Pathogenic variation of Mycosphaerella species infecting banana and plantain in Nigeria


  • M. Zandjanakou-Tachin,

    1. International Institute of Tropical Agriculture (IITA), Oyo Road, PMB 5320, Ibadan, Nigeria
    2. Laboratoire de Virologie et de Biotechnologie des Plantes, Université de Lomé, Ecole Supérieure d’Agronomie, BP 1515, Lomé, Togo
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  • P. S. Ojiambo,

    Corresponding author
    1. International Institute of Tropical Agriculture (IITA), Oyo Road, PMB 5320, Ibadan, Nigeria
    2. Department of Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
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  • I. Vroh-Bi,

    1. Central Biotechnology Laboratory, IITA, Oyo Road, PMB 5320, Ibadan, Nigeria
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  • A. Tenkouano,

    1. IITA, Humid Forest Ecoregional Center, BP 2008, Messa, Yaoundé, Cameroon
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  • Y. M. Gumedzoe,

    1. Laboratoire de Virologie et de Biotechnologie des Plantes, Université de Lomé, Ecole Supérieure d’Agronomie, BP 1515, Lomé, Togo
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  • R. Bandyopadhyay

    1. International Institute of Tropical Agriculture (IITA), Oyo Road, PMB 5320, Ibadan, Nigeria
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Mycosphaerella species that cause the ‘Sigatoka disease complex’ account for significant yield losses in banana and plantain worldwide. Disease surveys were conducted in the humid forest (HF) and derived savanna (DS) agroecological zones from 2004 to 2006 to determine the distribution of the disease and variation among Mycosphaerella species in Nigeria. Disease prevalence and severity were higher in the HF than in the DS zone, but significant (< 0·001) differences between agroecological zones were only observed for disease severity. A total of 85 isolates of M. fijiensis and 11 isolates of M. eumusae were collected during the survey and used to characterize the pathogenic structure of Mycosphaerella spp. using a putative host differential cultivar set consisting of Calcutta-4 (resistant), Valery (intermediate) and Agbagba (highly susceptible). Area under disease progress curve (AUDPC) was higher on all cultivars when inoculated with M. eumusae than with M. fijiensis, but significant (< 0·05) differences between the two species were only observed on Valery. Based on the rank-sum method, 8·3% of the isolates were classified as highly aggressive and 46·9% were classified as aggressive. About 11·5% of all the isolates were classified as least aggressive, and all of these were M. fijiensis. The majority of M. eumusae isolates (seven out of 11; 64%) were classified as aggressive. A total of nine pathotype clusters were identified using cluster analysis of AUDPC. At least one M. fijiensis isolate was present in all the nine pathotype clusters, while isolates of M. eumusae were present in six of the nine clusters. Isolates in pathotype clusters III and V were the most aggressive, while those in cluster VIII were the least aggressive. Shannon’s index (H) revealed a more diverse Mycosphaerella collection in the DS zone (= 1·81) than in the HF (= 1·50) zone, with M. fijiensis being more diverse than M. eumusae. These results describe the current pathotype structure of Mycosphaerella in Nigeria and provide a useful resource that will facilitate screening of newly developed Musa genotypes for resistance against two important leaf spot diseases of banana and plantain.


Mycosphaerella species that cause the ‘Sigatoka disease complex’ are the most economically important fungal pathogens affecting banana and plantain (Musa spp.) globally (Carlier et al., 1996). Mycosphaerella fijiensis is the causal agent of black leaf streak (black sigatoka) and is considered the most destructive member of the ‘Sigatoka disease complex’ wherever it occurs. Mycosphaerella musicola is the causal agent of yellow sigatoka leaf spot, while M. musae causes leaf speckle of Musa spp. Leaf speckle is considered of minor importance except in Australia (Stover, 1972). Mycosphaerella eumusae is the causal agent of eumusae leaf spot (previously known as septoria leaf spot) (Carlier et al., 2000) and is reported to affect banana and plantain cultivars resistant to M. fijiensis and M. musicola (Jones, 2002). Banana is the fourth most economically important food crop in West Africa. Within this region, banana and plantain production is concentrated at lower altitude zones that are characterized by frequent rainfall and warm temperatures, favouring the development of various leaf diseases. These leaf spot diseases significantly reduce the leaf area available for photosynthesis, which in turn reduces yield quantity and quality. For example, without use of fungicides, M. fijiensis has been reported to result in yield losses of 20–80% (Heslop-Harrison & Shwarzacher, 2007). The pathogen also causes premature ripening of the fruit (Marín et al., 2003), which reduces the quality of exported fruit.

