摘要: Estimating divergence times from molecular sequence data is central to reconstructing the evolutionary history of lineages. Although Bayesian relaxed-clock methods provide a principled framework for incorporating fossil information, their dependence on repeated evaluations of the full phylogenetic likelihood makes them computationally demanding for large genomic datasets. Furthermore, because disagreements in divergence-time estimates often arise from uncertainty or error in fossil placement and prior specification, there is a need for methods that are both computationally efficient and robust to fossil-calibration uncertainty. In this study, we introduce fast and accurate alternatives based on the phylogenetic pairwise composite likelihood, presenting two adjusted pairwise likelihood (APW) formulations that employ asymptotic moment-matching weights to better approximate the behavior of the full likelihood within a Bayesian MCMC framework. Extensive simulations across diverse fossil-calibration scenarios show that APW methods produce node-age estimates comparable to those obtained from the full likelihood while offering greater robustness to fossil misplacement and prior misspecification, due to the reduced sensitivity of composite likelihoods to local calibration errors. Applied to a genome-scale dataset of modern birds, APW methods recover divergence time patterns consistent with recent studies, while reducing computational cost by more than an order of magnitude. Overall, our results demonstrate that adjusted pairwise likelihoods provide a calibration-robust and computationally efficient framework for Bayesian node dating, especially suited for large phylogenomic datasets and analyses in which fossil priors may be uncertain or imperfectly placed.