Genetic diversity of bitter and sweet African bush mango trees (Irvingia spp., Irvingiaceae) in West and Central Africa

Abstract

Economically important food tree species in sub-Saharan Africa should be domesticated to enhance their production within agro forestry systems. The African bush mango trees (Irvingia species) are highly preserved and integrated in agro forestry systems in tropical Africa. However, the taxonomic debate related to the species or varietal status of the bitter and sweet fruited African bush mango trees hinders their domestication process and rational use. Amplified fragment length polymorphisms (AFLPs) and chloroplast simple sequence repeats (cpSSRs) were used in this study to assess the genetic diversity of African bush mango trees and to test the distinction between bitter and sweet fruited trees, sampled across Togo, Benin, Nigeria and Cameroon. Both the AFLPs and cpSSRs showed low genetic diversity for the Dahomey Gap bitter trees population. This is due to the higher fragmentation and the continuous reduction of this small sized population occurring in a limited forest ecosystem. The higher polymorphism and genetic diversity of the sweet mango tree populations in Benin and Togo showed the effects of domestication of materials of different geographical origin coupled with the frequent long distance transfer of genetic materials. When used separately, the AFLPs and cpSSRs failed to consistently discriminate the populations and type of trees. From the combined dataset, both markers differentiated geographically recognizable groups; bitter from sweet mango trees. However, Nigerian sweet mango trees clustered with the bitter ones. The suitability of AFLPs and cpSSRs to test our hypotheses within Irvingia needs to be thoroughly reassessed.

Full text

Vol. 14(45), pp. 3062-3074, 11 November, 2015
DOI: 10.5897/AJB12.2564
Article Number: 445036256209
ISSN 1684-5315
Copyright © 2015
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJB
African Journal of Biotechnology
Full Length Research Paper
Genetic diversity of bitter and sweet African bush
mango trees (Irvingia spp., Irvingiaceae) in West and
Central Africa
Vihotogbé, R.1,2*, van den Berg, R. G.1, Missihoun, A. A.3, Sinsin, B.2, and Sosef, M. S. M.1,4
1Biosystematic Group, Radix Building, Droevendaalsesteeg, 6708 PB Wageningen University, P.O. Box 647, 6700 AP,
Wageningen, The Netherlands.
2Faculty of Agronomic Sciences, University of Abomey-Calavi, 01 BP 526 Cotonou, Benin.
3Laboratoire de Génétique et des Biotechnologies (LGB/UAC), University of Abomey-Calavi, 01 BP 526 Cotonou, Benin.
4Botanic Garden Meise, Nieuwelaan 38, 1860 Meise, Belgium.
Received 20 August, 2012; Accepted 26 April, 2013
Economically important food tree species in sub-Saharan Africa should be domesticated to enhance
their production within agro forestry systems. The African bush mango trees (Irvingia species) are
highly preserved and integrated in agro forestry systems in tropical Africa. However, the taxonomic
debate related to the species or varietal status of the bitter and sweet fruited African bush mango trees
hinders their domestication process and rational use. Amplified fragment length polymorphisms
(AFLPs) and chloroplast simple sequence repeats (cpSSRs) were used in this study to assess the
genetic diversity of African bush mango trees and to test the distinction between bitter and sweet
fruited trees, sampled across Togo, Benin, Nigeria and Cameroon. Both the AFLPs and cpSSRs showed
low genetic diversity for the Dahomey Gap bitter trees population. This is due to the higher
fragmentation and the continuous reduction of this small sized population occurring in a limited forest
ecosystem. The higher polymorphism and genetic diversity of the sweet mango tree populations in
Benin and Togo showed the effects of domestication of materials of different geographical origin
coupled with the frequent long distance transfer of genetic materials. When used separately, the AFLPs
and cpSSRs failed to consistently discriminate the populations and type of trees. From the combined
dataset, both markers differentiated geographically recognizable groups; bitter from sweet mango
trees. However, Nigerian sweet mango trees clustered with the bitter ones. The suitability of AFLPs and
cpSSRs to test our hypotheses within Irvingia needs to be thoroughly reassessed.
Key words: AFLP, Benin, cpSSR, Togo, Dahomey Gap, Irvingia, taxonomy, domestication.
INTRODUCTION
The food tree species in African tropical forests are
important sources of food in sub Saharan Africa (Hladik
et al., 1996; Malaisse, 1997; FAO, 2008; Augustino et al.,
2011). Despite the increased food deficits, ecosystems
face very high destruction (Laurance, 1999; Archard et
al., 2002). Thus, domestication of the most important
food tree species used by local communities in their daily
diets remains a logical policy. This will strengthen

