.1. Khaya senegalensis ............................................................................................ 33
4.2.2. Pest outbreaks in stands of Khaya senegalensis ................................................. 34
4.2.3. Garcinia kola ...................................................................................................... 34
CONCLUSION ........................................................................................................................ 36
REFERENCES ......................................................................................................................... 37
x
List of figures
Figure 1: Study areas ............................................................................................................ - 5 -
Figure 2: Illustration of young (a) and adult (b) individuals of Khaya senegalensis ........... - 6 -
Figure 3: Illustration of the red form larva (A), green form larva (B), adult specimen of
Hypsipyla robusta (C), and its damages on the wood (D) [adapted from Griffiths (1996)] . - 7 -
Figure 4: Young stem of Garcinia kola shot in seedlings .................................................... - 8 -
Cliché: Djotan, 2018 ............................................................................................................. - 8 -
Figure 5: Spatial distribution of Khaya senegalensis and Hypsipyla robusta in the landscape of
interest ................................................................................................................................... - 9 -
Figure 6: Study areas, species parts, and distribution of occurrences .................................. - 9 -
Figure 7: Jackknife of regularized training gain (Khaya senegalensis) ................................... 13
Figure 8: Receiver Operating Characteristic (Khaya senegalensis) ......................................... 13
Figure 9: Spatial distribution of Khaya senegalensis under current climate ............................ 13
Figure 10: Projected distribution of the species at horizon 2055 under RCP4.5 (Khaya
senegalensis) ............................................................................................................................ 14
Figure 11: Projected distribution of the species at horizon 2055 under RCP8.5RCP8.5 (Khaya
senegalensis) ............................................................................................................................ 14
Figure 12: projected distribution of climate change impacts on the species at the horizon 2055
under RCP4.5 (Khaya senegalensis) ........................................................................................ 14
Figure 13: projected distribution of climate change impacts on the species at the horizon 2055
under RCP8.5 (Khaya senegalensis) ........................................................................................ 14
Figure 14: Jackknife of regularized training gain (Hypsipyla robusta) ................................... 16
Figure 15: Receiver Operating Characteristic (Hypsipyla robusta) ......................................... 16
Figure 16: Distribution of Hypsipyla robusta at present ......................................................... 16
Figure 17: Projected distribution of Hypsipyla robusta at horizon 2055 under RCP4.5 ......... 17
Figure 18: Projected distribution of Hypsipyla robusta at horizon 2055 under RCP8.5 ......... 17
Figure 19: projected distribution of climate change impacts on the species at the horizon 2055
under RCP4.5 (Hypsipyla robusta) .......................................................................................... 17
Figure 20: projected distribution of climate change impacts on the species at the horizon 2055
under RCP8.5 (Hypsipyla robusta) .......................................................................................... 17
Figure 21: Current overlap (Khaya X Hypsipyla) ................................................................... 19
Figure 22: Overlap in 2055 RCP4.5 (Khaya X Hypsipyla) ...................................................... 19
Figure 23: Overlap in 2055 RCP8.5 (Khaya X Hypsipyla) ...................................................... 19
xi
Figure 24: Response of K. senegalensis to the mean temperature of the coolest quarter ........ 20
Figure 25: Response of K. senegalensis to isothermality ......................................................... 20
Figure 26: Response of H. robusta to mean temperature of the coolest month ....................... 20
Figure 27: Response of H. robusta to the rainfall of the driest quarter .................................... 20
Figure 29: Jackknife of regularized training gain (Garcinia kola) .......................................... 22
Figure 28: Receiver Operating Characteristic (Garcinia kola) ................................................ 22
Figure 30: Distribution of predicted suitable areas for Garcinia kola from current scenario to
scenarios of 2055s .................................................................................................................... 22
Figure 31: Protected Areas Network along with climate change impact, with regard to suitability
areas for Garcinia kola ............................................................................................................. 26
Figure 32: Climate change impacts on the distribution of suitable areas of Garcinia kola across
municipalities and across protected areas network. ................................................................. 26
Figure 33: Distribution of forest pest Hypsipyla robusta outbreaks under current climate ..... 35
Figure 34: Distribution of climate change impacts zones across Benin for Khaya senegalensis,
and pest outbreak under future climate .................................................................................... 35
xii
List of tables
Table 1. Variables contribution to Khaya senegalensis model ................................................ 13
Table 2. Variables contribution to Hypsipyla robusta model .................................................. 16
Table 3. Variables contribution to Garcinia kola model ......................................................... 22
Table 4. Distribution of suitable areas and climate change impacts across protected areas (areas
in Km²) ..................................................................................................................................... 24
Table 5. Distribution of suitable areas and climate change impacts across municipalities (areas
in Km²) ..................................................................................................................................... 24
1
CHAPTER ONE: INTRODUCTION
1.1. Context
Ecosystem services” refer to conditions and processes through which natural ecosystems, and
species that make them up, sustain and fulfill human life (Daily, 1997). Those conditions and
processes maintain biodiversity and the production of ecosystem goods, such as seafood,
forage, timber, biomass fuels, natural fiber, and many pharmaceuticals, industrial products, and
their precursors (Daily, 1997). Ecosystem Services, that can be maintained and enhanced by
human (Comberti et al., 2015), are also widely understood as the “benefits that humans receive
from the natural functioning of healthy ecosystems” (Jeffers et al., 2015). Overall, twenty-three
(23) standardized ecosystem functions, grouped into four (4) categories to name regulation,
habitat, production, and information functions provide a much larger number of goods and
services (de Groot et al., 2002).
