dataesr/scientific-paragraphs-categorization huggingface dataset:
1 % (all >95. 4 %). The four Madagascan EACMCV DNA-B sequences were, however, considerably more diverse than both their DNA-A counterparts and the other sampled DNA-B sequences, and shared a mean identity of 91. 1 % (between 84. 5 and 94. 8 %) with one another. Interestingly, the 98 DNA-B components that did not obviously cluster with those of particular CMG species were quite similar, sharing a mean identity of 97. 6 % (all >90. 3 %; Table  1 ) and formed a monophyletic clade amongst all sampled DNA-B sequences. It is worth mentioning though that three of these sequences [Gen-Bank:KJ887689, GenBank:KJ887691, GenBank:KJ887687] were more genetically distinct than the rest of the Madagascan sequences and form an outgroup within the main monophyletic Madagascan DNA-B clade. These three sequences were also more distantly related to one another than the other Madagascan DNA-B sequences in this clade.
Recombination analyses As begomovirus genomes are known to be prone to recombination  [23, 24, 38] , analyses to detect such events were performed to (1) reveal the evident inter-species recombination patterns within the genomes of Madagascan CMGs, (2) compare these to known recombination patterns that are evident within mainland African CMGs and (3) identify the recombinant sequences in our datasets so as to account for these during subsequent phylogeographic analyses. A total of 45 inter-species recombination events were detected, 29 of which were found only in sequences sampled in Madagascar (Fig. 2 , Table  2 ). Amongst the remaining 16 detected recombination events are those confirming the mosaic structures of many previously described CMG recombinants including: (1) EACMMV (events 10 and 11;  [33] ; (2) EACMZV (Event 12;  [11, 33] ; (3) the Kenya variant of EACMV, formally named the KE2 strain (Event 28;  [13] ); (4) EACMCV (Event 14;  [18] ); and (5) EACMV-UG (Event 27;  [26] ; Table  2 ).
Isolate MG652A11 [GenBank: KJ888083] (Profile P16), which represents a new strain of EACMKV, is characterized by its full AC3, AC2 and C-terminal AC1 ORFs being of the EACMCV type. The remaining EACMKV recombinants (Profiles P13 and P14) have sequence segments derived from a currently unsampled Begomovirus species. Moreover, for some recombinants (Profiles P11, P12, P13 and P19), while most of their genomes (i. e. , the parts derived from their major parents) are SACMV-like, their full genome sequences are >91 % identical to EACMKV and are therefore classified as belonging to this species rather than to SACMV. This paradox can be explained by the high degrees of genetic similarity shared between SACMV and EACMKV on the complementary sense gene encoding genome regions relative to the CP encoding genome region such that the acquisition of a EACMV-like CP sequence by a SACMV variant would result in the variant being genetically more similar to EACMKV isolates than to SACMV isolates.
The number of events corresponding to each modality is indicated, and significant differences are represented by different letters above the ranges EACMKV and EACMCV were merged into an EACMVlike dataset due to their close relationship in this particular part of their genomes. Five EACMKV core CP sequences that were derived from other non-CMG species by recombination (Profiles P11, P12, P13 and P18; Fig. 2 , Additional file 3: Table  S1 ) were excluded from the EACMV-like dataset. Two additional datasets corresponding to ACMV and SACMV core CP sequences were also analysed. With the exception of a group of three ACMV sequences located in the north of Madagascar [GenBank:KJ888080, GenBank:KJ888082, GenBank:KJ888086], analysis of the core CP region of the Madagascan ACMV and SACMV isolates yielded no evidence of genetic or spatial population structure (results not shown), a result that was likely at least partially attributable to the extremely low degrees of genetic diversity observed amongst the isolates of these species.
5a ). The first axis of the analysis, which represents 27 % of the variance and a large positive autocorrelation index (Moran's I), permits clear separation of the three distinct groups inferred by the DAPC analysis. Additionally, when exploring the spatial structure of group 1 alone, a significant pattern of isolation by distance across a North/South gradient was detected (p-value = 5 × 10 -3 ; data not shown). The genetic structure of Madagascan CMG DNA-Bcomponents was investigated using their full sequences, for two datasets corresponding respectively to the 15 ACMV DNA-B components and the 98 monophyletic Madagascan EACMV-like isolates referred to as the EACMV-like DNA-B component dataset. Contrasting with the absence of structure that was evident for the core CP sequences of ACMV DNA-A components, the DAPC analysis performed on the 15 ACMV DNA-B components clearly distinguished sequences isolated in the North from those isolated in the centre of Madagascar (Fig.