traditional and regional strategies for agrobiodiversity
maintenance while enhancing the global production of
agro forestry systems.
The International Centre for Research in Agroforestry
(ICRAF, now called the World Agroforestry Centre) has
become a leading institution in traditional food tree
species domestication in West and Central Africa. Since
decades, the African bush mango trees (ABMTs) are
systematically preserved and integrated in various
traditional agroforestry systems in humid sub-Saharan
Africa (Okafor and Fernandes, 1987; Franzel et al., 1996;
Tabuna, 2001; Okunomo and Egho, 2010). ABMTs are
widely distributed and taxonomically ambiguous taxa
within the family of Irvingiaceae thus the bitter fruited and
sweet fruited forms are difficult to be distinguished. The
only way to easily differentiate them is by assessing the
bitterness versus sweetness of the mesocarp because
there are limited morphological differences between the
ABMTs (Harris, 1996).
However, these mangoes exhibit a high morphological
and phenological diversity, which is vital for
domestication and selection programs (Harris, 1996;
Atangana et al., 2002). There is also no clear relation
between biochemical properties and the type of ABMT
(Tchoundjeu and Atangana, 2007). Due to this overlap of
morphological, phenological and biochemical properties,
the correct taxonomy of ABMTs needs to be re-visited.
Okafor (1975) proposed the variety level for sweet and
bitter ABMTs, respectively, Irvingia gabonensis (Aubry-
LeComte ex O’Rorke) Baill. var. gabonensis and Irvingia
gabonensis var. excelsa (Mildbr.) Okafor. Although,
based on a thorough taxonomic revision (Harris 1996)
and a random amplified polymorphic DNA (RAPD)
analysis (Lowe et al., 2000), a distinction at species level
was suggested: I. gabonensis and I. wombolu
Vermoesen, for sweet and bitter trees, respectively. The
latter taxonomic grouping was not supported due to the
low reproducibility of the RAPD analysis.
Moreover, a sound quantitative morphological
comparison between bitter and sweet ABMTs is still
lacking. Still, in order to ensure proper in- and ex situ
conservation of the genetically diverse material and to
support its development and genetic improvement (Grace
et al., 2008), a clear taxonomic identity of the material is
essential. Furthermore, the geographic origin of ABMTs
that occur in the Dahomey gap (which is the wide
savannah area separating the West African forest into the
Upper and Lower Guinean forest blocks in Benin and
Togo) remains an important topic. Like RAPDs, the
Vihotogbé et al. 3063
amplified fragment length polymorphisms (AFLPs) are
dominant markers. However, the power of AFLPs to
reveal genetic diversity and difference even for closely
related species are demonstrated better than that of the
RAPDs. Thus, AFLPs were successfully used by Ude et
al. (2006) to separate population of the Lower Guinean
and Congolian forest block provenances of the sweet
ABMTs. Simple sequence repeat (SSR) are PCR based-
co-dominant markers, consist of repeats of short
nucleotide sequences and are proven to have great
importance in the studies of genome (Stafne and Clark,
2005). The chloroplast simple sequence repeats
(cpSSRs) are genetic markers with uniparental
inheritance used particularly to investigate phenomena of
evolution of species and to assess the phylogenetic rela-
tions between populations of species (Wills et al., 2005).
This study intends to revisit the pattern of genetic
diversity of ABMTs in answering two main questions: 1)
What is the genetic diversity and differentiation within and
between bitter and sweet ABMTs, and what do the
patterns tell us about the geographical origin of the
Dahomey Gap material? 2) Are our markers (AFLPs and
cpSSRs) suitable to consistently discriminate populations
and types of ABMTs?
MATERIALS AND METHODS
Sampling
Sweet and bitter ABMTs were sampled throughout the Dahomey
Gap and in Cameroon. Materials were also sampled in four gene
banks: IITA-Ibadan (International Institute for Tropical Agriculture)
and NAGRAB (National Centre for Genetic Resources and
Biotechnology) both in Nigeria and Kolbison and Mbalmayo both
established by the World Agroforestry Centre in Cameroon (Table
1). For each sampled tree, young leaves were collected and stored
in silica gel. DNA was extracted from each sample following the
protocol described in Fulton et al. (1995). An AFLP analysis was
first carried out on 33 samples (10 bitter and 23 sweet trees). This
was followed by an independent cpSSR analysis carried out with 47
samples (14 bitter and 33 sweet trees), including more individuals
from the Dahomey Gap and the regions postulated as ABMTs
genetic diversity centres by Lowe et al. (2000) and Ude et al. (2006)
in the Lower Guinean forest and the Congolian forest blocks. Thus,
in total, 59 accessions (39 in the Dahomey Gap and 20 from
Nigeria and Cameroon) were used in this study with 21 samples
common to both analyses (Table 1).
Genetic diversity
The AFLPs are more reliable genetic markers that generate large
number of bands and have high reproducibility. Therefore, they are
*Corresponding author. E-mail: rlvihotogbe@gmail.com. Tel: +22995451295. Fax: +22921303084.
Author(s) agree that this article remains permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License