Life on Earth depends on forest ecosystems services (Shvidenko et al., 2005; Waylen, 2006;
Pamlin & Armstrong, 2015). Those services are dynamics over time and space (Myers et al.,
2009). This dynamism is expressed through the scarcity of some resources that are beforehand
abundant but no more actually (Neuenschwander et al., 2011), the disappearance of some
resources that existed formerly but now extinct (Pimm and Raven, 2000), the shift of organisms’
potential ecological range (McClean et al., 2005) causing damages to resources that people
need (Sokpon and Ouinsavi, 2004), and the harshness of environmental conditions (IPCC,
2007; Hellmuth et al., 2007; Christensen et al., 2007; Massemin, 2015) required for peaceful
life. Climate change is part of causes of the shifts observed in the ecological ranges of species
(McClean et al., 2005). Insects are not safe from those shifts, and when it occurs, insects most
of the time appear in areas where they become pests for forest resources causing forest pest
invasion (IUFRO, 2010), destroying the quality and the quantity (Griffiths, 2001; Botha et al.,
2004; IUFRO, 2010) of forest production.
Methods for identification of areas possessing 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; Booth et al., 2014; Ganglo et
al., 2017). Known globally as Biodiversity Informatics, the domain of computation include
Ecological Niche Modelling that has many useful applications. Its use (Gbesso et al., 2013;
Saliou et al., 2014, Ganglo et al., 2017, Gbètoho et al., 2017) provided huge importance in
conservation and sustainable management of natural resources.
2
1.2. Statement of problems and justification
To provide growing populations with fuel woods, service woods, lumbers, and other forest
products while keeping at low rate the forest degradation under tropics, governments released
incentives for establishing and maintaining plantations all over the world (Piotto et al., 2003;
Parsons et al., 2006; Nikles et al., 2008; Foli et al., 2009). In Benin, great plantation efforts
have been made (Ganglo & De Foucault, 2006) between 1953 and 1959 with Tectona grandis
L.f. and Gmelina arborea Roxb., exotic species because of the failure of native species
plantations (Akpona et al., 2016). The necessity to produce high quality wood for the market
while conserving local biodiversity recalled for the introduction of indigenous tree species.
In Benin, Khaya senegalensis Desr. & Juss. was reconsidered in reforestation and plantation
projects since 2005 (Akpona et al., 2016). The introduction has started with the colonial
administration in 1935 where test plantations (Toffo, Atchérigbé, Kandi, Kouandé, Tanguiéta),
school plantations (Birni, Kouaba) and road plantations had been established (Akpona et al.,
2016). A high-value hardwood species (Sokpon et al., 2004), selectively logged (Glèlè Kakaï
and Sinsin, 2009), K. senegalensis is subject to forest pest’s attacks, specifically from Hypsipyla
robusta Moore (Griffiths, 2001; Sokpon et al., 2004) in the sub-region.
Another high value species is Garcinia kola (Heckel). Known as “bitter kola”, it is one of
multiple non-timber forest products that is of socio-economic importance in Benin
(Akoegninou et al., 2006; Assogbadjo et al., 2017) and in the Sub region (Yakubu et al., 2014).
G. kola is essentially planted for its seeds in the West African region, and belongs to the top ten
priority Non-Timber Forest Products in Benin (Assogbadjo et al., 2017). G. kola is also a
medicinal woody tree species that provides active compounds for the treatment of many
diseases (Esimone et al. 2002; Farombi et al., 2005). It occupies the third rank of medicinal
plants in Benin in terms of number of recipes in which the species is incorporated (Souza de,
2001). The species belongs to the Benin red list of the International Union for the Conservation
of the Nature (IUCN), and has been listed since 2011 as extinct in the wild (Neuenschwander
et al., 2011). Codjia et al. (2018) also confirmed its extinction from the naturally occurring state
in Benin. Garcinia trees are also currently decreasing in countries where it exists such as
Nigeria, as result of deforestation (Babalola and Agbeja, 2010). It is then urgent to drive
attention on a spatially and timely successful establishment of Garcinia plantation in Benin.
In addition to specific issues previously described for each species, it was also found that
climate changes shift the ecological range of species over time and space (McClean et al.,
2005), threaten biodiversity conservation areas (Araujo et al., 2011), and modify effects of
3
forest diseases on forest ecosystems (Sturrock et al., 2011). With regard to that, monitoring,
forecasting, planning and mitigation are proposed as forest and disease management tactics
(Sturrock et al., 2011). Ecological Niche Modelling and Gap Analysis are then relevant tools
to check for new opportunities that climate variabilities offer over time and space. Their use
will help not only in the management of the vulnerability of Khaya senegalensis with regard to
climate changes and forest pest invasion, but also in assessing possibilities of restoration,
conservation, and production of Garcinia kola in Benin.
1.3. Objective
1.3.1. Overall objective
The overall objective of the study is to inform natural resource managers, policy makers,
decision makers, private planted forest promotors, and conservationists on the threats and
possibilities offered by climate changes and its related consequences on the studied species, so
that they can incorporate the impacts of global changes as well to reforestation policies and
strategies of species valorization, as to the management of protected areas in the country.
1.3.2. Specific objectives
Specific objectives (SO) pursued by the study are as follow:
SO1: To assess the vulnerability of Khaya senegalensis to climate change and to the invasion
of Hypsipyla robusta in Benin over time and space; and
SO2: To analyze how far climate changes can help to restore and conserve Garcinia kola, an
extinct species in the wild in Benin over time and space.
1.3.3. Hypothesis
Hypothesis (H) that withstood the objectives of the study were as follow:
H1: As climate changes shift the ecological range of species over time and space (McClean et
al., 2005), threaten biodiversity conservation areas (Arujo et al., 2011), and modify effects of
forest diseases on forest ecosystems (IUFRO, 2010; Sturrock et al., 2011), it is possible that the
vulnerability of Khaya senegalensis to the invasion of Hypsipyla robusta (Griffiths, 2001;
Sokpon et al., 2004) varies over time and spaces.