4c ). This spatial clustering was confirmed with the sPCA analysis (p-value = 1 × 10 -3 ; Fig. 5c ). For EACMV-like DNA-B components, the DAPC analysis detected four clusters (Fig. 4b ). One cluster of three isolates [GenBank:KJ887689, GenBank:KJ887691, GenBank:KJ887687] (represented in purple in Fig. 4b ) corresponds to the three divergent EAMKV DNA-B sequences, all of which were sampled in the western parts of Madagascar. Interestingly, when we excluded these sequences from the dataset, the sPCA analysis indicated a pattern of isolation by distance (p-value = 1 × 10 -4 ) with a north/south spatial gradient (Fig. 5b )a finding consistent with our analysis of the core CP sequences. The patterns of isolation by distance detected in the EACMV-like DNA-A and DNA-B datasets and the ACMV DNA-B dataset suggests the occurrence of limitations on the long distance dissemination of viruses in Madagascar: a process which presumably must involve either transmission by B.
Nonetheless, when sampling dates were randomly shuffled between samples, the evolutionary rates inferred by the BEAST analyses were substantially lower than those inferred from the real data (Additional file 4: Figure  S3 ), suggesting that although the temporal signal in the data is not strong, it remains present. As a whole, however, these results indicated that all dates and evolutionary rates inferred during the subsequent phylogeographic analyses should be treated with caution. Using the spatial coordinates of our samples, we defined several discrete geographic sampling locations for each dataset (respectively seven and eight for the ACMV and EACMV-like datasets). By associating one of these discrete sampling location states to each analysed sequence, and accounting for sampling dates, the "discrete" phylogeographic analysis that we performed permitted us to identify when and where (amongst the seven or eight defined sampling locations) ancestral CMG sequences (represented by internal nodes on phylogenetic trees in the posterior distribution of trees) probably existed.
Discussion The co-occurrence in Madagascar of six CMG species within the same geographical area is a major opportunity to study the evolutionary, spatial and temporal dynamics of several distinct virus species concurrently evolving within the same ecological context. Towards this end we constructed viral sequence datasets representing the four most prevalent Madagascan CMG species (SACMV, ACMV, EACMKV and EACMCV) and compared their population structures and movement dynamics within the broader context of CMGs found elsewhere on the SWIO islands and on mainland Africa. We show both that despite sharing the same host and vector species, these four CMG species have distinct patterns of recombination and varying population structures, and that they have likely all been independently introduced to Madagascar.
We confirm previously described differences between recombination patterns in ACMV and other CMG species. Specifically, whereas ACMV is apparently an avid donor of small sequence fragments during inter-species recombination events, it has only very rarely been inferred to be a recipient of foreign sequence fragments of any size: a pattern that contrasts starkly with that of other CMG species which frequently exchange genomic fragments of widely varying sizes with one another. The only example (See figure on previous page. ) Fig. 6  Maximum clade credibility tree constructed from the ACMV core CP dataset. Branches are coloured according to the most probable location state of the node on their right (i. e. , the likely geographical location of the ancestral sequence represented by this node).
Instead it is apparent that EACMV-like variants have been directly transferred at least three times from Africa to Madagascar and that they have likely been circulating on this island for approximately the same time-period as those found on the Comoros and Seychelles archipelagos. Although our datasets were inadequate to provide a detailed description of the temporal movement dynamics of CMGs within Madagascar, the contrasting diversity and spatial dynamics of different CMG species on the island indicated that these species might display markedly different histories of introduction to the island. Specifically, whereas EACMV-like viruses appear to have been introduced multiple times to the islands, the two most prevalent CMGs on the island, ACMV and SACMV, both appear to have been introduced to the island only once in the very recent past.
