Harvey, M. G. | Search bioRxiv

bioRxiv2025

Song complexity in suboscine birds: evolutionary drivers and ecological constraints

Acoustic signal complexity varies widely in the animal kingdom for reasons that remain unclear. In birds, it is widely proposed that vocal complexity evolves as an honest signal of individual quality driven by sexual selection. Other hypotheses related to social interactions include competition for ecological resources (social selection) and intra-group communication in group- living animals, both of which may favour signal complexity. However, these hypotheses are rarely explored at macroevolutionary scales, particularly in the context of constraints on sound production, transmission and detection, leading to ongoing uncertainty about the evolutionary origins of complex vocal signals. Using Bayesian phylogenetic models, we test whether different forms of social communication and ecological constraints predict the temporal and spectral complexity of songs in 1,288 species of suboscine passerine birds. We found that song complexity was reduced by sexual selection, along with other limiting factors including large body size and dense vegetation. Conversely, territoriality boosted the temporal complexity of songs. These findings challenge the common assumption that sexual selection is the main driver of increased signal complexity, and instead highlight the role of social selection as a key component of multiple inter-related drivers and constraints.

evolutionary biology↗

bioRxiv2025

Efficient Inference of Macrophylogenies: Insights from the Avian Tree of Life

The exponential growth of molecular sequence data over the past decade has enabled the construction of numerous clade-specific phylogenies encompassing hundreds or thousands of taxa. These independent studies often include overlapping data, presenting a unique opportunity to build macrophylogenies (phylogenies sampling > 1,000 taxa) for entire classes across the Tree of Life. However, the inference of large trees remains constrained by logistical, computational, and methodological challenges. The Avian Tree of Life provides an ideal model for evaluating strategies to robustly infer macrophylogenies from intersecting datasets derived from smaller studies. In this study, we leveraged a comprehensive resource of sequence capture datasets to evaluate the phylogenetic accuracy and computational costs of four methodological approaches: (1) supermatrix approaches using concatenation, including the "fast" maximum likelihood (ML) methods, (2) filtering datasets to reduce heterogeneity, (3) supertree estimation based on published phylogenomic trees, and (4) a "divide-and-conquer" strategy, wherein smaller ML trees were estimated and subsequently combined using a supertree approach. Additionally, we examined the impact of these methods on divergence time estimation using a dataset that includes newly vetted fossil calibrations for the Avian Tree of Life. Our findings highlight that recently developed fast tree search approaches offer a reasonable compromise between computational efficiency and phylogenetic accuracy, facilitating inference of macrophylogenies.

evolutionary biology↗