H2: Shifts in the species ecological range over time and space (McClean et al., 2005) can incur
as well extension or regression of potentially favorable distribution areas for a given species
(Idohou et al., 2016). So, climate changes could help to restore, conserve and produce Garcinia
kola, an extinct species in the wild (Neuenschwander et al., 2011; Codjia et al., 2018) in Benin.
4
CHAPTER TWO: MATERIAL AND METHODS
2.1. Study areas
The current study was carried out in Benin, a country of about 115,762 km² (Hounkpe, 2013),
bordered by Togo to the West, Nigeria to the East, Burkina Faso and Niger to the North and the
Atlantic Ocean to the South (Figure 1). The country is stretched from latitudes 6
o
14’N and
12
o
25’N, and between longitudes 0
o
46’E and 3
o
51’E (DIVA-GIS, 2018). Benin encompasses
three main Agro Climatic Zones ranging from humid to semi-arid (Sinsin et al., 2004). Those
zones were classified as Guineo-Congolean zone (humid), Sudano-Guinean transition zone
(sub-humid), and Sudanian zone (semi-arid) by White (1986) following climatic patterns.
Guineo-Congolean and Sudano-Guinean transition zones are characterized by a tropical humid
climate with two rainy seasons (March-July and September-November) where the rainfall
maxima vary between 900 mm an 1110 mm annually (Adomou, 2005, Neuenschwander et al.,
2011). The relative humidity varies between 31 % and 98 % and the mean annual temperature
varies between 25°C and 29°C. As for the Sudanian zone, the climate is tropical with one rainy
season covering May to October and a long dry season covering November to May. The rainfall
maxima are obtained in June with about 1200 mm (Adomou, 2005; Neuenschwander et al.,
2011). The relative humidity varies from 18 % to 99 %, and the temperature from 24°C to 31°C.
Native vegetation in the Guineo-Congolean Zone (6°14’– 7°30’N) comprises fallows and small
forest patches, while in the transition zone (7°30’– 9°45’N), there are mosaics of woodlands.
As for the Sudanian Zone (9°45–12°25’N), native vegetation includes savannas and gallery
forests with trees and shrubs slightly covering the ground (Sinsin et al., 2004).
5
Figure 1: Study areas
2.2. Biological material
2.2.1. Khaya senegalensis Desr & Juss
K. senegalensis Desr & Juss is one of the most important tree species in the Meliaceae family
in West Africa. The species is native to Benin, Burkina Faso, Cameroon, Central African
Republic, Chad, Ivory Coast, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Mali, Niger,
Nigeria, Senegal, Sierra Leone, Sudan, Togo, and Uganda (Nikiema and Pasternak, 2008; Orwa
et al., 2009). Its common names are Cailcédrator Acajou d’Afriquein French, African
Mahogany” or “Dry zone Mahoganyin English, and “Acao” in Fon (local national dialect of
Benin). 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 doesn’t tolerate shade (Sokpon and Ouinsavi, 2002).
6
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 Agro
Climatic Zones 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 then how great
its importance is in pharmacy or medicine for people’s health, and ethnobotany. Figure 2 shows
below young and adult individuals of the species.
Figure 2: Illustration of young (a) and adult (b) individuals of Khaya senegalensis
2.2.2. Hypsipyla robusta Moore
Hypsipyla robusta Moore 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 Meliaceae family
(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, occurring through areas of Africa and the Asia/Pacific region (Griffiths, 2001).
The Caterpillars of this moth vary from reddish through grey to greenish. The head is dark
brown, and there is a black patch on the prothorax. The body has raised black spots on each
2a
March 5
th
, 2018
2b: PROTA,
December
3
rd
,
2008
7
segment. The caterpillars destroy the apical shoot, leading to side branching, and ultimately, a
deformed trunk. It is possible that the caterpillars prefer to eat the pith in the stem rather than
eating the leaves because the plant releases in the leaves, chemicals that deter phytophagous
insects (Griffiths, 1996). Figure 3 below illustrates the moth and its destruction pattern.
Figure 3: Illustration of the red form larva (A), green form larva (B), adult specimen of
Hypsipyla robusta (C), and its damages on the wood (D) [adapted from Griffiths (1996)]
2.2.3. Garcinia kola Heckel
Garcinia kola Heckel (Clusiaceae), commonly known as “bitter kola” is a medium-sized and
shade-tolerant tree with a cylindrical trunk that is slightly buttressed to the ground. It is endemic
to the humid lowland rainforest vegetation of the West and Central African sub regions, and is
found in coastal areas and lowland plains up to 300 m above sea level with an average of 2000-
2500 mm of annual rainfall, with temperatures ranging from 21.4 to 32.15 °C (Ntamag, 1997).
Its geographical distribution area extends from Congo to Sierra Leone (Vivien and Faure,
1985). The species is characterized by a low growth rate (Eyog-Matig et al., 2007), difficult
seedlings raising in nurseries, and a long gestation period before flowering and fruiting
(Adebisi, 2004). The effective dormancy-breaking recommended by Eyog-Matig et al. (2007)
were decoated seeds soaked in cold water, coated seeds placed in banana false trunk and coated
seeds soaked in pineapple juice where germination started 95, 102 and 110 days respectively
8
from sowing, with 130-141 days as mean germination times. However, it is possible to produce
stems by vegetative propagation (Yakubu et al., 2014; Kouakou et al., 2016). Ladipo (1995)
found that a mature tree can produce 85 to 1,717 fruits, with 208 to 6,112 seeds annually, mean
values being 834 fruits and 2,627 nuts per tree. Figure 4 shows below a young stem of Garcinia
kola.
Figure 4: Young stem of Garcinia kola shot in seedlings
Cliché: Djotan, 2018
2.3. Environmental and presence data
The area of interest for the present work is Benin. However, modeling across countries within
the sub-region was made for best results applicable in Benin (Fitzpatrick et al., 2009). Figures
5 and 6 show the spatial distributions of the species downloaded occurrences in the landscape
of interest. Those presence data have been downloaded on the site of Global Biodiversity
Information Facility (GBIF) for each species.
