dataesr/scientific-paragraphs-categorization huggingface dataset:
Deme size was of constant size N throughout the genealogy. This model was motivated by the observation of substructure in some species of dataset 1 and could be interpreted as repeated divergence events triggered by recurrent climate oscillations (see  [55] ), or by other vicariance or local adaptation events. In the ''constant'' population size scenario without divergence (Figure  4 , B), both samples derived from the same deme t1 = 1 generation ago. In each scenario, we simulated a DNA sequence of length 1000 bp and varied the deme size N and mutation rate m among runs to obtain average diversity and differentiation levels similar to those observed in our observed dataset 1. We repeated simulations 100 times in each run, and used the software Arlequin 3. 5. 1. 3 in batch mode  [68]  to compute summary statistics for the simulated data sets.
To assess the power of our data to detect genetic structure and to evaluate type I error, we recorded the number of significant differentiation tests (F ST ) respectively in the divergence and constant scenarios and compared them to the observed data. For the differentiation scenario that most closely matched the observed dataset 1, we repeated the simulation recovering n = 4 samples for each of four demes at t0. We then used AMOVA in Arlequin to compute the differentiation between populations with simulated substructure, F CT[(01)  (23) ] (where subscript numbers stand for the four demes), and the differentiation between groups composed of samples from different populations, F CT[(02)  (13) ]. This mimicked grouping the samples into W-E vs. N-S, with one of the two groupings corresponding to the true divergence history.
The fraction of simulations where F CT[(01)  (23) ]. F CT[(02)  (13) ] represents the power to identify the correct differentiation pattern between the two groupings considered. Shared spatial patterns of genetic structure. To geographically locate the most important genetic breaks and investigate whether taxa shared patterns of geographic differentiation, we searched for genetic barriers using the Monmonier maximum difference algorithm in the software BARRIER v. 2. 2  [74]. This analysis was restricted to dataset 2 because small sample sizes would imply low confidence in barrier location in dataset 1. The matrix of pair-wise differentiation (G ST ) between sampling locations was used together with the sampling coordinates to identify the three most important genetic barriers in each taxon.
Genetic Diversity and Relatedness of Haplotypes Sequences of the trnC-ycf6 region were deposited at Genbank under accession numbers KF487767-KF488074 (Table  S1 ). The trnC-ycf6 region had an average length of 870 bp (600-1022 bp) in dataset 1 and the number of SNPs varied between 0 and 26 across taxa, resulting in p T values ranging from 0 to 0. 0139 (mean 0. 0026, Table  1 ). In dataset 1, we detected between one and eight haplotypes per taxon when considering SNPs and between one and ten haplotypes when considering all polymorphic characters, for an average sample size of 16 individuals and eight sampling locations per taxon. Some high-diversity taxa displayed divergent lineages, with some haplotypes differing by six or more mutations without putative intermediate haplotypes: Anthonotha macrophylla, Strombosiopsis tetrandra and Erythrophleum ivorense/suaveolens (Figure  1 ).
Demographic Inference, Isolation by Distance and Phylogeographic Structure The null model of demographic expansion was rejected in five, and the spatial expansion model was rejected in two out of 12 taxa with P,0. 05 in dataset 1 (Table  2 ). In dataset 2, six out of eight tests rejected expansion models (Table  2 ). Isolation by distance of Table  3. Analysis of variance in 14 Afrotropical tree taxa under two evolutionary scenarios. haplotypes was found in ten out of fourteen taxa in dataset 1 and in all four species of dataset 2, evidenced by a significant negative regression slope of pairwise kinship, blog(F ij ), on the logarithm of geographic distance (Table  2 ). Isolation by distance was also observed for ordered haplotypes, with the same taxa, except Strombosiopsis tetrandra, showing a negative regression slope, blog(N ij ).
Patterns of Endemism and Differentiation In dataset 1, there was no significant difference of genetic diversity statistics between regions in any of the two evolutionary scenarios examined; however, some diversity statistics (h, Npriv and Ar 3 ) were found to differ significantly among taxa (Table  3 ). Genetic diversity, numbers of haplotypes and private haplotypes were generally similar between regions defined according to both evolutionary scenarios (Table  4 , Table  5 ). Only in M. excelsa in dataset 2, genetic diversity was larger in the S than in the N regions (non-overlapping 95% confidence intervals based on mean 61. 96 * SE). In dataset 2, private rarefied allelic richness Arpriv 18 was numerically larger in all four species in the E than in the W, whereas no common trend was observed in the N-S scenario.
