Jeremy M. Brown

Use of ephemeral habitats may lead to high vagility and an increase in gene flow and panmixia across a species’ range. Range expansions associated with habitat alterations may also spur connectivity among previously isolated habitats and reduce genetic diversity at the colonization front. A century and a half of historical and modern single nucleotide polymorphism (SNP) data for Bachman’s Sparrow (Peucaea aestivalis) demonstrate that panmixia is characteristic of this ephemeral habitat specialist, not a byproduct of a recent range expansion or retraction. Furthermore, high vagility has apparently dampened expected genetic signatures at the colonization front where no consistent signal of lower genetic diversity was observed in the extralimital region compared to other regions during expansion or retraction. Overall, our data support an emerging pattern of limited genetic structure for a species associated with ephemeral habitats, for which high vagility may have evolved to help individuals locate transient habitat across large spatial scales. Nevertheless, extensive habitat loss and fragmentation in recent decades have begun to influence genetic variation in P. aestivalis with small but significant increases in genetic structure. If this exceptionally vagile ephemeral habitat specialist is unable to reach remaining isolated habitat fragments, it is likely that barriers to its dispersal are limiting gene flow in other species, even those thought to be capable of traversing long distances.

While universities have traditionally trained undergraduate students for scientific careers, they often fail to equip them with the hard and soft skills necessary for success in STEM professions. Furthermore, the COVID-19 pandemic disrupted the ability of many students to gain research experience due to lockdowns, leading to a cohort with limited hands-on experience for their next career step. Training in evolutionary biology has the potential to enhance the workforce in STEM by equipping students with valuable, in-demand skills for a wide range of careers. The Louisiana Graduate Network in Applied Evolution (LAGNiAppE) is a one-year research training program aimed at strengthening scholars’ scientific capabilities. After a month-long bootcamp, participants work on independent projects under the guidance of a mentor, co-mentor, and near-peer mentors. At the beginning of the 2023–2024 cohort, a baseline survey was conducted on nine scholars to assess science-related skills, knowledge, attitudes, scientific thinking, confidence, understanding and expectations. A follow-up survey at the end of the program measured program-driven changes and feedback on programmatic processes. Results from these surveys indicated an improvement in scientific thinking, confidence, and understanding among scholars. Participants also reported a strong sense of belonging within the scientific community, and seven scholars expressed interest in pursuing careers in bioscience. This program effectively builds career readiness in STEM by providing research opportunities that foster positive multi-network mentorship experiences and build connections within the scientific community.

Variation in gene tree estimates is widely observed in empirical phylogenomic data and is often assumed to be the result of biological processes. However, a recent study using tetrapod mitochondrial genomes to control for biological sources of variation due to their haploid, uniparentally inherited, and non-recombining nature found that levels of discordance among mitochondrial gene trees were comparable to those found in studies that assume only biological sources of variation. Additionally, they found that several of the models of sequence evolution chosen to infer gene trees were doing an inadequate job of fitting the sequence data. These results indicated that significant amounts of gene tree discordance in empirical data may be due to poor fit of sequence evolution models and that more complex and biologically realistic models may be needed. To test how the fit of sequence evolution models relates to gene tree discordance, we analyzed the same mitochondrial data sets as the previous study using 2 additional, more complex models of sequence evolution that each include a different biologically realistic aspect of the evolutionary process: A covarion model to incorporate site-specific rate variation across lineages (heterotachy), and a partitioned model to incorporate variable evolutionary patterns by codon position. Our results show that both additional models fit the data better than the models used in the previous study, with the covarion being consistently and strongly preferred as tree size increases. However, even these more preferred models still inferred highly discordant mitochondrial gene trees, thus deepening the mystery around what we label the “Mito-Phylo Paradox” and leading us to ask whether the observed variation could, in fact, be biological in nature after all.

Reconstructing the evolutionary history of different groups of organisms provides insight into how life originated and diversified on Earth. Phylogenetic trees are commonly used to estimate this evolutionary history. Within Bayesian phylogenetics a major step in estimating a tree is in choosing an appropriate model of character evolution. While the most common character data used is molecular sequence data, morphological data remains a vital source of information. The use of morphological characters allows for the incorporation fossil taxa, and despite advances in molecular sequencing, continues to play a significant role in neontology. Moreover, it is the main data source that allows us to unite extinct and extant taxa directly under the same generating process. We therefore require suitable models of morphological character evolution, the most common being the Mk Lewis model. While it is frequently used in both palaeobiology and neontology, it is not known whether the simple Mk substitution model, or any extensions to it, provide a sufficiently good description of the process of morphological evolution. In this study we investigate the impact of different morphological models on empirical tetrapod datasets. Specifically, we compare unpartitioned Mk models with those where characters are partitioned by the number of observed states, both with and without allowing for rate variation across sites and accounting for ascertainment bias. We show that the choice of substitution model has an impact on both topology and branch lengths, highlighting the importance of model choice. Through simulations, we validate the use of the model adequacy approach, posterior predictive simulations, for choosing an appropriate model. Additionally, we compare the performance of model adequacy with Bayesian model selection. We demonstrate how model selection approaches based on marginal likelihoods are not appropriate for choosing between models with partition schemes that vary in character state space (i.e., that vary in Q-matrix state size). Using posterior predictive simulations, we found that current variations of the Mk model are often performing adequately in capturing the evolutionary dynamics that generated our data. We do not find any preference for a particular model extension across multiple datasets, indicating that there is no “one size fits all” when it comes to morphological data and that careful consideration should be given to choosing models of discrete character evolution. By using suitable models of character evolution, we can increase our confidence in our phylogenetic estimates, which should in turn allow us to gain more accurate insights into the evolutionary history of both extinct and extant taxa.

