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Species' life histories determine population demographics and thus the probability that introduced populations establish and spread. Life histories also influence which species are most likely to be introduced, but how such 'introduction biases' arise remains unclear. Here, we investigate how life histories affect the probability of trade and introduction in phylogenetic comparative analyses across three vertebrate classes: mammals, reptiles and amphibians. We find that traded species have relatively high reproductive rates and long reproductive lifespans. Within traded species, introduced species have a more extreme version of this same life history profile. Species in the pet trade also have long reproductive lifespans but lack 'fast' traits, likely reflecting demand for rare species which tend to have slow life histories. We identify multiple species not yet traded or introduced but with life histories indicative of high risk of future trade, introduction and potentially invasion. Our findings suggest that species with high invasion potential are favoured in the wildlife trade and therefore that trade regulation is crucial for preventing future invasions. Predicting which species will become invasive is vital because the harm they cause cannot always be mitigated once populations establish. Street et al. show that traded and introduced species have distinctive life histories with high invasion potential, helping to identify future invasion risks. [ABSTRACT FROM AUTHOR]

Biological phenotypes are products of complex evolutionary processes in which selective forces influence multiple biological trait measurements in unknown ways. Phylogenetic comparative methods seek to disentangle these relationships across the evolutionary history of a group of organisms. Unfortunately, most existing methods fail to accommodate high‐dimensional data with dozens or even thousands of observations per taxon. Phylogenetic factor analysis offers a solution to the challenge of dimensionality. However, scientists seeking to employ this modelling framework confront numerous modelling and implementation decisions, the details of which pose computational and replicability challenges.We develop new inference techniques that increase both the computational efficiency and modelling flexibility of phylogenetic factor analysis. To facilitate adoption of these new methods, we present a practical analysis plan that guides researchers through the web of complex modelling decisions. We codify this analysis plan in an automated pipeline that distils the potentially overwhelming array of decisions into a small handful of (typically binary) choices.We demonstrate the utility of these methods and analysis plan in four real‐world problems of varying scales. Specifically, we study floral phenotype and pollination in columbines, domestication in industrial yeast, life history in mammals and brain morphology in New World monkeys.General and impactful community employment of these methods requires a data scientific analysis plan that balances flexibility, speed and ease of use, while minimizing model and algorithm tuning. Even in the presence of non‐trivial phylogenetic model constraints, we show that one may analytically address latent factor uncertainty in a way that (a) aids model flexibility, (b) accelerates computation (by as much as 500‐fold) and (c) decreases required tuning. These efforts coalesce to create an accessible Bayesian approach to high‐dimensional phylogenetic comparative methods on large trees. [ABSTRACT FROM AUTHOR]

Understanding the role of traits in dispersal is necessary to improve our knowledge of historical biogeography, community assembly processes and predictions of species' future movements. Here we aimed to determine the relationship between three traits (coastal distribution, body size, position on the fast/slow life history continuum) and past dispersal probability on an evolutionary timescale in chameleons (Chamaeleonidae). Using species' distribution data we identified the nine most important biogeographic regions for all included chameleons (181/217 species). We compiled life history trait data and used phylogenetic factor analysis to infer independent body size and fast/slow life history trait axes. Finally, we tested whether traits and trait combinations related to biogeographic dispersal success in the past, using trait‐dependent biogeographic models. All three traits were associated with past biogeographical movements. Lineages having coastal distributions and those with large bodies had higher dispersal probabilities. Interestingly, chameleons with either a very fast or very slow life history were more successful dispersers than species with an intermediate strategy. Together, the three traits – coastal, large‐bodied and extreme life history – form a dispersal syndrome. Traits have played an important role in the biogeographic history of chameleons. While only fast traits have been linked to present‐day invasion success in reptiles, both extremes of the life history spectrum were likely advantageous for dispersal and establishment during past biogeographic movements. Fast‐living species may be less susceptible to stochastic extinction in the first phases of a colonization (due to rapid population growth), and slow‐living species may be less vulnerable to environmental stochasticity (due to low demographic variability). Our results call for broader analyses testing the general influence of life history strategy in biogeographic dispersal success, which would help explain species distribution patterns on Earth. [ABSTRACT FROM AUTHOR]

Comparative biologists are often interested in inferring covariation between multiple biological traits sampled across numerous related taxa. To properly study these relationships, we must control for the shared evolutionary history of the taxa to avoid spurious inference. An additional challenge arises as obtaining a full suite of measurements becomes increasingly difficult with increasing taxa. This generally necessitates data imputation or integration, and existing control techniques typically scale poorly as the number of taxa increases. We propose an inference technique that integrates out missing measurements analytically and scales linearly with the number of taxa by using a post-order traversal algorithm under a multivariate Brownian diffusion (MBD) model to characterize trait evolution. We further exploit this technique to extend the MBD model to account for sampling error or nonheritable residual variance. We test these methods to examine mammalian life history traits, prokaryotic genomic and phenotypic traits, and HIV infection traits. We find computational efficiency increases that top two orders-of-magnitude over current best practices. While we focus on the utility of this algorithm in phylogenetic comparative methods, our approach generalizes to solve long-standing challenges in computing the likelihood for matrix-normal and multivariate normal distributions with missing data at scale. for this article are available online. [ABSTRACT FROM AUTHOR]