摘要Comparative analyses in ecology and evolution often face the challenge of controlling for the effects of shared ancestry(phylogeny)from those of ecological or trait-based predictors on species traits.Phylogenetic Generalized Linear Models(PGLMs)address this issue by integrating phylogenetic re-lationships into statistical models.However,accurately partitioning explained variance among corre-lated predictors remains challenging.The phylolm.hp R package tackles this problem by extending the concept of"average shared variance"to PGLMs,enabling nuanced quantification of the relative importance of phylogeny and other predictors.The package calculates individual likelihood-based R2 contributions of phylogeny and each predictor,accounting for both unique and shared explained vari-ance.This approach overcomes limitations of traditional partial R2 methods,which often fail to sum the total R2 due to multicollinearity.We demonstrate the functionality of phylolm.hp through two case studies:one involving continuous trait data(maximum tree height in Californian species)and another focusing on binary trait data(species invasiveness in North American forests).The phylolm.hp package offers researchers a powerful tool to disentangle the contributions of phylogenetic and ecological predictors in comparative analyses.