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Article

Invasive Everywhere? Phylogeographic Analysis of the Globally Distributed Tree Pathogen Lasiodiplodia theobromae

by
James Mehl
1,
Michael J. Wingfield
1,
Jolanda Roux
2 and
Bernard Slippers
3,*
1
Department of Microbiology and Plant Pathology, DST-NRF Centre of Excellence in Tree Health Biotechnology (CTHB), Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa
2
Department of Plant and Soil Sciences, DST-NRF CTHB, FABI, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa
3
Department of Genetics, DST-NRF CTHB, FABI, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa
*
Author to whom correspondence should be addressed.
Forests 2017, 8(5), 145; https://doi.org/10.3390/f8050145
Submission received: 19 March 2017 / Revised: 20 April 2017 / Accepted: 22 April 2017 / Published: 27 April 2017
(This article belongs to the Special Issue Forest Pathology and Plant Health)
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Abstract

:
Fungi in the Botryosphaeriaceae are important plant pathogens that persist endophytically in infected plant hosts. Lasiodiplodia theobromae is a prominent species in this family that infects numerous plants in tropical and subtropical areas. We characterized a collection of 255 isolates of L. theobromae from 52 plants and from many parts of the world to determine the global genetic structure and a possible origin of the fungus using sequence data from four nuclear loci. One to two dominant haplotypes emerged across all loci, none of which could be associated with geography or host; and no other population structure or subdivision was observed. The data also did not reveal a clear region of origin of the fungus. This global collection of L. theobromae thus appears to constitute a highly connected population. The most likely explanation for this is the human-mediated movement of plant material infected by this fungus over a long period of time. These data, together with related studies on other Botryosphaeriaceae, highlight the inability of quarantine systems to reduce the spread of pathogens with a prolonged latent phase.

1. Introduction

The health of both native and planted forests is under increasing pressure from rapid changes in the environment (many related to the growing impacts of human society) or the introduction of non-native, invasive pathogens and pests [1,2]. The rise in the number of invasive pathogens and pests is thought to be driven primarily by increasing international movement and trade in plants and plant products [2,3]. This problem might be even more severe than previously realized, because quarantine mechanisms designed to reduce such movement are oblivious of the multitude of cryptic and endophytic microbes that occur asymptomatically within plants [3,4]. A prominent group of fungi that reflect this threat is the Botryosphaeriaceae.
The Botryosphaeriaceae includes many important plant pathogens such as well-known species in Botryosphaeria, Diplodia, Dothiorella, Lasiodiplodia, Macrophomina, and Neofusicoccum [5]. These fungi can persist endophytically within apparently asymptomatic plant material, from where they can cause disease when the host is stressed [4,6]. Many Botryosphaeriaceae species infect multiple plant hosts and commonly occur on both native and non-native hosts in a region [7,8,9,10,11]. Consequently, they can easily be spread when plants or plant material are moved between regions [3,4].
The majority of the Botryosphaeriaceae have relatively limited distributions [12,13,14,15]. This is perhaps not surprising given that their spread is closely linked with rainfall and associated wind dispersal, and is consequently expected to be relatively local [6,16]. While stepwise, long-distance spread would be possible, a continuous distribution of available hosts would be needed, making spread across oceans or other major physical barriers unlikely. A few species, however, have very broad global distributions, including Botryosphaeria dothidea, Diplodia sapinea, D. seriata, Dothiorella sarmentorum, Neofusicoccum parvum, and Lasiodiplodia theobromae [4,11,17,18,19,20]. These species are commonly associated with agriculture, forestry, or urban environments, and it is thought that human-assisted dispersal has played a significant role in their distributions [15,18,19].
A number of previous studies have suggested that human-assisted dispersal of the Botryosphaeriaceae might in some cases occur on a large scale. For example, D. sapinea has been introduced to all areas where Pinus species have been planted in the southern hemisphere [21]. Population genetic studies on this fungus suggest that, in most areas, these introductions have been so extensive that the diversity of the non-native populations exceeds that of some local native populations of the fungus [22,23]. Another example is N. parvum, which is also highly genetically diverse, with 12 lineages identified using microsatellite markers, many of which are shared between different countries and on different continents [18]. In the case of Macrophomina phaseolina, Sarr et al. [24] identified three lineages using DNA sequence data for six loci, also with shared geographic ranges. Analyses of a global collection of isolates of B. dothidea using two DNA sequence markers, showed that isolates grouped into two main haplotypes, with no structure based on either host genus or country of origin [19].
Lasiodiplodia theobromae is one of the most commonly reported species in the Botryosphaeriaceae. This fungus has been associated with at least 500 plant hosts from many tropical and subtropical regions globally [17,25]. However, many of these host associations and disease reports for L. theobromae predate the use of DNA sequencing for species identification, and at least some could be attributed to cryptic species related to L. theobromae [12,17]. In recent years, many cryptic species have been described for isolates previously treated as L. theobromae due to their morphological similarity, but that are distinct based mainly on DNA sequence data from two loci, the internal transcribed spacer ribosomal DNA (ITS) and translation elongation factor 1α (tef1α) [26,27,28]. At present, the genus Lasiodiplodia comprises 31 species [20], mostly distinguished using sequence data. Furthermore, Cruywagen et al. [27] recently showed that four species of Lasiodiplodia represent hybrid species, based on more complete isolate collections or sequence data of more loci than originally used. In view of all of these studies, there is no overall clarity on the host or geographic distribution of what can be considered L. theobromae sensu stricto, based on current DNA-based definitions of this taxon. It is also not clear where the fungus might have originated, where it is invasive, or to what extent humans have facilitated the dispersal of this fungus globally.
The first aim of this study was to screen a global collection of isolates putatively identified as L. theobromae and thus to identify a collection that represented L. theobromae sensu stricto based on DNA sequence data. Sequence data from four nuclear loci were then used to determine whether there was genetic structure amongst this global collection of L. theobromae isolates. Finally, we considered whether the data revealed a possible area of origin for the fungus.

2. Materials and Methods

2.1. Isolate Collections and DNA Extractions

A total of 426 fungal isolates designated as Botryosphaeria sp. or L. theobromae were obtained from the culture collection (CMW) of the Forestry and Agricultural Biotechnology Institute (FABI) at the University of Pretoria, Pretoria, South Africa. These isolates originated from collections made in Australia, Benin, Brazil, Cameroon, China, Colombia, Ecuador, Indonesia, Madagascar, Mexico, Oman, Peru, South Africa, Thailand, Uganda, the United States of America (USA), Venezuela, and Zambia (Figure 1). Several isolates identified as L. theobromae were also sourced from the culture collection of the Westerdijk Fungal Biodiversity Institute (previously known as the Centraalbureau voor Schimmelcultures), Utrecht, the Netherlands. In addition, sequences were sourced from GenBank for taxa labeled as “Botryosphaeria rhodina” or “Lasiodiplodia theobromae” and were included in datasets for analyses (Table 1).
Isolates assembled for this study were purified by transferring single hyphal tips to clean culture plates following the method described in Mehl et al. [30]. DNA was extracted from isolates using the method described by Wright et al. [31] with pellets suspended in 50 μL Tris Ethylenediaminetetraacetic acid (TE) buffer. DNA concentrations were determined using a NanoDrop® ND-1000 and accompanying software (NanoDrop Technologies, DuPont Agricultural Genomics Laboratories, Wilmington, DE, USA).

