bioRxivPaleontology
Early Tetrapodomorph Biogeography: Controlling for Fossil Record Bias in Macroevolutionary Analyses
Abstract
ABSTRACTThe fossil record provides direct empirical data for understanding macroevolutionary patterns and processes. Inherent biases in the fossil record are well known to confound analyses of this data. Sampling bias proxies have been used as covariates in regression models to test for such biases. Proxies, such as formation count, are associated with paleobiodiversity, but are insufficient for explaining species dispersal owing to a lack of geographic context. Here, we develop a sampling bias proxy that incorporates geographic information and test it with a case study on early tetrapodomorph biogeography. We use recently-developed Bayesian phylogeographic models and a new supertree of early tetrapodomorphs to estimate dispersal rates and ancestral habitat locations. We find strong evidence that geographic sampling bias explains supposed radiations in dispersal rate (potential adaptive radiations). Our study highlights the necessity of accounting for geographic sampling bias in macroevolutionary and phylogenetic analyses and provides an approach to test for its effect.View Full Text
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Modeling phylogenetic biome shifts on a planet with a past
M. J. Landis, E. J. Edwards, M. J. Donoghue·bioRxiv·Jun 1, 2020
- Early tetrapod diversification under neutral theory
E. M. Dunne, S. E. D. Thompson, … R. C. Close·bioRxiv·Feb 8, 2022
- Biogeographic dating of speciation times using paleogeographically informed processes
Michael J Landis·bioRxiv·Oct 8, 2015
- Spatially heterogeneous gene flow may hinder linking phylogeographic data to macroevolutionary patterns
Ethan F. Gyllenhaal, Lukas J. Musher·bioRxiv·Nov 4, 2025
- Machine Learning Biogeographic Processes from Biotic Patterns: A New Trait-Dependent Dispersal and Diversification Model with Model-Choice By Simulation-Trained Discriminant Analysis
Jeet Sukumaran, Evan P. Economo, L. Lacey Knowles·Systematic BiologyPeer-reviewed·Dec 28, 2015
- Apparent timescaling of fossil diversification rates is caused by sampling bias
Bouwe Rutger Reijenga, Roger A. Close·Current BiologyPeer-reviewed·Jan 23, 2025