Negative regression slopes with latitude were observed for withinpopulation allelic richness (Ar 3 ) in M. excelsa and for withinpopulation genetic diversity h in S. globulifera, i. e. southern populations of these species had higher diversity statistics (Table  S3 ). Differentiation estimated as F ST or N ST between regions in dataset 1 was significant (P,0. 05) for four or five taxa in the W-E scenario and for three or four taxa in the N-S scenario (Table  6 ). Erythrophleum ivorense/suaveolens and Scorodophloeus zenkeri showed W-E but not N-S differentiation, and S. globulifera displayed strong N-S, but only weak W-E differentiation; patterns were less clear for the remaining species. In dataset 2, all four species showed significant N-S differentiation, but only two showed W-E differentiation (Table  6 ).
A contribution of mutations to differentiation (N ST. N ST(perm. ) ) was detected in Erythrophleum ivorense/ suaveolens and Anthonotha macrophylla (W-E) and in G. suaveolens subsp. suaveolens var. suaveolens (N-S, dataset 2). Simulations showed that a population divergence scenario with mean levels of diversity and differentiation similar to those in dataset 1 and sample sizes of n = 8 per deme, i. e. simulated scenario A3, would lead to 26% significant differentiation tests at a level of a = 0. 05 based on F ST (Table  7 , Table  S4 ). In dataset 1, 29% (8 out of 28) differentiation tests were significant. If we assume that the variation associated to sampling different taxa is analogous to the stochastic variation in the coalescent process, this would suggest that dataset 1 had a detection power of population genetic structure similar to the one expected in a realistic differentiation scenario.
In a differentiation scenario with larger sample sizes, n = 30 per deme (scenarios A8 or A9), the detection power of genetic differentiation rose to 73%, indicating that differentiation tests in dataset 2 were about three times more likely  $ F CT[(02)  (13) ] in 98% of the cases. This suggested that the power of dataset 1 to distinguish differentiation patterns produced by alternative evolutionary scenarios was at least 58% (in 40% of cases no difference in differentiation patterns was detected, and in only 2% of cases a reversed differentiation pattern appeared). In simulations without genetic structure (B1-B4), only 0-4% of the tests detected a differentiation signal. This suggested a low level of false positive differentiation tests even for the small sample sizes of dataset 1.
Discussion This study shows that fourteen common Afrotropical rainforest tree taxa (dataset 1) exhibited variation in their patterns of genetic endemism and differentiation and did not show clear majority support for any of two simple evolutionary scenarios in response to physio-climatic features in the Lower Guinea, i. e. , 1) the W-E environmental gradient or 2) the N-S seasonal inversion. This observation allows several possible interpretations: 1) absence of a common pattern, or non-detection of a common pattern because it was too weak to be detected with the available sample sizes; 2) existence of a common genetic structure pattern but only for a subset of the study taxa; or 3) a confounding effect due to sampling ill-defined taxonomic species. We applied coalescent simulations and complementary sampling in a sub-sample of four of the fourteen taxa in order to further examine these possible Averages over 100 independent simulations are shown, see Figure  4  and Table  S4.
interpretations and to clarify our results. Interestingly, the complementary sampling distinguished an N-S differentiation pattern shared among the four species with larger samples. One caveat of our study is that the tree flora of Central African rainforests is not sufficiently well known to allow unambiguous species identification. The taxa considered in dataset 1 are specieslevel taxa according to current taxonomic classifications (except Erythrophleum ivorense/suaveolens) but some are most probably complexes of species that cannot be distinguished easily in the field (see material and methods) and, in any case, it cannot be taken for granted that any of the taxa considered in dataset 1 matches the biological species concept. Our study approach should generally remain valid for species complexes because we make no assumption on whether evolutionary forces affecting diversification led to differentiated populations or to distinct species.
inland) have only recently been inferred based on a phylogeographic study that compared the distribution of genetic diversity of vegetative specimens with the reproductive morphology in a few fertile specimens  [34]. When we excluded the cited taxa to control for the possible confounding effects, we still did not find majority support for any of the two simple evolutionary scenarios (results not shown). Our data did not find support for a common pattern of allelic endemism across species in dataset 1, as the numbers of private alleles were low and similar between regions in both scenarios. In dataset 2, there was a weak tendency for eastern locations to contain (numerically) more endemic alleles than western locations. This suggested that there was no general pattern in either region to harbour consistently older populations.
