Commonly used Bayesian diversification methods lead to biologically meaningful differences in branch-specific rates on empirical phylogenies.

Identifying along which lineages shifts in diversification rates occur is a central goal of comparative phylogenetics; these shifts may coincide with key evolutionary events such as the development of novel morphological characters, the acquisition of adaptive traits, polyploidization or other structural genomic changes, or dispersal to a new habitat and subsequent increase in environmental niche space. However, while multiple methods now exist to estimate diversification rates and identify shifts using phylogenetic topologies, the appropriate use and accuracy of these methods are hotly debated. Here we test whether five Bayesian methods-Bayesian Analysis of Macroevolutionary Mixtures (BAMM), two implementations of the Lineage-Specific Birth-Death-Shift model (LSBDS and PESTO), the approximate Multi-Type Birth-Death model (MTBD; implemented in BEAST2), and the Cladogenetic Diversification Rate Shift model (ClaDS2)-produce comparable results. We apply each of these methods to a set of 65 empirical time-calibrated phylogenies and compare inferences of speciation rate, extinction rate, and net diversification rate. We find that the five methods often infer different speciation, extinction, and net-diversification rates. Consequently, these different estimates may lead to different interpretations of the macroevolutionary dynamics. The different estimates can be attributed to fundamental differences among the compared models. Therefore, the inference of shifts in diversification rates is strongly method dependent. We advise biologists to apply multiple methods to test the robustness of the conclusions or to carefully select the method based on the validity of the underlying model assumptions to their particular empirical system.

The fundamental role of character coding in Bayesian morphological phylogenetics.

Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are four nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know a priori how many characters states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small.

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Comparison of Bayesian Coalescent Skyline Plot Models for Inferring Demographic Histories.

Bayesian coalescent skyline plot models are widely used to infer demographic histories. The first (non-Bayesian) coalescent skyline plot model assumed a known genealogy as data, while subsequent models and implementations jointly inferred the genealogy and demographic history from sequence data, including heterochronous samples. Overall, there exist multiple different Bayesian coalescent skyline plot models which mainly differ in two key aspects: (i) how changes in population size are modeled through independent or autocorrelated prior distributions, and (ii) how many change-points in the demographic history are used, where they occur and if the number is pre-specified or inferred. The specific impact of each of these choices on the inferred demographic history is not known because of two reasons: first, not all models are implemented in the same software, and second, each model implementation makes specific choices that the biologist cannot influence. To facilitate a detailed evaluation of Bayesian coalescent skyline plot models, we implemented all currently described models in a flexible design into the software RevBayes. Furthermore, we evaluated models and choices on an empirical dataset of horses supplemented by a small simulation study. We find that estimated demographic histories can be grouped broadly into two groups depending on how change-points in the demographic history are specified (either independent of or at coalescent events). Our simulations suggest that models using change-points at coalescent events produce spurious variation near the present, while most models using independent change-points tend to over-smooth the inferred demographic history.

The Expected Behaviors of Posterior Predictive Tests and Their Unexpected Interpretation.

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.

Bayesian Phylogenetic Analysis on Multi-Core Compute Architectures: Implementation and Evaluation of BEAGLE in RevBayes With MPI.

Phylogenies are central to many research areas in biology and commonly estimated using likelihood-based methods. Unfortunately, any likelihood-based method, including Bayesian inference, can be restrictively slow for large datasets-with many taxa and/or many sites in the sequence alignment-or complex substitutions models. The primary limiting factor when using large datasets and/or complex models in probabilistic phylogenetic analyses is the likelihood calculation, which dominates the total computation time. To address this bottleneck, we incorporated the high-performance phylogenetic library BEAGLE into RevBayes, which enables multi-threading on multi-core CPUs and GPUs, as well as hardware specific vectorized instructions for faster likelihood calculations. Our new implementation of RevBayes+BEAGLE retains the flexibility and dynamic nature that users expect from vanilla RevBayes. In addition, we implemented native parallelization within RevBayes without an external library using the message passing interface (MPI); RevBayes+MPI. We evaluated our new implementation of RevBayes+BEAGLE using multi-threading on CPUs and 2 different powerful GPUs (NVidia Titan V and NVIDIA A100) against our native implementation of RevBayes+MPI. We found good improvements in speedup when multiple cores were used, with up to 20-fold speedup when using multiple CPU cores and over 90-fold speedup when using multiple GPU cores. The improvement depended on the data type used, DNA or amino acids, and the size of the alignment, but less on the size of the tree. We additionally investigated the cost of rescaling partial likelihoods to avoid numerical underflow and showed that unnecessarily frequent and inefficient rescaling can increase runtimes up to 4-fold. Finally, we presented and compared a new approach to store partial likelihoods on branches instead of nodes that can speed up computations up to 1.7 times but comes at twice the memory requirements.