Poor fit between models of sequence or trait evolution and empirical data is known to cause biases and lead to spurious conclusions about evolutionary patterns and processes. Bayesian posterior prediction is a flexible and intuitive approach for detecting such cases of poor fit. However, the expected behavior of posterior predictive tests has never been characterized for evolutionary models, which is critical for their proper interpretation. Here, we show that the expected distribution of posterior predictive P-values is generally not uniform, in contrast to frequentist P-values used for hypothesis testing, and extreme posterior predictive P-values often provide more evidence of poor fit than typically appreciated. Posterior prediction assesses model adequacy under highly favorable circumstances, because the model is fitted to the data, which leads to expected distributions that are often concentrated around intermediate values. Nonuniform expected distributions of P-values do not pose a problem for the application of these tests, however, and posterior predictive P-values can be interpreted as the posterior probability that the fitted model would predict a dataset with a test statistic value as extreme as the value calculated from the observed data.

1 Reconstructing the evolutionary history of different groups of organisms provides insight into how life originated and diversified on Earth. Phylogenetic trees are commonly used to estimate this evolutionary history, providing a hypothesis of the events. Within Bayesian phylogenetics a major step in estimating a tree is in choosing an appropriate model of character evolution. In the case of most extinct species, our only source of information to decipher their phylogenetic relationships is through the morphology of fossils. We therefore use a model of morphological character evolution, the most common of which being the Mk Lewis model. While it is frequently used in palaeobiology, it is not known whether the simple Mk substitution model, or any extensions to it, provide a sufficiently good description of the process of morphological evolution. To determine whether or not the Mk model is appropriate for fossil data we used posterior predictive simulations, a model adequacy approach, to estimate absolute fit of the model to morphological data sets. We first investigate the impact that different versions of the Mk model have on key parameter estimates using tetrapod data sets. We show that choice of substitution model has an impact on both topology and branch lengths, highlighting the importance of model choice. Next, we use simulations to investigate the power of posterior predictive simulations for morphology. Having validated this approach we show that current variations of the Mk model are in fact performing adequately in capturing the evolutionary dynamics that generated our data. We do not find any preference for a particular model extension across multiple data sets, indicating that there is no ‘one size fits all’ when it comes to morphological data and that careful consideration should be given to choosing models of discrete character evolution. By using suitable models of character evolution, we can increase our confidence in our phylogenetic estimates, which should in turn allow us to gain more accurate insights into the evolutionary history of both extinct and extant taxa.

Genomic data have only sometimes brought resolution to the tree of life. Large phylogenomic studies can reach conflicting conclusions about important relationships, with mutually exclusive hypotheses receiving strong support. Reconciling such differences requires a detailed understanding of how phylogenetic signal varies among data sets. Two complementary strategies for better understanding phylogenomic conflicts are to examine support on a locus-by-locus basis and use support values that capture a larger range of variation in phylogenetic information, such as likelihood ratios. Likelihood ratios can be calculated using either maximum or marginal likelihoods. Despite being conceptually similar, differences in how these ratios are calculated and interpreted have not been closely examined in phylogenomics. Here, we compare the behavior of maximum and marginal likelihood ratios when evaluating alternate resolutions of recalcitrant relationships among major squamate lineages. We find that these ratios are broadly correlated between loci, but the correlation is driven by extreme values. As a consequence, the proportion of loci that support a hypothesis can change depending on which ratio is used and whether smaller values are discarded. In addition, maximum likelihood ratios frequently exhibit identical support for alternate hypotheses, making conflict resolution a challenge. We find surprising support for a sister relationship between snakes and iguanians across four different phylogenomic data sets in contrast to previous empirical studies. [Bayes factors; likelihood ratios; marginal likelihood; maximum likelihood; phylogenomics; squamates.]

The scale of data sets used to infer phylogenies has grown dramatically in the last decades, providing researchers with an enormous amount of information with which to draw inferences about evolutionary history. However, standard approaches to assessing confidence in those inferences (e.g., nonparametric bootstrap proportions [BP] and Bayesian posterior probabilities [PPs]) are still deeply influenced by statistical procedures and frameworks that were developed when information was much more limited. These approaches largely quantify uncertainty caused by limited amounts of data, which is often vanishingly small with modern, genome-scale sequence data sets. As a consequence, today’s phylogenomic studies routinely report near-complete confidence in their inferences, even when different studies reach strongly conflicting conclusions and the sites and loci in a single data set contain much more heterogeneity than our methods assume or can accommodate. Therefore, we argue that BPs and marginal PPs of bipartitions have outlived their utility as the primary means of measuring phylogenetic support for modern phylogenomic data sets with large numbers of sites relative to the number of taxa. Continuing to rely on these measures will hinder progress towards understanding remaining sources of uncertainty in the most challenging portions of the Tree of Life. Instead, we encourage researchers to examine the ideas and methods presented in this special issue of Systematic Biology and to explore the area further in their own work. The papers in this special issue outline strategies for assessing confidence and uncertainty in phylogenomic data sets that move beyond stochastic error due to limited data and offer promise for more productive dialogue about the challenges that we face in reaching our shared goal of understanding the history of life on Earth.[Big data; gene tree variation; genomic era; statistical bias.]