Current and future climates related environmental data were gathered for modeling potentially
suitable areas of species. Those environmental data were obtained from the website of AfriClim
at https://webfiles.york.ac.uk/KITE/AfriClim/GeoTIFF_150s/baseline_worldclim/ (Platts et
al., 2015) at the resolution 2.5 minutes (150s); format GeoTIFF at the extent of Africa. Those
layers belong to the climate timescale 1950-2000 (Hijmans, 2005), representing data for
modelling currently suitable areas for a given species or a set of living forms. As for projection
in the future, bioclimatic variables built under realistic representative concentration pathways
were also downloaded from AfriClim at
https://webfiles.york.ac.uk/KITE/AfriClim/GeoTIFF_150s/africlim_ensemble_v3_worldclim/
9
(Platts et al., 2015). The file set used was the ensemble v3 worldclim, containing the mean
value of AfriClim’s scenarii. The Representative Concentration Pathways 4.5 that is realistic
and optimistic (Meinshausen et al, 2011), and the Representative Concentration Pathways 8.5
that is realistic and pessimistic (Meinshausen et al, 2011), at the horizon 2055 were used for
projection of models to the future. AfriClim website was used because climate experts had set
up data with regard to the climate specificity and ecological realities in Africa (Platts et al.,
2015), so that the use of those data lead to meaningful results on the continent.
Figure 5: Spatial distribution of K. senegalensis and H. robusta in the landscape of interest
(Djotan, 2018)
Figure 6: Study areas, species parts, and distribution of occurrences (Djotan, 2018)
10
2.4. Model fitting
Maximum Entropy modelling (MaxEnt) has been used to model the potentially favorable areas
for species of interest under present and future climates (Philips et al., 2006; Pearson et al.,
2007). Future predictions were done using the optimistic Representative Concentration
Pathways 4.5 (RCP 4.5) and the pessimistic one (RCP 8.5), that are both realistic (Meinshausen
et al, 2011) at horizon 2055. For each RCP, the ensemble mean v3 model (Platts et al., 2015)
was used. As for the MaxEnt parameters, default values were used as recommended by Dossou
et al. (2016). But in addition to default parameters of MaxEnt, 25 percent of presence points
have been randomly set as test points. Moreover, based on our knowledge on the sampling
effort in each of West African Countries, a bias file have been defined for each species. Models
have been run step by step, and at each run based on cross-validation sampling, variables that
have to be excluded were selected. By the ways, the most explanatory variables in the
distribution of the species were kept while giving each variable a reasonable chance to show its
importance in the species’ distribution model building. With the most representative variables,
one next model had been run using bootstrap as sampling method with 10 replications.
2.5. Statistical analysis
Statistics helped in selecting variables that may be included in the model. Those statistics were
twofold. First group of statistics were those computed by the modelling algorithm itself
(MaxEnt). It included 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 included 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 and
biology of the species. Classification thresholds were selected in the table of threshold of
MaxEnt outputs based on the objectives of our study.
2.6. Spatial analysis
2.6.1. Khaya senegalensis and Hypsipyla robusta
Geographic Information systems (GIS) and its related tools have been used to process and
present the results of the modeling. The threshold values from the results generated by the last
cross validation sampling based MaxEnt model, were used to define the suitable areas, and the
degree of this suitability across the country. Those thresholds were applied to the mean value
of logistic probability distributions obtained from 10 replicates bootstrap based MaxEnt model.
11
The suitable areas were split into two classes: highly suitable and suitable according to values
of the probability with reference to defined thresholds. Here, two thresholds were used, the “10
percentile training presence” (1
st
thr) used by Fandohan et al. (2015) and the “Equal test
sensitivity and specificity” (2
nd
thr) used by Ganglo et al. (2017). So we had unfavorable zone
(logistic probability below the 1
st
thr), suitable zone (logistic probability between 1
st
thr and 2
nd
thr), and highly suitable zone (logistic probability above 2
nd
thr). Microsoft Excel 2016 and
QGIS Wien 2.16.3 were used to compute environmental preferences of the species on the basis
of the most important variables previously retained. The process was done both for Khaya
senegalensis and Hypsipyla robusta, whose suitable areas were later overlaid. Areas classified
as areas at risk of biological attacks have been identified and corresponded to areas potentially
very favorable to both species. Those analysis were also done using future climates models,
pointing out the impact of climate change over time and space on both species.
2.6.2. Garcinia kola
The logistic probability corresponding to the threshold “10 percentile training presence” was
chosen from the last cross validation sampling based MaxEnt model to classify using
Geographical Information Systems (GIS), the areas as suitable or unsuitable. The suitable areas
are partitioned into two classes: highly suitable when the probability of presence is higher than
or equal to 0.5 and suitable when the probability is between the threshold and 0.5. The “10
percentile training presence” corresponds to the logistic probability above which, when
sampled to grid values, the 10 percent least suitable presence points can’t fall. This threshold
classified fairly the continuous maps for Garcinia kola. The model used for the classification
was the average of the ones that came from the bootstrap sampling with 10 replications. The
high confidence prediction areas (HCPA) was defined, and corresponded to areas where a
model for current climate, and both models (RCP 4.5 and RCP 8.5) for future climate, revealed
favorable conditions for the species. The HCPA was used to recommend restoration and
conservation actions in favor of Garcinia kola. Quantum GIS was used to perform gap analysis
across Benin’s protected areas network and municipalities. The proportion of protected areas
and the proportion of municipalities that were shown to be in the HCPA were computed and
compared. The same computations were done to assess the climate change impact on the
species’ potentially suitable areas.