Members of the ‘Sigatoka disease complex’ have the ability to adapt to new environments where banana and plantain are cultivated. For example, M. fijiensis, which is typically associated with low and warm elevations, has now adapted to cooler climates at higher elevations (Arzanlou et al., 2007). In addition, some banana cultivars previously known to be resistant expressed a susceptible reaction to some M. fijiensis isolates in certain regions after several seasons of widespread commercial use of fungicides. For instance, Yangambi km 5, considered resistant, expressed a susceptible reaction when inoculated with isolates from the Pacific Islands (Fullerton & Olsen, 1995) and Cameroon (Mouliom-Perfoura, 1999). Similarly, the hybrids FHIA-01 and SH-3436 were reported to be resistant when evaluated in Honduras but were susceptible when introduced in Cuba (Alvarez, 1997). Based on evolutionary studies, it was shown that Mycosphaerella populations maintain a relatively high level of genetic diversity (Carlier et al., 2000) and this diversity can allow the pathogen to adapt to newly introduced host genotypes that have previously been resistant. Studies on pathogenic variability provide a means of monitoring the present state of the interaction between pathogen virulence and plant resistance for a specific pathogen and host-plant population (Ramstedt et al., 2002). Anecdotal reports have indicated that M. eumusae are more pathogenic on Musa than M. fijiensis (Carlier et al., 2000; Jones, 2002). Furthermore, it was reported that M. eumusae was able to cause disease on Musa cultivars that were highly resistant to M. fijiensis (Jones, 2002). However, no systematic studies on the pathogenic variation of members of the ‘Sigatoka disease complex’ have been conducted to support these observations.

Studies on pathogenic variation for members of the ‘Sigatoka disease complex’ are very limited in the literature. A literature search yielded only one comprehensive report (Fullerton & Olsen, 1995) that focused only on M. fijiensis. The main findings from that study were: (i) different isolates of M. fijiensis had different virulence combinations, and (ii) resistance in some genotypes such as Calcutta-4 and Yangambi km 5, was isolate-specific and there were isolates capable of overcoming resistance in these two genotypes. Selection criteria in breeding programmes in West Africa are primarily oriented towards selection for quantitative resistance to M. fijiensis (Ortiz & Vuylsteke, 1994). This implies that early-generation breeding lines need to be screened against a range of pathogen genotypes that reflect the variation of a specific fungal population. However, there is no inventory of dominant pathotypes of Mycosphaerella spp. and their distribution within the region to better design germplasm-evaluation and cultivar-development breeding programmes.

Genetic variation among isolates of Mycosphaerella spp. in two geographical zones in Nigeria was assessed recently (Zandjanakou-Tachin et al., 2009). The results revealed significant genetic differentiation in Mycosphaerella spp. and a higher intraspecific variation in M. eumusae than in M. fijiensis. When complemented with data from genetic variation studies, comprehensive studies on pathogenic variation of Mycosphaerella spp. can aid in monitoring the dynamics of virulent isolates and haplotypes and facilitate breeding for disease resistance. Thus, this study was undertaken to establish the distribution of ‘Sigatoka disease complex’ and to characterize the pathogenic variation of Mycosphaerella spp. in the main banana and plantain production zones in Nigeria. Previous studies indicated that the response of different cultivars of banana and plantain to Mycosphaerella spp. is more quantitative than qualitative (Vakili, 1968; Ortiz & Vuylsteke, 1994). Thus, the term ‘aggressiveness’, rather than ‘virulence’, is used to describe the interaction between Mycosphaerella spp. and banana and plantain.

