Biology subjects

Holland, B. R.

Publications and source records attributed to Holland, B. R..

4 recordsLinked to original sources

bioRxiv2025

Robust Phylogenetics

We present a robust phylogenetic inference method, called the trimmed log-likelihood method, which effectively identifies fast-evolving, saturated, or erroneous sites in both simulated and empirical multiple sequence alignments. This method avoids circularity by dynamically identifying and removing sites without relying on an initial tree, allowing the specific sites removed to change as tree topology and branch lengths are estimated. Our analyses demonstrate that this method outperforms existing approaches, such as the Slow-Fast method, Tree Independent Generation of Evolutionary Rate approach, and Le Quesne Probability statistics, by removing fewer sites while still inferring phylogenies with comparable or greater accuracy. Implemented in IQ-TREE2, the trimmed log-likelihood method is user-friendly with a simple command-line interface. However, challenges remain in addressing heterogeneous evolutionary processes including compositional biases, such as GC bias. Despite these challenges, our approach offers a practical solution for improving phylogenetic inference by automatically and dynamically identifying sites to down-weight during phylogenetic analyses. We recommended that researchers compare trees inferred by varying the proportion of down-weighted sites to monitor changes in tree topology and to identify a set of candidate tree topologies for further consideration.

evolutionary biology↗

bioRxiv2022

Multi-Response Phylogenetic Mixed Models: Concepts andApplication

The scale and resolution of trait databases and molecular phylogenies is increasing rapidly. These resources permit many open questions in comparative biology to be addressed with the right statistical tools. Multi-response (MR) phylogenetic mixed models (PMM) offer great potential for multivariate analyses of trait evolution. While flexible and powerful, these methods are not often employed by researchers in ecology and evolution, reflecting a specialised and technical literature that creates barriers to usage for many biologists. Here we present a practical and accessible guide to MR-PMM. We begin with a review of single-response (SR) PMM to introduce key concepts and outline the limitations of this approach for characterizing patterns of trait co-evolution. We emphasise MR-PMM as a preferable approach to analyses involving multiple species traits, due to the explicit decomposition of trait covariance across levels in the model hierarchy. We discuss multilevel distributional models, meta-analyses, multivariate models of evolution, and extensions to non-Gaussian response traits. We highlight techniques for causal inference using precision matrices, as well as advanced topics including prior specification and latent factor models. Using simulated data and visual examples, we discuss interpretation, prediction, and model validation. We implement many of the techniques discussed in example analyses of plant functional traits to demonstrate the general utility of MR-PMM in handling complex real world datasets. Finally, we discuss the emerging synthesis of comparative techniques made possible by MR-PMM, highlight strengths and weaknesses, and offer practical recommendations to analysts. To complement this material, we provide extensive online tutorials including side-by-side model implementations in two popular R packages, MCMCglmm and brms.

evolutionary biology↗

bioRxiv2018

A new phylogenetic protocol: Dealing with model misspecification and confirmation bias in molecular phylogenetics

Molecular phylogenetics plays a key role in comparative genomics and has an increasingly-significant impacts on science, industry, government, public health, and society. In this opinion paper, we posit that the current phylogenetic protocol is missing two critical steps, and that their absence allows model misspecification and confirmation bias to unduly influence our phylogenetic estimates. Based on the potential offered by well-established but under-used procedures, such as assessment of phylogenetic assumptions and tests of goodness-of-fit, we introduce a new phylogenetic protocol that will reduce confirmation bias and increase the accuracy of phylogenetic estimates. DedicationTo the memory of Rossiter H. Crozier (1943-2009), an evolutionary biologist, who, with his great generosity and wide-reaching inquisitiveness, inspired students and scientists in Australia, and abroad.

evolutionary biology↗

bioRxiv2017

GHOST: Recovering Historical Signal from Heterotachously-evolved Sequence Alignments

Molecular sequence data that have evolved under the influence of heterotachous evolutionary processes are known to mislead phylogenetic inference. We introduce the General Heterogeneous evolution On a Single Topology (GHOST) model of sequence evolution, implemented under a maximum-likelihood framework in the phylogenetic program IQ-TREE (http://www.iqtree.org). Simulations show that using the GHOST model, IQ-TREE can accurately recover the tree topology, branch lengths and substitution model parameters from heterotachously-evolved sequences. We develop a model selection algorithm based on simulation results, and investigate the performance of the GHOST model on empirical data by sampling phylogenomic alignments of varying lengths from a plastome alignment. We then carry out inference under the GHOST model on a phylogenomic dataset composed of 248 genes from 16 taxa, where we find the GHOST model concurs with the currently accepted view, placing turtles as a sister lineage of archosaurs, in contrast to results obtained using traditional variable rates-across-sites models. Finally, we apply the model to a dataset composed of a sodium channel gene of 11 fish taxa, finding that the GHOST model is able to infer a subtle component of the historical signal, linked to the previously established convergent evolution of the electric organ in two geographically distinct lineages of electric fish. We compare inference under the GHOST model to partitioning by codon position and show that, owing to the minimization of model constraints, the GHOST model is able to offer unique biological insights when applied to empirical data.

evolutionary biology↗