CHAPTER THREE: RESULTS
3.1. Khaya senegalensis and Hypsipyla robusta
3.1.1. Khaya senegalensis
12
Model evaluations indicated that model for Khaya senegalensis was robust and yielded
predictions statistically significant, better than random. The training data AUC (Area Under the
Curve) 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 (Receiver Operating Characteristic) 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). 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 that the prediction of the model was accurate. The distribution of the species in
the landscape of interest is shown on Figure 9, indicating that both species showed good
distribution pattern across that landscape.
Table 1 and Figure 7 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 most the distribution of the species. High
temperatures in coolest quarter are unfavorable for Khaya senegalensis (Figure 24). It tolerates
extremes values of isothermality and values between 57.5 °C and 67.5 °C are critical for the
species (Figure 25). Figures 8, 9, 10, 11, 12 and 13 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
7) 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 were above 0.90 (Figure 8). 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 9, 10 and 11). For climate
change impacts on the species, Figures 12 and 13 show stable areas in green, positive impact
areas in violet, and negative impact areas in red.
13
Table 1. Variables contribution to Khaya senegalensis model
Variable
Percent contribution (%) Permutation importance (%)
Bio11 31.1
26.9
Bio3 20.8
21.7
Bio7 18.5
9.3
Bio6 10.7
14
Bio14 9.8
8.1
Bio16 9.2
20.1
Figure 7: Jackknife of regularized training gain (Khaya senegalensis)
Figure 8: Receiver Operating Characteristic (Khaya senegalensis)
Figure 9: Spatial distribution of Khaya senegalensis under current climate
14
Figure 10: Projected distribution of the species at horizon 2055 under
RCP4.5 (Khaya senegalensis)
Figure 11: Projected distribution of the species at horizon 2055 under
RCP8.5RCP8.5 (Khaya senegalensis)
Figure 12: projected distribution of climate change impacts on the
species at the horizon 2055 under RCP4.5 (Khaya senegalensis)
Figure 13: projected distribution of climate change impacts on the species
at the horizon 2055 under RCP8.5 (Khaya senegalensis)
15
3.1.2. 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 were, with the selected
variables respectively 0.902 and 0.880. The mean test data AUC was 0.856 and the standard
deviation was 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 was stable.
Table 2 and Figure 14 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 26) and needs a
minimum of rainfall in driest quarter (Figure 27). Figures 15-20 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 at the horizon 2055 under RCP4.5, and projected distribution of climate
change impacts on the species at the horizon 2055 under RCP8.5.
The contribution table and the jackknife chart of regularized training gain (Table 2 and Figure
14) 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 well the training and test
data AUCs are above 0.90 (Figure 15). 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 16, 17 and 18). For climate
change impacts on the species, figures 19 and 20 show stable areas in green, positive impact
areas in violet, and negative impact areas in red.
16
Table 2. Variables contribution to Hypsipyla robusta model
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
Figure 14: Jackknife of regularized training gain (Hypsipyla robusta)
Figure 15: Receiver Operating Characteristic (Hypsipyla robusta)
Figure 16: Distribution of Hypsipyla robusta at present
17
Figure 17: Projected distribution of Hypsipyla robusta at horizon 2055
under RCP4.5
Figure 18: Projected distribution of Hypsipyla robusta at horizon 2055 under
RCP8.5
Figure 19: projected distribution of climate change impacts on the
species at the horizon 2055 under RCP4.5 (Hypsipyla robusta)
Figure 20: projected distribution of climate change impacts on the species at the
horizon 2055 under RCP8.5 (Hypsipyla robusta)
18
3.1.3. Khaya senegalensis in interaction with Hypsipyla robusta over time
and space
It was revealed that Khaya senegalensis and Hypsipyla robusta share four variables out of the
six most contributing ones retained. Those variables were 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 time and space. Figures 21-23 show respectively the current overlaps
between the two species, overlaps in 2055 RCP4.5, and overlaps in 2055 RCP8.5.
19
Figure 21: Current overlap (Khaya X Hypsipyla)
Figure 22: Overlap in 2055 RCP4.5 (Khaya X Hypsipyla)
Figure 23: Overlap in 2055 RCP8.5 (Khaya X Hypsipyla)
For maps showing overlaps, there are three different types of
areas: areas where only Khaya senegalensis was projected to
prosper (green color on the maps); areas where only Hypsipyla
robusta was projected to find its preference (red color on the
maps); and areas where both species are likely to occur (violet
on the maps).
20
Figure 24: Response of K. senegalensis to the mean temperature of the
coolest quarter
Figure 25: Response of K. senegalensis to isothermality
Figure 26: Response of H. robusta to mean temperature of the coolest
month
Figure 27: Response of H. robusta to the rainfall of the driest quarter
21
3.1.4. 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 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’s territory. 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 and 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 (figures 33 and 34).
3.2. Garcinia kola
3.2.1. Model validation
The Area Under the Curve (AUC) associated to the model was 0.941 while the one associated
to its test was 0.936 (Figure 28). The “10th percentile training presence” threshold gave a value
of 0.275, 0.000 for test omission rate and 0.098 for the training omission rate. The True Skill
Statistic (TSS) calculated with that threshold gave a value of 0.790. As for the Partial ROC, the
minimal ratio was 1.01 and the maximal one was 1.15. All ratios on 1000 iterations were well
above 1. These statistics indicated that our models were very good, predictive and performed
better than random. The value of the standard deviation among runs was 0.032 for the AUC
test, showing that the model is stable and didn’t fluctuate randomly. Variables bio 17, 6, 2, 3,
and bio 4 were the most significant among bioclimatic variables we input in the algorithm
MaxEnt (Figure 29). Bio 17 is the rainfall of the driest quarter (mm); Bio 6 is the minimal
temperature of the coolest month (°C x10); Bio 2 is the mean diurnal range in temperature (°C
x10); Bio 3 is the isothermality (°C x10); and Bio 4 is the temperature seasonality (°C x10).
Table 3 shows the contribution of retained variables.
