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Research Article
Cryptic host-associated differentiation and diversity: unravelling the evolutionary dynamics of the plant pathogen Lasiodiplodia
expand article infoYa-Zhu Ko‡, Huei-Chuan Shih§, Meng-Shin Shiao|, Yu-Chung Chiang‡
‡ National Sun Yat-sen University, Kaohsiung, Taiwan
§ Meiho University, Pingtung, Taiwan
| Mahidol University, Bangkok, Thailand
¶ Kaohsiung Medical University, Kaohsiung, Taiwan

Corresponding author: Meng-Shin Shiao ( msshiao@gmail.com )

Corresponding author: Yu-Chung Chiang ( yuchung@mail.nsysu.edu.tw )

Academic editor: Sinang Hongsanan
© 2025 Ya-Zhu Ko, Huei-Chuan Shih, Meng-Shin Shiao, Yu-Chung Chiang.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Ko Y-Z, Shih H-C, Shiao M-S, Chiang Y-C (2025) Cryptic host-associated differentiation and diversity: unravelling the evolutionary dynamics of the plant pathogen Lasiodiplodia. IMA Fungus 16: e147543. https://doi.org/10.3897/imafungus.16.147543
Open Access

Abstract

Lasiodiplodia, a genus within the Botryosphaeriaceae family, comprises significant plant pathogens with a broad host range and global distribution, posing a substantial threat to agricultural production. Our recent study revealed the complexity of this genus by identifying numerous potential cryptic species within the seemingly generalist L. theobromae. To fully understand this species’ complexity, higher-resolution genetic markers are required. Therefore, this study employed a comprehensive analysis of multiple transferable microsatellite markers to verify Lasiodiplodia species delimitation and examine the fine-scale genetic structure and diversity of Lasiodiplodia species, particularly L. theobromae. The study identified four distinct genetic groups within L. theobromae, each showing high genetic diversity. The phylogenetic relationships of these groups align with the evolutionary history of their host plants. This finding suggests that host-pathogen co-evolution is shaped by shared ancestral variation, limited gene flow, isolation and natural selection. These insights enhance our understanding of managing economically important Lasiodiplodia plant pathogens and highlight the significance of genetic diversity and host preferences in developing effective control measures.

Key words:

Fungal pathogens, genetic diversity, host-associated differentiation, Lasiodiplodia, microsatellite

Introduction

Lasiodiplodia encompasses numerous phytopathogenic fungal species causing various disease symptoms in different plant hosts in tropical and subtropical regions (Punithalingam 1980; Farr and Rossman 2021). The observed symptoms include fruits and seeds rot, necrotic lesions, neck rot, foliage yellowing, panicle brown rot, decline, branch dieback, stem-end rot, gummosis and stem canker (Burgess et al. 2006; Damm et al. 2007; Abdollahzadeh et al. 2010; de Oliveira Costa et al. 2010; Sakalidis et al. 2011; Ismail et al. 2012; Marques et al. 2013; Machado et al. 2014; Netto et al. 2014). Although Lasiodiplodia species were reported to be one of the most aggressive pathogens in Botryosphaeriaceae, various levels of aggressiveness were reported in different Lasiodiplodia species or isolates (de Oliveira Costa et al. 2010; Marques et al. 2013; Cruywagen et al. 2017; Rodríguez-Gálvez et al. 2017; Ali et al. 2020).

The classification of Lasiodiplodia species traditionally relies on several characteristics: the symptoms of infected plants, host pathogenicity, characteristics of pathogen culture and asexual reproduction types (Punithalingam 1976; Coutinho et al. 2017). However, these characteristics may vary under different environmental conditions. The variability complicates species identification and often leads to inconsistent conclusions (Coutinho et al. 2017). Recent studies have dedicated the effort to elucidate the phylogenetic relations of Lasiodiplodia species (Crous et al. 2006; Alves et al. 2008; Abdollahzadeh et al. 2010; Wang et al. 2011; Phillips et al. 2013; Slippers et al. 2013; Ko et al. 2023). The results consistently indicated the possible existence of multiple cryptic species in L. theobromae.

Lasiodiplodia theobromae was first documented in cacao (Nowell 1923) as a morphological variable and multi-host ascomycete with predominantly asexual reproduction. Our previous study focusing on phylogenetic relations of species in Botryosphaeriaceae in Taiwan also supported the possibility of cryptic species and genetic variation within Lasiodiplodia (Ko et al. 2023). Notably, L. theobromae isolates were found to be paraphyletic, with some of the isolates grouping with L. brasiliense (Ko et al. 2023). This finding highlighted the ongoing challenges in accurately delineating species boundaries within this genus. Therefore, an in-depth genetic analysis is necessary to uncover hidden diversity further and clarify taxonomic uncertainties in Lasiodiplodia.

The generalist pathogen L. theobromae adapts to various hosts and infects different host tissues. However, true generalist pathogens are rare, as molecular studies often reveal cryptic species within what were once thought to be generalist species (Le Gac et al. 2007; Pintye et al. 2015; Beckstead et al. 2016). Although generalist plant pathogens can infect a wide range of hosts, they often comprise multiple strains that exhibit varying degrees of genetic differentiation, host preference or narrower host ranges. This has been observed in several studies on generalist plant pathogens (Fournier and Giraud 2008; Pintye et al. 2015; Walker et al. 2015; Feurtey et al. 2016; Menardo et al. 2017). This variability underscores their adaptive potential, where such pathogens can maintain broad host ranges while maintaining host-specific specialisations amongst certain strains, leading to differential impacts on plant fitness. The dynamic interplay between generalist tendencies and effective specialisation highlights their capability to adaptively infect various hosts, while also exerting context-dependent selective pressures (Benítez et al. 2013; Beckstead et al. 2016). In the case of L. theobromae, previous studies have predominantly reported low genetic differentiation amongst samples from various hosts or geographic regions (Burgess et al. 2003; Mohali et al. 2005; Shah et al. 2010; Begoude Boyogueno et al. 2012; Mehl et al. 2017). However, some research has detected weak genetic differentiation unrelated to geography and host (Burgess et al. 2003; Mohali et al. 2005; Begoude Boyogueno et al. 2012; Mehl et al. 2017; Rêgo et al. 2019).

Interestingly, despite L. theobromae being primarily considered an asexually reproducing organism, genetic diversity studies have yielded significantly varied results (Cardoso and Wilkinson 2008; Begoude Boyogueno et al. 2012; Sangeetha et al. 2012; Al-Sadi et al. 2013; Rêgo et al. 2019). These notable discrepancies in genetic diversity studies raise a crucial question: Are the effects of host species diversity and geographic distribution more influential than previously thought? To address this, examining the genetic structure of L. theobromae across various ecological contexts is crucial, especially considering the complexities of generalist pathogens. Given the inconclusive nature of current genetic differentiation patterns, a comprehensive study of L. theobromae’s genetic structure and its relationship with isolate origin, host and cultivars is necessary to understand this adaptable pathogen better.

In our previous study, four genetic markers were applied to elucidate phylogenetic relationships of Lasiodiplodia species and the closely-related species infected fruits in Taiwan (Ko et al. 2023). The four markers included intergenic spacer (ITS) of genomic rDNA, nuclear ribosomal small subunit (SSU), nuclear ribosomal large subunit (LSU), translation elongation factor 1-alpha (EF1), β-tubulin gene (TUB) and mitochondrial ribosomal small subunit (mtSSU). These markers provided appropriate resolution for the phylogenetic relationships of the investigated species. However, as mentioned, we observed potential host preferences in Lasiodiplodia species and possible cryptic species of L. theobromae that the four genetic markers may not adequately reveal. Genetic markers with higher intraspecific variation are necessary to uncover the species’ evolutionary mode and genetic differentiation.

In this study, we aim to use transferable microsatellites, which can be amplified using identical primer pairs across closely-related species, to address the questions mentioned above. Although microsatellite loci have been developed, based on the genome of L. theobromae, their “transferability” between Lasiodiplodia species and other members of the Botryosphaeriaceae remains limited (Burgess et al. 2003; Slippers et al. 2004; Cardoso and Wilkinson 2008; Baird et al. 2010; Nagel et al. 2020; Nagel et al. 2020). Therefore, this study aims to achieve three key objectives: (1) Evaluate the cross-species applicability of microsatellite markers within the Botryosphaeriaceae, including six species of genus Lasiodiplodia and two species of genus Neofusicoccum collected from important fruit crops in Taiwan; (2) Conduct a comprehensive genetic diversity and differentiation analysis while examining the degree of host or origin specificity to identify distinct genetic clusters; (3) Examine the demographic history of Lasiodiplodia species and assess the influence of gene flow on their evolutionary trajectory.

