A multivariate phylogenetic comparative method incorporating a flexible function between discrete and continuous traits

Haba, Yuki; Kutsukake, Nobuyuki

doi:10.1007/s10682-019-10011-6

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Many evolutionary hypotheses invoke causal relationships between continuous and discrete traits. However, currently MR-PMM is one of few methods available to estimate correlations between continuous and discrete traits in a fully phylogenetic framework (see also Haba and Kutsukake 2019). Despite complications arising from non-linear latent variable transformations (see Section 3.0.5), this has enabled several long-standing evolutionary hypotheses to be addressed in recent years (e.g., Downing et al 2020;Cornwallis et al 2017).…”

Section: Strengths and Weaknessesmentioning

confidence: 99%

“…The motivation for developing multivariate phylogenetic comparative methods is now broadly appreciated (Adams and Collyer 2018;Uyeda et al 2015Uyeda et al , 2018Garamszegi 2014). Modern approaches aim to move beyond phylogenetic regression and variance partitioning of individual Gaussian variables (e.g., Lynch 1991;Pagel 1999;Blomberg et al 2003), to methods capable of evaluating the strength, direction, and conservatism of eco-evolutionary relationships within networks of continuous and discrete variables (Westoby et al, 2023;Haba and Kutsukake, 2019;Brommer et al, 2019;Hadfield, 2010). These multivariate techniques are applicable to a broad range of species traits, from morphology, physiology, and behavior, to environmental tolerance limits and niche characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Response Phylogenetic Mixed Models: Concepts and Application

Halliwell

Yates

Holland

2022

Preprint

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The scale and resolution of trait databases and molecular phylogenies is increasing rapidly. These resources permit many questions in macro evolution and ecology to be addressed with the right statistical tools. Phylogenetic mixed models (PMM), particularly multi-response (MR) implementations, offer great potential for analyses of trait evolution. While flexible and powerful, these models can be conceptually challenging. The literature describing PMM is also highly technical, creating additional barriers for many biologists. Here we present an accessible and practical guide to the PMM. We begin by outlining key concepts in mixed modelling, emphasising the use of covariance matrices to model correlative structure in the data. We present a simple notation for PMM, of which phylogenetic generalised least squares (PGLS) is a special case. We outline the limitations of single-response (SR) models such as PGLS for characterising patterns of trait variation, and emphasise that MR models will often be a preferable approach to analyses involving multiple species traits. Using simulated data and visual examples, we demonstrate the capacity of MR-PMM to partition trait covariance into phylogenetic and independent components. We discuss interpretation, prediction, and model validation, including methods based on leave-one-out cross validation. We then apply this approach to a real-world data set of leaf traits in Eucalyptus to show how MR-PMM can offer a more nuanced understanding of trait correlations compared to SR approaches. Finally, we highlight common pitfalls and offer practical recommendations to analysts. To complement this material, we provide an online tutorial including additional examples, as well as side-by-side implementations in two popular R packages, MCMCglmm and brms.

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Section: Strengths and Weaknessesmentioning

confidence: 99%