Synergizing Bayesian and Heuristic Approaches: D-BPP Uncovers Ghost Introgression in <i>Panthera</i> and <i>Thuja</i>

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 candidates 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 reliably 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 introgression, 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 introgression 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.