Materials and methods

Lasiodiplodia and other Botryosphaeriaceae Isolates from fruit crops in Taiwan

In this study, genomic DNA samples were employed from a comprehensive survey of Lasiodiplodia species infecting fruit crops in Taiwan (Ko et al. 2023). We obtained 209 genomic DNA samples from single spores of Lasiodiplodia and Neofusicoccum from various host plant families: Myrtaceae — Syzygium samarangense (wax apple), Psidium guajava (guava) and Syzygium taiwanicum; Anacardiaceae — Mangifera indica (mango); Annonaceae — Annona squamosa (sugar apple); Caricaceae — Carica papaya (papaya); Malvaceae — Theobroma cacao (cocoa); Boraginaceae — Cordia dichotoma; Zingiberaceae — Alpinia; and Musaceae — Musa spp. (banana). Species identification was conducted using a multi-locus phylogenetic approach, employing the internal transcribed spacer (ITS) region, translation elongation factor 1-α (TEF1-α) and β-tubulin (TUB2) gene sequences. There were 145 Lasiodiplodia genomic DNA samples, including six species: 63 L. theobromae, 11 L. brasiliensis, 18 L. hormozganensis, 13 L. pseudotheobromae, 26 L. rubropurpurea and 14 L. iranensis. Additionally, 64 genomic DNA samples from other genera within the Botryosphaeriaceae family, including 12 Neofusicoccum mangiferae and 52 Neofusicoccum parvum (Suppl. material 2: table S1).

Microsatellite genotyping

Microsatellite markers amplified in 20 μl PCR mix containing 0.5 μl of template DNA, 2 μl of 10× reaction buffer, 2 μl of dNTP Mix (2 mM), 2 μl of each forward and reverse primer (2 mM), 0.5 μl Taq polymerase (0.2 U μl-1; Promega) and 11 μl sterile water. The gradient PCR reactions of initial denaturation of 94 °C for 5 min, followed by 35 cycles of the 45 s at 94 °C, 45 s at 50–65 °C, the 50 s at 72 °C and set the final extension of 7 min at 72 °C were performed using the Labnet MultiGene 96-well Gradient Thermal Cycler (Labnet, Edison, New Jersey, USA).

After confirming the optimum annealing temperature (Ta) for each primer, all samples performed the PCR amplification reaction. The PCR products were separated and assessed using 10% polyacrylamide gel electrophoresis (a mix of 30% acrylamide, 5× TBE buffer, 10% ammonium peroxydisulphate and tetramethyl ethylenediamine) performed in 1× TBE as the electrophoresis buffer at 70 V for 16 h, stained with ethidium bromide and visualised (used ethidium bromide) under UV light exposure. The alleles’ patterns and sizes were recorded digitally using Quantity One ver. 4.62 (Bio-Rad Laboratories, Hercules, California, USA).

Assessment of transferability, gene diversity and recombination

Six Lasiodiplodia species and two Neofusicoccum species were isolated from various fruit trees in Taiwan. These species were used to evaluate the cross-species and cross-genus transferability of twenty-two microsatellite markers. PCR amplification of microsatellite markers was performed on twenty isolates from eight species, followed by fragment length analysis to assess transferability and polymorphisms under the previously described conditions. The polymorphism information content (PIC) for each primer was calculated using Cervus 3.0.7 to estimate allele variation (Kalinowski et al. 2007).

The allelic richness (Ar) and private allelic richness (Ap: the unique alleles number in a population) of Lasiodiplodia and Neofusicoccum species were calculated in HP-Rare (Kalinowski 2005), which is the fundamental parameter of genetic diversity. Poppr v.2.9.2, an R package, is convenient for analysing genetic data of mixed reproduction modes with sexual and clonal production (Kamvar et al. 2014). Consequently, this study employed Poppr v.2.9.2 to calculate Nei’s unbiased gene diversity (Hexp) (Kamvar et al. 2014).

Assessing genotypic diversity is crucial for analysing the genetic structure of pathogen and microbial populations (Grünwald et al. 2003). We evaluated the multilocus genotype (MLG) diversity of Lasiodiplodia species by calculating various parameters, including the observed Multilocus genotypes (MLG). To account for differences in sample sizes, we estimated the number of expected MLG (eMLG) using the rarefaction method, which standardizes the comparison to the smallest sample size (Grünwald et al. 2003). Genotypic diversity parameters were also measured using the Shannon-Wiener Index of MLG diversity (H) (Shannon and Weaver 1949), Stoddart and Taylor’s Index of MLG diversity (G) (Stoddart and Taylor 1988), Simpson’s Index (Lambda) and Evenness (E.5) (Grünwald et al. 2003). The clonal fraction of Lasiodiplodia species was calculated as 1 − (number of MLGs / number of isolates), which is defined as the proportion of the fungal isolates sampled originating from asexual reproduction (Zhan et al. 2003).

Random association of alleles at different loci, leading to gametic equilibrium in populations, is one of the long-term consequences of genetic recombination. Therefore, analysis of fungal populations for potential clonal or mixed clonal and sexual reproduction relies on testing the null hypothesis of random mating by detecting linkage disequilibrium amongst loci (Milgroom 1996; McDonald and Linde 2002). We used Poppr v.2.9.2 to calculate the index of association (IA) and its standardised form (rbarD) to detect signatures of multilocus linkage. Significant deviation from the null model, which assumes no linkage amongst markers, was assessed using four permutation algorithms per population dataset (Brown et al. 1980; Smith et al. 1993; Burt et al. 1996; Milgroom 1996; Agapow and Burt 2001).

Genetic differentiation and genetic groups analysis

We conducted a hierarchical analysis of molecular variance (AMOVA) using Arlequin v.3.5 (Excoffier and Lischer 2010) to evaluate the partitioning of genetic variance across different levels and assess the significance of F-statistics through 999 permutations. Additionally, we calculated genetic distances amongst Lasiodiplodia species, based on the proportion of shared alleles (DPS) using the microsatellite analyser (MSA) v.4.05 (Dieringer and Schlötterer 2003).

To comprehensively examine the genetic structure patterns and identify genetic groups, we employed multiple software tools for cross-validation. These included Principal Coordinate Analysis (PCoA), Bayesian-based analysis (Structure), Discriminant Analysis of Principal Components (DAPC) and GENELAND. These analyses were utilised to investigate the genetic structure of Lasiodiplodia species collected from various fruit plants across different regions in Taiwan.

To evaluate the relationships amongst isolates, we conducted a multivariate analysis using PCoA in GenAlEx v.6.5 (Peakall and Smouse 2012). This distance-based model utilises a pairwise genetic distance matrix derived from Jaccard’s similarity coefficient to visualise genetic affinities between samples.

Structure clustering analyses employ Bayesian clustering to classify individuals into source populations based on allele frequencies (Pritchard et al. 2000). This method utilises the Markov Chain Monte Carlo (MCMC) estimation to analyse the distribution of genetic variation and group individuals with similar characteristics. The algorithm assigns individuals to K ancestry clusters, estimates variant frequencies and re-assigns individuals under the assumptions of Hardy-Weinberg and linkage equilibrium (Hubisz et al. 2009; Porras-Hurtado et al. 2013). In our study, we implemented an admixture model with population identifiers. We calculated posterior probabilities of K clusters for each individual using 105 burn-in periods and 106 MCMC replicates. To ensure robust results, we conducted twenty independent Markov chain runs. We performed the analysis on partitioned datasets to examine lower-level genetic structure. First, Lasiodiplodia species would be analysed separately to censor the degree of genetic composition admixed amongst Lasiodiplodia species. Second, the datasets of L. theobromae were analysed independently to check the cryptic genetic groups. We utilised Structure Harvester to evaluate likelihood values across multiple K values and iterations (Earl 2012). CLUMPP was employed to align multiple replicates of the chosen K cluster, addressing multimodality issues (Jakobsson and Rosenberg 2007). For a visual representation of the Structure output, we generated bar plots using the POPHELPER R package (Francis 2017).