Materials and methods

Survey of Musa fields and analysis of disease intensity data

Surveys of ‘Sigatoka disease complex’ were conducted from 2004 to 2006 in Musa fields located in two agroecological zones in Nigeria, namely: the humid forest (HF) and derived savanna (DS) agroecological zones (Fig. 1). A total of 72, 64 and 50 fields were surveyed in the HF zone in 2004, 2005 and 2006, respectively. In DS, no fields were surveyed in 2004, while 65 and 45 fields were surveyed in 2005 and 2006, respectively. In both zones, different fields were sampled in different years. The two agroecological zones lie within areas with the largest production of cooking banana in West Africa. The HF zone lies between latitudes 4°25′ and 7°25′N and longitudes 2°40′ and 9°10′E and has a bimodal rainfall distribution averaging 1500–2000 mm annually, and maximum temperatures ranging from 27 to 32°C. The DS zone lies within latitudes 6°8′ and 9°30′N and longitudes 2°40′ and 12°15′E and has a bimodal rainfall distribution averaging from 1300 to 1500 mm annually, and maximum temperatures ranging from 25 to 35°C. The geographical location of each surveyed field was recorded with a handheld geographical positioning system device (NAV 5000DLX, Magellan Systems Corporation). Fields were randomly selected along survey routes at minimum distances of 5–10 km apart. To determine disease severity, 20 plants were randomly selected in each field and disease severity was scored on three to five randomly selected leaves on each plant using a 0–6 scale (Stover & Dickson, 1970). The average score from the two leaves randomly selected on each plant represented the disease severity score for the entire plant, while the mean score across the 20 plants represented the disease severity of a single field.

Figure 1.

 Map showing approximate location of sampling sites in two agroecological zones (derived savanna and humid forest) where banana and plantain leaves infected by ‘Sigatoka disease complex’ were collected to determine the distribution and pathogenic variation of Mycosphaerella species in Nigeria.

Disease prevalence in each agroecological zone and sampling year was calculated as the percentage of infected fields relative to the total number of fields surveyed. Prior to analysis, disease severity data collected during the survey using the 0–6 scale were converted to percentages using the midpoint method (Campbell & Madden, 1990). In each year, disease severity in each agroecological zone was calculated as the mean of all fields surveyed within the respective zone. Disease prevalence and disease severity data were then subjected to analysis of variance (anova) in which years and agroecological zones were considered as blocking and treatment factors, respectively. Differences in disease prevalence and disease severity between agroecological zones were determined using Fisher’s protected least significant difference (LSD) test. All analyses were performed in sas (v. 9.2; SAS Institute).

Isolate collection and identification of Mycosphaerella species

Infected leaves collected from Musa plants during the field surveys were transported to the IITA plant pathology laboratory in Ibadan. A single ascospore of the fungus was isolated from infected leaf samples as described by Stover (1976) and single-ascospore isolates were cultured on potato dextrose agar medium as described by Twizeyimana et al. (2007). A total of 96 isolates were recovered from infected banana leaves collected during the survey. Ribosomal coding DNA was sequenced and compared in 96 isolates of Mycosphaerella spp. and single nucleotide polymorphisms (SNPs) were used to identify the species as described by Zandjanakou-Tachin et al. (2009). Based on the SNP analysis, 85 isolates were identified as M. fijiensis and 11 as M. eumusae. None of the isolates collected during the survey were identified as either M. musicola or M. musae.

Inoculum preparation and host genotypes

For each Mycosphaerella sp., mycelial fragments were used as inoculum in the pathogenic variation experiments. Under field conditions, banana and plantain are infected by either conidia or ascospores of Mycosphaerella spp. However, production of conidia in vitro is very inconsistent within and across isolates, whilst mycelia fragments provide consistent infections under in vitro conditions (Donzelli & Churchill, 2007) and result in high levels of disease severity (Twizeyimana et al., 2007) compared to conidial inoculations. Prior to sporulation of cultures, mycelia were scraped and fragments were ground in sterile distilled water using a pestle for about 2 min. The mixture was then filtered through two layers of cheesecloth and then stirred. A drop of Tween 80 was added, and the suspension was adjusted with sterile distilled water to a concentration of 1 × 106 mycelial fragments mL−1 using a haemocytometer. Prior to inoculation, a drop of 1% Triton X-100 was added to the inoculum suspension to enable mycelia to adhere to leaf surfaces.

Three well-characterized Musa genotypes, Agbagba (ABB), Dwarf Valery (AAA, henceforth referred to as Valery) and Calcutta-4 (AA), were used in this study. The cultivars were selected based on their known reaction to ‘Sigatoka disease complex’. Agbagba (False Horn plantain) is a highly susceptible cultivar, Valery (dessert banana) has an intermediate reaction, while Calcutta-4 (plantain) is highly resistant to the ‘Sigatoka disease complex’ (Ortiz & Vuylsteke, 1994). The detached leaf assay (Twizeyimana et al., 2007) was used to evaluate the reaction of these genotypes to collected fungal isolates. Briefly, two leaf sections measuring 4 × 3 cm were surface-sterilized in 1% NaOCl for 90 s and washed in five changes of sterile distilled water. Two leaf pieces were then placed in a 9-cm-diameter plastic Petri dish with the adaxial surface appressed on 1% technical agar (Oxoid) amended with 5 p.p.m. gibberellic acid. Leaf pieces were inoculated with mycelia suspension by pipetting 40-μL droplets of the suspension at four points onto the abaxial side of the leaves. The Petri plates were sealed with Parafilm and placed in an incubator at 25°C with a 12/12-h light/dark cycle. Petri plates were arranged in the incubator in a completely randomized design with two replications. Starting 5 days after inoculation, leaf pieces were rated for disease severity as leaf area infected using the 0–6 scale (Stover & Dickson, 1970) every 5 days for a total of 8 weeks. The entire experiment was performed twice.