2.2. PCR Amplifications, DNA Sequencing, and Confirmation of Species Identity

Isolate identities were confirmed as L. theobromae using data from four loci; the ITS rDNA (including the ITS1, 5.8S nuclear ribosomal RNA (nrRNA) and ITS2), tef1α, β-tubulin-2 (tub2) and RNA polymerase II (rpb2) loci. Preliminary identification was done for all isolates using maximum likelihood phylogenetic analysis of sequence data from the tef1α locus, which was then supported by data for the other three loci. The dataset for tef1α included all other Lasiodiplodia species known at the time of the analyses.
For PCR amplifications, the primer sets ITS1 and ITS4 [32], EF1F and EF2R [33], EF688F and EF1251R [34], Bt-2a and Bt-2b [35], and RPB2-LasF and RPB2-LasR [27] were used to amplify the ITS, tef1α, tub2, and rpb2 loci, respectively. PCR mixes were the same as those that included KAPA Taq and MyTaq DNA polymerases as described by Mehl et al. [36] and PCR cycling conditions and product visualization were the same as those used by Mehl et al. [37]. PCR product purification and sequencing were done as described by Mehl et al. [30] and sequences were examined and edited using MEGA 6 [38].
Sequence datasets were aligned using MAFFT 6 [39] with the G-INS-I algorithm selected and alignment errors corrected visually. For the tef1α dataset that included isolates of species other than L. theobromae, the best nucleotide substitution model was determined using JMODELTEST 2.1.3 [40] with the corrected Akaike Information Criterion selected. The dataset was analyzed with PHYML 3.0.1 [41] using the same model parameters as determined by JMODELTEST and the robustness of the generated tree was evaluated using 1000 bootstrap replicates. Sequences generated in this study were deposited in GenBank (Table 1).

2.3. Haplotype Assignment and Networks

To ascertain the number of haplotypes for each dataset and to identify where haplotypes occurred, sequence datasets were generated for each locus separately, along with one combined dataset for the ITS and tef1α regions. The combined dataset was generated because it included the majority of isolates and provided a better representation of the diversity inherent in the populations and regions. For each dataset, isolates were assigned to different haplotypes using the map program in Mobyle SNAP Workbench [42]. Sites that violated the infinite sites model, as well as indels, were removed prior to assigning haplotypes. Median joining haplotype networks were then constructed for each dataset, as well as for the combined dataset using NETWORK 4.6.1.3 [43,44].

2.4. Population and Regional Structure and Diversity

To determine whether there was genetic structure present in the datasets and to test for potential population subdivision, haplotype assignments for all four loci, as determined by Mobyle SNAP Workbench, were analyzed using the program STRUCTURE 2.3.4 [45,46]. STRUCTURE uses a Bayesian clustering algorithm to evaluate the possibility of multiple lineages being present. Two sets of analyses were made, the first of which evaluated whether there was genetic structure in the dataset for all isolates. The second set of analyses involved grouping isolates into five populations based on the continent of origin (North America, South America, Africa, Eurasia, and Australasia) and then running STRUCTURE analyses on pairs of populations to determine whether there was genetic structure between any of the populations (10 pairs including every possible combination).
For all analyses, burnin was set at 300,000 and the number of Markov Chain Monte Carlo (MCMC) repeats was set at 900,000, so that more than 1,000,000 repeats were done to generate robust results. Initially lambda was computed based on five runs at K = 1. The model selected entailed admixture with independent allele frequencies and the lambda value computed. Twenty iterations were done for each value of K = 1 to K = 10. Results were parsed through STRUCTURE HARVESTER [47] and the DeltaK [48] output used to identify possible subpopulations.
Population statistics, including gene and nucleotide diversities, were inferred using ARLEQUIN 3.5.1.2 [49] on the ITS, tef1α, combined ITS and tef1α, and tub2 sequence datasets for every geographic country and region assigned. Pairwise population differentiation (ΦST) comparisons were computed for all populations and regions using ARLEQUIN on the same dataset.

2.5. Putative Geographic Origin of Lasiodiplodia theobromae

To determine the possible centre of origin for L. theobromae, scenarios of how populations could have arisen were simulated and the summary statistics of these compared to those of the observed dataset using DIYABC 2.0.4 [50]. For these analyses, the sequence datasets of isolates (with data from all four loci) were grouped according to continent of origin, similar to the arrangements for the second set of analyses using STRUCTURE. To determine whether any of the populations could be ancestral, pairs of populations were evaluated using three possible scenarios (Figure 2): scenario 1—the first population is ancestral to both, scenario 2—the second population is ancestral to both, scenario 3—both populations diverged from an unknown ancestral population. For each scenario, 1,000,000 datasets were simulated.
Posterior probabilities of scenarios for each analysis step were computed using polychotomous logistic regression on 1% of the simulated datasets closest to the dataset provided. The best scenario was the one having the highest probability and with 95% confidence intervals that did not overlap with those of the other scenarios tested.

3. Results

3.1. Isolate Collections and Confirmation of Species Identity

The tef1α sequence dataset that included all isolates, as well as representatives of other Lasiodiplodia species, consisted of 340 characters (151 parsimony informative, 22 parsimony uninformative, 167 constant). The model selected by JMODELTEST was HKY (transitions:transversions (ti/tv) = 1.719, γ = 0.407). The resulting tree contained a clade of 255 isolates, from 26 countries, that was considered to represent L. theobromae sensu stricto as it included authentic isolates of this species (Figure S1). Of these, 95 isolates represented a global collection assembled over many years and stored in the CMW culture collection. The other isolates sampled from this collection grouped with Botryosphaeria dothidea, D. pseudoseriata, L. brasiliense, L. crassispora, L. gilanensis, L. gonubiensis, L. hormozganensis, L. iraniensis, L. laeliocattleyae, L. mahajangana, L. margaritacea, L. parva, L. pseudotheobromae, L. viticola, Neofusicoccum parvum, and N. vitifusiforme (data not shown) and were thus excluded. Four isolates were from the collection of the Westerdijk Fungal Biodiveristy Institute. The remaining sequences for 156 additional isolates were sourced from GenBank (Table 1, Figure 1). Thus, all subsequent analyses were based on data for this core group of 255 isolates from 52 plant hosts.
Countries considered in the analyses were grouped into eight geographic regions, including north America (Hawaii, Mexico, Puerto Rico, United States of America—USA), western south America (Colombia, Ecuador, Peru, Venezuela), eastern south America (Brazil, Uruguay), western Africa (Benin, Cameroon), southern and eastern Africa (Madagascar, South Africa, Uganda, Zambia), Middle East and Europe (Egypt, Iran, Italy, Oman), Asia (China, Indonesia, Korea, Thailand), and Australasia (Australia, Papua New Guinea) (Table 1 and Table 2).

