ArticlePloS one2017
Model selection with multiple regression on distance matrices leads to incorrect inferences.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Benchmarking the Mantel test and derived methods for testing association between distance matrices.Molecular ecology resources · 2025Article
- Understanding the effect of sociodemographic and psychological latent characteristics on flex-route transit acceptance.PloS one · 2023Article
- The Easter Egg Weevil (Pachyrhynchus) genome reveals syntenic patterns in Coleoptera across 200 million years of evolution.PLoS genetics · 2021Article
- Coupled beta diversity patterns among coral reef benthic taxa.Oecologia · 2021Article
- Mechanisms governing avian phylosymbiosis: Genetic dissimilarity based on neutral and MHC regions exhibits little relationship with gut microbiome distributions of Galápagos mockingbirds.Ecology and evolution · 2020Article
- Landscape genetics of wolverines (Journal of mammalogy · 2020Article
- Alternative Quantifications of Landscape Complementation to Model Gene Flow in Banded Longhorn Beetles [Frontiers in genetics · 2020Article
- Correlates of different facets and components of beta diversity in stream organisms.Oecologia · 2019Article
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Authors and funding
7 authors.
Funding
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Abstract
In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.
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