3064 Afr. J. Biotechnol.
Table 1. ABMT accessions and types of analysis applied.
Accession
Type of ABMT
Provenance: Site of collection and country
Population
Types of analysis applied
P2
Sweet
Pobè, South Benin
SDG
AFLP
POB21
Sweet
Pobè, South Benin
SDG
AFLP
IP2
Sweet
Pobè, South Benin
SDG
AFLP
Ip4
Sweet
Pobè, South Benin
SDG
AFLP
Coco6
Sweet
Calavi, South Benin
SDG
AFLP
NPA4
Sweet
Parakou, North Benin
SDG
AFLP
NPA6
Sweet
Parakou, North Benin
SDG
AFLP
MBM1
Sweet
Sangmelima, South Cameroon
STA
AFLP
IW3BAD5
Bitter
Badou, Southwest Togo
BDG
AFLP
FNGB
Bitter
NAGRAB Gene bank, Nigeria
BTA
AFLP
DNGB
Bitter
NAGRAB Gene bank, Nigeria
BTA
AFLP
IWSAK1
Bitter
Centre Cameroon, ICRAF Kolbison Gene Bank
BTA
AFLP
POB20
Sweet
Pobè, South Benin
SDG
AFLP + SSR
Coco1
Sweet
Calavi, South Benin
SDG
AFLP + SSR
Coco5
Sweet
Calavi, South Benin
SDG
AFLP + SSR
NPA7
Sweet
Parakou, North Benin
SDG
AFLP + SSR
NPA9
Sweet
Parakou, North Benin
SDG
AFLP + SSR
TG1
Sweet
Atakpamè, Centre Togo
SDG
AFLP + SSR
TG18
Sweet
Lomé, South Togo
SDG
AFLP + SSR
BAD1
Sweet
Badou, Southwest Togo
SDG
AFLP + SSR
WAMP2
Sweet
Badou, Southwest Togo
SDG
AFLP + SSR
IGIBDGB2
Sweet
IITA Gene bank, Nigeria
STA
AFLP + SSR
IGIBDGB1
Sweet
IITA Gene bank, Nigeria
STA
AFLP + SSR
Limb
Sweet
Limbé, Southwest Cameroon
STA
AFLP + SSR
Limbe6
Sweet
Limbé, Southwest Cameroon
STA
AFLP + SSR
IGGBWAC
Sweet
ICRAF Kolbison Gene Bank, Yaoundé
STA
AFLP + SSR
NGMK1
Sweet
Sangmelima, South Cameroon
STA
AFLP + SSR
NMKIW02
Bitter
Mamfé, South Cameroon, Mbalmayo Gene bank
BTA
AFLP + SSR
BSIW07
Bitter
Mamfé, South Cameroon, Mbalmayo Gene bank
BTA
AFLP + SSR
IWSAK2
Bitter
Centre Cameroon, ICRAF Kolbison Gene Bank
BTA
AFLP + SSR
CENRAD
Bitter
CENRAD Gene bank
BTA
AFLP + SSR
BAD4kiw
Bitter
Badou, Southwest Togo
BDG
AFLP + SSR
TGIW2
Bitter
Badou, Southwest Togo
BDG
AFLP + SSR
P2
Sweet
Pobè, South Benin
SDG
SSR
POB21
Sweet
Pobè, South Benin
SDG
SSR
CALI
Sweet
Calavi, South Benin
SDG
SSR
TORI13J
Sweet
Calavi, South Benin
SDG
SSR
TORI25
Sweet
Calavi, South Benin
SDG
SSR
Djot6
Sweet
Couffo, South Benin
SDG
SSR
LALO1G
Sweet
Couffo, South Benin
SDG
SSR
VODassa
Sweet
Dassa Centre Benin
SDG
SSR
Djoug
Sweet
Djougou, North Benin
SDG
SSR
Peninsou
Sweet
Peninsoulou, North Benin
SDG
SSR
Lom1
Sweet
Lomé, South Togo
SDG
SSR
L2
Sweet
Lomé, South Togo
SDG
SSR
TG4
Sweet
Lomé, South Togo
SDG
SSR
TG12
Sweet
Lomé, South Togo
SDG
SSR
Atak
Sweet
Atakpamè, Centre Togo
SDG
SSR