22
Table 3. Variables contribution to Garcinia kola model
Variable Percent
contribution Permutation
importance
rainfall of the driest quarter (mm) 74.3 63
minimal temperature of the coolest month (°C x10) 18.5 1.6
mean diurnal range in temperature (°C x10) 3.6 2.8
isothermality (°C x10) 2.9 29.5
temperature seasonality (°C x10) 0.8 3.1
Values are in percentages
Figure 29: Jackknife of regularized training gain (Garcinia kola)
Figure 28: Receiver Operating Characteristic (Garcinia kola)
Figure 30: Distribution of predicted suitable areas for Garcinia kola
from current scenario to scenarios of 2055s
23
3.2.2. Suitable areas for the species
Considering the results that we obtained from the models, the evolution of the climate is in
favor of the extension of the climate envelop-based potential ecological niche of Garcinia kola.
In fact, it was found from the present scenario that the favorable areas range from the coastal
areas of Benin (Southern part) to the latitude of Zogbodomè (6.95
o
N). Meanwhile, a projection
in future at 2055 using RCP4.5 revealed that the species can enlarge its potentially suitable
areas from the coastal areas to the latitude of center Glazoué (8.19
o
N). We found that it was
much better according to the projection we did with the RCP8.5, a pessimistic scenario. This
latter showed us that the species can have its favorable areas extended beyond the latitude at
the center of Glazoué, it is projected to reach the upper latitude of Glazoué and the beginning
of the municipality of Bassila (8.56
o
N). So, we can rely on our estimate saying that climate
change is in favor Garcinia kola species in Benin, whether we consider either the optimistic or
the pessimistic scenario. Moreover, whether a scenario is pessimistic or optimistic depends then
on the species that we consider and the landscape as well. Figure 30 shows more details on the
maps.
3.2.3. Climate change impacts and conservation of Garcinia kola
3.2.3.1. Protected areas network
Areas that were projected to belong to the HCPA encompass four (4) protected areas (Table 4).
But the projection using RCP 4.5 at 2055s gave suitable areas that cover eleven (11) more
protected areas as results of positive climate change impacts (Table 4). As for the one using
RCP 8.5 at 2055, twelve (12) more protected areas were found in addition to the stable one, as
results of positive climate change impacts (Table 4). We also remarked that there were no areas
with negative climate change impacts. Figure 31 shows more details of the climate change
impacts on the distribution of suitable areas for Garcinia kola across protected areas network
(PAN).
3.2.3.2. Municipalities
Many municipalities fell into the potentially suitable areas for Garcinia kola, equally well under
current climate as under the future’s one with both RCPs. The trend was the same as the one
observed in the distribution of the suitable areas across PAN over time and space. Overall forty
(40) municipalities belonged to the HCPA (Table 5). The changes in the climate borne by the
RCP 4.5 showed twenty-four (24) more suitable municipalities for the species as positive
impacts of climate change (Table 5). As for the information we obtained by the RCP 8.5 at
2055, 26 more municipalities were added to those that were suitable with all scenarios together
(Table 5). No negative climate change impacts were indicated. The percentage of municipalities
24
that were suitable for the species is far above the percentage of protected areas network that
were predicted as suitable (7.44% versus 0.93%) when we consider the domain of maximal
prediction confidence (HCPA) (Figure 32). The trend remains similar with the RCPs considered
individually (Tables 4-5).
Table 4. Distribution of suitable areas and climate change impacts across protected areas
(areas in Km²)
Protected Areas Suitable Areas / Climate change impacts
All Scenarios RCP4.5 RCP8.5
FC Agoua 0 44.46 691.19
FC Atcherigbe 0 31.13 31.13
FC Dassa-Zoume 0 32.57 32.57
FC Dogo 0 319.88 319.88
FC Ketou 0 129.95 129.95
FC Logozohe 0 26.1 26.1
FC Monts Kouffe 0 0 221.45
FC Oueme Boukou 0 0 231.88
FC Oueme-Boukou 0 231.88 0
FC Savalou 0 14.35 14.35
FC Setto 0 13.03 13.03
FC Toui-Kilibo 0 0 51.94
FC Pahou 8.59 - -
FC Agrime 27.59 - -
FC Djigbe 47.22 - -
FC Lama 178.39 - -
Total 261.79 843.35 1763.47
Percentage of total PAN 0.931486514 3.000760731 6.274680176
Table 5. Distribution of suitable areas and climate change impacts across municipalities
(areas in Km²)
Municipalities Suitable areas / Climate change Impacts
All Scenarios RCP4.5 RCP8.5
Bante 0 473.45 2432.01
Bassila 0 0 199.22
Boukounbe 0 0 21.04
Cove 0 443.54 443.54
Dassa 0 1715.84 1715.84
Djidja 0 2181.85 2203.02
Glazoue 0 1111.81 1781.67
Ketou 0 1764.53 1764.53
Savalou 0 2311.23 2688.29
Save 0 1845.39 2236.07
Tchaourou 0 213.96 1260.49
Zangnanado 0 536.04 536.04
Abomey 4.02 146.73 146.73
25
Za-kpota 4.37 400.29 400.29
Aplahoue 4.43 970.82 970.82
Ouinhi 9.1 287.27 287.27
Bohicon 24.29 132.59 132.59
Klouekanme 25.5 398.89 398.89
Agbangnizoun 46.12 144.84 144.84
Porto-Novo 50.85 - -
Adjara 61.88 - -
Cotonou 70.57 - -
Pobe 73.93 303.09 303.09
Avrankou 85.54 - -
Toviklin 95.65 38.41 38.41
Akpro-Misserete 95.7 - -
Djakotome 113.5 96.65 96.65
Seme-Kpodji 137.44 - -
Aguegue 150.55 - -
Dangbo 159.27 - -
Come 162.28 - -
Athieme 163.79 - -
Ifangni 170.06 - -
So-Ava 176.93 - -
Adja-Ouere 181.36 273.07 273.07
Grand-Popo 220.65 - -
Dogbo-Tota 262.71 - -
Ouidah 264.55 - -
Bonou 267.6 3.91 3.91
Houeyogbe 296.31 - -
Adjohoun 306.26 - -
Kpomasse 309.04 - -
Lokossa 321.27 - -
Tori-Bossito 334.64 - -
Bokpa 387.01 - -
Allada 397.63 - -
Lalo 422.1 0.92 0.92
Sakete 431.76 - -
Abomey-Calavi 488.03 - -
Toffo 547.35 - -
Ze 687.04 - -
Zogbodome 695.52 120.19 120.19
Total 8706.6 15915.31 20599.43
Percentage of total municipalities 7.440585812 13.60108766 17.6040965
26
Figure 31: Protected Areas Network along with climate change impact, with regard to
suitability areas for Garcinia kola
Figure 32: Climate change impacts on the distribution of suitable areas of Garcinia kola
across municipalities and across protected areas network.