If the W-E environmental gradient had enabled persistence of rainforest taxa in the moister Atlantic zone only during past climate oscillations  [18, 33] , higher allele endemism would have been expected in the W only. Our results did not concur with this expectation. Our simulation approach indicated that the power to detect a common differentiation pattern across taxa (with significant F ST tests) was ca. 26% in dataset 1 and ca. 73% in dataset 2, given the level of differentiation observed in our data. In dataset 1, three to five out of 14 taxa showed significant population differentiation compatible with the proposed W-E or N-S scenarios, which was close to the expected proportion from simulations. The comparison of F CT values between the two alternative groupings of simulated demes indicated that F CT was (strictly) larger in the true differentiation scenario in 58% of the cases vs.
only 2% in the false scenario in samples analogous to those of dataset 1. This approach, i. e. , the comparison of F ST values between alternative groupings of our samples was therefore a more powerful approach to detect the correct scenario than F ST tests. Our data displayed, respectively, six F ST values larger in the W-E than in the N-S scenario, and six values larger in the reverse comparison. This suggested that, given the power of dataset 1, there was no majority support for neither of the two hypothesized scenarios. Simulations also showed that the strength of the differentiation signal had a strong effect on the power (Table  7 ): in the simulated scenario (A6) with mean F ST = 0. 43, sample sizes of n = 8 per deme had 85% power to detect differentiation. We can therefore conclude that our taxa from dataset 1 probably had idiosyncratic histories, or, if they had been exposed to the same evolutionary scenario, the differentiation effect was not strong enough, and/or there was too high population substructure, to make it detectable as a common pattern across taxa.
In dataset 2, all four species exhibited N-S differentiation, but only two showed W-E differentiation. This suggests that a common N-S pattern might exist, at least across the four examined species, and this result was also supported by the barrier density analysis (see below). Simulations also indicated that we could confidently discard the hypothesis that all taxa would exhibit panmixia in the LG since the rate of false positive differentiation tests was low in simulations (0-4%). The apparent idiosyncratic genetic structure patterns observed in dataset 1 could partially be explained by the lack of a common pattern of bottlenecks or postglacial expansion, which contrasts with comparative phylogeographic studies in other regions of the world  [11, 78]. The only common pattern across taxa that we could observe with some degree of confidence in dataset 1 was isolation by distance within taxa, which is expected when dispersal is limited in space  [79].
Dataset 2 showed that IBD patterns were detected similarly in the corresponding taxa of both datasets, and the visual comparison of haplotype maps also suggested that the small datasets provided fair information on haplotype diversity and its geographic organization. The power to detect differentiation patterns was considerably improved in dataset 2 (see above) and a common genetic break zone was identified by the barrier density analysis separating the N from the S of the LG for these taxa (G. suaveolens subsp. suaveolens var. suaveolens, M. excelsa, S. globulifera and T. acuminata), in agreement with the expectation of the N-S evolutionary scenario. A similar genetic break zone in the LG region has been suggested based on nuclear markers in a review by Hardy et al. [36]  which included also the following species: Santiria trimera  [45] , Distemonanthus benthamianus  [44] , Irvingia gabonensis  [43]  and Aucoumea klaineana  [42] , and moreover, floristic analysis on local assemblages of tree species also suggested the existence of a floristic differentiation between N and S of the climatic hinge  [37].
Our study, with the lack of a common pattern using small multi-taxa datasets, and congruent or partially congruent differentiation patterns according to two scenarios in larger datasets of fewer species, suggests that indepth comparative phylogeographic work across a wide range of taxa requires a good understanding of evolutionary processes between closely related species and in species complexes. More detailed population genetic case studies are therefore necessary, including studies on species complexes, before specific physioclimatic or species-inherent features (e. g. , climatic tolerance, mating system or substrate affinity  [13, [80] [81] [82] ) can be proposed as drivers of any shared phylogeographic patterns in African tropical rainforest trees.
Acknowledgments We thank  B. Senterre ,  Y. Issembe, T. Ste  ´vart, members of the O. J. Hardy  group and field assistants for help with sampling. S. C. Gonzalez-Martı  ´nez and  G. Nieto Feliner , as well as three anonymous reviewers and the editor  Paul V. A. Fine  provided helpful comments to previous versions of the manuscript. J. Martı ´nez Ferna  ´ndez helped with Fig. 2.
147 * ns Strombosiopsis tetrandra ns ns 20. 147 *** 20. 037 ns ns Symphonia globulifera + ns 20. 103 *** 20. 175 *** *** Trichoscypha acuminata ns ns 20. 073 * 20. 067 + ns Dataset 2 Greenwayodendron suaveolens subsp. * *** 20. 268 *** 20. 401 *** ** suaveolens var. suaveolens (Engl. & Diels) Verdc. Milicia excelsa (Welw. ) C. C. Berg + ** 20. 096 *** 20. 179 *** ns Symphonia globulifera L. f. * ns 20. 126 *** 20. 173 *** ** Trichoscypha acuminata Engl. * *** 20. 093 *** 20. 092 *** ns blog(F ij ) and P(IBD_F ij ), slope and test of isolation by distance; blog(N ij ) and P(IBD_N ij ) slope and test of isolation by distance using ordered alleles; P(blog(N ij ),blog(N ij perm. )), test for phylogeographic structure. For details, see materials and methods. +, P,0. 1; *, P,0. 05; **, P,0. 01; ***, P,0.