Vihotogbé et al. 3065
Table 1. Contd.
BAD5
Sweet
Badou, Southwest Togo
SDG
SSR
IGGBWACII
Sweet
Centre Cameroon, ICRAF Kolbison Gene Bank
STA
SSR
MBUM
Sweet
Sangmelima, South Cameroon
STA
SSR
KGH1
Bitter
Kougnonhou, Southwest Togo
BDG
SSR
KGH2
Bitter
Kougnonhou, Southwest Togo
BDG
SSR
KGH3
Bitter
Kougnonhou, Southwest Togo
BDG
SSR
KGH4
Bitter
Kougnonhou, Southwest Togo
BDG
SSR
NKIW19
Bitter
Mamfé, South Cameroon, Mbalmayo Gene bank
BTA
SSR
BSIW324
Bitter
Mamfé, South Cameroon, Mbalmayo Gene bank
BTA
SSR
T2BSIW16
Bitter
Mamfé, South Cameroon, Mbalmayo Gene bank
BTA
SSR
IWSAK3
Bitter
Centre Cameroon, ICRAF Kolbison Gene Bank
BTA
SSR
AFLP, Amplified fragment length polymorphisms; cpSSRs, chloroplast simple sequence repeats (or microsatellites), SDG, sweet ABMTs from
the Dahomey Gap, BDG, bitter ABMTs from the Dahomey Gap, STA, sweet ABMTs from Tropical Africa, BTA, bitter ABMTs from Tropical Africa.
widely used in genetic analysis (Berchowitz et al., 2001;
Assogbadjo et al., 2010) as opposed to the RAPDs with low
reproducibility (Powell et al., 1996). Microsatellites (or simple
sequence repeats, SSRs) and especially chloroplast microsatellites
(cpSSRs), have the power to reveal genetic diversity as well as
phylogenetic relationships and hybridization between plant species
(Wills et al., 2005; Panwar et al., 2010). Thus AFLPs and cpSSRs
were used for these analyses.
AFLP data
The AFLP analysis was carried out at the Biosystematics Group,
University of Wageningen, The Netherlands, following the
procedure of Vos et al. (1995) with minor modifications. Three
primer combinations previously identified to amplify sweet mango
tree material (Ude et al., 2006) were used to analyze all the 33
samples. These included: E38M59 (Eco ACT/ se CTA), E40M62
(Eco AGC/Mse CTT) and E33M48 (Eco AAG/Mse CAC). PCR
reactions were performed using a MJ PTC200 thermocycler. Prior
to the selective amplification, the EcoR1 primer was fluorescently
labelled with IRD700. AFLP fragments were separated on a
LYCOR 4300 (Westburg, The Netherlands), and the resulting
profiles were scored using the Quantar software (Key Gene
Products, Wageningen, The Netherlands 2000) to produce the
presence/absence data matrix.
cpSSR data
The cpSSR analysis was carried out in the Laboratory of Genetics
and Biotechnology of the University of Abomey-Calavi, Benin.
Eighteen (18) chloroplast SSR primers were tested on independent
samples (bitter and sweet mango tree accessions). They included:
CCMP 2, NTCP 8, NTCP 9, NTCP 30, NTCP 37, NTCP 39, NTCP
40, NTCP 5, NTCP 16, NTCP 19, NTCP 25, NTCP 26, NTCP 27,
NTCP 29, NTCP 32, NTCP 33, NTCP 34 and NTCP 38. Among the
18 primers, seven of them (CCMP 2, NTCP 8, NTCP 9, NTCP 30,
NTCP 37, NTCP 39 and NTCP 40) that amplified the chloroplast
DNA of ABMT material were retained and used to test the
amplification and polymorphism of complete set of 47 samples.
PCR reaction was performed using a Peltier-Effect Cycling PTC
100 thermocycler programmed for an initial denaturation at 94°C for
4 min, followed by 35 cycles at 94°C for 30 s per cycle, annealing
temperature (55 - 60°C) for 1 min, a step at 72°C for 1 min, and a
final extension step at 72°C for 5 min. Migration of the PCR
products was visualized with denaturing polyacrylamide gel (5%)
electrophoresis and then revealed with silver nitrate in accordance
with Chair et al. (2005). The electrophoresis bands were scored to
generate a presence/absence data matrix.
Data analysis
Genetic diversity and structure
Three datasets were considered in this study: the AFLP set, the
cpSSRs set and the one containing the accessions that showed a
result for both AFLP and cpSSR. An analysis of the genetic
diversity and population structure based on allele frequency using
AFLP-SURV version 1.0 (Vekemans, 2002) was performed on each
dataset. The type of ABMT (sweet versus bitter) was considered as
well as the geographical origin of the sample. Four geographic
‘populations’ were considered: (i) bitter mango trees from the
Dahomey Gap (Benin and Togo = population BDG), (ii) bitter
mango trees from Tropical Africa (population BTA), (iii) sweet
mango trees from the Dahomey Gap (Benin and Togo = population
SDG) and (iv) sweet mango trees from Tropical Africa (population
STA; Table 1). For each dataset, we computed the mean Nei
genetic diversity (Nei, 1973) for each population, the global genetic
differentiation (Fst statistics) and the pair wise genetic distance
among populations and between sweet and bitter mango trees.
Assuming there was no genetic structure among populations under
Hardy-Weinberg equilibrium (Vekemans, 2002), the significance of
the genetic differentiation was assessed by comparing the
observed Fst with the distribution of obtained Fst using 100 random
individual permutations.
Identification of populations and distinction between sweet
and bitter ABMTs
To assess the effectiveness of the genetic markers used in the
discrimination of the four geographically recognized populations as
well as the two types of ABMT, a cluster analysis was carried out on
each dataset and a dendrogram was produced using the well as