27
CHAPTER FOUR: DISCUSSION, RECOMMENDATIONS AND
CONCLUSION
4.1. Discussion
4.1.1. 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 a tree species and its insect pest in order to point out the impacts
of the insect (Hypsipyla robustaI), 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 prediction of interactions between species other time and space. On the other side, we
explored the possibilities offered by climate changes for the restoration, the conservation, and
the production of Garcinia kola. Here again, we focused our attention on the positive impacts
of climate changes, while most of research papers tried to find out only its negative impacts.
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. Such correlation
between daily phenomena point out the applications of this field of study in health and security
related domains, and in biology as well. 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 (L.) R. M. King & H. Rob. under current
and future climates. Such studies raise awareness of people on the dangers our resources are
and will be exposed to, and give a range of solutions for their conservation according to the
obtained results. 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. It is also a prospective study
aiming at searching for areas favorable to the restoration of a species that is extinct in the wild
in Benin.
4.1.2. Climate changes impacts on Khaya senegalensis
28
The Beninese districts that didn’t meet fully Khaya senegalensisenvironmental 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, considered as dry areas savanna mahogany tree (Nikiema et al., 2008). Kandi is shown
highly suitable for Khaya, this shows the evidence that the model did 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
also have played a key role in 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 zones 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 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 accounted for 3% (RCP4.5) and 2% (RCP8.5) of Benin area,
while the significantly negative impacts zone accounted 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
29
species (Rosenzweig et al., 2008) and many more are expected to shift with future climate
change. Unfortunately, few of them had pointed 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
those resources to climate changes over time and space. Anticipating likely effects of climate
changes on species distribution (Peterson et al., 2011) and transferring prediction models 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 over time and space, 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.
4.1.3. 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. Then, the country is supposed to be exposed to the species’ presence.
This presence could be driven by the suitability of the areas to its host plant Khaya senegalensis
30
since the species is known to be 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 areas of distribution. 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 upon 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. However, the significantly negative impacts zone will account for 66% regardless
the Representative Concentration Pathways (RCP). Any area didn’t show up 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. The matters are what the species is, what the spatial scale is and what the
used scenario is. In instance, 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 opposing shift is going to be observed for Hypsipyla robusta upon 2055. In
fact, Hypsipyla robusta, seen as natural resource will suffer from these changes. However, a
planted forest instigator who is willing to settle plantation of Khaya senegalensis will consider
that future climatic conditions provide best environments for his business.
4.1.4. 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 emerged 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 zones
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
those sites. A projection to the 2055s showed for both scenarii (RCP4.5 and RCP8.5) that almost
no more areas will be available for Hypsipyla robusta, and then Khaya senegalensis can be
31
easily grown 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 affirmed 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
may be better for plantations of Khaya senegalensis.
4.1.5. Relevance of modeling Garcinia kola ecological niche with regard to
stated problems
Some previous studies including those of Assogbadjo et al. (2017) recommended appropriate
incentives for the valuation of priority species such as Garcinia kola. Our results led to some
recommendations that may be a useful guide to be used by resource managers and decision
makers in implementing the incentives. In fact,
Cuni-Sanchez et al. (2010)
stated that fundamental
niche and potential distributions are convenient when the purpose of the modelling is
introduction of a species in a geographic region. Moreover, it is worth knowing the
environmental requirement of a species, its potentially suitable areas, and its potential response
to climate change for conservation and management purposes (Bowe and Haq, 2010).
Therefore, Blach-Overgaad et al. (2010) and Bowe and Haq (2010) recognized that Ecological
Niche Modelling of threatened species, agroforestry species, pests, and invasive species is
useful to recommend policy and decision makers on their management. Other studies from
Gbètoho et al. (2017) indicated that Ecological Niche Modelling could also be used for
exploring species that could be used for restoration of secondary forests. The selection of
variables to include in the model differs from one scientist to another. It was found that the
distribution of a species at large scale depends mainly on climate (Vayreda et al., 2013).
However, contrary to Gbètoho et al. (2017) who selected “a priori” four variables that they
found to be the most biologically relevant in plant ecology in tropical West Africa and easy to
interpret, and Adjahossou et al. (2016) who selected variables only on the correlations between
32
them before running the model, we gave chance to all variables, even those that are shown to
be correlated to reveal how well it contributes to the model building before making a decision
to remove it. Our study is in line with all previous sources of information about recommended
strategies for the conservation and the restoration of Garcinia kola in its potentially suitable
areas.
(
Neuenschwander et al. (2011) reported that the species’ nut trading and its use for vegetable
toothbrush constitute the main threats; and that the species occurs in inhabited areas, but may
also occur in dense humid forests and riparian forests. Our findings confirm their statement
because models showed first that the species may find its climatic preferences in dense forests
in southern Benin, and can be grown in some cities always in southern Benin. The same authors
recommended that conservation efforts should include the restoration of the species in natural
occurrence sites, and that further research may profitably focus on the distribution, ecology,
regeneration, and silviculture of the species. Our study tackled its distribution emphasizing
climate change. There are now some advances based on research for vegetative propagation of
Garcinia kola (Kouakou et al., 2016) and this may ease multiplication and introduction of the
species in urban forestry, agroforestry and home garden systems. However, many other
parameters may drive the introduction of Garcinia kola in urban areas.
