Supplementary data

TableS1: Characteristics of 16 microsatellite loci isolated from M.ulei. Ta, annealing temperature.

Locus / GenBank Accession nos / Primer sequences (5’-3’) / Repeat motif of cloned allele / Ta (C°) / Expected Size (bp) / Number of alleles / Percentage of null alleles / Nei Gene Diversity / Previous Name (Le Guen et al. 2004)
µMU01 / GQ420364 / F CGACGAGATTGAATGTTGTGTT
R CGTGGACATCTCGACTTTCTC / [TG]13 / 60 / 140 / 4 / 0.0 / 0.48 / Mu01
µMU03 / GQ420365 / F CGACTCGCTGGAAAAGATG
R GCAGTTCGGGTCAGTGATTT / [GT]5GC[GT]7 / 60 / 103 / 3 / 0.0 / 0.41 / Mu08
µMU05 / GQ420355 / F GGATCTGCATGGTGAGTCG
R TGCTGGCATCTGCATCTATC / [CGC]5(…)[TGGA]5 / 60 / 107 / 2 / 2.1 / 0.02
µMU06 / GQ420356 / F ACGTATTACCCTCACCAC
R GAGGCCTCGCAACACTTC / [CA]15 / 60 / 134 / 4 / 1.0 / 0.46
µMU08 / AY228718 / F GTCCGGGATCTTGAAACAGC
R ATTCTCTCCCGTCATCCTCA / [CA]8 / 60 / 120 / 4 / 0.0 / 0.66 / Mu11
µMU09 / GQ420357 / F ACAGTCATTGCACCCGTTCT
R GACCTTCGTTTCACCTCCAC / [GT]6 / 60 / 138 / 3 / 4.2 / 0.39
µMU11 / GQ420358 / F CTCCACTTGTCACGAACCAG
R CAGAATGCGACCGACGTG / [CT]9[GT]7TT[GT]4 / 60 / 193 / 4 / 3.1 / 0.54
µMU13 / GQ420359 / F GACGGGCGGTTATTTCATC
R CTGCGTCATCTGGTAACTGC / [TG]3[[TG]3CG]9(…)[TG]14 / 60 / 199 / 8 / 7.3 / 0.75
µMU14 / GQ420366 / F CTTTCCGCGGACACTGC
R ACAGGCCAGCTGGTTCATC / [CA]5(…)[CA]5 / 60 / 212 / 5 / 2.1 / 0.79 / Mu02
µMU16 / AY228713 / F ATGGGTGTCCGTAAGTGCTC
R CAGCCACCCACCCACAAG / [TG]11 / 60 / 192 / 6 / 3.1 / 0.50 / Mu05
µMU24 / GQ420360 / F CGCTTTGGTGTGGTTAGTGA
R CAACCAAGAAGGCGAAGAAG / [GA]31 / 60 / 280 / 8 / 8.3 / 0.74
µMU28 / GQ420367 / F GCTGTTTAGGAGCCTGTTCG
R TCCATCTCCTCCAAATCGAC / [CA]14 / 60 / 348 / 4 / 5.2 / 0.66 / Mu07
µMU35 / GQ420368 / F AGCTGTGGGGCTTACATTTG
R CGCATCAGAAACTCCCAGTC / [GT]30 / 60 / 377 / 6 / 1.0 / 0.71 / Mu12
µMU37 / GQ420361 / F CAACATCATGAAGGGTAAGTTCC
R TTGTTGCCCATGACTGAAGA / [AC]7CTCC[CT]9 / 60 / 139 / 5 / 0.0 / 0.72
µMU38 / GQ420362 / F CCGTAGATCTGCGTGTCTCTC
R GCCAGTGATGCTGACTCTTG / CG[CA]3(…)CG[CA]4(…)CG[CA]6 / 60 / 205 / 4 / 1.0 / 0.65
µMU41 / GQ420363 / F AACCTAGGATACAGTCACAC
R ACCGATGACCTTGTTGGAAG / [CA]7 / 60 / 272 / 3 / 0.0 / 0.56

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Table S2: Comparison between scenarios belonging to a predefined class, with and without bottleneck. Pi, Pj, Pk and Pl are the populations represented in Figure3. Probability of scenario was computed on 1% closest simulated data sets. The best scenario with the best posterior probability across the 12 scenarios is in bold.