Ar, rarefied haplotype richness in a random sample of 3 sequences in dataset 1 or 18 sequences in dataset 2; Npriv, number of private haplotypes; Arpriv, rarefied private haplotypic richness; nc, not calculated. doi:10. 1371/journal. pone. 0084307. t005 1. 70 0. 05 9. 54 2. 43 3. 61 4 2. 00 0 9. 99 14 4. 14 3 108 0. 598(0. 026) 8 42 0. 512(0. 017) 2 31 0. 805(0. 042) 15 39 0. 529(0. 084) 5 2. 05 0. 21 6. 03 0. 28 3. 97 2 2. 17 1 6. 48 7 2 0 0. 585(0. 059) 5 0. 266(0. 056) 3 0. 822(0. 067) 8 0. 451(0. 060) 2 61 85 35 34 1. 71 0. 18 8. 63 1. 52 4. 03 3 2. 18 1 10 8 4. 30 2 0. 540(0. 055) 7 0. 415(0. 042) 3 0. 909(0. 044) 10 0. 582(0. 076) 5 89 99 18 42 1. 70 0. 00 6. 61 0. 05 4. 03 3 2 0 7. 98 12 2. 83 0 0. 648(0. 036) 7 0. 349(0. 090) 2 0. 847(0. 029) 14 0. 582(0. 093) 3 80 Milicia excelsa (Welw.
335 ** 0. 280 + 0. 074 0. 257 Symphonia globulifera 0. 098 + 0. 146 + 0. 263 *** 0. 491 *** + Trichoscypha acuminata 0. 233 + 0. 167 20. 061 20. 025 Number of tests with P,0. 05 4 5 2 4 3 0 Number of tests with P,0. 10 8 7 3 5 6 1 Dataset 2 Greenwayodendron suaveolens subsp. suaveolens var. 0. 180 *** 0. 154 *** 0. 186 *** 0. 299 *** * suaveolens (Engl. & Diels) Verdc. Milicia excelsa (Welw. ) C. C. Berg 20. 013 20. 009 0. 255 *** 0. 220 *** Symphonia globulifera L. f. 0. 111 *** 0. 148 ** 0. 185 *** 0. 252 *** Trichoscypha acuminata Engl. 20. 005 20. 013 0. 079 * 0. 156 *** Estimates of differentiation statistics are followed by results from one-sided permutation tests for population structure. +, P,0. 10; *, P,0. 05; **, P,0. 01; ***, P,0. 001; ns, not significant; nc, not calculated. doi:10.
Acknowledgements We express much gratitude to  Dr Peter Schwendinger ,  Curator of Geneva Museum , for his extraordinarily kind assistance and collaboration, which allowed us to conduct this study. With regard to SEM, we are grateful to  Mr Sylvain Pont  ( Direction des Collections -SEM-EDS), Muséum National d'Histoire Naturelle, Paris, France , for the secondary SEM images. We also express the sincere gratitude to  Mrs Catherine Rausch De 20 N. Fernandez  et al. Downloaded by [North West University] at 04:41 19 September 2014.
Genome Screening, eps Gene Identification and Nomenclature Genomic sequences were recovered from databases or produced by GeT-PlaGe Genotoul (Castanet Tolosan France) and Macrogen (Seoul Korea) (unpublished). All 36 new sequences were annotated by RAST (Rapid Annotation using Subsystem Technology, rast. nmpdr. org) and Kaas (KEGG Automatic Annotation Server)  [21]. These sequences have been deposited at DDBJ/ EMBL/GenBank under the accession numbers listed in Table  1. The versions described in this paper for eps gene content are versions XXXX01000000. Multilocus sequence typing (MLST) was performed for all strains according to the procedure described by Bilhere et al. [19]  with some modifications. The sequence type (ST) of each strain was constructed from six housekeeping genes: gyrB, g6pd, pgm, dnaE, purK and rpoB whose sequences were obtained by genome analysis in Seed Viewer application of RAST.