3066 Afr. J. Biotechnol.
Table 2a. Results of the genetic diversity analysis with AFLP-SURV.
Genetic diversity
Population level
Type level
BTA
BDG
SDG
STA
Bitter
Sweet
AFLPs
Segregating fragments (%)
96.5
97.2
Polymorphism (%)
22
66.7
85.1
60.3
66.7
76.6
Nei’s genetic diversity
0.091
0.263
0.304
0.234
0.221
0.264
cpSSRs
Segregating fragments (%)
100
100
Polymorphism (%)
55
70
65
90
60
80
Nei’s genetic diversity
0.202
0.273
0.289
0.235
0.240
0.278
AFLPs + cpSSRs
Segregating fragments (%)
83.9
85.1
Polymorphism (%)
24.8
50.9
70.8
63.4
50.9
67.1
Nei’s genetic diversity
0.105
0.232
0.251
0.211
0.210
0.245
AFLP, Amplified fragment length polymorphisms; cpSSRs, chloroplast simple sequence repeats (or microsatellites), SDG, sweet
ABMTs from the Dahomey Gap, BDG, bitter ABMTs from the Dahomey Gap, STA, sweet ABMTs from Tropical Africa, BTA, bitter
ABMTs from Tropical Africa. *Highest figures in bold.
the two types of ABMT, a cluster analysis was carried out on each
dataset and a dendrogram was produced using the unweighted pair
group method with arithmetic mean (UPGMA) method based on
Jaccard similarity index (Jaccard, 1908) in Past software (Hammer
et al., 2001):
(1)
Where, for a random pair of individuals, a = number of totally loci
scored present for the two individuals, b = number of loci scored
present exclusively present for only one individual and c = number
of loci exclusively present for only the second individual.
When classifying individuals using the principal component
analysis (PCA), there is unclear grouping pattern due to the
abundance of factors of low contribution to an existing pattern in the
dataset. Therefore, the PCA axes that explained a high percentage
of the total variance within the dataset or the factors correlated with
those PCA often used in a subsequent multivariate analysis to get a
better signal from the dataset (Mohammadi and Prasanna, 2003;
Bidogeza et al., 2009). Because the separate use of the AFLPs and
cpSSRs data generated more confusing patterns, only the
combined AFLPs + cpSSRs dataset was used in the rest of the
analysis. First, all the alleles with no variability (totally shared
presence and absence) in the AFLPs + cpSSRs dataset were
excluded. A principal coordinate analysis (PCoA) was performed in
order to highlight the main groups yielded in the cluster analysis.
The AFLPs + cpSSRs datasets without the totally shared presence
and absence alleles were used to detect relationship among our
sample. First, a PCA was performed and the axes that accumulated
at least 70% of the total variation within the dataset were retained.
The loci that were highly correlated (at least 70%) with those axis
were used to produce a Neighbor Joining (NJ) tree using
Kulczynski similarity index. Like Jaccard index, the Kulczynski
similarity index (Kulczynski, 1927) does not integrate totally shared
absent alleles and is one of the most consistent similarity index
used in systematic and taxonomy (Boyce and Ellison, 2001).
Especially, this index is influenced by the total number of loci that
make the different individuals between two randomly chosen
individuals (Kronberg, 1987).
(2)
RESULTS
For the AFLPs analysis, a total of 141 polymorphic alleles
were scored for all the 33 individuals. The cpSSR
analysis yielded 20 polymorphic alleles (one to six per
locus). Thus, a total of 161 polymorphic alleles were
available for the set of 21 samples with both AFLP and
cpSSR results.
Genetic diversity and differentiation of ABMTs
The AFLP analysis indicated that the mean number of
fragments scored as present for an individual tree was
47. The number of segregating fragments in the dataset
was high (96.5%). The polymorphism was higher within
the Dahomey Gap sweet mango tree population and
lowest in the Dahomey Gap bitter mango trees
population. The same tendency was observed regarding
the within population Nei genetic diversity. The sweet
mango tree population in the Dahomey Gap showed
significantly highest genetic diversity, while the bitter
ones in this eco-region displayed the lowest diversity
(Table 2a). The test for genetic differentiation among