3.2. Haplotype Assignment and Networks

The ITS dataset (252 isolates) consisted of 333 characters (two parsimony informative, 23 parsimony uninformative, 308 constant) and yielded 11 haplotypes with 17 fixed single nucleotide polymorphisms (SNPs) (Table S1, Figure 3a). The tef1α dataset (255 isolates) consisted of 216 characters (five parsimony informative, 11 parsimony uninformative, 200 constant) and yielded eight haplotypes with 14 SNPs (Table S1, Figure 3b). The tub2 dataset (153 isolates) consisted of 309 characters (six parsimony informative, nine parsimony uninformative, 294 constant) and yielded 12 haplotypes with 15 SNPs (Table S1, Figure 3c). The rpb2 dataset (73 isolates) consisted of 535 characters (zero parsimony informative, zero parsimony uninformative, 535 constant) and yielded a single haplotype. The combined ITS and tef1α dataset consisted of 549 characters (seven parsimony informative, 34 parsimony uninformative, 508 constant) and yielded 17 haplotypes (Figure 4).
There was no clear grouping of isolates based on region of origin. Analyses of the ITS and tub2 loci (Figure 3) showed that one haplotype was most common. The rpb2 dataset was not analyzed further as it constituted only one haplotype. For the tef1α dataset and the combined dataset of ITS and tef1α, two closely related (separated by a single mutation) haplotypes were most common. These common haplotypes represented isolates sourced from all eight regions sampled (Figure 3 and Figure 4, Table S2).
An analysis of haplotypes (Table S3) showed that Asia and North America had the greatest number of unique haplotypes (10 and four, respectively) across all three loci (ITS, tef1α, and tub2). For the remaining regions, one to three unique haplotypes were detected. When considering the individual loci, three unique ITS haplotypes and six unique tub2 haplotypes were observed amongst isolates from Asia. For all other regions, two or fewer unique haplotypes were found. Upon closer examination, these unique haplotypes were confined to specific countries. Two of the five isolates collected from the USA (North America) had unique haplotypes, while 15 isolates collected from three locations in China over a period of four years had unique haplotypes.

3.3. Population and Regional Structure and Diversity

There was no evidence of sub-populations present in either set of the STRUCTURE analyses. In the first set of analyses that considered all isolates, the significantly highest DeltaK value was at K = 8 populations, but the corresponding barplot showed that no structure was present (Figure 5). Similarly, in the second set of analyses that evaluated genetic structure between the pairs of populations, the highest DeltaK values obtained differed for each population pair tested and varied from K = 2 to K = 8. However, the corresponding barplots for these values of K all showed that no structure was present in the data (Figure S2a–j).
Gene diversity was low for most countries and regions sampled. High gene diversity (>0.4) was detected for individual loci in countries including USA, Peru, Uganda, China, Indonesia, and Thailand, and in North America (Table 2). High nucleotide diversity was detected in the above-mentioned countries, as well as in Ecuador, Venezuela, Brazil, Cameroon, South Africa, Oman and Australia, and in several regions including western and eastern South America, western Africa, and Australasia (Table 2).
When combining the gene and nucleotide diversities across the three individual loci (ITS, tef1α, tub2) (Table 2), the greatest diversity overall was recorded for North America (HE = 0.798, π × 10−3 = 6.146). High gene diversity (HE > 0.4) was also detected for Australasia (HE = 0.606, π × 10−3 = 6.146), Middle East and Europe (HE = 0.554, π × 10−3 = 2.420), western South America (HE = 0.432, π × 10−3 = 3.933), and southern and eastern Africa (HE = 0.418, π × 10−3 = 4.208). Asia and western Africa had low levels of gene diversity, but high levels of nucleotide diversity (Asia: HE = 0.312, π × 10−3 = 6.008; western Africa: HE = 0.220, π × 10−3 = 5.099). Eastern South America had the lowest diversity overall (HE = 0.281, π × 10−3 = 3.703).
Most populations were not highly genetically differentiated, based on ΦST values. The greatest genetic differentiation was seen in the north American and western African populations, with moderate to very high levels of genetic differentiation [51] compared to the other populations assessed (Table 3).

3.4. Putative Geographic Origin of Lasiodiplodia theobromae

Posterior probabilities for all of the scenarios tested for the pairs of populations were low (Table S3) when a posterior probability of 0.7 or more was considered high. Ninety-five percent (95%) confidence intervals for different scenarios for the same pairwise comparison often overlapped (Table S4), indicating a lack of resolution in choosing one specific scenario over the others. These results are likely due to the lack of variation in the markers. However, they support the conclusions of other analyses reported above that did not identify any specific region as an evolutionary origin of the fungus over others.

4. Discussion

Results of this study suggest that isolates associated with L. theobromae collected from many different hosts and countries of the world represent a single globally distributed species, with no obvious phylogeographic structure. This was evident from various analyses on sequence datasets for four loci (only three of which were variable) in 255 isolates from 52 hosts from all continents other than Antarctica. We thus contend that the only likely explanation for this result is the large-scale human dispersal of this fungal species.
The lack of population structure in L. theobromae on a global scale is in contrast to studies on other broadly distributed fungi that infect commercially cultivated plants or are medically important (e.g., [52,53,54]). These previous studies have typically revealed phylogeographic structure within species, with multiple cryptic lineages linked to geographic regions, leading to the conclusion that, for fungi, “nothing is generally everywhere” [54,55]. Subsequent studies have shown that lineages in some of these fungi (e.g., Fusarium graminearum and Histoplasma capsulatum) represent cryptic species [56,57]. An exception to this rule is Aspergillus fumigatus, which has very small (2–3 μm), wind-dispersed conidia. This special case is hypothesized to possibly arise from human influence, especially through environmental impact, which has created ideal habitats for the fungus [58,59].
Amongst the Botryosphaeriaceae, the shared genetic diversity across continents is not unique to L. theobromae. Neofusicoccum parvum also appears to have a similar global distribution of diversity [18]. Recently, Marsberg et al. [19] reported a similar lack of structure amongst a global collection of B. dothidea isolates. All three of these species have exceptionally broad host ranges across many plant families, and this has no doubt facilitated their broad distribution. Furthermore, N. parvum was reported to be more common in human-associated and disturbed environments, such as plantations, orchards, and urban environments [15], which could facilitate invasion (similar to A. fumigatus). Lasiodiplodia theobromae, B. dothidea, and N. parvum are ideal systems in which to further test these hypotheses regarding the role of host and human association in facilitating invasions.
The absence of phylogeographic structure amongst global collections of Botryosphaeriaceae such as L. theobromae is surprising in the light of their spore dispersal mechanism. Spores of the Botryosphaeriaceae, including those of L. theobromae, emerge in a sticky matrix and are relatively large (the most common spores, conidia, range between 10–35 × 8–15 μm; [12]) and are naturally dispersed by wind and rain splash [6,16,60,61,62]. Consequently spores are not expected to be spread over large distances or across geographic barriers and certainly not between continents. The limited ability of these fungi to disperse over long distances would be expected to result in a vicariant population structure with differences at a regional level between populations. The lack of population structure and dominance of identical multilocus haplotypes on distant continents can only be explained by assisted dispersal. In this case, human-mediated movement of plant material [1,3,63] has most likely facilitated this global dispersal.
A large number of the plant hosts from which isolates of L. theobromae were obtained for the present study are commercially important and traded globally as part of the nursery trade, or cultivated either for agriculture (e.g., Carica papaya, Mangifera indica, and Vitis vinifera) or forestry (e.g., Acacia mangium, Eucalyptus species). The Botryosphaeriaceae, including L. theobromae, are common endophytes in such plants and plant products, including fruits [4,64]. Endophytic infections by these fungi are typically invisible and are thus not detected by quarantine systems [3,19,65]. The present study highlights how widely species of the Botryosphaeriaceae, specifically L. theobromae, can be spread as a consequence of such human-assisted movement.
Results of this study were consistent with those of previous studies that used microsatellite markers to study populations of L. theobromae [66,67,68]. These previous studies considered populations of isolates from Mexico, South Africa, Venezuela, India, and Cameroon, and detected extensive gene flow and shared genotypes from different hosts [66,67,68] and from different countries [66]. Our analyses provide a broader representation with consistent results, including publicly available data combined with data from our own collection of L. theobromae isolates.
No clear centre of origin for L. theobromae emerged from this study based on gene diversity. The greatest cumulative diversity obtained by combining the diversities for the individual loci was detected for the North American collections. Population differentiation tests highlighted the North American and west African populations as being moderately to fairly distinct from the rest. The North American and Asian regions had higher numbers of unique haplotypes (four and ten respectively), but these haplotypes were present only in some countries (USA and China, respectively).
The diversity of L. theobromae in the USA was especially noticeable given that only a few isolates were available for that country. Further sampling would be needed to confirm whether this reflects a possible native population or is the result of introductions through trade with various other regions [55]. It has been shown for other organisms, for example lizards, that the invasive populations could be more diverse than native populations if introduced multiple times and from various isolated native populations [69]. This has also been observed in fungi such as D. sapinea in parts of its invasive range (e.g., in South Africa; [22,23]).
This study provides a valuable foundation for future studies that will investigate the genetic structure, movement, and origins in L. theobromae and other important species of the Botryosphaeriaceae. The loci used were chosen to allow for the inclusion of publicly available sequence data so as to obtain a more comprehensive global perspective. We excluded cryptic lineages based on previous studies that have resolved the taxonomy of Lasiodiplodia spp. and have defined these lineages as distinct species, including hybrid species [12,27]. As such, the current collection represents a valuable resource to represent a sensu stricto definition of the species. This information can now serve as a basis for further collections targeted at more isolated areas that could reveal the potential origin of the fungus. Other markers, such as microsatellite markers, would also provide further insights into origins and patterns of spread of this fungus. However, this will require greater numbers of isolates and ideally a more structured sampling regime than was possible for this study [18].