Sequence treatment was performed by using BioEdit 7. 2. 3 and the phylogenetic tree was constructed by the neighbor-joining method with a Kimura twoparameter distance model, using MEGA 4 software  [22]. Bootstrap values were obtained after 1,000 iterations. From the 3 genomes sequences publicly available at the beginning of our work (genomes of strains O. oeni PSU-1, ATCC BAA-1163 and AWRI B429), we created a database of 82 protein sequences (Table  S1 , panel initial database), potentially associated with the EPS metabolism including glycosyltransferases, flippases (wzx) and polymerases (wzy) but also glycoside-hydrolases and protein sequences involved in the synthesis of precursors (sugar nucleotides). The 47 other annotated genome sequences were then analyzed for the presence of orthologs of these 82 proteins (BLASTP).
Once an ortholog was identified, the gene genomic environment was examined. In addition, all the genes encoding proteins different from those in the initial database (identity , 70%), but displaying significant homology (BLASTP or TBLASTX cutoff level of 1e 230 ), suggesting proteins with related enzymatic activity, were listed and their genomic environment was analyzed. A second analysis was done by searching, among the proteins deduced from the annotated genomes, the conserved motifs of glycoside-hydrolases and glycosyltransferases. Both methods gave the same results, i. e. the same list of eps genes and proteins. To assign protein functions, we used the Pfam database (http://pfam. sanger. ac. uk/). Glycosyltransferase genes were also assigned to GT families, based on the CAZy database. Genes were named (Table  S1 ) according to the bacterial polysaccharide gene nomenclature (BPGN) system  [23] : this system is applicable to all species; it distinguishes different classes of genes and provides a single name for all genes of a given function.
pneumoniae, S. sanguis, S. sanguinis, S. agalactiae, Leuconostoc citreum, Ln. mesenteroides, L. lactis, Pediococcus acidilactici, Enterococcus faecalis, Bifidobacterium bifidum, Bacillus coagulans or Bacteroides dorei. Few of these species are encountered in wine environment, but very few wine bacteria genomes have been sequenced and published at the time of this study. Sequence analysis of the protein sequences deduced from the 15 models of cluster eps2 led to identify (Figure  3 , Table  S1 , panel eps2): N 3 highly conserved regulatory proteins (AraC, Wzd, Wze), N 13 distinct polymerase (Wzy), displaying low identity with the sequences in the database. WodC encoded in model 9304 of eps2 may be a 14 th polymerase, N 9 flippases families: B422/PSU1 (99% identity), BAA-1163/ 9805 (80% identity), 0502/9304/0607/C52/C23 (more than 75% identity), 0501, B429, 9517, S13, 277, S12, N 3 alleles of priming glycosyltransferases WobA (WobA PSU1 , WobA B429 , WobA S12 ),  N 5 putative rhamnosyltransferases WobB, WobF, WobJ, WobS and WobU (1GT-1, 4GT-2), N 4 putative galactosyltransferases, WocK, WocS, WodQ and WodS (1 GT-1, 2 GT-2, 1 GT-28), N 3 putative choline phosphotransferases (LicD 277 , LicD C23 , LicD C52 ), N 1 putative glucosyltransferase, WobE, N 53 glycosyltransferases, whose substrate specificity could not be predicted by sequence analysis and, among them, 24 glycosyltransferases classified in GT-2, 19 in GT-1, 2 in GT-4 and 8 not associated with a CAZy family, N 4 putative acetyltransferases and 2 putative pyruvyltrans- ferases, N 3 UDP-glucose-dehydrogenase (UgdB 553 , Ugd 0501 , Ugd S12 ), 2 glycerol-3-P-cytidyltransferase (TagD 277 , TagD C23 ), 1 nucleotidyltransferase (Abp1 C52 ) and 1 epimerase (Abp2 C52 ), N and 6 proteins with unknown function (WocE, WocP, WocT, WodC, wodK, wodU).
On the other hand, only a few strains were apparently able to produce CDP-glycerol (proteinTagD provided by eps2 models 277 or C23) or UDP-N-Acetyl mannosamine (Mna provided by eps2 model C52). Regarding the biosynthesis of NDP-arabitinol, the genes abp1 and abp2 were found in the C52 genome (in cluster eps2) but the deduced proteins exhibited moderate identities with proteins Abp1 and Abp2 found in the databases (37% and 30%). Finally, the biosynthetic pathway for CDP-choline (LicA and LicC) was not found in any of the studied genomes, although three models of cluster eps2 (8 strains involved) encoded a choline phosphotransferase (LicD). Nevertheless, we cannot exclude that these functions are performed by highly divergent proteins in O. oeni. Additional glycosyltransferase genes. Another element may contribute to the modulation of the structure of the EPS produced by O.