2025/07/03 by Jacob D. Gardner, Joanna Baker, Chris Venditti +1 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · Earth and Planetary Sciences · Environmental Science · Mathematics · #Biology #Computer science #Data set #Evolution and Paleontology Studies #Evolutionary biology #Gene #Generalized least squares #Genetic diversity and population structure #Genetics #Least-squares function approximation #Mathematics #Ordinary least squares #Partial least squares regression #Phylogenetic comparative methods #Phylogenetic tree #Phylogenetics #Predictive modelling #Regression #Set (abstract data type) #Species Distribution and Climate Change #Statistics #Trait
paper · pdf · doi:10.1038/s41467-025-61036-1
published in Nature Communications 16(1), 6130 (Nature Portfolio)
openalex publication_date 2025/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
Inferring unknown trait values is ubiquitous across biological sciences-whether for reconstructing the past, imputing missing values for further analysis, or understanding evolution. Models explicitly incorporating shared ancestry amongst species with both known and unknown values (phylogenetically informed prediction) provide accurate reconstructions. However, 25 years after the introduction of such models, it remains common practice to simply use predictive equations derived from phylogenetic generalised least squares or ordinary least squares regression models to calculate unknown values. Here, we use a comprehensive set of simulations to demonstrate two- to three-fold improvement in the performance of phylogenetically informed predictions compared to both ordinary least squares and phylogenetic generalised least squares predictive equations. We found that phylogenetically informed prediction using the relationship between two weakly correlated (r = 0.25) traits was roughly equivalent to (or even better than) predictive equations for strongly correlated traits (r = 0.75). A critique and comparison of four published predictive analyses showcase real-world examples of phylogenetically informed prediction. We also highlight the importance of prediction intervals, which increase with increasing phylogenetic branch length. Finally, we offer guidelines to making phylogenetically informed predictions across diverse fields such as ecology, epidemiology, evolution, oncology, and palaeontology.