5. Conclusions

The results of this study, together with other recent investigations on diversity amongst global populations of Botryosphaeriaceae, have highlighted the fact that human-mediated movement of plant material infected by these fungi can facilitate their movement globally. The extent of movement of this serious pathogen around the world suggests a major shortcoming in the ability of quarantine systems to inhibit or stop its movement. These fungi, and their hosts, are also likely to increasingly be influenced by global climate change. Because the earth is subjected to more extreme weather events, plants are likely to become increasingly stressed and more susceptible to disease by pathogens [70], including opportunistic and generalist pathogens such as the Botryosphaeriaceae. Consequently, the Botryosphaeriaceae, including L. theobromae, will become increasingly prominent and important for the management of health in both native and commercially cultivated woody plants. Serious attention should be given to strategies that could reduce the extent of such movement. Such management strategies are likely to also be relevant to the numerous other endophytes and potential latent pathogens that inhabit plants and plant material traded around the world.

Supplementary Materials

The following are available online at www.mdpi.com/1999-4907/8/5/145/s1, Figure S1: Maximum likelihood tree of the tef1α sequence dataset for the initial identification of isolates for inclusion in this study. Included were type and paratype strains of other Lasiodiplodia species, Figure S2: STRUCTURE output from pairwise comparisons of populations. Each plot includes the DeltaK analysis from STRUCTURE HARVESTER (top) and the corresponding barplot for the highest value of K. Pairwise comparisons as follows: (a) north America and south America, (b) north America and Africa, (c) north America and Eurasia, (d) north America and Australasia, (e) south America and Africa, (f) south America and Eurasia, (g) south America and Australasia, (h) Africa and Eurasia, (i) Africa and Australasia and (j) Eurasia and Australasia, Table S1: Polymorphic sites for the respective haplotypes for the ITS, tef1α and tub2 datasets, Table S2: Haplotype assignments for every isolate used in this study, based on the sequence datasets, Table S3: Summary of haplotypes obtained and unique haplotypes (listed in brackets) found for each locus, Table S4: Posterior probabilities (with 95% confidence intervals in parentheses) of pairwise comparisons for three scenarios to test for possible ancestry between populations done in DIYABC. In scenario 1, population 1 is ancestral to both. In scenario 2, population 2 is ancestral to both. In scenario 3, both populations diverged from an unknown source population.

Acknowledgments

We thank the Department of Science and Technology (DST)-National Research Foundation (NRF) Centre of Excellence in Tree Health Biotechnology (CTHB) and members of the Tree Protection Co-operative Programme (TPCP), South Africa, for financial support. Mr. Victor Kalbskopf and Ms. Elmien Slabbert assisted the lead author with some of the laboratory work required for this study and their assistance is gratefully acknowledged.

