A unified framework for phylogenetic and spatial meta-analysis: concepts, implementation, and practical guidance

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Meta-analyses in ecology and evolution, and related fields, can include effect sizes structured by shared evolutionary history or spatial distance. In this tutorial paper, we show that phylogenetic and spatial meta-analyses can be formulated within the same theoretical framework based on correlated random effects. From this perspective, the two approaches differ only in how distance is defined: evolutionary time in phylogenetic meta-analyses versus geographic distance in spatial ones, while sharing the same underlying statistical logic. This unified view clarifies relationships among commonly used correlation structures and reveals their direct correspondence across phylogenetic and spatial settings. Building on this framework, we illustrate how researchers can implement phylogenetic and spatial meta-analytic models in several widely used R packages, including metafor, glmmTMB, and brms. Using published datasets, we demonstrate how researchers can express equivalent model specifications across frequentist and Bayesian frameworks and how these models allocate variance across hierarchical levels. We also present practical issues related to model identifiability and data structure and highlight considerations for specifying and interpreting correlated meta-analytic models. Although we draw examples from ecology and evolutionary biology, the same framework can be applied to meta-analyses in many other fields, for example, epidemiology and public health, education and social policy, and linguistics and cultural evolution. https://doi.org/10.32942/X2WT1B Ecology and Evolutionary Biology Hierarchical modelling, Quantitative analysis, Brownian motion, Ornstein–Uhlenbeck process, Meta-analysis Published: 2026-04-09 12:02 Last Updated: 2026-04-09 12:02 CC BY Attribution 4.0 International Conflict of interest statement: None Data and Code Availability Statement: https://ayumi-495.github.io/phylo_spatial_tutorial/ Language: English Views: 350 Downloads: 55