Vihotogbé et al. 3067
Table 2b. Pairwise Fst statistics among populations.
Parameter
BTA
BDG
SDG
STA
AFLP
BTA
0
-
-
-
BDG
0.149
0
-
-
SDG
0.0731
0.2407
0
-
STA
0
0.1587
0.0789
0
cpSSRs
BTA
0
-
-
-
BDG
0.0772
0
-
-
SDG
0
0.1244
0
-
STA
0.0437
0.3268
0.0411
0
AFLPs + cpSSRs
BTA
0
-
-
-
BDG
0.2511
0
-
-
SDG
0.132
0.1618
0
-
STA
0.1646
0.2916
0.096
0
AFLP, Amplified fragment length polymorphisms; cpSSRs, chloroplast simple
sequence repeats (or microsatellites), SDG, sweet ABMTs from the Dahomey Gap;
BDG, bitter ABMTs from the Dahomey Gap; STA, sweet ABMTs from Tropical Africa;
BTA, bitter ABMTs from Tropical Africa. *Highest figures in bold.
populations indicated a global Fst value of 0.108 (P =
0.024). The pairwise Fst values among populations was
higher between bitter and sweet mango tree populations
within the Dahomey Gap and no genetic difference was
found between bitter and sweet tree populations outside
of the Dahomey Gap (Table 2b). Sweet mango trees
showed a higher polymorphism and Nei genetic diversity
than bitter mango trees (Table 2a). However, the
difference based on this AFLP data was not significantly
different (global Fst = 0.034; P = 0.0639). The Fst between
bitter and sweet mango tree populations were very low
(0.011).
The cpSSR results showed that the mean number of
fragments scored as present at individual tree level was 8
and all the 20 scored loci have segregation power. The
polymorphism was highest in the sweet trees population
outside the Dahomey Gap and lowest for the Dahomey
Gap bitter trees. The sweet tree populations in the
Dahomey Gap and the bitter one in tropical Africa
presented the highest genetic diversity, while the bitter
tree population in the Dahomey Gap presented the
lowest diversity again (Table 2a). No clearly significant
genetic differentiation was found among populations
(global Fst = 0.105; P = 0.048  0.05) even though the
highest pairwise Fst was found between bitter tree
population of the Dahomey Gap and the sweet trees of
Tropical Africa (Table 2b).
Considering bitter versus sweet trees, the genetic
diversity was highest in the sweet mango trees (Table
2a). However, based on the cpSSRs there was no
significant genetic differentiation amongst the two types
(Fst = 0.0537; P = 0.077), and the Fst between bitter and
sweet tree populations was low (0.0540).
The combined AFLPs + cpSSRs data also indicated a
high number of alleles with segregating power (83.9%),
with 24 alleles presenting no variability. The within
population proportion of polymorphism and the Nei
genetic diversity showed the same tendency as the
separate AFLPs and cpSSRs results: the highest value
was calculated for the sweet trees population from the
Dahomey Gap and the lowest in the bitter trees
population from this eco-region (Table 2a). A significant
genetic differentiation was detected among populations
(global Fst = 0.0176; P = 0.016). The bitter tree population
in the Dahomey Gap and the sweet one in Tropical Africa
had the highest pairwise Fst while the lowest distance
was shown between the sweet tree population in the
Dahomey Gap and that in Tropical Africa (Table 2b).
Considering sweet and bitter mango trees, 85.1% of the
combined AFLP and SSR alleles had segregation power.
The proportion of polymorphic loci was higher within
sweet trees than within bitter trees (Table 2b).
The Nei genetic diversity was 0.2453 and 0.21 for
sweet and bitter trees, respectively. The genetic
differentiation between sweet and bitter trees was low (Fst
= 0.0335) and not significantly different (global Fst =