While Structure is effective for analysing recent mixtures amongst differentiated groups, additional methodologies are required for comprehensive demographic and historical analysis (Falush et al. 2016; Novembre 2016). Considering the diverse reproductive modes and mating systems of fungi, which often deviate from random mating conditions, DAPC emerges as a more appropriate tool for analysing various organisms. This model-free multivariate approach for DAPC (Jombart et al. 2010) was implemented using R v.4.1.0 with the adegenet 2.1.3 package (Jombart 2008). DAPC integrates discriminant analysis (DA) with principal component analysis (PCA), ensuring uncorrelated variables and a reduced number of variables compared to analysed individuals. It employs a sequential K-means algorithm to infer complex genetic clusters without prior group information, making it suitable for potentially clonal pathogen populations. This method optimises the discrimination of individuals into pre-defined groups by maximising between-group variance, while minimising within-group variance (Jombart et al. 2010). In our analysis, we initially employed the find.clusters() method to identify clusters (with a maximum of 40 evaluation groups) and assign samples. The optimal group number (K) was determined, based on Bayesian Information Criterion (BIC) differences. We then verified the number of principal components retained in the DAPC analysis using the xvalDapc function with default parameters (Miller et al. 2020).

While the DAPC approach offers valuable insights, its linear combination of genotypes may not fully capture complex genetic structures with multimodal distributions and non-linear patterns (Sugiyama 2007; Qin et al. 2021). To complement this analysis, we employed GENELAND v.4.9.2 in R v.4.4.1 to delineate genetic boundaries and cross-validate results with other grouping methods (Guillot et al. 2005; Guillot 2008; Guillot et al. 2012). The Bayesian clustering algorithm implemented in the GENELAND is based on a spatial cluster model, assuming that populations are spatially separated with few gene flows. GENELAND implements a spatial cluster model that assumes populations are spatially distinct with few gene flows, incorporating geographical coordinates into its analysis. We conducted the analysis using a correlated allele frequencies model, executing ten independent MCMC runs (100,000 iterations, thinning = 100, burn-in = 10,000). The maximum number of populations (K) was set to 40, with the Poisson process rate and Poisson-Voronoi tessellation nuclei maxima set at 100 and 300, respectively. The optimal K value was identified, based on the run exhibiting the highest likelihood (posterior density).

Estimation of multilocus SSR genotype evolutionary relationships

We constructed a Minimum Spanning Network of Lasiodiplodia species using multilocus SSR genotypes to visualise genetic relationships and investigate potential recombination events. We employed Poppr v.2.9.2 to define MLGs and calculated Bruvo’s distance, which is recommended for microsatellite data due to its stepwise mutation model. Bruvo’s distance incorporates microsatellite repeat numbers, with a distance value of 0.1 representing a single mutation step. This approach provides a robust framework for analysing genetic relatedness within the Lasiodiplodia species complex (Bruvo et al. 2004).

Isolation-with-migration analyses

We used IMa3 (Hey et al. 2018) to analyse demographic parameters across six Lasiodiplodia species and distinct genetic groups of L. theobromae. Our methodology incorporated the hidden genealogy approach (-j0) for phylogenetic topology estimation and employed the Stepwise Mutation Model for microsatellite data analysis. All the present and their ancestral populations (species) had the effective population size parameter (denoted by q). Migration rate parameters (denoted by m) between all pairs of source and target populations were provided. The splitting times of (denoted by t) of internal nodes corresponding to each ancestral population were also estimated. We calibrated demographic quantities using a mutation rate range of 2.80 × 10-6–2.50 × 10-5 per year. The microsatellite mutation rate was estimated, based on the noncoding DNA mutation rate in filamentous Ascomycetes (1.12 × 10-9 and 1.00 × 10-8 substitutions/site/year) (Kasuga et al. 2002) and then multiplied by 2500 to yield average microsatellite mutation rate of 2.80 × 10-6–2.50 × 10-5 (Dettman and Taylor 2004). The analysis comprised two independent runs, each sampling 200,000 genealogies with a 10% burn-in period. To visualise the isolation-with-migration model results, we employed the IMfig programme (Hey 2010; Hey et al. 2018).

Results

Transferability and polymorphisms of microsatellite markers amongst species of Botryosphaeriaceae

Fungal samples were extensively collected from important fruit crops in Taiwan. Eight species in the Botryosphaeriaceae were identified, including six species of Lasiodiplodia and two species of Neofusicoccum (Suppl. material 2: table S1). We first examined the transferability of microsatellite markers developed from L. theobromae in previous studies amongst all eight species. Sixteen of 22 makers could be successfully amplified and showed polymorphism in Lasiodiplodia and Neofusicoccum species (Table 1). These 16 markers demonstrated moderate to high values of Polymorphism Information Content (PIC), ranging from 0.33 (LAS0304) to 0.92 (LAS2122) (Table 1). Several microsatellite markers showed unique sizes for specific Lasiodiplodia species (Suppl. material 2: table S2). For instance, 176 bp of LAS2930 and 408 bp of LAS2122 were unique to L. pseudotheobromae and L. iranensis. L. rubropurpurea exhibited unique allele sizes in two markers: LAS2526 (386 bp, 392 bp) and LAS1718 (256 bp) (Suppl. material 2: table S2). Notably, LAS2324 failed to amplify in L. pseudotheobromae, providing another distinctive feature for species identification. Both unique allele sizes and the absence of amplification can aid in differentiating Lasiodiplodia species.

Table 1.
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Repeat motifs, primer sequences, fragment sizes (bp) based on L. theobromae, optimised annealing temperatures (Ta), polymorphism information content (PIC) and FST values of 16 polymorphic microsatellite loci used in this study. *p < 0.001.

Primer Primer sequence (5’ to 3’) Repeat motif Fragment size (bp) Ta (°C) PIC FST
LAS1314 F: 5’-GAGTTGTTAGTGCGGGCGCC-3’ A5(GA)3(GAAGAAA)2(GA)3 A5(CGG)3 317 63 0.43 0.32*
R: 5’-GCAGCCCCACAATTCACCAG-3’
LAS1516 F: 5’-GCCAGATCCGTGCCCACTG-3’ (CT)3(AG)3-TCTCTT7 335 63 0.89 0.23*
R: 5’-CATGCAGAGGTCGCAAAGTG-3’
LAS2122 F: 5’-GGAAGATGATGGGATGGTTGC-3’ (CA)5T6(GCT)3G7T8 387 58 0.92 0.26*
R: 5’-GTACAAGAACGAACTCCGGGT-3’
LAS2728 F: 5’-CGAACAGGGTTTCGTGACGT-3’ (GA)3(GAC)4(TTC)3(CG)4(TCGC)3(GT)7(GA)3(CTCTCG)3 462 58 0.81 0.31*
R: 5’-CTCATATCTCGCCGGTTGCC-3’
LAS3536 F: 5’-GGCATCACAACGACCAACCC-3’ (GCTT)10(GGA)5(CGT)4(GCT)5 379 63 0.87 0.31*
R: 5’-GCGAGAGTCGCAAGTACAGC-3’
LAS0304 F: 5’-GACTCATTCACGGTCTCATGG-3’ T5(CT)2CA(CT)5G5AG4(GT)4 361 57 0.33 0.24*
R: 5’-GTGGAGCGGAACTGTCTGCT-3’
LAS1718 F: 5’-GATCTTCCAGCTCTTCGGCC-3’ Sequence rich in A repeats 254 57 0.88 0.34*
R: 5’-GACACTGCAGTAGGTTAGCGG-3’
LAS3334 F: 5’-GCTCCGTTGCGCAAGAGCAG-3’ (CCCTTTCCTCTTCTTT)(GCT)5 276 57 0.87 0.29*
R: 5’-GTCTTGTCTGAACGCCTTCGC-3’
LAS3738 F: 5’-GGTTACTCGACGATGATCTCC-3’ (GATGTGTGT)4(GTGTTGGTGTGTTGTGT) 135 57 0.89 0.20*
R: 5’-CAGTCACTTACCACGACACC-3’
LAS2930 F: 5’-GACGAGGTCAAGGGCGACA-3’ (CGA)3(CAA)7(GCA)3 191 52 0.84 0.47*
R: 5’-CCTCCATGTCGGATTCCTTG-3’
LAS2526 F: 5’-GTATTGCAAGGTGAGCAAGAG-3’ (GC)7(CA)11(CA)4T7 433 55 0.64 0.80*
R: 5’-GTAGATGGCGTGTATCATCCT-3’
LAS3132 F: 5’-GGGTGTGTTACCCGAATCAG-3’ (GT)4 437 56 0.89 0.18*
R: 5’-CGCCATTTGCTTGCCTACAGC-3’
LAS2324 F: 5’-CAAAGCGATTGTACGCGGGT-3’ (CT)3(AGTG)8(GGGCT)7T13 456 56 0.88 0.34*
R: 5’-CACGGTTGGACCAACCCGTG-3’
LAS 01 F: 5’-GAGGGTTTTGTGCTCCATGT-3’ (CA)6 202 57 0.61 0.51*
R: 5’-GGAAAACGGTGGTCAAAGAA-3’
LAS 08 F: 5’-CTCGTTAGGAAGGAAAGCAT-3’ (GGT)7 188 58 0.74 0.39*
R: 5’-GAACTATCCCCGCATCTACT-3’
LAS 09 F: 5’-GGGAAAATAAAATGGTCTGG-3’ (GA)9 143 58 0.67 0.36*
R: 5’-GAAACCCTTGTTCCATGC-3’

