Saccharomycotina yeasts defy long-standing macroecological patterns.

The Saccharomycotina yeasts ("yeasts" hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild. Here, we trained machine learning models on 12,816 terrestrial occurrence records and 96 environmental variables to infer global distribution maps at ~1 km2 resolution for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversity. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high-diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many long-standing macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and a positive relationship between latitude and range size (Rapoport's rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

The evolutionary and functional significance of germline immunoglobulin gene variation.

The recombination between immunoglobulin (IG) gene segments determines an individual's naïve antibody repertoire and, consequently, (auto)antigen recognition. Emerging evidence suggests that mammalian IG germline variation impacts humoral immune responses associated with vaccination, infection, and autoimmunity - from the molecular level of epitope specificity, up to profound changes in the architecture of antibody repertoires. These links between IG germline variants and immunophenotype raise the question on the evolutionary causes and consequences of diversity within IG loci. We discuss why the extreme diversity in IG loci remains a mystery, why resolving this is important for the design of more effective vaccines and therapeutics, and how recent evidence from multiple lines of inquiry may help us do so.

On the Decoupling of Evolutionary Changes in mRNA and Protein Levels.

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here, we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level; this observation held true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and the translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic data.

The Major Features of Macroevolution.

Evolutionary dynamics operating across deep time leave footprints in the shapes of phylogenetic trees. For the last several decades, researchers have used increasingly large and robust phylogenies to study the evolutionary history of individual clades and to investigate the causes of the glaring disparities in diversity among groups. Whereas typically not the focal point of individual clade-level studies, many researchers have remarked on recurrent patterns that have been observed across many different groups and at many different time scales. Whereas previous studies have documented various such regularities in topology and branch length distributions, they have typically focused on a single pattern and used a disparate collection (oftentimes, of quite variable reliability) of trees to assess it. Here we take advantage of modern megaphylogenies and unify previous disparate observations about the shapes embedded in the Tree of Life to create a catalog of the "major features of macroevolution." By characterizing such a large swath of subtrees in a consistent way, we hope to provide a set of phenomena that process-based macroevolutionary models of diversification ought to seek to explain.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations.

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

Evaluating methods for B-cell clonal family assignment.

The adaptive immune response relies on a diverse repertoire of B-cell receptors, each of which is characterized by a distinct sequence resulting from VDJ-recombination. Upon binding to an antigen, B-cells undergo clonal expansion and in a process unique to B-cells the overall binding affinity of the repertoire is further enhanced by somatic hypermutations in the receptor sequence. For B-cell repertoires it is therefore particularly important to analyze the dynamics of clonal expansion and patterns of somatic hypermutations and thus it is necessary to group the sequences into distinct clones to determine the number and identity of expanding clonal families responding to an antigen. Multiple methods are currently used to identify clones from sequences, employing distinct approaches to the problem. Until now there has not been an extensive comparison of how well these methods perform under the same conditions. Furthermore, since this is fundamentally a phylogenetics problem, we speculated that the mPTP method, which delimits species based on an analysis of changes in the underlying process of diversification, might perform as well as or better than existing methods. Here we conducted extensive simulations of B-cell repertoires under a diverse set of conditions and studied errors in clonal assignment and in downstream ancestral state reconstruction. We demonstrated that SCOPer-H consistently yielded superior results across parameters. However, this approach relies on a good reference assembly for the germline immunoglobulin genes which is lacking for many species. Using mPTP had lower error rates than tailor-made immunogenetic methods and should therefore be considered by researchers studying antibody evolution in non-model organisms without a reference genome.

Synthesizing the phylogenetic evidence for mutualism-associated diversification.

Mutualisms are associations in which interacting species provide services or resources to each other. It has been suggested that being party to a mutualism can spur the diversification of the interacting species due to several potential hypothesized mechanisms. There is empirical evidence to both support and refute this prediction. However, this evidence comes from a variety of different methodologies, some of which have been found to be unreliable when the phylogenetic model is misspecified, and different data types and it is therefore difficult to weigh together. Here, we synthesize phylogenetic comparative datasets and analyze the data in a consistent manner using both sister-clade comparisons and hidden-trait state-dependent speciation and extinction models. The results are mixed-for the majority of the datasets we find no evidence for an effect on diversification rates in either direction, with several showing significant positive associations and a few showing significant negative associations. In contrast to the generally mixed findings between datasets, we find that qualitative results to be consistent when analyzing taxonomically overlapping datasets using different methods, suggesting that the detected variation in diversification is due to the nature of the mutualism and not due to differences in methodology.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution.

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well-described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred model for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Decoupling of evolutionary changes in mRNA and protein levels.