Pathotype determination for Mycosphaerella species

Prior to analysis, disease severity data recorded on the 0–6 scale were converted to percentage leaf area infected as described above and data from the two runs were tested for homogeneity of error variance using analysis of variance. Results indicated no heterogeneity in error variance and data were pooled over runs and replications for subsequent analyses.

For each isolate and host genotype, disease severity recorded over time was used to calculate area under disease progress curve (AUDPC). In the first set of analyses, the rank-sum method (Onyeka et al., 2005) was used to classify isolates into different categories of aggressiveness based on AUDPC values for Agbagba (AUC1), Valery (AUC2) and Calcutta-4 (AUC3). To calculate the rank-sum, AUC1, AUC2 and AUC3 for each isolate were assigned ranks from the smallest to the largest using the rank procedure of sas. The sum of the ranks (Xn) was computed for each isolate and compared with the grand mean of the rank-sums across all isolates (Gn). Deviation (D) of each isolate from the grand mean was calculated using the equation = ([Xn − Gn]/σ) × 2, where σ is the standard deviation. Deviations to the right (positive) of the grand mean on the mean distribution curve are more aggressive, while deviations to the left (negative) of the grand mean are less aggressive. Isolates within 2·5 and >2·5 deviations to the right of Gn were classified as aggressive and highly aggressive, respectively, while isolates within −2·5 and >−2·5 deviations to the left Gn were classified as moderately aggressive and least aggressive, respectively.

Aggressiveness of the isolates was further examined using a multivariate approach. Prior to this analysis, the univariate procedure of sas was used to determine the distribution of AUDPC values for all isolates. From the univariate analysis, three severity categories were defined based on the departure from the grand mean for AUDPC across all isolates and host genotypes: the first, second and third categories consisted of AUDPC values that were one standard deviation above, around and below the grand mean, respectively. The first category was assigned a value of 1 and defined as highly aggressive, while the second and third categories were assigned values of 2 and 3, respectively, and were defined as aggressive and least aggressive, respectively. Principal component analysis was used to examine the multivariate structure of Mycosphaerella as described by Chakraborty et al. (2010) using severity classes (i.e. 1, 2 and 3) as separate variates. Principal component scores were calculated from the resulting data matrix of 96 observations (isolates) and three variates (host genotypes). Pathotype clusters were determined using the average linkage method and clustering criteria and the first two principal components were plotted to visualize clustering of isolates. Principal component analysis was conducted in sas using the procedure princomp.

Pathogenic diversity of Mycosphaerella species

The diversity of M. fijiensis and M. eumusae in the two agroecological zones was estimated using Shannon’s index of diversity (Shannon & Weaver, 1949) using the equation: inline image, where ni is the number of isolates in the ith pathotype cluster and N is the total number of isolates in the population A, which was 50 and 46 in the DS and HF zones, respectively. The Shannon index reflects both the number of pathotypes per population and the relative evenness of their frequencies (Grünwald et al., 2003). Two other indices of species diversity, genetic richness and evenness were also estimated as described elsewhere (Ludwig & Reynolds, 1988). Genetic richness (R) was calculated using the equation: Ssub/nsub, in which Ssub and nsub denote number of different pathotypes and size of the smallest sample, respectively. Genetic evenness (E) was estimated as: H/ni, where H denotes Shannon’s index of diversity and ni is the number of isolates of the ith pathotype (Ludwig & Reynolds, 1988).


Severity and prevalence of ‘Sigatoka disease complex’ in agroecological zones

‘Sigatoka disease complex’ was present in 15, 12 and 12 fields in the HF zone in 2004, 2005 and 2006, respectively, while 0, 9 and 7 fields were infected in 2004, 2005 and 2006, respectively, in the DS zone. Disease severity differed significantly (< 0·05) between agroecological zones during the study period. The disease was most severe in the HF zone and disease severity was significantly (< 0·001) higher in this zone than in the DS zone (Table 1). Across the 3 years of study, disease severity was approximately 70% higher in the HF than in the DS zone. Similarly, disease prevalence was higher in the HF zone than in the DS zone, but differences between the two agroecological zones were not statistically significantly (= 0·0614).

