Data from: Synergizing Bayesian and heuristic approaches: D-BPP uncovers ghost introgression in Panthera and Thuja

Hybridization involving extinct or unsampled (“ghost”) lineages profoundly influences species’ evolutionary histories, but detecting such introgression remains methodologically challenging. We introduce D-BPP, a framework that integrates the heuristic D-statistic (or ABBA-BABA test) with Bayesian phylogenomic inference (implemented in BPP) to efficiently infer phylogenetic networks. In D-BPP, we first employ the D-statistic to rapidly identify candidate introgression events on a predefined bifurcating species tree; then we leverage the Bayesian test in BPP to rigorously validate these can- didates and sequentially add them to the species tree, retaining only those events with strong statistical support. When the species tree is ambiguous, D-BPP identifies the most probable topology by comparing introgression models in a Bayesian framework. Through dedicated simulation analyses, we show that the D-BPP workflow has high power: the D-statistic reli- ably detects the presence of introgression, BPP accurately discriminates among alternative introgression scenarios, and the key procedural steps of the pipeline are empirically well-justified. Critically, our framework excels at detecting ghost intro- gression, which is often unidentifiable or overlooked by existing methods—whether heuristic or full-likelihood. Applied to genomic datasets from Panthera (big cats) and Thuja (conifers), D-BPP uncovered previously undetected ghost introgres- sion events in both clades, underscoring the pervasive role ghost lineages have played across diverse taxa. By combining the computational efficiency of heuristic D-statistics with the robust statistical rigor of full-likelihood Bayesian inference, D-BPP provides a practical and powerful approach for reconstructing complex reticulate evolutionary histories.