Author Contributions

James Mehl conducted the laboratory work, analyzed the data, and drafted the manuscript. Bernard Slippers, Jolanda Roux, and Michael J. Wingfield conceived the study, assembled collections of isolates, assisted with the analyses, contributed to and assisted in writing the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Sites (black circles) and biogeographic regions (shaded) where isolates originated from. Map source: [29].
Figure 1. Sites (black circles) and biogeographic regions (shaded) where isolates originated from. Map source: [29].
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Figure 2. Scenarios evaluated to determine possible ancestry between any of the pairs of populations tested. In scenario 1, population 1 is ancestral to both. In scenario 2, population 2 is ancestral to both. In scenario 3, both populations diverged from an unknown source population.
Figure 2. Scenarios evaluated to determine possible ancestry between any of the pairs of populations tested. In scenario 1, population 1 is ancestral to both. In scenario 2, population 2 is ancestral to both. In scenario 3, both populations diverged from an unknown source population.
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Figure 3. Haplotype networks generated for the (a) internal transcribed spacer rDNA (ITS), (b) translation elongation factor 1α (tef1α), and (c) β-tubulin-2 (tub2) loci. Only one haplotype resulted from analysis of the RNA polymerase II (rpb2) locus and is not included. Colours represent the different regions isolates were obtained from.
Figure 3. Haplotype networks generated for the (a) internal transcribed spacer rDNA (ITS), (b) translation elongation factor 1α (tef1α), and (c) β-tubulin-2 (tub2) loci. Only one haplotype resulted from analysis of the RNA polymerase II (rpb2) locus and is not included. Colours represent the different regions isolates were obtained from.
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Figure 4. Haplotype network generated for the combined ITS and tef1α dataset. Colours represent the different regions isolates were obtained from. Haplotypes designated by Roman numerals (I–XVII). Open circles represent inferred haplotypes.
Figure 4. Haplotype network generated for the combined ITS and tef1α dataset. Colours represent the different regions isolates were obtained from. Haplotypes designated by Roman numerals (I–XVII). Open circles represent inferred haplotypes.
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Figure 5. Structure output on the combined dataset of all four loci. The output from the DeltaK analysis from STRUCTURE HARVESTER (top) resulted in the highest peak at K = 8 populations, but the corresponding barplot (bottom) showed no structure.
Figure 5. Structure output on the combined dataset of all four loci. The output from the DeltaK analysis from STRUCTURE HARVESTER (top) resulted in the highest peak at K = 8 populations, but the corresponding barplot (bottom) showed no structure.
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Table
Table 1. List of isolates used for genetic analyses. Isolates are ordered geographically, moving from North America eastwards to Australia. Countries in each region are arranged alphabetically. Sequences from GenBank are italicized.
Table 1. List of isolates used for genetic analyses. Isolates are ordered geographically, moving from North America eastwards to Australia. Countries in each region are arranged alphabetically. Sequences from GenBank are italicized.
RegionCountry, LocalityIsolateHostPlant FamilyITStef1αtub2rpb2
North AmericaHawaiiCBS111530Leucospermum sp.ProteaceaeFJ150695EF622054KU887531KU696382
MexicoBOM230Carica papayaCaricaceaeKR001856KT075154
MexicoBOS104Car. papayaCaricaceaeKR001857KT075158
MexicoBOT112Car. papayaCaricaceaeKT075139KT075155
MexicoBOT359Car. papayaCaricaceaeKR001859KT075159
MexicoLAM118Car. papayaCaricaceaeKT075141KT075156
Puerto RicoK286Mangifera indicaAnacardiaceaeKC631660KC631656KC631652
Puerto RicoK8Man. indicaAnacardiaceaeKC631659KC631655KC631651
Puerto RicoPHLO10Dimocarpus longanSapindaceaeKC964547KC964554KC964550
Puerto RicoPHLO9Dim. longanSapindaceaeKC964546KC964553KC964549
USACBS124.13Unknown DQ458890DQ458875DQ458858KY472887
USA, FloridaCMW34107Eucalyptus amplifoliaMyrtaceaeKY473070KY473018
USA, FloridaSEFL3Vaccinium sp.EricaceaeJN607091JN607114JN607138
USA, Florida, ApopkaUF05161Vacc. corymbosumEricaceaeGQ845096GQ850468
USA, Florida, Alaucha CountryWFF92Vacc. corymbosumEricaceaeGQ845095GQ850467
Western South AmericaColombia, AndesCMW34303Unknown KY473031KY472979
EcuadorCMW4694Schizolobium parahybaFabaceaeKY473033KY472981KY472913KY472842
EcuadorCMW4695Sch. parahybaFabaceaeKF886707KF886730KY472914KY472843
EcuadorCMW4696Sch. parahybaFabaceaeKY473034KY472982KY472915
EcuadorCMW9273Sch. parahybaFabaceaeKY473035KY472983KY472916KY472844
Ecuador, EsmeraldasCMW22924Sch. parahybaFabaceaeKF886709KF886732KY472911KY472840
Ecuador, EsmeraldasCMW22926Sch. parahybaFabaceaeKY473032KY472980KY472912KY472841
PeruCMW31861Theobroma cacaoMalvaceaeKY473048KY472996KY472935
PeruCMW31867Th. cacaoMalvaceaeKY473049KY472997KY472936KY472862
PeruCMW31899Th. cacaoMalvaceaeKY473050KY472998KY472937KY472863
Peru, Cienneguillo Norte, PiuraLA-SJ1Vitis viniferaVitaceaeKM401976KM401973
Peru, Sol-Sol, PiuraLA-SOL1Vts. viniferaVitaceaeKM401974KM401971
Peru, San Vicente, PiuraLA-SV1Vts. viniferaVitaceaeKM401975KM401972
Venezuela, GuayanaA10Acacia mangiumFabaceaeJX545093JX545113JX545133
Venezuela, GuayanaA13Ac. mangiumFabaceaeJX545094JX545114JX545134
Venezuela, AcariguaCMW13490Euc. urophyllaMyrtaceaeKY473071KY473019KY472962KY472888
Venezuela, CojedesCMW13501Ac. mangiumFabaceaeKY473072KY473020KY472963KY472889
Venezuela, Falcon StateCMW13519Pinus caribaea var. hondurensisPinaceaeKY473073KY473021KY472964KY472890
Venezuela, Falcon StateCMW13527Pin. caribaea var. hondurensisPinaceaeKY473074KY473022KY472965KY472891
Eastern South AmericaBrazilARM122Jatropha curcasEuphorbiaceaeKF553895KF553896
Brazil, Vicosa, MGCDA 425Cocos nuciferaArecaceaeKP244697KP308475KP308531
Brazil, Vicosa, MGCDA 444Coc. nuciferaArecaceaeKP244699KP308477KP308532
Brazil, Vicosa, MGCDA 450Coc. nuciferaArecaceaeKP244688KP308478KP308533
Brazil, Vicosa, MGCDA 455Coc. nuciferaArecaceaeKP244689KP308463KP308534
Brazil, Juazeiro, BACDA 465Coc. nuciferaArecaceaeKP244701KP308465KP308535