Assessment of genetic diversity and inbreeding in eight species

A total of 216 alleles were identified from 16 microsatellite loci and 144 multilocus genotypes (MLGs) were identified amongst 146 Lasiodiplodia samples. The allelic richness (Ar) and private allelic richness (Ap) were highest in N. parvum (Ar: 6.63; Ap: 2.52) and lowest in L. brasiliensis (Ar: 2.98; Ap: 0.05) (Suppl. material 2: table S3). Amongst the six Lasiodiplodia species, L. rubropurpurea exhibited the highest allelic richness and private allelic richness (Ar: 5.63; Ap: 1.45).

While MLG numbers varied across species, expected MLG counts (eMLG) remained relatively consistent. The highest MLG diversity was observed in L. theobromae (H: 4.14; G: 63), while the lowest was observed in L. brasiliensis (H: 2.27; G: 9.31). Nei’s unbiased gene diversity (Hexp) spanned from 0.71 (L. rubropurpurea) to 0.42 (L. brasiliensis). Evenness (E.5) values were consistently high across species (0.96–1.00) (Table 2).

Table 2.
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Parameters of genotype diversity in six Lasiodiplodia species, based on microsatellite data generated by the poppr() function.

Species N MLG eMLG SE H G lambda E. 5 Hexp Clonal fraction I A rbarD
L. brasiliensis 11 10 10.00 0.00 2.27 9.31 0.89 0.96 0.42 0.09 4.52* 0.31*
L. hormozganensis 18 17 10.60 0.48 2.81 16.20 0.94 0.97 0.70 0.06 1.79* 0.12*
L. pseudotheobromae 13 13 11.00 0.00 2.56 13.00 0.92 1.00 0.49 0.00 0.78* 0.06*
L. rubropurpurea 27 27 11.00 0.00 3.3 27.00 0.96 1.00 0.71 0.00 0.43* 0.03*
L. theobromae 63 63 11.00 0.00 4.14 63.00 0.98 1.00 0.69 0.00 0.84* 0.06*
L. iranensis 14 14 11.00 0.00 2.64 14.00 0.93 1.00 0.64 0.00 0.97* 0.07*
Total 146 144 11.00 0.10 4.96 142.11 0.99 0.99 0.78 0.01 0.49* 0.03*

N: Number of isolates; MLG: Multilocus genotypes (MLG) observed; eMLG: The number of expected MLG at the smallest sample size (≥ 10) based on rarefaction; SE: Standard error based on eMLG; H: Shannon-Wiener Index of MLG diversity; G: Stoddart and Taylor’s Index of MLG diversity; Lambda: Simpson’s Index; E.5: Evenness; Hexp: Nei’s unbiased gene diversity; IA: The index of association; RbarD: The standardised index of association; *Significant at p < 0.01.

All six Lasiodiplodia species exhibited low clonal fractions and high genetic diversity. To detect evidence of recombination, the index of association (IA) and its standardised index of association (rbarD) were calculated, showing low, yet statistically significant values across all Lasiodiplodia species. L. brasiliensis demonstrated the highest values (IA: 4.52; rbarD: 0.31), whereas L. rubropurpurea exhibited the lowest (IA: 0.43; rbarD: 0.03). The null hypothesis of the random mating was rejected by detecting the linkage disequilibrium of alleles (Table 2).

Species delineation and genetic divergence were observed in Lasiodiplodia species amongst host fruit plants

Since different evolutionary scenarios have been proposed for Lasiodiplodia in literature, we performed several analyses to elucidate its evolutionary pattern.

PCoA demonstrated that the microsatellite markers grouped Lasiodiplodia species into three major clusters (Suppl. material 2: fig. S1A). The first cluster included L. pseudotheobromae and L. iranensis. The second cluster consisted mostly of L. rubropurpurea, with three samples from L. theobromae. The third cluster contained L. hormozganensis, most samples of L. theobromae and L. brasiliensis.

Next, we investigated whether genetic variations in fungal isolates correlated with the host species or infection sites. The results revealed that genetic groupings had a clear association with host plants for each Lasiodiplodia species [Suppl. material 2: fig. S1B]. For instance, wax apple isolates formed distinct clusters in L. hormozganensis, L. brasiliense, L. rubropurpurea and L. theobromae. Similarly, mango isolates clustered distinctly in L. hormozganensis, L. pseudotheobromae, L. brasiliense and L. theobromae. Additionally, L. hormozganensis isolates from bananas and L. theobromae isolates from cocoa and papaya formed their own distinct clusters (Suppl. material 2: fig. S1B). However, no significant genetic grouping was observed between different infection sites (fruits and stems) (Suppl. material 2: fig. S1B). For example, while L. brasiliensis and L. rubropurpurea in mango showed distinct clustering based on host plants, they showed no differentiation based on infection site. Similarly, L. theobromae isolates from fruits and stems showed substantial genetic overlap in their groupings (Suppl. material 2: fig. S1B).

Significant genetic differentiation amongst species was identified in Lasiodiplodia species, with FST values ranging from 0.07 to 0.48 (Suppl. material 2: table S4). The lowest genetic differentiation was observed between L. hormozganensis and L. theobromae (FST = 0.07), while the highest was between L. brasiliensis and L. pseudotheobromae (FST = 0.48) (Suppl. material 2: table S4). Furthermore, analysis of genetic distances, based on shared alleles, revealed values ranging from 0.40 to 0.77, with the lowest DPS value amongst L. hormozganensis, L. theobromae and L. brasiliensis, while the highest was between L. brasiliensis and L. rubropurpurea (Suppl. material 2: table S4).

Analysis of genetic differentiation amongst Lasiodiplodia species across various host groups revealed significant differentiation within Lasiodiplodia species, with all FST values exceeding 0.1. For L. theobromae, most host groups showed significant genetic differentiation, except for sugar apple, guava and mango groups, which displayed non-significant differentiation (Table 3). For example, the genetic differentiation of L. brasiliensis between wax apple and mango is significant (FST = 0.7), even greater than the differences between Lasiodiplodia species (Table 3 and Suppl. material 2: table S4).

Table 3.
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List of pairwise genetic distance values (FST) values amongst different host species of Lasiodiplodia species. Bold letters indicated that the data were significant (Significance Level = 0.05).