3068 Afr. J. Biotechnol.
Figure 1a. UPGMA dendrogram for AFLP data of the 33 accessions based on Jaccard’s
similarity index.
0.0333; P = 0.064).
Cluster analysis
The dendrogram based on the AFLP results (Figure 1a)
showed no clear pattern among populations and no clear
distinction between bitter and sweet mango trees. The
majority of individuals from each considered population
were spread across many clusters. However, apart from
few accessions, there is a tendency for the Togo (bitter
and sweet) and Benin (sweet) materials to cluster
together (Figure 1a and Table 1).
The dendrogram based on the cpSSR results (Figure
1b and Table 1) showed an even less clear pattern with
higher similarity among individuals and completely failed
to discriminate between the two types of ABMTs or
geographically defined populations.
The dendrogram resulting from the cluster analysis of
the combined APLP + cpSSR data (Figure 1c and Table

Vihotogbé et al. 3069
Figure 1b. UPGMA dendrogram for cpSSR data of the 47 accessions based on
Jaccard’s similarity index.
1) was more discriminative than those obtained from the
separate AFLP and cpSSR datasets. Apart from one
accession from Togo (WAMP2), four geographically
distinct groups could be distinguished from the lower to
upper position: (i) all sweet trees from Benin, (ii) sweet
and bitter trees from the Lower Guinean forest bloc
(southern Nigeria and Mamfé region in South-west
Cameroon), (iii) sweet and bitter trees from Central and
South Cameroon, and (iv) bitter and sweet trees from
Togo.
The first two axes of the PCoA on the combined
dataset with 137 alleles (Figure 2 and Table 1) accounted
for 62.5% of the variance (46.13 and 16.38% for
coordinate 1 and 2, respectively). The PCoA tended to
separate the bitter from sweet ABMTs, with the two sweet
trees from Nigeria (IGIBGB1 and IGIBGB2) falling within
the bitter tree group. Within each of the two groups, the
populations were not clearly distinguishable apart from
the sweet trees from Benin as shown in Figures 1a and c.
Forty-eight (48) alleles were highly correlated (at least

3070 Afr. J. Biotechnol.
Figure 1c. UPGMA dendrogram for AFLP + cpSSR data of the 21 accessions based on
Jaccard’s similarity index.
70%) with the first 8 PCA axes accounting for 72.8% of
the total variation. The clustering obtained with NJ based
on the Kulczynski similarity index of these alleles (Figure
3) confirmed the pattern in the PCA. The inability of the
combined AFLP + cpSSR data to accurately separate
populations and the clustering of the sweet trees from
Nigeria within the bitter tree cluster was also confirmed.
DISCUSSION
ABMTs genetic diversity: Failure of AFLPs and
cpSSRs or influence of domestication
For all the three datasets considered, the lowest
polymorphism and genetic diversity was observed in the

Vihotogbé et al. 3071
Figure 2. Plot of first two principal coordinates based on Jaccard’s similarity index with the 48 AFLPs + cpSSRs
for the 21 common accessions. DG, Dahomey Gap; NC, Nigeria and Cameroon.
bitter mango tree population in the Dahomey Gap, while
the highest values for these parameters was noted
among the cultivated sweet tree population from the
same eco-region. These results are not consistent with
those of Lowe et al. (2000) and Ude et al. (2006) who
indicated a higher genetic diversity for ABMTs in
Cameroon and Nigeria. Environmental transformation
through logging, extension of agricultural productive
space through yearly bush fires, and urbanisation are the
main causes of biodiversity loss in Tropical Africa (FRIG,
2003; Sodhi, 2007; Jose, 2012). Fragmentation and
decreased population size have changed the climatic
characteristics of the Volta forest region; a unique
ecosystem in which bitter trees are found in the wild in
the Dahomey Gap (Vihotogbé et al., 2014a). Additionally,
the economic potential of bush mangoes’ seed highly
threatens the ABMTs, since the market of this non timber
forest product relies mostly on natural populations
(Agbor, 1994; Lowe et al., 2000). Thus, the population
size of wild bitter ABMTs has decreased in the Volta
forest region in their entire distribution range due to a lack
of sufficient natural regeneration (Agbor, 1994; Zapfack
and Ngobo-Nkongo, 2002; Vihotogbé et al., 2014a).
Consequently, the reduction of their ecological variability
has narrowed their morphological and genetic diversity.
Nevertheless, the domestication and cultivation of sweet
trees in various climatic zones in the Dahomey Gap, has
helped to preserve or increase the existing diversity
(Casas et al., 2005; Jose, 2012). Although, the
provenance of sweet ABMTs in the Dahomey Gap is
unknown (Harris, 1996; Asaah et al., 2003; Lesley and
Brown, 2004; Ude et al., 2006, Vihotogbe et al., 2014a),
their higher diversity in this eco-region may well be due to
the fact that the ongoing traditional domestication
process in this region includes material from
geographically different origins: the Upper and Lower
Guinean forest blocks as well as the Congolian forest
region. We conclude that this is a consequence of the
random genetic material transfer between and within
local communities, not only for ABMTs but related to any
economically important food tree species in agroforestry
systems (Jose, 2012).