Scenario Class / Pi / Pj / Pk / Pl / Probability of scenario (logistic regression) / Confidence Interval
Second-round scenarios with bottleneck
Class I / Brazil / Ecuador / Guatemala / French Guiana / 0.000 / [0.000-0.000]
Class II / Ecuador / Guatemala / Brazil / French Guiana / 0.000 / [0.000-0.000]
Class III / Brazil / Ecuador / Guatemala / French Guiana / 0.000 / [0.000-0.000]
Class IV / Ecuador / Brazil / Guatemala / French Guiana / 0.014 / [0.011-0.017]
Class V / Brazil / Ecuador / Guatemala / French Guiana / 0.118 / [0.082-0.154]
Class VI / Ecuador / Guatemala / Brazil / French Guiana / 0.869 / [0.833-0.905]
Second-round scenarios without bottleneck
Class I / Brazil / Ecuador / Guatemala / French Guiana / 0.000 / [0.000-0.000]
Class II / Ecuador / Guatemala / Brazil / French Guiana / 0.000 / [0.000-0.000]
Class III / Brazil / Ecuador / Guatemala / French Guiana / 0.000 / [0.000-0.000]
Class IV / Ecuador / Brazil / Guatemala / French Guiana / 0.000 / [0.000-0.000]
Class V / Brazil / Ecuador / Guatemala / French Guiana / 0.000 / [0.000-0.000]
Class VI / Ecuador / Guatemala / Brazil / French Guiana / 0.000 / [0.000-0.000]

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TableS3: Model checking for the six best scenarios of each class. BRA=Brazil, ECU=Ecuador, GTM=Guatemala, GUF=French Guiana, HET=gene diversity (Nei, 1987), MGW=mean Garza and Williamson’s M index (Garza and Williamson, 2001) across loci, N2P=mean number of alleles between populations, V2P=mean allele size variance between populations, DAS=shared allele distance (Chakraborty and Jin, 1993) between populationsa.

Probability
Summary statistics / Observed value / Best scenario of Class I / Best scenario of Class II / Best scenario of Class III / Best scenario of Class IV / Best scenario of Class V / Best scenario of Class VI
HET BRA / 0.2336 / 0,3650 / 0.3185 / 0.7630 / 0.8040 / 0.3575 / 0.6385
HET ECU / 0.2775 / 0,1410 / 0.1340 / 0.0055** / 0.4690 / 0.8435 / 0.3790
HET GTM / 0.1598 / 0,5640 / 0.6560 / 0.8490 / 0.8315 / 0.8220 / 0.6325
HET GUF / 0.2195 / 0,7740 / 0.8405 / 0.8375 / 0.7040 / 0.8225 / 0.6435
MGW BRA / 0.6766 / 0,3325 / 0.4545 / 0.2120 / 0.2580 / 0.5120 / 0.3100
MGW ECU / 1.4648 / 0,9930** / 0.9880* / 1.0000*** / 0.9570* / 0.8560 / 0.9700*
MGW GTM / 1.6393 / 0,8870 / 0.6920 / 0.5540 / 0.7010 / 0.6830 / 0.8090
MGW GUF / 0.3677 / 0,0070** / 0.0030** / 0.0050** / 0.0190* / 0.0090** / 0.0215*
N2P BRA&ECU / 2.6875 / 0,0665 / 0.0285* / 0.5905 / 0.7620 / 0.7760 / 0.5740
N2P BRA&GTM / 2.6875 / 0,2995 / 0.2185 / 0.6850 / 0.9620* / 0.7960 / 0.7835
N2P BRA&GUF / 3.8125 / 0,8780 / 0.7540 / 0.9505* / 0.8570 / 0.6760 / 0.7770
N2P ECU&GTM / 2.0000 / 0,0080** / 0.1475 / 0.1025 / 0.3620 / 0.8365 / 0.2215
N2P ECU&GUF / 3.2500 / 0,4660 / 0.3425 / 0.3635 / 0.3315 / 0.6950 / 0.2935
N2P GTM&GUF / 3.2500 / 0,8880 / 0.8825 / 0.9375 / 0.7735 / 0.8380 / 0.6325
V2P BRA&ECU / 1.2370 / 0,0780 / 0.0240* / 0.0600 / 0.3585 / 0.3055 / 0.2450
V2P BRA&GTM / 1.1814 / 0,1005 / 0.0310* / 0.0370* / 0.4250 / 0.3080 / 0.2810
V2P BRA&GUF / 27.6895 / 0,9960** / 0.9740* / 0.9860* / 0.8390 / 0.7910 / 0.8690
V2P ECU&GTM / 0.2401 / 0,0030** / 0.0320* / 0.0000*** / 0.1195 / 0.2455 / 0.0410*
V2P ECU&GUF / 24.9809 / 0,9960** / 0.9690* / 0.9580* / 0.7880 / 0.7510 / 0.8270
V2P GTM&GUF / 26.1170 / 0,9920** / 0.9750* / 0.9780* / 0.8160 / 0.7830 / 0.8590
DAS BRA&ECU / 0.5418 / 0,9830* / 0.9975** / 0.7770 / 0.1180 / 0.1440 / 0.5090
DAS BRA&GTM / 0.4068 / 0,8925 / 0.9695* / 0.9840* / 0.0300* / 0.0425* / 0.2090
DAS BRA&GUF / 0.1786 / 0,2805 / 0.5320 / 0.6465 / 0.8565 / 0.8815 / 0.8345
DAS ECU&GTM / 0.5856 / 0,9940** / 0.6175 / 0.5975 / 0.1670 / 0.0590 / 0.6470
DAS ECU&GUF / 0.1583 / 0,1785 / 0.4505 / 0.5955 / 0.8185 / 0.8260 / 0.8050
DAS GTM&GUF / 0.0865 / 0,0540 / 0.2015 / 0.2880 / 0.5435 / 0.5815 / 0.5135