Table 1. Severity and prevalence of ‘Sigatoka disease complex’ (caused by Mycosphaerella spp.) of banana and plantain based on disease surveys conducted from 2004 to 2006 in two agroecological zones in Nigeria
Agroecological zoneDisease variable
Severity (%)aPrevalence (%)b
  1. aDisease severity refers to leaf area infected (%) based on a sample of 20 plants in each of the surveyed fields.

  2. bDisease prevalence denotes the percentage of fields in which black leaf streak symptoms were observed relative to the total number of fields surveyed in each agroecological zone.

Humid forest31·721·1
Derived savanna9·914·6
Least significant difference (α = 0·05)5·112·7

Aggressiveness of Mycosphaerella species and pathotype determination

All isolates of M. fijiensis and M. eumusae resulted in disease symptoms following inoculation of host genotypes. However, the actual level of disease severity (AUDPC) varied among isolates and host genotypes. Isolate, host genotype and isolate × genotype interaction all significantly (< 0·005) affected disease severity. Across cultivars, AUDPC was significantly (< 0·0001) higher on Agbagba than on Calcutta-4, while AUDPC values for Valery were intermediate (Fig. 2). Although disease severity was higher on all cultivars when they were inoculated with M. eumusae than with M. fijiensis, significant differences between the two species were only observed on Valery (Fig. 2). On cv. Valery, AUDPC values were 36% higher when inoculated with M. eumusae than with M. fijiensis.

Figure 2.

 Mean disease severity (area under disease progress curve) on three banana and plantain cultivars inoculated with 85 isolates of Mycosphaerella fijiensis and 11 isolates of Mycosphaerella eumusae. For each vertical bar, vertical lines represent the standard errors of the mean. Cultivar Valery is moderate susceptible, Agbagba highly susceptible and Calcutta-4 resistant to ‘Sigatoka disease complex’.

Classification of Mycosphaerella isolates using the rank-sum method indicated that the majority (46·9%) were classified as aggressive and only 8·3% were highly aggressive (Table 2). About 11·5% and 33·3% of all the isolates were classified as least aggressive and moderately aggressive, respectively. Isolates classified as least aggressive were all M. fijiensis, while only two isolates of Meumusae were classified as moderately aggressive. Most of the M. eumusae isolates collected (seven out of 11; 64%) during this study were classified as aggressive. Among the eight isolates classified as highly aggressive across all the three cultivars, two (25%) were M. eumusae (Table 2).

Table 2. Aggressiveness classification of selected Mycosphaerella spp. as identified by the rank-sum method following inoculation of a putative host differential set of three banana cultivarsa
IsolatebDisease scoreIsolate rankingClassPathotype cluster
AgbagbaCalcutta-4Valery a b
  1. LA, least aggressive; MA, moderately aggressive; AG, aggressive; HA, highly aggressive isolates.

  2. aFor each cultivar, disease score rank is based on AUDPC whereby the isolates that resulted in the lowest and highest AUDPC values were given ranks of 1 and 96, respectively; = rank-sum (across genotypes); = deviation from the grand mean (G) of the rank-sums ([= (a − G)/standard deviation] ×2). Standard deviation = 75·2.

  3. bIsolates shown in regular and bold fonts are M. fijiensis and M. eumusae, respectively.

  4. zGrand mean of the rank-sums (G).

Iso57 43 25 4 72 −1·9 MA II
Iso56 9 89 39 137 −0·2 MA V
Iso54 35 44 75 154 0·3 AG I
Iso64 78 9 89 176 0·7 AG IV
Iso62 17 89 70 176 0·8 AG V
Iso30 79 23 91 193 1·3 AG IV
Iso13 49 71 77 197 1·4 AG III
Iso20 48 79 95 222 2·1 AG VI
Iso11 95 61 68 224 2·1 AG III
Iso12 77 90 86 253 2·9 HA VI
Iso21 80 85 92 257 3·0 HA VI

Principal component analysis revealed that the Mycosphaerella isolates used in this study were composed of nine pathotype clusters (Fig. 3). The three host cultivars, Agbagba, Calcutta-4 and Valery, had large positive or negative loadings on the first three principal components (Table 3). In addition, the first and second principal components accounted for 83% of the standardized variance (Table 3). When isolates were delineated by species, at least one isolate of M. fijiensis was present in all the nine pathotype clusters, while isolates of M. eumusae were present in six of the nine clusters (Table 4). Mycosphaerella isolates in pathotype clusters III, V and VI were the most aggressive, while isolates in cluster VIII were the least aggressive. Pathotype cluster I constituted the majority of Mycosphaerella isolates that were moderately aggressive.