LBR LHO LPSE LRU LTH LIR
SS MI SS PG MI AS MB SS PG MI SS PG MI C SS PG MI CP AS TC CD AL PG MI AS TC
LBR(SS)
LBR(MI) 0.7
LHO(SS) 0.6 0.8
LHO(PG) 0.6 0.7 0.8
LHO(MI) 0.6 0.5 0.7 0.5
LHO(AS) 0.5 0.4 0.5 0.5 0.4
LHO(MB) 0.7 0.7 0.8 0.8 0.6 0.4
LPSE(SS) 0.6 0.5 0.7 0.6 0.5 0.4 0.5
LPSE(PG) 0.7 0.6 0.7 0.6 0.5 0.5 0.6 0.3
LPSE(MI) 0.7 0.7 0.8 0.7 0.6 0.5 0.7 0.3 0.4
LRU(SS) 0.5 0.4 0.5 0.5 0.4 0.3 0.5 0.4 0.5 0.5
LRU(PG) 0.5 0.4 0.5 0.5 0.4 0.3 0.4 0.4 0.4 0.4 0.2
LRU(MI) 0.7 0.7 0.8 0.7 0.6 0.5 0.6 0.4 0.5 0.6 0.3 0.2
LRU(C) 0.7 0.7 0.9 0.8 0.6 0.5 0.8 0.5 0.6 0.7 0.3 0.3 0.5
LTH(SS) 0.3 0.4 0.4 0.4 0.4 0.3 0.4 0.3 0.4 0.4 0.3 0.4 0.4 0.4
LTH(PG) 0.4 0.3 0.4 0.3 0.3 0.2 0.4 0.3 0.4 0.4 0.3 0.3 0.3 0.4 0.2
LTH(MI) 0.4 0.3 0.5 0.4 0.3 0.3 0.4 0.4 0.5 0.5 0.4 0.4 0.5 0.5 0.3 0.2
LTH(CP) 0.5 0.5 0.5 0.6 0.5 0.2 0.5 0.5 0.5 0.6 0.4 0.4 0.5 0.5 0.4 0.3 0.4
LTH(AS) 0.5 0.3 0.5 0.5 0.4 0.2 0.4 0.4 0.5 0.5 0.4 0.3 0.5 0.5 0.2 0.1 0.2 0.3
LTH(TC) 0.5 0.5 0.5 0.6 0.5 0.3 0.5 0.5 0.6 0.6 0.4 0.4 0.6 0.6 0.3 0.3 0.3 0.3 0.3
LTH(CD) 0.6 0.8 0.9 0.8 0.5 0.4 0.8 0.6 0.6 0.7 0.5 0.4 0.7 0.9 0.3 0.3 0.4 0.4 0.3 0.4
LTH(AL) 0.6 0.7 0.8 0.8 0.5 0.2 0.8 0.5 0.6 0.7 0.4 0.4 0.7 0.8 0.3 0.3 0.3 0.4 0.3 0.3 0.9
LIR(PG) 0.7 0.7 0.9 0.7 0.5 0.4 0.8 0.4 0.5 0.6 0.4 0.4 0.7 0.8 0.3 0.3 0.4 0.5 0.4 0.6 0.9 0.9
LIR(MI) 0.5 0.4 0.5 0.4 0.4 0.3 0.4 0.2 0.3 0.3 0.3 0.3 0.4 0.4 0.3 0.3 0.3 0.4 0.3 0.4 0.3 0.3 0.2
LIR(AS) 0.7 0.7 0.8 0.8 0.6 0.5 0.8 0.5 0.5 0.6 0.5 0.5 0.7 0.8 0.4 0.4 0.5 0.5 0.5 0.6 0.8 0.8 0.7 0.3
LIR(TC) 0.8 0.8 0.9 0.8 0.7 0.6 0.8 0.5 0.6 0.7 0.6 0.5 0.8 0.9 0.5 0.5 0.5 0.6 0.6 0.7 0.9 0.9 0.8 0.3 0.8

Note: [Species code] LTH: L. theobromae; LBR: L. brasiliensis; LHO: L. hormozganensis; LPSE: L. pseudotheobromae; LRU: L. rubropurpurea; LIR: L. iranensis. [Host species code] SS: Syzygium samarangense; PG: Psidium guajava; MI: Mangifera indica; CP: Carica papaya; AS: Annona squamosa; TC: Theobroma cacao; MB: Musa basjoo; C: unknown; AL: Alpinia; CD: Cordia dichotoma.

Examination of accumulated genetic variation across different classes indicated that variation was primarily distributed within populations (43.75%), amongst populations within Lasiodiplodia species (29.84%) and amongst species (20.59%). Notably, the majority of genetic variation was observed within populations (57.86%) in L. theobromae (Suppl. material 2: table S5). The fixation index was significantly high amongst Lasiodiplodia species (FCT = 0.21), amongst populations within species (FSC = 0.38) and within populations (FST = 0.57 and FST = 0.37), indicating significant genetic differentiations at all levels (Suppl. material 2: table S5).

Genetic structures of six Lasiodiplodia species

To further elucidate the genetic structure of six Lasiodiplodia species, we conducted DAPC, Structure and GENELAND analyses to determine optimal clustering and genetic structure patterns.

DAPC analysis identified an optimal clustering solution of five groups (K = 5), with 20 principal components retained as recommended by the xvalDAPC function. The two primary discriminant analysis axes explained 73.6% of the total variance, with PC1 accounting for 45.12% and PC2 for 28.48% (Suppl. material 2: figs S2A, S3A). The analysis with K = 5 revealed that L. rubropurpurea (LRU) formed a distinct cluster, while L. pseudotheobromae (LPSE) and L. iranensis (LIIR) grouped into a unique cluster as well (Fig. 1A and Suppl. material 2: fig. S3A). In contrast, L. theobromae (LTH), L. hormozganensis (LHO) and L. brasiliensis (LBR) exhibited diverse genetic compositions, warranting further analysis to uncover potential cryptic genetic groups (Fig. 1A).

Figure 1. 

Bar plots for the genetic structure of Lasiodiplodia species, based on microsatellite data generated by (A) DAPC, (B, C) Structure and (D) GENELAND. A vertical colour line represents each individual and the same colour indicates that the individual belongs to the same cluster. Black lines separate different species. The species codes are as follows: LBR: L. brasiliense, LHO: L. hormozganensis, LPSE: L. pseudotheobromae, LRU: L. rubropurpurea, LTH: L. theobromae, LIR: L. iraniensis.

Structure analysis identified the optimal cluster is two clusters (K = 2), followed by twelve (K = 12) and three (K = 3) clusters amongst six Lasiodiplodia species and two Neofusicoccum species (Suppl. material 2: figs S2C, S4). At K = 12, the analysis effectively distinguished between Lasiodiplodia and Neofusicoccum species (Suppl. material 2: fig. S4). The analysis demonstrated high efficiency in discriminating amongst Botryosphaeriaceae species using the microsatellite markers. The analysis also revealed partial intraspecies variations linked to host differences in L. theobromae, L. rubropurpurea, L. brasiliensis and L. hormozganensis, particularly from wax apple, guava and mango (Suppl. material 2: fig. S4).

Structure analysis of six Lasiodiplodia species independently revealed that the optimal number of clusters was two (K = 2), followed by four (K = 4) (Fig. 1B, C, Suppl. material 2: fig. S2D, table S6). At K = 4, the clustering pattern demonstrated substantial concordance with DAPC analysis (K = 5), where L. rubropurpurea formed a distinct cluster and L. pseudotheobromae and L. iranensis grouped into a separate cluster. However, DAPC analysis exhibited superior resolution in detecting fine-scale cryptic genetic groups within L. hormozganensis, L. theobromae and L. brasiliensis (Fig. 1A, C). GENELAND analysis identified K = 20 as the optimal number of clusters based on the highest posterior density (Fig. 1D and Suppl. material 2: fig. S2F). The genetic structure of the Lasiodiplodia species showed no correlation with geographic distribution. Instead, the genetic structure pattern revealed distinct host-associated differentiation amongst Lasiodiplodia species (Fig. 1D).

Intraspecific cryptic genetic structure in Lasiodiplodia theobromae: delineation and characterisation of cryptic genetic groups

Given the substantial evidence of intraspecific genetic structure within L. theobromae, we conducted comprehensive analyses of its genetic structure using multiple analytical approaches: DAPC, Structure and GENELAND. To investigate potential drivers of genetic differentiation, we examined the genetic structure patterns in relation to two biological factors: host species affiliation and infection site.