Conclusion The results of this study highlight the complexity of CMD in Madagascar, with each CMG species having their own epidemiological and evolutionary dynamics (See figure on previous page. ) Fig. 7  Maximum clade credibility tree constructed from the EACMV-like core CP dataset. Branches are coloured according to the most probable location state of the node on their right (i. e. , the likely geographical location of the ancestral sequence represented by this node). The large black circle around one of the nodes indicates that the state probability at this node is less than 0. 5 (i. e. , there is less than 50 % confidence in the indicated location being the actual place where this ancestral sequence existed). The time-scale of evolutionary changes represented in the tree is indicated by the scale bar above it.
Alignments, virus classification and phylogenetic trees All sequence alignments were produced using the MUSCLE  [44]  method as implemented in Geneious 6. 1. 7 (Biomatters Ltd, New Zealand) and MEGA 5. 2  [45]  with default parameters. All alignments were manually edited. A pairwise nucleotide identity matrix was calculated for full length DNA-A sequences using SDT v1. 2  [46]  and was used to assign each sequence to a viral species and strain, according to the ICTV approved begomovirus species (>91 % DNA-A identity) and strain (>94 % DNA-A identity) demarcation thresholds  [36]. Approximately-maximum-likelihood phylogenetic trees were constructed using the computer program FastTree (v2. 1,  [47] ), with the GTR-CAT nucleotide substitution model and a gamma distribution of 20 substitution rates categories.
Recombination analyses Detection of potential recombinant sequences, identification of sequences closely related to parental sequences and localization of recombination breakpoints were carried out using the RDP  [48] , GENECONV  [24] , BOOTSCAN  [49] , MAXIMUM CHI SQUARE  [50] , CHIMAERA  [49] , SISCAN  [51] , and 3SEQ  [52]  recombination detection methods implemented in RDP4  [53]. The recombination analysis was performed on a dataset of 626 DNA-A sequences, containing 351 new CMG DNA-A sequences (279 from Madagascar and Comoros and 72 from the Central African Republic and Chad) aligned together with 114 CMG sequences recently isolated from the Comoros and Seychelles archipelagos  [32] , 152 representative CMG DNA-A sequences available from GenBank and 9 DNA-A-like sequences of monopartite begomoviruses from Africa and Madagascar.
Despite the high diversity of DNA-B sequences and the inherent difficulties in generating accurate alignments of these, an additional recombination analysis was performed on a dataset of 336 DNA-B sequences containing 187 new CMG sequences (117 from Madagascar and Comoros and 70 from the Central African Republic and Chad) aligned together with 55 DNA-B sequences from the Comoros and Seychelles archipelagos  [32] , and 94 representative CMG DNA-B sequences available from GenBank. For the sake of both computational speed and the facilitation of further analyses, divergent CMG taxa (corresponding to ACMV, SACMV, and EACMV-like [EACMV, EACMKV and EACMCV] for DNA-A; ACMV, EACMCV, and EACMV/SACMV-like for DNA-B) were analysed separately, using the "select group" option in RDP4. Default settings for the different detection methods and a Bonferroni corrected p-value cut-off of 0.
Genetic and spatial structure of CMGs in Madagascar The existence of potential genetic structure in Madagascan CMG populations was investigated using the Discriminant Analysis of Principal Components (DAPC;  [54] ) method implemented in the R 3. 0. 1 Adegenet package  [55, 56]. DAPC is a multivariate method designed both to infer clusters of genetically related sequences, and to describe the variability between these sequence clusters. Firstly, a principal component analysis (PCA) of allelic diversity was performed to reduce the number of variables before using a sequential K-means clustering procedure to infer an optimal number of groups (selected using the Bayesian information criterion). Then, a first step of data transformation was performed using a second PCA on the raw data, to obtain a lower number of uncorrelated variables describing the dataset.
Subsequently, a discriminant analysis (DA;  [57] ) was performed upon these variables in order to maximize the variation between groups (with the number of groups having been defined in the first step) while minimizing the variation within each group. This method had the advantage of not relying on a particular genetic model. Analysis of the repartition of each group with respect to the DA axes was used to yield information relating to the genetic structure of the dataset. In addition to analysing genetic structure within the Madagascan CMG populations, we also looked for evidence of spatial structure within the distributions of these sequences across Madagascar. For this we used spatial principal components analysis (sPCA;  [55] ), which was also implemented in the R Adegenet package. This method relies on a modification of classical PCA to take the spatial autocorrelation of genetic variability into account.