Variation in gene expression across lineages is thought to explain much of the observed phenotypic variation and adaptation. The protein is closer to the target of natural selection but gene expression is typically measured as the amount of mRNA. The broad assumption that mRNA levels are good proxies for protein levels has been undermined by a number of studies reporting moderate or weak correlations between the two measures across species. One biological explanation for this discrepancy is that there has been compensatory evolution between the mRNA level and regulation of translation. However, we do not understand the evolutionary conditions necessary for this to occur nor the expected strength of the correlation between mRNA and protein levels. Here we develop a theoretical model for the coevolution of mRNA and protein levels and investigate the dynamics of the model over time. We find that compensatory evolution is widespread when there is stabilizing selection on the protein level, which is true across a variety of regulatory pathways. When the protein level is under directional selection, the mRNA level of a gene and its translation rate of the same gene were negatively correlated across lineages but positively correlated across genes. These findings help explain results from comparative studies of gene expression and potentially enable researchers to disentangle biological and statistical hypotheses for the mismatch between transcriptomic and proteomic studies.

Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution.

Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene-tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein-Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene-tissue combinations. Second, we find that for 61% of gene-tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.

Saccharomycotina yeasts defy longstanding macroecological patterns.

The Saccharomycotina yeasts ("yeasts" hereafter) are a fungal clade of scientific, economic, and medical significance. Yeasts are highly ecologically diverse, found across a broad range of environments in every biome and continent on earth1; however, little is known about what rules govern the macroecology of yeast species and their range limits in the wild2. Here, we trained machine learning models on 12,221 occurrence records and 96 environmental variables to infer global distribution maps for 186 yeast species (~15% of described species from 75% of orders) and to test environmental drivers of yeast biogeography and macroecology. We found that predicted yeast diversity hotspots occur in mixed montane forests in temperate climates. Diversity in vegetation type and topography were some of the greatest predictors of yeast species richness, suggesting that microhabitats and environmental clines are key to yeast diversification. We further found that range limits in yeasts are significantly influenced by carbon niche breadth and range overlap with other yeast species, with carbon specialists and species in high diversity environments exhibiting reduced geographic ranges. Finally, yeasts contravene many longstanding macroecological principles, including the latitudinal diversity gradient, temperature-dependent species richness, and latitude-dependent range size (Rapoport's rule). These results unveil how the environment governs the global diversity and distribution of species in the yeast subphylum. These high-resolution models of yeast species distributions will facilitate the prediction of economically relevant and emerging pathogenic species under current and future climate scenarios.

Conceptual and empirical bridges between micro- and macroevolution.

Explaining broad molecular, phenotypic and species biodiversity patterns necessitates a unifying framework spanning multiple evolutionary scales. Here we argue that although substantial effort has been made to reconcile microevolution and macroevolution, much work remains to identify the links between biological processes at play. We highlight four major questions of evolutionary biology whose solutions require conceptual bridges between micro and macroevolution. We review potential avenues for future research to establish how mechanisms at one scale (drift, mutation, migration, selection) translate to processes at the other scale (speciation, extinction, biogeographic dispersal) and vice versa. We propose ways in which current comparative methods to infer molecular evolution, phenotypic evolution and species diversification could be improved to specifically address these questions. We conclude that researchers are in a better position than ever before to build a synthesis to understand how microevolutionary dynamics unfold over millions of years.

The scaling of diversification rates with age is likely explained by sampling bias.

Numerous phylogenetic studies reported the existence of a pervasive scaling relationship between the ages of extant eukaryotic clades and their estimated diversification rates. The causes of this age-rate-scaling (ARS), whether biological and/or artifactual, remain unresolved. Here we fit diversification models to thousands of eukaryotic time-calibrated phylogenies to explore multiple potential causes of the ARS including parameter non-identifiability, model inadequacy, biases in taxonomic practice, and an important and ubiquitous form of sampling bias-preferentially analyzing larger extant clades. We distinguish between two mechanism by which such sampling biases can cause an ARS: First, by favoring clades that happen to be unusually large merely by chance (i.e., due to the stochastic nature of the cladogenic process), thus leading to rate overestimation, and second, by favoring clades that have truly higher diversification rates. We find that, of the proposed explanations, only sampling biases are likely to contribute to the observed ARS. We develop methods for fully correcting for sampling bias mechanism 1, and find that despite these corrections a substantial ARS remains. We then confirm using simulations that preferring trees with truly higher rates (mechanism 2) likely explains this residual ARS. Since we do not have a completely unbiased sample of clades, including extinct ones, for phylogenetic analyses, it is difficult to demonstrate unambiguously that sampling biases are the sole cause of the ARS. Sampling biases are, however, a parsimonious and plausible explanation for this widely observed macroevolutionary pattern, and this has implications for how we interpret the distribution of diversification rate estimates in extant clades.

Niche expansion and adaptive divergence in the global radiation of crows and ravens.

The processes that allow some lineages to diversify rapidly at a global scale remain poorly understood. Although earlier studies emphasized the importance of dispersal, global expansions expose populations to novel environments and may also require adaptation and diversification across new niches. In this study, we investigated the contributions of these processes to the global radiation of crows and ravens (genus Corvus). Combining a new phylogeny with comprehensive phenotypic and climatic data, we show that Corvus experienced a massive expansion of the climatic niche that was coupled with a substantial increase in the rates of species and phenotypic diversification. The initiation of these processes coincided with the evolution of traits that promoted dispersal and niche expansion. Our findings suggest that rapid global radiations may be better understood as processes in which high dispersal abilities synergise with traits that, like cognition, facilitate persistence in new environments.