4.1.6. Climate change and conservation or restoration of Garcinia kola in
Municipalities or in Protected Areas Networks
Our study focused on the distribution and biogeography of the species, and then gave
background on areas where the species can be restored. The suitable areas shown by our models
for Garcinia kola are also conform to the ecological and geographical descriptions made on the
species by Vivien and Faure (1985) and Ntamag (1997). The favorable zones according to our
results corresponded to parts of the Guinean zone in Benin, and the coastal areas; and this
highlights the evidence of the concentration of suitable areas in southern Benin. The protected
forests that may be retained for the conservation of the species are all in the southern Benin, the
part of the country that belonged to the High Confidence Prediction Areas (HCPA). Meanwhile,
the biggest part of PAN is located in the northern part of the country. So, just a part of protected
areas can conserve a given species. This information is an addition to the conclusions of
Houehanou et al. (2013) and Adjahossou et al. (2016), who noted that
some protected areas are
threatened by unsustainable use of the existing resources.
The use of the HCPA is means to keep at
the lowest level as possible the prediction error, giving more confidence to the users of our
results. HCPA represented areas that were shown suitable for Garcinia kola as well under
33
current climate as under future climates regardless of used scenario among RCP4.5, and RCP8.5
at 2055s.
Positive climate change impacts include some municipalities, and some Protected Areas in the
center Benin were predicted to be suitable for hosting the species upon 2055s. In contrast to the
findings of some authors who modeled other species, for example Ganglo et al. (2017) who
modeled Dialium guineense, climate change has only positive impacts on the distribution of
Garcinia kola. But it is worth mentioning that different thresholds had been used for the
classification, and this may create a slight difference on the classification results that they
observed. It is unfortunate that many protected areas are in the northern parts of the country,
and those protected areas may not guarantee sustainable conservation sites for G. kola. Areas
where G. kola may find its climatic envelope are medium to high population density areas. So,
as remarked on one hand by Sogbohossou and Akpona (2006; 2007), Santini (2013), Idohou et
al. (2014), Salako et al. (2014), and Gbedomon et al. (2015; 2016) that home gardens, botanical
gardens in cities, urbanization through the green spaces and any other area in urban centers
maintain the urban biodiversity, their remark aligns with our findings. On the other hand, urban
biodiversity is essentially influenced by human pressure (Sogbohossou and Akpona, 2006;
2007; Santini, 2013), giving more importance to the results that we obtained emphasizing the
importance of urban forestry and agroforestry promotion.
4.2. Perspectives and Recommendations
4.2.1. Khaya senegalensis
Climate changes are going to be probably helpful for releasing Khaya senegalensis from
Hypsipyla robusta attacks. The 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,
all we suggest is to promote the tree’s plantation across the country. These plantations have to
be settled in protected areas projected to be favorable upon the 2055s (Figures 33 and 34).
Stable zones and positive climate change impacts zones are highly recommended for planting
Khaya senegalensis. 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 started, we suggest that the Government orders road
builders to plant the species along roads, and that city offices give incentives to land owners to
introduce the species in their home.
34
4.2.2. Pest outbreaks in stands of Khaya senegalensis
Many authors 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
al., 2014). It is then worth on one side considering our spatial analysis results for plantations of
Meliaceae (Figures 33 and 34) 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, it is interesting that some researches are going on 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, neither biological, nor
chemical, nor silvicultural control couldn’t be enough to stop its attack on the host. Meanwhile,
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 analysis are
performed will certainly be better. Maps on figures 33 and 34 are recommended for high quality
Khaya senegalensis wood production, and for adaptation to climate change.
4.2.3. Garcinia kola
Evidence in response to research questions related to Garcinia kola revealed that decision
makers and resources managers may not rely only on the protected areas network to conserve
and restore the species in Benin. Our municipalities can provide a greater chance for Garcinia
kola (an extinct species in the wild) to extend its occurrence areas due to likely climate changes.
The percentage of municipalities that were suitable for the species is far above the percentage
of PAN that were predicted as suitable habitats. Municipalities and Protected Areas that may
be able to host conservation measures and actions toward enhancing the survival and habitat
35
expansion of the species were identified. Thus, decision makers and resource managers may
focus on those identified sites. Overall, there are suitable natural and human habitable spaces
to introduce successfully Garcinia kola. With regard to the synthesis of suitability areas shown
in Tables 4-5, it is worth introducing the species in farmland, homeland, cities, streets, public
spaces, and home gardens by Urban Forestry. However, some additional researches are needed
to focus on some key physiological and horticultural aspects. First, a better understanding of
the root system requirement of the species. On the other hand, more documented evidence of
the interactions between humans and this species when introduced in cultivation is needed, as
another important research focus.
Figure 33: Distribution of forest pest Hypsipyla robusta outbreaks under current climate
Figure 34: Distribution of climate change impacts zones across Benin for Khaya senegalensis,
and pest outbreak under future climate
36
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 upon 2055
due to climate changes. Details can be obtained from our results on the urban districts, even
cities where future situations are favorable for both species (biological impact zone), where the
tree species can be grown successfully (without pest outbreaks). 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. As for Garcinia kola, modeling its ecological niche
gave background on its environmental envelop. More insights have been found on whether the
evolution of climate is in favor of sustaining the species or not. Garcinia kola is a valuable tree
species, which can benefit from predicted climate changes in Benin, regardless of the scenario.
Each research question found an appropriate response at the conclusion of the study, and all the
hypothesis are verified and confirmed.
37
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