Unlike PCA, which seeks to find independent synthetic variables that maximize variance, sPCA variables are selected to maximize the product of the variance and a spatial autocorrelation index: Moran's I. One can choose the variables which have a large associated variance and a large positive Moran's I, therefore representing patterns of isolation by distance (distant individuals tend to be more genetically distant), or a negative Moran's I (close individuals tend to be more genetically distant), respectively referred to in the package as global and local structures. The significance of any structure was inferred using a permutation test implemented in the package. Specifically, for each dataset we employed a Delaunay triangulation network to describe connections between our isolates, and tested the significance of inferred spatial structures using 9999 permutations.
Phylogeography of CMGs on the South-West Indian ocean islands The spatial and temporal dynamics of CMGs in the SWIO islands were investigated using a discrete symmetric diffusion model implemented within the Bayesian inference framework of the computer program BEAST v1. 8. 2  [58]. A first dataset, called EACMV-like, was assembled from 244 EACMV, EACMKV, and EACMCV sequences isolated from the Comoros (117; including the islands of Mayotte, Anjouan, Grande Comore and Moheli), Seychelles  (12) , Madagascar  (51) , and the African continent (64). Each of the sequences had a corresponding GPS coordinate and sampling date, and was assigned to one of the following eight discrete sampling locations: East Africa, West Africa, Seychelles, Mayotte, Anjouan, Grande Comore, Moheli and Madagascar. A second dataset, called ACMV, was assembled from 218 ACMV sequences isolated from Madagascar (93), and the African continent (123).
e. , viruses in the ACMV or EACMV-like datasets that had not recombinationally acquired core CP regions from other divergent Begomovirus species) were included in the analyses. The GTR + I + G model was selected as the best fit nucleotide substitution model using RDP4, for all four datasets and the BEAST analyses were performed using a lognormal relaxed clock model with a Bayesian SkyGrid coalescent tree prior with 100 grid points; both of which were selected as the best-fitting models using the path sampling and stepping-stone model selection procedures  [59]. Three independent runs comprising a total of either 400 or 800 million iterations of the Marko chain were performed for each dataset and where necessary, combined using LogCombiner after the removal of an appropriate burn-in to ensure that the effective sample sizes for parameters all exceeded 200.
The Maximum Clade Credibility trees (MCC) was constructed using TreeAnnotator and was visualized using the computer program FigTree (available at http://tree. bio. ed. ac. uk/software/figtree/). A Bayesian stochastic search variable selection (BSSVS) approach, described in detail by Lemey et al. [60] , was used to identify well-supported epidemiological links between locations using Bayes factor (BF) tests  [61] , with BF values >5 taken as representing significant evidence of migration between the discrete sampling locations considered. In order to estimate biases due to differences in sampling sizes between the discrete locations, the analyses were also carried out as above but with the location states of the sequences randomized using an additional operator in the MCMC procedure. The location state probabilities of the root node determined during these analyses were compared with those determined for the datasets analysed without the location state randomization setting.
As only two African SACMV sequences exist in the databases, the history of SACMV movements between continental Africa and Madagascar could not be properly investigated using a discrete phylogeographic analysis approach. Finally, two additional phylogeographic analyses were performed on datasets consisting of 215 full-length EACMV-like DNA-B components (corresponding to 98 Madagascan sequences, 51 form the Comoros archipelagos and 65 from mainland Africa) and 95 full-length ACMV DNA-B components (corresponding to 15 from Madagascar and 80 from mainland Africa). The same parameters and procedures used for the DNA-A analyses were applied for the DNA-B analyses except that seven discrete sampling location states were used for the EACMV-like DNA-B phylogeographic analysis (corresponding to East Africa, Central Africa, Grande Comore, Mayotte, Anjouan, Moheli and Madagascar) and four discrete sampling location states were used for the ACMV DNA-B phylogeographic analysis (corresponding to West Africa, Central Africa, East Africa and Madagascar).