a Significance levels are indicated by asterisks (*, ** and *** for Q0.05, Q0.01 and Q0.001, respectively).

FigureS1: Delta K (ΔK black triangles) calculated as described in Evanno et al. (2005) and probability of the data evaluated for a given K (Ln(P(X|K)) black squares). For K=2, 3 and 4, the height of the ΔK module value is an indicator of the strength of the signal detected by structure (Evanno et al., 2005). The increase rate of Ln(P(X|K) also show that K=2, 3 and 4 are of interest.

FigureS2: Principal coordinate decomposition analysis based on the DAS distance matrix between M.ulei individual multilocus genotypes. BRA=Brazil, ECU=Ecuador, GTM=Guatemala, GUF=French Guiana.

FigureS3: Comparison between simulated and observed summary statistics (mean number of alleles for each population and Fst between populations). This comparison was done for the best Class VI scenario without (main title of plots in black) and with bottleneck (main title of plots in red). Each grey histogram depicts the distribution of one summary statistics in simulated datasets. The values of observed summary statistics are signaled by black vertical line. A two-sided permutation test was performed for each distribution. P-values are indicated in the main title of each histogram. Summary statistics of observed dataset are significantly (P<0.01) different from summary statistics of the simulated datasetsbased on the scenario without bottleneck. On the contrary, summary statistics of observed dataset lie in the distribution of summary statistics of the simulated datasetsbased on the scenario with bottleneck. BRA=Brazil, ECU=Ecuador, GTM=Guatemala, GUF=French Guiana.

FigureS4: Principal Component Analysis of summary statistics of 50.000 simulated datasets with the six second-round scenarios with (here denoted 1 to 6) and without (here denoted 7 to 12) bottleneck. Observed dataset is projected on the plan formed by the two first principal components (blue point) and is surrounded by simulated dataset based on ClassVI scenario with bottleneck.

Literature cited in supplementary data

Chakraborty R, Jin L (1993). Determination of relatedness between individuals using DNA-fingerprinting. Hum Biol65: 875–895.

Evanno G, Regnaut S, Goudet J (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol14: 2611–2620.

Garza JC, Williamson EG (2001). Detection of reduction in population size using data from microsatellite loci. Mol Ecol10: 305–318.

LeGuen V, Rodier-Goud M, Troispoux V et al.(2004). Characterization of polymorphic microsatellite markers for Microcyclus ulei, causal, agent of South American leaf blight of rubber trees. Mol Ecol Notes4: 122–124.

Nei M (1987). Molecular Evolutionary Genetics. Columbia University Press, New York.

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