Brazil, Juazeiro, BACDA 467Coc. nuciferaArecaceaeKP244702KP308473KP308536
Brazil, Juazeiro, BACDA 469Coc. nuciferaArecaceaeKP244691KP308466KP308537
Brazil, Juazeiro, BACDA 472Coc. nuciferaArecaceaeKP244692KP308467KP308538
Brazil, Sao Francisco ValleyCMM 0307Vts. viniferaVitaceaeKJ450879KJ417879
Brazil, Sao Francisco ValleyCMM 0310Vts. viniferaVitaceaeKJ450880KJ417880
Brazil, Sao Francisco ValleyCMM 0384Vts. viniferaVitaceaeKJ450876KJ417876
Brazil, Sao Francisco ValleyCMM 0455Vts. viniferaVitaceaeKJ450878KJ417878
Brazil, Sao Francisco ValleyCMM 0820Vts. viniferaVitaceaeKJ450877KJ417877
BrazilCMM1476Man. indicaAnacardiaceaeJX464083JX464057
BrazilCMM1481Man. indicaAnacardiaceaeJX464095JX464021
BrazilCMM1517Man. indicaAnacardiaceaeJX464060JX464054
BrazilCMM2168Car. papayaCaricaceaeKC484817KC481572
BrazilCMM2179Car. papayaCaricaceaeKC484787KC481569
BrazilCMM2183Car. papayaCaricaceaeKC484824KC481573
BrazilCMM2190Car. papayaCaricaceaeKC484780KC481518
BrazilCMM2193Car. papayaCaricaceaeKC484826KC481550
BrazilCMM2208Car. papayaCaricaceaeKC484776KC481575
BrazilCMM2209Car. papayaCaricaceaeKC484784KC481578
BrazilCMM2210Car. papayaCaricaceaeKC484783KC481577
BrazilCMM2231Car. papayaCaricaceaeKC484775KC481515
BrazilCMM2232Car. papayaCaricaceaeKC484785KC481521
BrazilCMM2235Car. papayaCaricaceaeKC484779KC481517
BrazilCMM2237Car. papayaCaricaceaeKC484819KC481547
BrazilCMM2238Car. papayaCaricaceaeKC484771KC481512
BrazilCMM2239Car. papayaCaricaceaeKC484786KC481522
BrazilCMM2241Car. papayaCaricaceaeKC484790KC481571
BrazilCMM2261Car. papayaCaricaceaeKC484789KC481579
BrazilCMM2262Car. papayaCaricaceaeKC484822KC481581
BrazilCMM2265Car. papayaCaricaceaeKC484772KC481574
BrazilCMM2267Car. papayaCaricaceaeKC484777KC481576
BrazilCMM2268Car. papayaCaricaceaeKC484818KC481580
BrazilCMM2269Car. papayaCaricaceaeKC484821KC481585
BrazilCMM2276Car. papayaCaricaceaeKC484820KC481548
BrazilCMM2278Car. papayaCaricaceaeKC484781KC481519
BrazilCMM2280Car. papayaCaricaceaeKC484773KC481513
BrazilCMM2282Car. papayaCaricaceaeKC484827KC481551
BrazilCMM2294Car. papayaCaricaceaeKC484828KC481552
BrazilCMM2295Car. papayaCaricaceaeKC484774KC481514
BrazilCMM2297Car. papayaCaricaceaeKC484823KC481582
BrazilCMM2303Car. papayaCaricaceaeKC484816KC481546
BrazilCMM2306Car. papayaCaricaceaeKC484788KC481570
BrazilCMM2310Car. papayaCaricaceaeKC484782KC481520
BrazilCMM2327Car. papayaCaricaceaeKC484778KC481516
BrazilCMM2328Car. papayaCaricaceaeKC484825KC481549
BrazilCMM3612Jat. curcasEuphorbiaceaeKF234546KF226692KF254929
BrazilCMM3647Jat. curcasEuphorbiaceaeKF234548KF226704KF254932
BrazilCMM3654Jat. curcasEuphorbiaceaeKF234555KF226716KF254939
BrazilCMM3831Jat. curcasEuphorbiaceaeKF234556KF226717KF254940
BrazilCMM4019Mangifera indicaAnacardiaceaeJX464096JX464026
BrazilCMM4021Man. indicaAnacardiaceaeJX464064JX464047
BrazilCMM4033Man. indicaAnacardiaceaeJX464081JX464032
BrazilCMM4039Man. indicaAnacardiaceaeJX464065JX464041
BrazilCMM4041Man. indicaAnacardiaceaeKC184891JX464042
BrazilCMM4042Man. indicaAnacardiaceaeJX464070JX464017
BrazilCMM4043Man. indicaAnacardiaceaeJX464087JX464056
BrazilCMM4046Man. indicaAnacardiaceaeJX464091JX464027
BrazilCMM4047Man. indicaAnacardiaceaeJX464082JX464025
BrazilCMM4048Man. indicaAnacardiaceaeJX464093JX464048
BrazilCMM4050Man. indicaAnacardiaceaeJX464062JX464024
BrazilCMM4499Anacardium occidentaleAnacardiaceaeKT325578KT325587
BrazilCMM4508Ana. occidentaleAnacardiaceaeKT325576KT325588
BrazilCMM4513Ana. occidentaleAnacardiaceaeKT325577KT325589
BrazilCMW32099Unknown KY473028KY472971KY472897
Brazil, Vicosa, MGCOAD 1788Coc. nuciferaArecaceaeKP244698KP308476KP308528
Brazil, Vicosa, MGCOAD 1789Coc. nuciferaArecaceaeKP244700KP308474KP308529
Brazil, Juazeiro, BACOAD 1790Coc. nuciferaArecaceaeKP244703KP308468KP308530
Brazil, Catuana, CearáIBL340Spondias purpureaAnacardiaceaeKT247466KT247472KT247475
Brazil, Itapipoca, CearaIBL375Talisia esculentaSapindaceaeKT247467KT247473KT247474
Brazil, Buique, PiauíIBL404Ana. occidentaleAnacardiaceaeKT247468KT247470KT247476
Brazil, Buique, PiauíIBL405Ana. occidentaleAnacardiaceaeKT247469KT247471KT247477
Uruguay, PaysandúFi2359Malus domesticaRosaceaeKR071127KT191041
Western AfricaBeninCMW33290Adansonia digitataBombacaceaeKY473027KY472970KY472896KY472828
Cameroon, Mbalmayo-BilinkCMW28311Terminalia ivorensisCombretaceaeGQ469932GQ469898KY472898KY472829
Cameroon, KribiCMW28317Ter. catappaCombretaceaeFJ900602FJ900648KY472899KY472830
Cameroon, KribiCMW28319Ter. catappaCombretaceaeFJ900603FJ900650
Cameroon, KribiCMW28547Ter. mentalyCombretaceaeGQ469919KY472972KY472900KY472831
Cameroon, KribiCMW28548Ter. mentalyCombretaceaeGQ469920KY472973KY472901KY472832
Cameroon, KribiCMW28550Ter. mentalyCombretaceaeGQ469921KY472974KY472902KY472833
Cameroon, Mbalmayo-EbogoCMW28570Ter. ivorensisCombretaceaeGQ469923GQ469896KY472903KY472834
Cameroon, Mbalmayo-EbogoCMW28571Ter. ivorensisCombretaceaeGQ469924GQ469897KY472904KY472835
Cameroon, Mbalmayo-EbogoCMW28573Ter. ivorensisCombretaceaeGQ469925KY472975KY472905KY472836
Cameroon, Mbalmayo-EkombitieCMW28625Ter. ivorensisCombretaceaeGQ469933KY472976KY472906KY472837
Cameroon, LombelCMW36127Ad. digitataBombacaceaeKY473029KY472977KY472907
Southern and Eastern AfricaMadagascar, MadamoCMW27810Ter. catappaCombretaceaeFJ900605FJ900651KY472923KY472851
South Africa, MpumalangaCMW18422Pin. patulaPinaceaeDQ103544DQ103562
South Africa, MpumalangaCMW18423Pin. patulaPinaceaeDQ103545DQ103563
South Africa, MpumalangaCMW18425Pin. patulaPinaceaeDQ103546DQ103561 KY472864
South Africa, MpumalangaCMW22663Pterocarpus angolensisFabaceaeFJ888468FJ888450 KY472865
South Africa, MpumalangaCMW22664Pt. angolensisFabaceaeFJ888469FJ888451
South Africa, Kwazulu-NatalCMW24125Sclerocarya birreaAnacardiaceaeKU997372KU997111 KY472866
South Africa, MpumalangaCMW25212Man. indicaAnacardiaceaeKU997392KU997128KU997566
South Africa, LimpopoCMW26616Euphorbia ingensEuphorbiaceaeKY473051KY472999KY472941KY472867
South Africa, LimpopoCMW26630Euph. ingensEuphorbiaceaeKY473052KY473000KY472942KY472868
South Africa, Kwazulu-NatalCMW26715Ter. catappaCombretaceaeFJ900604FJ900649KY472943KY472869
South Africa, Kwazulu-NatalCMW32018Pin. elliottiiPinaceaeKY473053KY473001KY472944KY472870
South Africa, MpumalangaCMW32498Pin. patulaPinaceaeKY473054KY473002KY472945KY472871
South Africa, MpumalangaCMW32536Pin. elliottiiPinaceaeKY473055KY473003KY472946KY472872
South Africa, Kwazulu-NatalCMW32544Pin. elliottiiPinaceaeKY473056KY473004KY472947KY472873
