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. 2018 Apr 3;8:5591. doi: 10.1038/s41598-018-23707-6

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Competing demographic scenarios of Psittacanthus calyculatus divergence. Five evolutionary scenarios were built and tested using DIYABC: (A) simple split model (scenario 1), in which CALY (Pop1), HYBR (Pop2) and SCHI (Pop3) diverged simultaneously at t1; (B) isolation with admixture model (scenario 2), in which Pop2 (HYBR) was generated by admixture between Pops 1 (CALY) and 3 (SCHI) at t1, then CALY merged with SCHI at t2; (C) hierarchical split model 1 (scenario 3), in which HYBR merged with CALY at t1, then both populations merged with SCHI at t2; (D) hierarchical split model 2 (scenario 4), in which HYBR merged with SCHI at t1, then both populations merged with CALY at t2; (E) hierarchical split model 3 (scenario 5), in which CALY merged with SCHI at t1, then both populations merged with HYBR at t2. The posterior probability of scenarios was assessed using a weighted logistic regression on the 1% of simulated datasets closest to the observed data and, for the best-supported scenario (scenario 2); (F) Results of a logistical model comparing the posterior probability of each scenario with the number of simulations used to calculate it; (G) A model checking procedure was applied using a PCA on test statistic vectors to visualize the fit between the simulated and observed datasets. Note the large cloud of data from the prior and observed datasets centred on a small cluster from the posterior predictive distribution, suggesting that the best-supported scenario explained the observed data well. Prior and posterior probabilities of parameters t1 (H) and ra (=admixture rate) are provided (I).