For the DAPC analysis of L. theobromae, the optimal cluster number was determined to be K = 4, with 20 principal components retained as recommended by the xvalDAPC function. The two primary discriminant analysis axes explained 85.28% of the total variance, with PC1 accounting for 46.72% and PC2 for 38.56% (Suppl. material 2: fig. S2B, S3B). Based on the DAPC analysis with K = 4, we evaluated the genetic differentiation in relation to host species and infection sites. The results revealed that genetic differentiation was associated with host species differences (Fig. 2A), but showed no clear relationship with infection site variation (Fig. 2E). When examining the genetic composition of isolates arranged by host species, host-specific clustering was evident, with isolates from cocoa and papaya forming a single cluster, while those from mango and sugar apple predominantly grouped together. Wax apple isolates split into two genetic groups and guava isolates shared genetic composition with a subset of wax apple isolates, except for one sample (Fig. 2A).

Figure 2. 

Genetic structure analysis of Lasiodiplodia theobromae, based on microsatellite data. Bar plots representing the genetic structure of L. theobromae populations as inferred by different clustering methods: (A and E) DAPC analysis; (B, C, F and G) Structure analysis; and (D) GENELAND analysis. Each vertical line represents an individual, with colours indicating cluster membership. Panels A-D show clustering by host species, while panels E-G show clustering by infected sites. The Structure analysis is presented with two different optimal K values (B, C and F, G) to illustrate potential substructure.

DAPC classification results revealed distinct genetic compositions within L. theobromae, with four genetic groups (G1-G4) showing minimal admixture (Fig. 3A and Suppl. material 2: fig. S3B). To examine host-specific associations, we evaluated the distribution of host species across these genetic groups. The analysis revealed clear host-specific patterns: G1 primarily consisted of mango isolates, with a few sugar apple samples; G2 exclusively contained wax apple isolates; G3 and G4 included isolates from multiple host species, with cocoa and papaya isolates appearing uniquely in G4 (Fig. 3A). To assess potential morphological distinctions, L. theobromae isolates were cultured on PDA at 25 °C for four weeks to evaluate phenotypic differences amongst genetic groups. Despite observable variations in culture morphology amongst the four genetic groups (Suppl. material 2: fig. S5), these morphological differences alone were insufficient to distinguish between genetic groups reliably.

Figure 3. 

Proportional representation of four genetic groups in Lasiodiplodia theobromae and their corresponding minimum spanning network. A, B the pie charts display the host species’ proportion across four genetic groups of L. theobromae inferred by DAPC and structure analysis C minimum spanning network illustrating the genetic relationships amongst the four genetic groups calculated via Bruvo’s distance, based on MLGs.

Both Structure and GENELAND analyses provided complementary evidence for host-associated genetic differentiation in L. theobromae. Structure analysis identified four optimal clusters (K = 4) that aligned with DAPC findings (Suppl. material 2: fig. S2E, table S6 and Figs 2A, C, 3A, B), while GENELAND analysis revealed finer-scale subdivision with thirteen clusters (K = 13) (Fig. 2D and Suppl. material 2: fig. S2G). Minor variations in group composition were observed, particularly for guava and sugar apple samples (Figs 2A, C, 3A, B). Both analyses showed that genetic differentiation was significantly associated with host species (Fig. 2B–D), but independent of infection site differences (Fig. 2E–G). Notably, the genetic structure showed no correlation with geographical distribution, as evidenced by the extensive overlap of genetic groups across sampling locations, particularly in wax apple and guava populations (Fig. 2D). As DAPC has proven reliable for analysing fungi with complex mating systems, we conducted subsequent demographic estimations using the four genetic groups (G1-G4) identified through DAPC analysis.

Multilocus genotype (MLG) evolutionary relationships

To investigate the phylogenetic relationships amongst different species and elucidate the evolutionary relationships between the four genetic groups within L. theobromae, we conducted comprehensive phylogenetic analyses. The minimum spanning network revealed that L. theobromae samples exhibited high polymorphism and complex genetic relationships amongst isolates. Lasiodiplodia pseudotheobromae and L. iranensis showed the closest phylogenetic relationships (Suppl. material 2: fig. S3C). This pattern was similar to the Neighbour-Joining relationship derived from the combined sequence dataset by Ko et al. (2023) (Ko et al. 2023).

The minimum spanning network analysis of L. theobromae revealed distinct and well-defined clustering patterns when colour-coded by the four genetic groups (G1-G4). The network showed clear genetic group segregation, with only four isolates clustering outside their designated groups (Fig. 3C). G1, primarily composed of mango isolates, exhibited a closer phylogenetic relationship to G3, consisting mainly of guava isolates. On the other hand, G4, comprising mostly papaya and cocoa isolates, showed a more distant phylogenetic relationship to G2, which included wax apple isolates, than to G1 and G3 (Fig. 3C).

Network analysis of the remaining five Lasiodiplodia species revealed significant host-associated genetic differentiation despite their comparatively limited sample sizes relative to L. theobromae (Suppl. material 2: fig. S6). This host-specific genetic structuring was particularly pronounced in L. rubropurpurea and L. hormozganensis, where distinct genetic clusters were observed among isolates from wax apple, mango and guava hosts Suppl. material 2: fig. S6B, E).

Demographic parameter estimation

Isolation-with-Migration models were employed to evaluate gene flow rates and effective population sizes. Amongst the six Lasiodiplodia species analysed, L. theobromae exhibited the highest effective population size, while L. brasiliensis demonstrated the lowest. Notably, L. theobromae’s ancestral effective population size approximated its current size. L. iranensis presented the second-highest effective population size, significantly larger than its ancestral size (Fig. 4, Suppl. material 2: table S7, fig. S8A, B).

Figure 4. 

Representation of the Isolation with Migration model generated by IMa3 and the IMfig programme for the six Lasiodiplodia species. The corresponding phylogenetic topology is presented in Suppl. material 2: fig. S7 (A and D), with phylogeny depicted as a set of hierarchical boxes. Ancestor boxes are located between their corresponding descendants. The width of each box is proportional to the estimated effective population size (Ne), with grey arrows indicating the 95% confidence interval of the Ne value. Green arrows show migration direction (*p < 0.05, **p < 0.01, * ** p < 0.001). Values above the green arrows represent unscaled migration rate parameters. The species codes are as follows: 0: L. brasiliense, 1: L. hormozganensis, 2: L. pseudotheobromae, 3: L. rubropurpurea, 4: L. theobromae, 5: L. iraniensis.

Inter-species migration rates amongst Lasiodiplodia species were found to be exceptionally low. The highest migration rate was observed between L. brasiliensis and L. hormozganensis (Fig. 4, Suppl. material 2: table S7, fig. S8C). L. theobromae demonstrated minimal or no migration with other Lasiodiplodia species, except for minor gene flow with L. rubropurpurea and L. brasiliensis. It is worth noting that the migration rate from L. brasiliensis to L. theobromae exceeded that in the opposite direction (Fig. 4 and Suppl. material 2: table S7).

Examination of the four genetic groups (G1-G4) within L. theobromae revealed that G3 and G4 possessed higher effective population sizes compared to G1 and G2. Interestingly, the current effective population size of G2 was significantly larger than its ancestral size (Suppl. material 2: fig. S7D, E, table S8). Migration rates amongst these four genetic groups were determined to be low (Suppl. material 2: fig. S7F, E, table S8).

Discussion

Strategies for the rapid identification of Lasiodiplodia species with high-efficiency microsatellite markers

Rapid and accurate identification of Lasiodiplodia and Neofusicoccum species is crucial in plant pathology. These two genera frequently co-occur in the field, but traditional identification methods using mycelial culture morphology are unreliable because of similar colony characteristics and the time-consuming process of spore formation. This situation emphasises the need for molecular markers that enable swift and precise species identification. Although microsatellite markers are widely used in population genetics, they have limitations at higher taxonomic levels, especially regarding cross-genus transferability. Caution is therefore warranted when making broad phylogenetic inferences solely from microsatellite data.

Despite these constraints, transferable microsatellite markers remain invaluable for investigating genetic diversity and evolutionary patterns amongst closely related pathogens. Our research identified sixteen polymorphic microsatellite loci that efficiently distinguish multiple Lasiodiplodia species and differentiate them from Neofusicoccum at an early culture stage. While microsatellite-based markers may not fully resolve more distantly related taxa across the entire Botryosphaeriaceae, they remain invaluable for inferring genetic diversity patterns, revealing population structures and detecting cryptic lineages within closely related Lasiodiplodia species.