Figure 3.

 Principal component and cluster analysis on disease severity (area under disease progress curve) after three banana and plantain cultivars were inoculated with 85 isolates of Mycosphaerella fijiensis and 11 isolates of Mycosphaerella eumusae. Sample size (n) shown within each cluster (circle) represents the number of isolates, plotted in a plane defined by the first two principal components (PC1 × PC2). Roman numerals adjacent to each cluster are the pathotype cluster groupings described in the main text, while ‘*’ indicates the position of a representative isolate within a cluster.

Table 3. Eigenvectors for the first three principal components from the analysis of area under disease progress curve for a putative differential set of banana cultivars inoculated with 11 and 85 isolates of Mycosphaerella eumusae and Mycosphaerella fijiensis, respectively
Differential genotypePrincipal componenta
  1. aValues have been multiplied by 100 and rounded to the nearest whole integer.

Variation explained (%)533017
Table 4. Mean and standard deviation (in parenthesis) for disease severity on three banana cultivars inoculated with 96 isolates of Mycosphaerella species in different pathotype clusters
Mycosphaerella speciesPathotype clusterNumber of isolatesDisease severity (area under disease progress curve)a
  1. aValery is moderate susceptible, Agbagba is highly susceptible, and Calcutta-4 is resistant to ‘Sigatoka disease complex’.

M. fijiensis I311349 (204)229 (78)982 (229)
II202010 (286)299 (77)1286 (189)
III142068 (278)571 (155)1389 (135)
IV81909 (390)292 (102)1779 (339)
V42189 (12)544 (141)1910 (236)
VI41176 (12)219 (26)143 (202)
VII21324 (379)605 (10)1483 (47)
VIII1153 (0)85 (0)80 (0)
IX12109 (0)1011 (0)276 (0)
Mean1587 (220)428 (94)1036 (235)
M. eumusae I12021 (241)639 (73)2037 (256)
II12121 (53)211 (71)1994 (65)
III22280 (723)485 (42)1544 (46)
IV21267 (108)661 (45)1351 (253)
V21579 (0)367 (0)1554 (0)
VI31680 (0)271 (0)583 (0)
Mean1871 (150)488 (64)1613 (205)

Geographical distribution of pathotype clusters

Across all agroecological zones, the most frequent pathotype clusters were I (33%), II (22%), III (17%) and IV (10%). The distribution of the nine pathotype clusters differed between agroecological zones. All except cluster VII were present in the HF zone, and all except clusters VIII and IX were present in the DS zone (Fig. 4). The most frequent (≥8%) pathotype clusters in the HF zone were clusters I, II and III, while clusters I, II, and IV were the most frequent in the DS zone. The most aggressive pathotype cluster V was more frequent in the DS (36%) than the HF (12·5%) zone, while the least aggressive pathotype cluster VIII was present only in the HF zone, at a low frequency (1%).

Figure 4.

 Frequency distribution of nine pathotype clusters in the derived savanna and humid forest agroecological zones of Nigeria based on principal component analysis of disease severity data for 96 isolates of Mycosphaerella collected from banana and plantain.

Shannon’s index for diversity revealed a more diverse Mycosphaerella collection in the DS than the HF agroecological zone, with H values of 1·81 and 1·50, respectively (Table 5). At the species level, M. fijiensis was the most diverse, with H values ranging from 1·2 to 1·73, while M. eumusae was comparatively less diverse, with H values ranging from 0·73 to 1·20. Both M. fijiensis and M. eumusae were consistently more diverse in the DS than the HF zone (Table 5). The distribution of Mycosphaerella spp. within the sample collection (i.e. evenness, E) was also higher in the DS than the HF zone, with E values of 0·46 and 0·39, respectively. At the species level, E was higher for M. eumusae than for M. fijiensis and this evenness pattern was also consistent across the two agroecological zones. The number of Mycosphaerella genotypes contained in the collection (i.e. genotypic richness, R) was slightly higher in the HF (= 0·20) than in the DS (= 0·18) zone. As was the case with evenness, R was higher for M. eumusae than for M. fijiensis in both agroecological zones (Table 5).