South Africa, Kwazulu-NatalCMW32549Pin. elliottiiPinaceaeKY473057KY473005KY472948KY472874
South Africa, Kwazulu-NatalCMW32571Pin. elliottiiPinaceaeKY473058KY473006KY472949KY472875
South Africa, Kwazulu-NatalCMW32603Pin. elliottiiPinaceaeKY473059KY473007KY472950KY472876
South Africa, Kwazulu-NatalCMW32604Pin. elliottiiPinaceaeKY473060KY473008KY472951KY472877
South Africa, Kwazulu-NatalCMW32606Pin. elliottiiPinaceaeKY473061KY473009KY472952KY472878
South Africa, Kwazulu-NatalCMW32651Pin. elliottiiPinaceaeKY473062KY473010KY472953KY472879
South Africa, Kwazulu-NatalCMW32666Pin. elliottiiPinaceaeKY473063KY473011KY472954
South Africa, Kwazulu-NatalCMW32669Pin. elliottiiPinaceaeKY473064KY473012KY472955KY472880
South Africa, MpumalangaCMW33658Man. indicaAnacardiaceaeKY473065KY473013KY472956
South Africa, GautengCMW38120Vachellia karrooFabaceaeKC769935KC769843KC769887
South Africa, GautengCMW38121Vac. karrooFabaceaeKC769936KC769844KC769888
South Africa, GautengCMW38122Vac. karrooFabaceaeKC769937KC769845KC769889
South Africa, GautengCMW39290Vac. karrooFabaceaeKF270061KF270021
South Africa, GautengCMW39291Vac. karrooFabaceaeKF270062KF270022
South Africa, Kwazulu-NatalCMW41214Barringtonia racemosaLecythidaceaeKP860842KU666547KP860765KU587889
South Africa, Kwazulu-NatalCMW41222Bar. racemosaLecythidaceaeKP860836KU666549KP860759KU587881
South Africa, Kwazulu-NatalCMW41223Bar. racemosaLecythidaceaeKP860837KU666548KP860760KU587882
South Africa, Kwazulu-NatalCMW41360Bar. racemosaLecythidaceaeKP860841KP860686KP860764KU587888
South Africa, Kwazulu-NatalCMW42341Bar. racemosaLecythidaceaeKP860843KU587945KU587866
South Africa, Kwazulu-NatalMTU53Sygygium cordatumMyrtaceaeKY052943KY024622KY000125
UgandaCMW10130Vitex donnianaLamiaceaeAY236951AY236900AY236929KY472883
Uganda, MbaleCMW18420Casuarina cunninghamiiCasuarinaceaeDQ103534DQ103564KY472959KY472884
Uganda, MbaleCMW32245Cas. cunninghamiiCasuarinaceaeKY473068KY473016KY472960KY472885
Uganda, MbaleCMW32246Cas. cunninghamiiCasuarinaceaeKY473069KY473017KY472961KY472886
Zambia, SamfyaCMW30103Syz. cordatumMyrtaceaeFJ747640FJ871114
Zambia, SamfyaCMW30104Syz. cordatumMyrtaceaeFJ747641FJ871115
Zambia, SamfyaCMW30105Syz. cordatumMyrtaceaeFJ747642FJ871116
Middle East and EuropeEgyptBOT23Man. indicaAnacardiaceaeJN814400JN814427
EgyptBOT4Man. indicaAnacardiaceaeJN814395JN814422
EgyptBOT5Man. indicaAnacardiaceaeJN814376JN814403
EgyptBOT6Man. indicaAnacardiaceaeJN814399JN814426
EgyptBOT7Man. indicaAnacardiaceaeJN814396JN814423
EgyptBOT9Man. indicaAnacardiaceaeJN814392JN814419
IranCJA198Unknown GU973871GU973863
IranCJA199Unknown GU973872GU973864
IranIRAN1233CUnknown GU973868GU973860
IranIRAN1496CMan. indicaAnacardiaceaeGU973869GU973861
IranIRAN1499CMan. indicaAnacardiaceaeGU973870GU973862
Italy, FoggiaB159Vts. viniferaVitaceaeKM675760KM822731
Italy, CerignolaB202Vts. viniferaVitaceaeKM675761KM822732
Italy, CerignolaB215Vts. viniferaVitaceaeKM675762KM822733
Italy, CerignolaB342Vts. viniferaVitaceaeKM675763KM822734
Italy, CerignolaB85Vts. viniferaVitaceaeKM675759KM822730
Oman, BarkaCMW20506Man. indicaAnacardiaceaeKY473037KY472985KY472924KY472852
Oman, BarkaCMW20508Man. indicaAnacardiaceaeKY473038KY472986KY472925KY472853
Oman, BarkaCMW20511Man. indicaAnacardiaceaeKY473039KY472987KY472926KY472854
Oman, BarkaCMW20512Man. indicaAnacardiaceaeKY473040KY472988KY472927KY472855
OmanCMW20537Unknown KY473041KY472989KY472928KY472856
OmanCMW20542Unknown KY473042KY472990KY472929
OmanCMW20543Unknown KY473043KY472991KY472930KY472857
OmanCMW20546Unknown KY473044KY472992KY472931KY472858
OmanCMW20560Unknown KY473045KY472993KY472932KY472859
OmanCMW20573Unknown KY473046KY472994KY472933KY472860
OmanCMW20579UnknownKY473047KY472995KY472934KY472861
AsiaChina, Fangshan, PingtungB838Man. indicaAnacardiaceaeGQ502456GQ980001GU056852
China, Guantian, TainanB852Man. indicaAnacardiaceaeGQ502457GQ980002GU056851
China, ChiayiB886Man. indicaAnacardiaceaeGQ502452GQ980005GU056847
China, Guantian, TainanB902Man. indicaAnacardiaceaeGQ502459GQ980004GU056849
China, Guantian, TainanB918Man. indicaAnacardiaceaeGQ502458GQ980003GU056850
China, Guantian, TainanB961Man. indicaAnacardiaceaeGQ502453GQ979999GU056845
China, Guantian, TainanB965Man. indicaAnacardiaceaeGQ502454GQ980000GU056854
ChinaBL1331Albizia falcatariaFabaceaeKU712499KU712500KU712501
ChinaCBS122127Homo sapiens EF622017EF622018
China, GuangDong ProvinceCERC1983Polyscias balfourianaAraliaceaeKP822979KP822997KP823012
China, GuangDong ProvinceCERC1985Pol. balfourianaAraliaceaeKP822980KP822998KP823013
China, GuangDong ProvinceCERC1988Pol. balfourianaAraliaceaeKP822981KP822999KP823014
China, GuangDong ProvinceCERC1989Euc. GU hybridMyrtaceaeKP822982KP823000KP823015
China, GuangDong ProvinceCERC1991Euc. GU hybridMyrtaceaeKP822983KP823001KP823016
China, GuangDong ProvinceCERC1996Euc. GU hybridMyrtaceaeKP822984KP823002KP823017
China, GuangDong ProvinceCERC2049Bougainvillea spectabilisNyctaginaceaeKP822985KP823003KP823018
China, GuangDong ProvinceCERC3820Rosa rugosaRosaceaeKR816831KR816837KR816843
China, GuangDong ProvinceCERC3821R. rugosaRosaceaeKR816832KR816838KR816844
China, GuangDong ProvinceCERC3822R. rugosaRosaceaeKR816833KR816839KR816845
China, GuangDong ProvinceCERC3823R. rugosaRosaceaeKR816834KR816840KR816846
China, GuangDong ProvinceCERC3824R. rugosaRosaceaeKR816835KR816841KR816847
China, GuangDong ProvinceCERC3825R. rugosaRosaceaeKR816836KR816842KR816848
China, Dong Men Forest FarmCMW24701Euc. GU hybridMyrtaceaeHQ332193HQ332209KY472908KY472838
China, Dong Men Forest FarmCMW24702Euc. GU hybridMyrtaceaeHQ332194HQ332210KY472909KY472839
ChinaCMW33957Eucalyptus sp.MyrtaceaeKY473030KY472978KY472910
ChinaFXPZVts. viniferaVitaceaeKR232666KR232660KR232674
ChinaHD1332Alb. falcatariaFabaceaeKU712502KU712503KU712504
ChinaHN74Hevea brasiliensisEuphorbiaceaeKT947466KU925617KU925616
China, Guangxi ProvinceL1Man. indicaAnacardiaceaeKR260791KR260808KR260820
China, Guangxi ProvinceL2Man. indicaAnacardiaceaeKR260792KR260809KR260821
China, Guangxi ProvinceL3Man. indicaAnacardiaceaeKR260793KR260810KR260822
China, Guangxi ProvinceL4Man. indicaAnacardiaceaeKR260794KR260811KR260823
China, Guangxi ProvinceL5Man. indicaAnacardiaceaeKR260795KR260812KR260824
China, Guangxi ProvinceL6Man. indicaAnacardiaceaeKR260796KR260813KR260825
China, Guangxi ProvinceL7Man. indicaAnacardiaceaeKR260797KR260814KR260826