Importantly, as dates and rates inferred from BEAST analyses are strongly affected by the range of the sampling dates, the temporal signal of each of our datasets was investigated using two methods. Firstly, we used the computer program Path-O-Gen 1. 4 (available at http:// tree. bio. ed. ac. uk/software/pathogen/) which, when provided with a phylogenetic tree, performs a linear regression between the sampling dates of the sequences and their genetic distances from the root (i. e. , the point in the tree that, assuming a molecular clock model of evolution would best represent the most recent common ancestor of all the sequences used to construct the tree). A correlation coefficient close to one would indicate evidence of the corresponding sequence dataset being compatible with a strict clock evolutionary model (i.
e. , with equal rates of nucleotide substitution across all the branches of the phylogeny). Alternatively, a correlation coefficient between 0 and 1 would imply that the use of a relaxed clock evolutionary model is more appropriate. A negative or zero correlation coefficient would imply that the data are absolutely not clocklike and then that the genetic variability evident in the data could not be properly related to sampling times (i. e. , the absence of sufficient temporal signal in the dataset to confidently infer coalescence times or substitution rates). Secondly, we performed a tip-date randomization technique previously described in other studies  [29, 62]. For each dataset, ten independent randomizations of the sampling dates associated with each sequence were performed. The mean substitution rate distribution of these randomized datasets was inferred with BEAST, and then compared with the mean substitution rates inferred from the real datasets.
Acknowledgements ADB is a recipient of a PhD fellowship from the  Conseil Général de La Réunion  and  CIRAD. This work was funded by the  Centre de recherche et de veille sanitaire sur les maladies émergentes dans l'océan Indien (CRVOI) , the  European Union (FEDER) , the  Région Réunion and CIRAD. AV and DPM are supported by the  National Research Foundation  (South Africa). GWH is supported by the  South African National Research Foundation , Thuthuka grant. Computational analyses have been performed on the supercomputer facilities of the  Université de la Réunion.
We also did not observe terminal inverted repeats flanking the DLSs, which are hallmarks of proliferation by transposition (data not shown). 1  and S2. Bootstrap values correspond to 1000 replications and are indicated for values. 50 %. Sequences of 100 aa corresponding to translation of the integrated DLSs in M. persicae (see Table  1 ) and A. pisum genomes (see Table  S2 ) sharing similarity with NS1 of parvoviruses were aligned with densovirus sequences arising from: Aedes albopictus densovirus (AaDNV; GenBank accession no. NC_004285. 1), infectious hypodermal and haematopoietic necrosis virus (IHHNV; NC_002190. 2), Bombyx mori densovirus 5 (BmDNV5; NC_004287. 1), Fenneropenaeus chinensis hepatopancreatic densovirus (FchDV-1; NC_014357. 1), JcDNV (NC_004284. 1), MpDNV (NC_005040. 1), DplDNV (EU851411.
2 ), which suggested a potential transcription of NSint_1 and/or NSint_4. Indeed, the high sequence identity between these two ORFs prevented detection of each specific mRNA. The specificity of the amplification reactions was confirmed by sequencing the amplified fragments. In contrast, no fragments or fragments of unexpected size were obtained using the other sets of primers amplifying potential transcripts from CPint_1 on scaffold_1839 or NSint_2 and/or NSint_3 on scaffold_794, suggesting an absence of transcription of these potential ORFs (data not shown). In a second step, in order to measure the rate of accumulation of the viral transcripts detected by RT-PCR, four independent real-time RT-PCRs were performed on polyadenylated mRNAs extracted from whole aphids, and the results of one representative experiment is presented in Fig.
As the predicted viral ORFs were not all transcribed, and the transcript levels from transcribed ORFs appeared unequal,  the presence of functional promoters was assessed (Neural Network Promoter Prediction software, http://www. fruitfly. org/seq_tools/promoter. html; data not shown). In scaffold_1839, potential promoters upstream of CPint_2 were identified, whereas no promoter sequences were detected upstream of CPint_1. This observation could explain the lack of CPint_1 transcription observed in our experiments. On the same scaffold, potential promoters that could control transcription of NSint_1 were also found. On the other scaffold (scaffold_794), promoter-like sequences with the potential to control transcription of CPint_1 and CPint _2 were found upstream of each ORF. Transcription of NSint_1 and/or NSint_4 could also be explained by the presence of promoter-like sequences upstream of these two ORFs.