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Within-species trait variation may be the result of genetic variation, environmental variation, or measurement error, for example. In phylogenetic comparative studies, failing to account for within-species variation has many adverse effects, such as increased error in testing hypotheses about evolutionary correlations, biased estimates of evolutionary rates, and inaccurate inference of the mode of evolution. These adverse effects were demonstrated in studies that considered a tree-like underlying phylogeny. Comparative methods on phylogenetic networks are still in their infancy. The impact of within-species variation on network-based methods has not been studied. Here, we introduce a phylogenetic linear model in which the phylogeny can be a network to account for within-species variation in the continuous response trait assuming equal within-species variances across species. We show how inference based on the individual values can be reduced to a problem using species-level summaries, even when the within-species variance is estimated. Our method performs well under various simulation settings and is robust when within-species variances are unequal across species. When phenotypic (within-species) correlations differ from evolutionary (between-species) correlations, estimates of evolutionary coefficients are pulled towards the phenotypic coefficients for all methods we tested. Also, evolutionary rates are either underestimated or overestimated, depending on the mismatch between phenotypic and evolutionary relationships. We applied our method to morphological and geographical data from Polemonium. We find a strong negative correlation of leaflet size with elevation, despite a positive correlation within species. Our method can explore the role of gene flow in trait evolution by comparing the fit of a network to that of a tree. We find marginal evidence for leaflet size being affected by gene flow and support for previous observations on the challenges of using individual continuous traits to infer inheritance weights at reticulations. Our method is freely available in the Julia package PhyloNetworks.
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Reducing the Biases in False Correlations Between Discrete Characters
Abstract The correlation between two characters is often interpreted as evidence that there exists a significant and biologically important relationship between them. However, Maddison and FitzJohn (in The unsolved challenge to phylogenetic correlation tests for categorical characters. Syst. Biol. 2015;64:127–136) recently pointed out that evidence of correlated evolution between two categorical characters is often spurious, particularly, when the dependent relationship stems from a single replicate deep in time. Here we will show that there may, in fact, be a statistical solution to the problem posed by Maddison and FitzJohn naturally embedded within the expanded model space afforded by the hidden Markov model (HMM) framework. We demonstrate that the problem of single unreplicated evolutionary events manifests itself as rate heterogeneity within our models and that this is the source of the false correlation. Therefore, we argue that this problem is better understood as model misspecification rather than a failure of comparative methods to account for phylogenetic pseudoreplication. We utilize HMMs to develop a multirate independent model which, when implemented, drastically reduces support for correlation. The problem itself extends beyond categorical character evolution, but we believe that the practical solution presented here may lend itself to future extensions in other areas of comparative biology. [Macroevolution; model adequacy; phylogenetic comparative methods; rate heterogeneity].
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- Award ID(s):
- 1916558
- PAR ID:
- 10465466
- Editor(s):
- Smith, Stacey
- Date Published:
- Journal Name:
- Systematic Biology
- Volume:
- 72
- Issue:
- 2
- ISSN:
- 1063-5157
- Page Range / eLocation ID:
- 476 to 488
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/sysbio/syac066