This finding enhances the markers’ utility in distinguishing between different Lasiodiplodia species and Neofusicoccum, an aspect not previously examined (Burgess et al. 2003; Begoude Boyogueno et al. 2012; Mehl et al. 2013; Rêgo et al. 2019). Our results demonstrate that careful marker selection enables efficient identification and preliminary discrimination amongst closely related Lasiodiplodia and Neofusicoccum species, providing a foundation for more comprehensive phylogenetic and epidemiological studies.

Genetic diversity and recombination of Lasiodiplodia species from fruit crops in Taiwan

A comprehensive analysis of the fine-scale genetic diversity and structure of L. theobromae is essential for understanding its adaptive and evolutionary potential. Our study of six Lasiodiplodia species found that L. rubropurpurea exhibited the highest allelic richness, even surpassing L. theobromae, indicating significant adaptability potential. We also observed that the gene diversity of L. theobromae, L. hormozganensis and L. iranensis was higher than previously reported across various regions and host species (Begoude Boyogueno et al. 2012; Al-Sadi et al. 2013; Rêgo et al. 2019). Notably, L. hormozganensis showed greater gene diversity than L. theobromae, suggesting substantial evolutionary potential in Taiwan. The high genetic diversity amongst all Lasiodiplodia species is primarily due to the wide range of host species and geographic regions included in the study. These findings underscore the importance of comprehensive sampling for accurately assessing genetic diversity in Lasiodiplodia species.

The reproductive modes of Lasiodiplodia species are crucial for understanding their genetic structure and diversity. Although direct observation of sexual stages is rare, molecular techniques provide valuable insights into their reproduction strategies. Our study revealed low but significant values of IA and rbarD, along with low clonal fraction values across all Lasiodiplodia species. These findings suggest that the Lasiodiplodia species which we studied employ a selfing or mixed reproductive system (combining asexual, selfing, occasional sexual and parasexual reproduction) rather than the strictly asexual reproduction suggested by previous studies (McDonald and Linde 2002; Baskarathevan et al. 2012; Bihon et al. 2012a; Bihon et al. 2012b; Rêgo et al. 2019). The observed high genetic diversity and distinct genetic groups indicate a mixed reproductive system, where genetic exchange occurs through hyphal fusion, sexual recombination and parasexual cycles amongst diverse strains. This complex reproductive strategy likely contributes to the adaptability and diversity seen in Lasiodiplodia species.

Genetic differentiations with low gene flow and a high proportion of shared alleles amongst Lasiodiplodia species

The IMa3 analysis revealed generally low gene flow amongst the studied Lasiodiplodia species and between genetic groups of L. theobromae. This finding contradicts previous hypotheses suggesting extensive gene flow of L. theobromae (Mohali et al. 2005; Shah et al. 2010; Boyogueno et al. 2012; Al-Sadi et al. 2013; Mehl et al. 2017). The restricted dispersal may be attributed to the large spore size of L. theobromae (21–31 × 13–15.5 μm) (Phillips et al. 2013), which may limit its capacity for long-distance aerial transmission. Field observations support this, showing limited conidia movement in L. theobromae vineyards (Úrbez-Torres et al. 2010). Studies of other Botryosphaeriaceae species also demonstrate that spores mainly spread through short-distance mechanisms like water splash (Ahimera et al. 2004; Baskarathevan et al. 2012). Although some researchers have proposed wind-mediated dispersal (Mehl et al. 2017; Rêgo et al. 2019), this conclusion stems from studies of related Botryosphaeriaceae species rather than L. theobromae itself (Swart et al. 1987; Pusey 1989; Amponsah et al. 2009; Úrbez-Torres et al. 2010; Úrbez-Torres 2011; Mehl et al. 2013). Currently, no direct evidence exists for the wind dispersal of L. theobromae conidia.

Restricted gene flow alone cannot fully explain the genetic differentiation patterns observed within and amongst Lasiodiplodia species. Our analyses revealed substantial admixture amongst L. theobromae, L. hormozganensis and L. brasiliensis, despite limited dispersal. The genetic differentiation patterns showed a stronger correlation with host differences than geographic origin, indicating the influence of natural selection forces. Intriguingly, certain Lasiodiplodia species maintain distinct taxonomic boundaries (Ko et al. 2023) despite sharing a high proportion of alleles. This phenomenon likely results from shared ancestral variation, commonly found in recently diverged species with large effective population sizes (Pamilo and Nei 1988). This hypothesis is supported by our IMa3 analysis, which demonstrated recent divergence and large effective population sizes amongst Lasiodiplodia species.

Host-associated differentiation of Lasiodiplodia species

Our study utilised multiple genetic structure analyses (DAPC, Structure, GENELAND) which consistently revealed distinct cryptic genetic groups within L. theobromae. While Structure analysis aligned with DAPC results and GENELAND provided finer-scale details, DAPC was primarily used for defining conservative clusters due to potential overestimation issues with GENELAND (Frantz et al. 2009). The four genetic groups (G1-G4) of L. theobromae identified align with host species phylogeny, suggesting host-pathogen co-evolution. Specifically, groups G2 and G3 predominantly comprised isolates from Myrtaceae (wax apple, guava), G4 included isolates from Malvaceae and Caricaceae (cocoa, papaya) and G1 was primarily associated with Anacardiaceae (mango).

The four genetic groups' relationships mirror their host phylogenetic patterns (Bdeir et al. 2016; Lin and Chung 2017). This alignment strongly suggests that host species diversity exerts significant disruptive selection pressure, driving genetic differentiation and potentially host specialisation within the pathogen population, reflecting host-pathogen co-evolutionary dynamics. Host variation-induced disruptive selection can drive parasite populations to differentiate, even in sympatry, resulting in new host races or sibling species. This phenomenon is particularly prevalent in predominantly asexual fungi (Kawecki 1998; Giraud et al. 2008).

This observed host-associated differentiation aligns with broader evolutionary hypotheses proposed for the Botryosphaeriales. Slippers et al. (2013) proposed that the order Botryosphaeriales likely originated in the Cretaceous period and that the majority of its diversification occurred during the Tertiary period. This period coincided with the extensive radiation and expansion of woody angiosperms. They hypothesised that the distribution and evolution of host plants played a significant role in driving the diversification of these fungi (Slippers et al. 2013). De Wet et al. (2008) also suggest that the ancestors of the Botryosphaeriaceae initially evolved on angiosperms before later colonising and diversifying on gymnosperms (De Wet et al. 2008). Previous research by De Wet et al. (2008) and Slippers et al. (2013) hypothesised potential host-associated co-evolution within the Botryosphaeriaceae, though without direct empirical evidence. Our current study’s findings of distinct genetic groups corresponding to host plant phylogeny and the observed patterns of host-associated differentiation provide additional empirical evidence supporting these earlier hypotheses about host-pathogen co-evolution in Botryosphaeriaceae.

The genetic differentiation patterns emphasise natural selection’s role in shaping pathogen diversity. We hypothesise that soft selective sweeps are a key mechanism driving this host-associated adaptation. In these sweeps, genetic diversity persists as selection acts on beneficial alleles that are already present at high frequencies (Garud et al. 2013; Lin et al. 2017). This aligns with our findings of high diversity, increased interspecies differentiation at specific target loci and large, stable effective population sizes across the studied Lasiodiplodia species. This mechanism is increasingly recognised as the predominant mode of recent rapid adaptive events in numerous species (Hernandez et al. 2011; Garud et al. 2013; Messer and Petrov 2013; Garud et al. 2015). These conclusions are further supported by previous sequence analysis results (Ko et al. 2023).

Conclusions

In conclusion, this study provides a comprehensive investigation into the genetic diversity, structure and evolutionary dynamics of Lasiodiplodia species, particularly L. theobromae. Transferable microsatellite markers, successfully applied across eight species, proved highly efficient in revealing genetic relationships and fine-scale population structures of these economically significant plant pathogens. Our analyses revealed high genetic diversity across all Lasiodiplodia species. Using multiple clustering approaches, we identified cryptic genetic groups within L. theobromae and host-associated differentiation across Lasiodiplodia species. Notably, genetic structure was influenced more by host specificity than geographic location, suggesting host-driven selection plays a significant role in pathogen evolution. Our findings revealed shared ancestral variation and limited gene flow amongst species, with intraspecific genetic clusters corresponding to host plant phylogeny, lending further support to the hypothesis of host-pathogen co-evolution in Lasiodiplodia. These findings contribute to more precise species delimitation and identification methods, enhance taxonomic resolution and provide information for effective management strategies for Lasiodiplodia species in agricultural settings. This study establishes a solid foundation for future research on the population genetics and evolutionary biology of Lasiodiplodia and related genera within the Botryosphaeriaceae.