China, Guangxi ProvinceL8Man. indicaAnacardiaceaeKR260798KR260815KR260827
China, Guangxi ProvinceL9Man. indicaAnacardiaceaeKR260799KR260816KR260828
China, Guangxi ProvinceL10Man. indicaAnacardiaceaeKR260800KR260817KR260829
China, Guangxi ProvinceL11Man. indicaAnacardiaceaeKR260801KR260818KR260830
China, Guangxi ProvinceL15Man. indicaAnacardiaceaeKR260802KR260819KR260831
China, SichuanMht-5Actinidia deliciosaActinidiaceaeJQ658976JQ658977JQ658978
China, ShanghaiSHYAGVitis viniferaVitaceaeJX275794JX462302JX462276
China, ZhejiangZHn411Pyrus pyrifoliaRosaceaeKC960899KC961038KC960992
Indonesia, SumatraCMW22881Euc. grandisMyrtaceaeKY473036KY472984KY472917KY472845
Indonesia, LogasCMW23003Ac. mangiumFabaceaeEU588629EU588609KY472918KY472846
Indonesia, LogasCMW23008Ac. mangiumFabaceaeEU588630EU588610KY472919KY472847
Indonesia, LogasCMW23018Ac. mangiumFabaceaeEU588633EU588613KY472920KY472848
Indonesia, TesoCMW23031Ac. mangiumFabaceaeEU588631EU588611KY472921KY472849
Indonesia, LogasCMW23073Ac. mangiumFabaceaeEU588632EU588612KY472922KY472850
KoreaML1001Man. indicaAnacardiaceaeJN542561JN542563
KoreaML1005Man. indicaAnacardiaceaeJN542562JN542564
Thailand, PrajinburiCMW15680Euc. camaldulensisMyrtaceaeKY473066KY473014KY472957KY472881
Thailand, PrajinburiCMW15682Euc. camaldulensisMyrtaceaeKY473067KY473015KY472958KY472882
Thailand, Chiang MaiCPC 22766Pin. kesiyaPinaceaeKM006436KM006467
Thailand, Chiang MaiCPC 22780Manilkara zapotaSapotaceaeKM006442KM006473
Thailand, Chiang MaiCPC 22798Syz. samarangenseMyrtaceaeKM006454KM006485
Thailand, Chiang MaiMFLUCC12 0293Tectona grandisLamiaceaeKM396896KM409634KM510354
AustralasiaAustraliaCMW40630Syzygium sp.MyrtaceaeKY473023KY472966KY472892KY472825
AustraliaCMW40635Syz. novosumMyrtaceaeKY473024KY472967KY472893
AustraliaCMW40636Syz. novosumMyrtaceaeKY473025KY472968KY472894KY472826
AustraliaCMW40637Syz. novosumMyrtaceaeKY473026KY472969KY472895KY472827
Darwin, AustraliaMUCC737Ad. gregoriiBombacaceaeGU199387GU199407
Papua New Guinea, MadangCBS164.96Fruit along coral reef coastAY640255AY640258KU887532KU696383
Table
Table 2. Standard genetic and nucleotide diversity measures for isolates collected in each country and region, for the ITS, tef1α, combined ITS and tef1α, and tub2 sequence datasets. Included are sample size (N), number of haplotypes found (H), gene diversity (HE) and nucleotide diversity (π). Sample sizes are also recorded for the tub2 dataset as sequence data for this locus was not available for all isolates. Totals for each region are also listed.
Table 2. Standard genetic and nucleotide diversity measures for isolates collected in each country and region, for the ITS, tef1α, combined ITS and tef1α, and tub2 sequence datasets. Included are sample size (N), number of haplotypes found (H), gene diversity (HE) and nucleotide diversity (π). Sample sizes are also recorded for the tub2 dataset as sequence data for this locus was not available for all isolates. Totals for each region are also listed.
RegionCountryNITStef1αITS + tef1αtub2
HHEπ (×10−3)HHEπ (×10−3)HHEπ (×10−3)NHHEπ (×10−3)
North AmericaHawaii11001001001100
Mexico51001001000000
Puerto Rico41001001004100
USA530.3564.27120.3561.64640.3854.210220.6672.157
Total1530.1291.54630.4053.74640.1932.110720.2640.854
Western South AmericaColombia11001001000000
Ecuador610030.3845.33130.3842.0976100
Peru610020.3031.40320.3030.552320.5331.726
Venezuela620.3030.91020.3031.40330.3031.104620.3030.981
Total1920.1020.30830.2012.79240.1761.2851530.1290.833
Eastern South AmericaBrazil7610040.0973.13140.0971.2321920.1850.597
Uruguay11001001000000
Total7710040.0963.10640.0961.2221920.1850.597
Western AfricaBenin11001001001100
Cameroon1110030.2225.13130.2222.01910100
Total1210030.2205.09930.2202.00611100
Southern and Eastern AfricaMadagascar11001001001100
South Africa3230.0640.95320.1120.52030.0620.5602940.1192.302
Uganda410020.4291.98420.4290.781420.4291.387
Zambia31001001000000
Total4030.0510.76020.2481.14740.0720.7823440.1192.301
Middle East and EuropeEgypt61001001000000
Iran51001001000000
Italy51001001000000
Oman1130.1731.04010030.1730.6311120.1730.560
Total2730.0730.43620.3081.42440.1510.8251120.1730.560
AsiaChina4330.6060.54630.1081.00350.0800.7264270.1534.939
Indonesia610020.4852.24520.4850.8836100
Korea21001001000000
Thailand620.3030.91020.4852.24530.3941.4353100
Total5740.0430.51830.1391.28860.0750.8215170.1304.202
AustralasiaAustralia520.3562.13510020.3561.2954100
Papua New Guinea11001001001100
Total620.3031.82020.3031.40330.3031.6565100
All 255110.001 80.003 170.001 153120.002
Table
Table 3. Pairwise population differentiation (ΦST) comparisons between the regions that isolates were obtained from, based on the combined ITS and tef1α dataset.
Table 3. Pairwise population differentiation (ΦST) comparisons between the regions that isolates were obtained from, based on the combined ITS and tef1α dataset.
RegionNNorth AmericaWestern South AmericaEastern South AmericaWestern AfricaSouthern and Eastern AfricaMiddle East and EuropeAsiaAustralasia
North America15
Western South America190.047
Eastern South America770.0260.014
Western Africa120.0400.1650.121-
Southern and Eastern Africa400.1890.0510.1050.367
Middle East and Europe270.1090.0080.0450.2720.01
Asia570.1660.0320.0800.3430.0060.002
Australasia60.0870.0410.0870.2050.0750.0560.068

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MDPI and ACS Style

Mehl, J.; Wingfield, M.J.; Roux, J.; Slippers, B. Invasive Everywhere? Phylogeographic Analysis of the Globally Distributed Tree Pathogen Lasiodiplodia theobromae. Forests 2017, 8, 145. https://doi.org/10.3390/f8050145

AMA Style

Mehl J, Wingfield MJ, Roux J, Slippers B. Invasive Everywhere? Phylogeographic Analysis of the Globally Distributed Tree Pathogen Lasiodiplodia theobromae. Forests. 2017; 8(5):145. https://doi.org/10.3390/f8050145

Chicago/Turabian Style

Mehl, James, Michael J. Wingfield, Jolanda Roux, and Bernard Slippers. 2017. "Invasive Everywhere? Phylogeographic Analysis of the Globally Distributed Tree Pathogen Lasiodiplodia theobromae" Forests 8, no. 5: 145. https://doi.org/10.3390/f8050145

APA Style

Mehl, J., Wingfield, M. J., Roux, J., & Slippers, B. (2017). Invasive Everywhere? Phylogeographic Analysis of the Globally Distributed Tree Pathogen Lasiodiplodia theobromae. Forests, 8(5), 145. https://doi.org/10.3390/f8050145

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