Table 5. Indices of diversity, evenness and richness for Mycosphaerella species collected from banana and plantain in two agroecological zones in Nigeria
IndexDerived savannaHumid forest
Mycosphaerella spp. M. fijiensis M. eumusae Mycosphaerella spp. M. fijiensis M. eumusae
  1. H, Shannon & Weaver’s index for genetic diversity; E, evenness – measures how genotypes are distributed within a population; R, richness – number of genotypes in a population.

H 1·811·731·201·501·200·73
E 0·460·460·580·390·320·66
R 0·180·220·750·200·222·00


Knowledge of the pathogenic variation of fungal pathogens is a valuable tool in plant breeding screening programmes because it provides information that can be used to target cultivars with specific resistance to match prevailing pathotype groups in specific regions. In this study, substantial diversity was observed in Mycosphaerella spp. infecting Musa in different agroecological zones in Nigeria. ‘Sigatoka disease complex’ was more widespread and severe in the HF than the DS agroecological zone. Using a set of three putative host differential genotypes, pathogen isolates were grouped into nine pathotype clusters that represent the current range of aggressiveness of Mycosphaerella spp. in Nigeria. At the species level, M. fijiensis isolates were consistently more diverse than those of M. eumusae across the HF and DS agroecological zones.

Disease prevalence was approximately one and a half times higher in the HF than in the DS agroecological zone and similarly, disease severity was three times higher in the HF zone than in the DS zone. This high disease intensity in the HF agroecological zone may be caused by the prevailing annual rainfall and temperatures. The mean annual rainfall in the HF zone ranges from 1500 to 2000 mm and maximum temperatures range from 27 to 32°C. In contrast, the annual rainfall range in the DS zone is 1300–1500 mm and maximum temperatures range from 25 to 35°C. High rainfall, relative humidity and cool temperatures (optimum 26°C) favour disease development (Romero & Sutton, 1997), while temperatures >31°C greatly reduce disease development (Jacome & Schuh, 1992). Moisture is required for germination of ascospores and conidia and, therefore, for infection. In the presence of moisture, temperature determines the extent of disease development (Jacome & Schuh, 1992). Thus, the higher rainfall and comparatively lower temperatures may explain the higher levels of disease prevalence and severity in the HF zone. The significance of rainfall and temperature in determining the degree and extent of plant diseases in different geographical zones has been reported for several diseases including soyabean rust in Nigeria (Twizeyimana et al., 2009) and fusarium head blight in China (Qu et al., 2008).

The only comprehensive study on the pathogenic variability of Mycosphaerella focused on M. fijiensis (Fullerton & Olsen, 1995) and did not examine pathogenic variation in other Mycosphaerella species. Furthermore, although this previous study (Fullerton & Olsen, 1995) that tested 54 isolates of M. fijiensis (two isolates from Nigeria) revealed a very diverse population, it did not establish the number of pathotype clusters and their distribution across different geographical zones. The present study quantified the pathogenic variation in populations of M. fijiensis and M. eumusae and established how the diversity of each species is distributed across two agroecological zones in Nigeria. The results indicated that the collection of M. eumusae and M. fijiensis was composed of seven and nine pathotype clusters, respectively, with pathotype clusters III, V and VI being the most aggressive. Isolates within these two most aggressive clusters constituted approximately 30% of the total pathogen population evaluated in this study. Pathotype cluster VIII was the least aggressive and was present only in the HF zone. Although the frequency of isolates in the latter pathotype was small, Musa genotypes exposed to only isolates of this pathotype in a screening programme and deemed resistant in HF zone may exhibit a susceptible reaction when re-evaluated in the DS zone.