3072 Afr. J. Biotechnol.
Figure 3. Kulczynski similarity-based Neighbor Joining tree of the 48 AFLPs + cpSSRs loci. DG, Dahomey
Gap; NC, Nigeria and Cameroon.
In general, for the three datasets, no significant genetic
differentiation was found between bitter and sweet
ABMTs. Similarly, no genetic differentiation among
populations was observed with the AFLP dataset.
Genetic differentiation was detected within the cpSSR
and AFLP + cpSSR datasets but the pattern was not the
same for the two datasets. The dominance of cultivated
material in our samples (collected in the field as well as in
gene banks) resulted in the expression of artificially
generated variation.
Comparatively few sharp bands could be scored with
the AFLP and the cpSSR products. This weakness was
noted during an AFLP study by Ude et al. (2006) who
used 12 pairs of primers. The genetic diversity among
populations and most importantly between bitter and
sweet mango trees posed important questions
concerning the suitability of the genetic markers used in
our study. Thus, including wild materials from every eco-
region in the entire distribution range of ABMTs and using
sound genetic analyses’ methods would be of great
importance in the evaluation of the genetic diversity, the
influence of domestication and the genetic adaptability of
ABMTs.
Suitability of the markers
Apart from the sweet mango tree population from Benin,
which formed the most consistent and distinct clusters
throughout our analyses, none of the methods used in
this study clearly separated the ABMTs into
geographically distinct populations (Figures 1 to 3). The
PCA and NJ (Figures 2 and 3) showed a distinction
between the two types, with the exception of the sweet
tree samples from Nigeria which clustered with the bitter
trees (Figures 2 and 3). This was also observed in the
study of Lowe et al. (2000), but was explained in terms of
inaccuracy of sampling. In our study, the fact that neither
the PCA plots nor the NJ dendrogram were able to
discriminate either geographical populations or fruit types

implied that the markers used to achieve this goal were
probably unsuitable. This idea corroborated with the fact
that we know that in the area where bitter and sweet
ABMTs co-occur (in the Volta forest region) successful
gene flow between bitter and sweet trees is hardly
expected for phenological reasons: (i) very short co-
flowering time, (ii) flowers abortion on all sweet trees after
this co-flowering time and (iii) consistent overall
difference in phenology between both types (Vihotogbé et
al., 2014b). Further support comes from the presence of
ecological differences between both types (Ainge and
Brown, 2004; Vihotogbé et al., 2014a). Therefore, the
unclear species distinction from the AFLP and cpSSR
data might be attributed to the high level of sweet ABMTs
diversity (Kelleher et al., 2005). So, in conclusion, we
attributed the failure of AFLPs, cpSSRs and AFLPs +
cpSSRs to distinguish populations to the effect of
domestication and large scale transfer of genetic material
via seeds of the economically and nutritionally
appreciated ABMT morphotypes.
Conflict of interests
The authors have not declared any conflict of interest.
ACKNOWLEDGEMENTS
We are grateful to the Dutch Organization for
International Cooperation in Higher Education (NUFFIC,
the Netherlands), to the International Foundation for
Science (IFS / Grant No: D/4672-1, Stockholm, Sweden)
and to the donors of both institution for funding this
research. The authors are particularly grateful to the Late
Robert Kaman (former employee of the Laboratory of
Botany, University of Lomé, Togo) for his assistance
during genetic material collection. The AFLP Analysis
was fully conducted by Nynke Groendijk-Wilders and Ria
Vrielink of the Biosystematics group (Wageningen
University, the Netherlands). We thank them for their
support. Lastly, Prof. Clement Agbangla and Prof.
Corneille Ahanahanzo (Laboratory of Genetics and
Biotechnology, Faculty of Sciences and Techniques,
University of Abomey-Calavi in Benin Rep.) authorized
the cpSSR analysis to be performed in their Laboratory
and we thank them. Paulin Sedah (MSc) and Rolande
Dagba (MSc) (Research Assistants in this laboratory)
conducted cpSSR this analysis. We are grateful to them.
Abbreviations: AFLP, Amplified fragment length
polymorphisms; cpSSRs, chloroplast simple sequence
repeats (or microsatellites); SDG, sweet ABMTs from the
dahomey gap; BDG, bitter ABMTs from the dahomey
Vihotogbé et al. 3073
gap; STA, sweet ABMTs from Tropical Africa; BTA, bitter
ABMTs from Tropical Africa; UPGMA, unweighted pair
group method with arithmetic mean.
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