The effects of host phylogenetic coverage and congruence metric on Monte Carlo-based null models of phylosymbiosis

  1. James G. DuBose

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Abstract

Variation in host-associated microbial communities often parallels patterns of phylogenetic divergence between hosts, a pattern known as phylosymbiosis. Understanding of this phenomenon relies initially on quantifying phylosymbiotic signals from across a broad range of host taxa. Quantifying signals of phylosymbiosis is typically achieved by calculating how congruent a host’s phylogenetic tree is with a dendrogram that represents patterns of dissimilarity in their associated microbial communities. To statistically assess the degree of congruence, several studies have constructed null models using a Monte Carlo approach to randomly sample trees. Although this approach is becoming more common, it has several features that warrant benchmarking to advise its further use. This approach relies on quantification of congruence between a host’s phylogenetic tree its microbial community dendrogram. Therefore, it is important to establish how choice of congruence metric influences null model-based inferences. Furthermore, phylosymbiotic signals may manifest at different scales of host divergence, and it is important to establish the extent of host phylogenetic breadth needed to reliably detect a phylosymbiotic signal. To help improve our study of phylosymbiosis, here I examine how power and type 1 error (false positive) rates associated with this approach varies with choice of congruence metric and host phylogenetic coverage. Furthermore, I examine variation in sensitivity given uncertainty in tree estimation, as well as how well null congruence models align with expectations of community assembly that is completely neutral with respect to host phylogeny. I generally found that model performance increased rapidly with increasing tree sizes, suggesting lower limits on the host phylogenetic breadth needed to reliably detect phylosymbiotic signals with this approach. Furthermore, I found several notable variations in performance between congruence metrics, which translated into different inferences regarding signal detection. Overall, these findings suggest that Monte Carlo sampling across tree space can be an effective way to quantify phylosymbiotic signals and highlight key considerations for its implementation. 

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  1. Version published to 10.24072/pcjournal.667
  2. Peer Community in Evolutionary Biology

    For anyone seeking to understand how, "from (a) simple beginning, endless forms most beautiful and most wonderful have been, and are being, evolved" [Darwin, 1859], the relatively simple rules of template-based DNA synthesis, mutation and vertical inheritance offer powerful lenses into the past, provided selection and drift are taken into account. These principles allow us to read genomic similarity as a record of shared ancestry, giving rise to phylogenetics and its tree-like reconstructions of evolutionary history, commonly referred to as phylogenetic trees. 

    One striking observation when zeroing on specific lineages is that the community composition of microrganisms that live in and on them are more similar to one another than to the microbial communties of other hosts sharing the same environments or the same diets (the two factors more obviously linked to the microbial communities). Relationships between community compositions in these cases also generate tree-like branching patterns, more precisely called dendrograms, sometimes uncannily mirroring the phylogenetic relationships among the organisms who serve as their hosts. This sort of phylogenetic signal of microbial communities associated with related hosts is called phylosymbiosis [Brucker & Bordenstein, 2013]. While diverse ecological and evolutionary processes can generate mirrored host-microbiome 'trees' [Kohl, 2020], rigorous quantification is required to understand them. DuBose [2025] tackles this methodological challenge by evaluating how different tree-congruence metrics and null models perform when detecting phylosymbiotic signals.

    At least three approaches have been used to quantify phylosymbiosis [Lim & Bordenstein, 2020]. Matrix-correlation methods compare host phylogenetic distances with microbiome beta-diversity distances (quantified by Bray-Curtis, Jaccard or UniFrac methods), typically using Mantel [Groussin et al., 2017] or Procrustes tests [Qin et al., 2021]. Phylogenetic comparative methods, though less common in this context, evaluate phylogenetic signal through measures such as Blomberg's K and Pagel's lambda [Donohue et al., 2022] or process-based models like multivariate Brownian motion [Perez-Lamarque et al., 2023]. Topological congruence methods, the most widely used, assess whether a host phylogeny and a microbiome dendrogram are more topologically similar than expected by chance. But the "devil is in the details" regarding what constitutes "chance" and whether different topological congruence metrics agree on the strength of the signal. 

    DuBose offers a thorough study of both aspects. First, the author implements a clever neutral community assembly model (following reviewer suggestions) to compare against standard random tree sampling, validating the approach as a robust reference for testing null hypotheses. Second, the study evaluates a large range of congruence metrics available in the widely used TreeDist and phangorn packages. The reproducibility of this method was assessed by a PCI data editor and this recommender; users interested in testing the full range of metrics can easily modify the provided scripts.

    While one may miss alternative model trees to represent more biologically grounded processes and most results align with theoretical expectations (e.g. signal is more evident in larger trees), random tree sampling remains common yet under-benchmarked, reference in the literature [Brooks et al., 2016; Travelline et al., 2020; Graham et al.,  2025]. DuBose provides the first systematic exploration of its properties, demonstrating that Monte Carlo sampling across tree space serves as a meaningful null framework. This work clearly outlines key caveats and implementation choices researchers must consider, making it a necessary methodological resource for the field.

     

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  3. Version published to 10.1101/2025.02.07.637028 on bioRxiv