Contrary to the report by Fullerton & Olsen (1995), the present study observed a very diverse virulence spectrum for M. fijiensis isolates from Nigeria. The number of isolates and the Musa genotypes used as putative differential hosts could explain differences between the results here and those of Fullerton & Olsen (1995). For example, this study used a set of 85 M. fijiensis isolates while Fullerton & Olsen (1995) used only two isolates. The probability of observing differences in virulence is certainly higher when a larger sample of isolates is examined. Secondly, the host differential set used by Fullerton & Olsen (1995) was composed of 20 genotypes (that included Calcutta-4) selected by breeders and plant pathologists at an international workshop in Costa Rica. However, most of the host genotypes in the international differential set were consistently susceptible to all isolates of M. fijiensis (Fullerton & Olsen, 1995). To be useful, only host genotypes capable of discriminating between isolates of the pathogen should be used in characterizing pathogenic diversity. As such, host genotypes that are consistently susceptible (besides the susceptible check) are of limited use as a differential set. Thus, the results reported by Fullerton & Olsen (1995) may not have accurately reflected the pathogenic variability in M. fijiensis at that time. In the present study, the putative differential set consisted of three Musa genotypes that differentially responded to both M. fijiensis and M. eumusae. Based on this set, nine pathotype clusters were identified and three pathotype clusters were associated only with M. fijiensis. If each of the three putative differentials carried a unique resistance factor against each of the pathogen isolates tested, then, theoretically, up to 23 (=8) pathotype clusters can be differentiated (Chakraborty et al., 1996). This theoretical number is close to the number of clusters reported in this study, suggesting that the range of pathogenic variation captured by the putative differential set was adequate for the Nigerian isolates. Nonetheless, additional studies are needed to characterize the pathogenic variability of Mycosphaerella from a broader set of isolates from major banana and plantain growing regions in West Africa.

One goal of this study was to evaluate the possible correlation between pathogenic variability and genetic diversity of Mycosphaerella. A previous study (Zandjanakou-Tachin et al., 2009) revealed genetically diverse Mycosphaerella spp. and grouped M. eumusae and M. fijiensis isolates into seven and 14 SNP haplotypes, respectively. The present study also demonstrated a highly diverse population with respect to aggressiveness based on the same set of isolates. However, there was no clear clustering of isolates with respect to haplotype and pathogen aggressiveness. For example, among the six M. fijiensis isolates within the highly aggressive group, three were of the same haplotype, while the remaining three isolates were of a different haplotype. Similarly, among the seven isolates of M. eumusae classified as aggressive, five belonged to the same SNP haplotype, while three belonged to a different haplotype. The lack of concordance between pathogenic and genetic diversity is not unique to Mycosphaerella spp. and was reported for Colletotrichum gloeosporioides from the forage legume Stylosanthes guianensis (Kelemu et al., 1999), Ralstonia solanacearum from tomato (Jaunet & Wang, 1999) and Phakopsora pachyrhizi from soyabean (Twizeyimana et al., 2011). The degree to which genotypic polymorphisms can differentiate pathotypes into distinct groups depends on the heterogeneity among pathotypes and adequate homogeneity among isolates within a pathotype (Kelemu et al., 1999). Thus, if the amount of genetic diversity within a pathotype is very large relative to the magnitude of variability among the various pathotypes, then the differences that distinguish the pathotypes will be diminished. Leung et al. (1993) assumed that absence of association between pathogenic and genetic diversity indicates that either the pathogen population is strongly selected by host genotype or the pathotype, as defined by interactions with specific host cultivars, is not the main unit of pathogen evolution. Most of the banana and plantain cultivars grown in Nigeria are susceptible to ‘Sigatoka disease complex’. This susceptibility of Musa genotypes to Mycosphaerella spp. could help maintain high pathogenic and genetic diversity, which could lead to no association between these two aspects of diversity, as demonstrated with R. solanacearum from tomato (Jaunet & Wang, 1999). Pathogenic variation can also evolve independently of molecular markers (Goodwin et al., 1995) leading to a lack of association between pathogenic variation and genetic diversity. Furthermore, Mycosphaerella spp. are highly sexual (Chen & McDonald, 1996) and the high level of genetic recombination can also result in no correlation between pathogenic variability and genetic diversity.

In summary, pathogenic diversity of Mycosphaerella spp. from banana and plantain was established in Nigeria. The inventory of the dominant pathotypes generated from this study will facilitate screening for resistance to ‘Sigatoka disease complex’ by enabling putatively resistant Musa genotypes to be targeted to match the prevailing pathotypes in respective locations. Furthermore, the availability of Mycosphaerella isolates collected in this study with known aggressiveness on specific host genotypes offers an opportunity for rapid screening of new sources of resistance. Additional studies are recommended to identify Mycosphaerella pathotypes in different regions in major banana and plantain growing areas in West Africa. The number, structure and composition of the pathotype clusters will become clearer as data based on more pathogen isolates, especially M. eumusae isolates, collected from diverse banana and plantain growing areas, are progressively added and analysed.


The authors gratefully acknowledge the financial support from Third World Organization for Women in Science (TWOWS) to the first author. This study was also supported in part by financial support from the Directorate General for Development Cooperation (DGDC, Belgium) to the Strategic Musa Improvement Project at IITA.