Acknowledgements

We sincerely appreciate Dr. Min-Nan Tseng for his invaluable suggestions, which greatly assisted in the development of this research.

Additional information

Conflict of interest

The authors have declared that no competing interests exist.

Ethical statement

No ethical statement was reported.

Adherence to national and international regulations

All the fungal strains used in this study have been legally obtained, respecting the Convention on Biological Diversity (Rio Convention).

Funding

This research was funded by the National Science and Technology Council, Taiwan [MOST 108-2621-B-110-003-MY3, MOST 109-2313-B-110-005, MOST 111-2621-B-110-001 and NSTC 112-2621-B-110-001-MY3] to Y.C.C. and by partial financing (the Higher Education Sprout Project) of NSYSU. This research was also supported by Mahidol University (Fundamental Fund: fiscal year 2025 by National Science Research and Innovation Fund (NSRF) FF-031/2568, FF-030/2568) and Mahidol University Strategic Fund (MU-SRF-RS-32 C/67) to MSS.

Author contributions

YZK: collected samples, designed and performed research, analysed data and wrote the paper. HCS: designed and performed research and analysed data. MSS: designed and performed research, analysed data and wrote the paper. YCC: collected samples, designed and performed research, analysed data and wrote the paper.

Author ORCIDs

Ya-Zhu Ko https://orcid.org/0000-0002-5810-6487

Meng-Shin Shiao https://orcid.org/0000-0001-6655-1700

Yu-Chung Chiang https://orcid.org/0000-0002-0551-9309

Data availability

The original contributions presented in the study are included in the article/Suppl. material 1 as Raw Data Sheet. Further enquiries can be directed to the corresponding author.

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Supplementary materials

Supplementary material 1 

Raw data sheet

Ya-Zhu Ko, Huei-Chuan Shih, Meng-Shin Shiao, Yu-Chung Chiang

Data type: xlsx

Explanation note: The original contributions presented in the study.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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Supplementary material 2 

Additional information

Ya-Zhu Ko, Huei-Chuan Shih, Meng-Shin Shiao, Yu-Chung Chiang

Data type: pdf

Explanation note: table S1. Lists of isolated numbers, locations, hosts and population codes for Lasiodiplodia, Neofusicoccum and Botryosphaeria species. table S2. Transferability and allele size range (bp) of microsatellite markers for Lasiodiplodia and Neofusicoccum species. table S3. Estimates of genetic diversity by 16 polymorphic microsatellite loci of Lasiodiplodia and Neofusicoccum species. Ar: allelic richness, Ap: private allelic richness. table S4. List of pairwise genetic distance values (FST) (upper diagonal) and the proportion of shared alleles (DPS) (lower diagonal) amongst Lasiodiplodia species, based on microsatellite data. table S5. Summary of molecular variance (AMOVA) for microsatellite data of six Lasiodiplodia species and L. theobromae at distinct hierarchical levels. table S6. Mean LnP(K) and ΔK for each cluster using Bayesian assignment test in Structure of Lasiodiplodia species, based on the sequence and microsatellite data. The bold fonts indicate that the results are adopted and presented in subsequent tables. table S7. Estimated the IMa3 Model parameters of Lasiodiplodia species, based on the microsatellite data. The phylogenetic topology using the maximum parsimony phylogenetic tree (refers to fig. 23). The species codes are 0: L. brasiliense, 1: L. hormozganensis, 2: L. pseudotheobromae, 3: L. rubropurpurea, 4: L. theobromae, 5: L. iraniensis. The N, M and T are effective population size (q), migration rate (m) and divergence time (t) scaled by the mutation rate (2.80 × 10-6–2.50 × 10-5 per year). The N0-N5 represent the effective population size of species 0-5. The N6-N10 correspond to the ancestral effective population sizes on nodes of the tree topology. The Mi > j represents the migration rate from species i to species j forwards in time. The T means the divergence time on nodes of the tree topology. table S8. Estimated the demographic parameters in four genetic groups of L. theobromae, based on the microsatellite data by using IMa3. The population topology of four genetic groups was estimated use with the “-j0”. The N, M and T are effective population size (q), migration rate (m) and divergence time (t) scaled by the mutation rate (2.80 × 10-6–2.50 × 10-5 per year). N0-N3: effective population size. N4-N6: ancestral effective population sizes on nodes of the population topology. Mi > j: migration rate from species i to species j forwards in time. T: divergence time on nodes of the population topology. The genetic group codes are as follows: 0: G1, 1: G2, 2: G3, 3: G4. fig. S1. Principal coordinate analysis (PCoA) graphed in 2-dimensional space, based on SSR-based genetic distance data for (A) six Lasiodiplodia species, (B) Six Lasiodiplodia species with information on host and infected sites. Coord. 1 and Coord. 2 refer to the first and second principal coordinates, respectively. fig. S2. Statistical analyses for determining optimal cluster numbers in Lasiodiplodia species using microsatellite data. (A-B) DAPC: Bayesian Information Criterion (BIC) used to infer optimal cluster numbers for (A) six Lasiodiplodia species and (B) L. theobromae. (C-E) Structure: Delta K values and mean log-likelihood values (LnP(K)) for (C) Lasiodiplodia and Neofusicoccum species, (D) six Lasiodiplodia species and (E) L. theobromae. (F-G) GENELAND: Average density of inferred K for (F) Lasiodiplodia species and (G) L. theobromae. fig. S3. Discriminant Analysis of Principal Components and Minimum Spanning Network analyses of Lasiodiplodia species, based on microsatellite genotypes. (A) DAPC scatter plot for six Lasiodiplodia species and (B) L. theobromae. Each dot represents an individual and circles denote different groups identified by DAPC. Inset shows eigenvalues of the analysis. Colours indicate distinct species and genetic groups (G1-G4). Species codes: LBR: L. brasiliense, LHO: L. hormozganensis, LPSE: L. pseudotheobromae, LRU: L. rubropurpurea, LTH: L. theobromae, LIR: L. iraniensis. Numbers in parentheses after species codes represent sample sizes. (C) Minimum Spanning Network analyses of Lasiodiplodia species calculated using Bruvo’s distance, based on multilocus genotypes (MLGs). fig. S4. Bar plots for the genetic structure of Lasiodiplodia and Neofusicoccum species, based on microsatellite data generated by Structure. A vertical colour line represents each individual and the same colour indicates that the individual belongs to the same cluster. Black lines separate different species. The species codes are as follows: LBR: L. brasiliense, LHO: L. hormozganensis, LPSE: L. pseudotheobromae, LRU: L. rubropurpurea, LTH: L. theobromae, LIR: L. iraniensis. fig. S5. Morphological variability amongst the four genetic groups of L. theobromae from different isolations. Culture growing on PDA after four weeks at 25 °C. fig. S6. The minimum spanning network calculated via Bruvo’s distance, based on MLGs for microsatellite data of (A) L. brasiliense; (B) L. hormozganensis; (C) L. pseudotheobromae; (D) L. iraniensis; and (E) L. rubropurpurea using poppr in R. The different colour corresponds to different sample parts (stem and fruit). The red letters represent that the sample is isolated from the stem. fig. S7. Multilocus posterior distribution of unscaled demographic parameter estimates for six Lasiodiplodia species and four genetic groups of L. theobromae based on IMa3 analysis. (A, D) Best-fitting phylogenetic topologies used with -j0 command for Lasiodiplodia species and four genotype clusters of L. theobromae, respectively; (B, E) Posterior probability distributions for effective population sizes of Lasiodiplodia species and four genotype clusters of L. theobromae, respectively; (C, F) Posterior probability distributions for migration rates amongst Lasiodiplodia species and four genotype clusters of L. theobromae, respectively. The species codes are as follows: LBR: L. brasiliense, LHO: L. hormozganensis, LPSE: L. pseudotheobromae, LRU: L. rubropurpurea, LTH: L. theobromae, LIR: L. iraniensis.

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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