Nucleotide Substitution Model Selection Is Not Necessary for Bayesian Inference of Phylogeny With Well-Behaved Priors.

Model selection aims to choose the most adequate model for the statistical analysis at hand. The model must be complex enough to capture the complexity of the data but should be simple enough not to overfit. In phylogenetics, the most common model selection scenario concerns selecting an adequate substitution and partition model for sequence evolution to infer a phylogenetic tree. Previously, several studies showed that substitution model under-parameterization can bias phylogenetic studies. Here, we explored the impact of substitution model over-parameterization in a Bayesian statistical framework. We performed simulations under the simplest substitution model, the Jukes-Cantor model, and compare posterior estimates of phylogenetic tree topologies and tree length under the true model to the most complex model, the $\text{GTR}+\Gamma+\text{I}$ substitution model, including over-splitting the data into additional subsets (i.e., applying partitioned models). We explored 4 choices of prior distributions: the default substitution model priors of MrBayes, BEAST2, and RevBayes and a newly devised prior choice (Tame). Our results show that Bayesian inference of phylogeny is robust to substitution model over-parameterization and over-partitioning but only under our new prior settings. All 3 current default priors introduced biases for the estimated tree length. We conclude that substitution and partition model selection are superfluous steps in Bayesian phylogenetic inference pipelines if well-behaved prior distributions are applied and more effort should focus on more complex and biologically realistic substitution models.

Exploring genome gene content and morphological analysis to test recalcitrant nodes in the animal phylogeny.

An accurate phylogeny of animals is needed to clarify their evolution, ecology, and impact on shaping the biosphere. Although datasets of several hundred thousand amino acids are nowadays routinely used to test phylogenetic hypotheses, key deep nodes in the metazoan tree remain unresolved: the root of animals, the root of Bilateria, and the monophyly of Deuterostomia. Instead of using the standard approach of amino acid datasets, we performed analyses of newly assembled genome gene content and morphological datasets to investigate these recalcitrant nodes in the phylogeny of animals. We explored extensively the choices for assembling the genome gene content dataset and model choices of morphological analyses. Our results are robust to these choices and provide additional insights into the early evolution of animals, they are consistent with sponges as the sister group of all the other animals, the worm-like bilaterian lineage Xenacoelomorpha as the sister group of the other Bilateria, and tentatively support monophyletic Deuterostomia.

Rapidly changing speciation and extinction rates can be inferred in spite of nonidentifiability.

The birth-death model is commonly used to infer speciation and extinction rates by fitting the model to phylogenetic trees with exclusively extant taxa. Recently, it was demonstrated that speciation and extinction rates are not identifiable if the rates are allowed to vary freely over time. The group of birth-death models that have the same likelihood is called a congruence class, and there is no statistical evidence to favor one model over the other. This issue has led researchers to question if and what patterns can reliably be inferred from phylogenies of only extant taxa and whether time-variable birth-death models should be fitted at all. We explore the congruence class in the context of several empirical phylogenies as well as hypothetical scenarios. For these empirical phylogenies, we assume that we inferred the true congruence class. Thus, our conclusions apply to any empirical phylogeny for which we robustly inferred the true congruence class. When we summarize shared patterns in the congruence class, we show that strong directional trends in speciation and extinction rates are shared among most models. Therefore, we conclude that the inference of strong directional trends is robust. Conversely, estimates of constant rates or gentle slopes are not robust and must be treated with caution. Interestingly, the space of valid speciation rates is narrower and more limited in contrast to extinction rates, which are less constrained. These results provide further evidence and insights that speciation rates can be estimated more reliably than extinction rates.

Inferring the demographic history of the North American firefly Photinus pyralis.

The firefly Photinus pyralis inhabits a wide range of latitudinal and ecological niches, with populations living from temperate to tropical habitats. Despite its broad distribution, its demographic history is unknown. In this study, we modelled and inferred different demographic scenarios for North American populations of P. pyralis, which were collected from Texas to New Jersey. We used a combination of ABC techniques (for multi-population/colonization analyses) and likelihood inference (dadi, StairwayPlot2, PoMo) for single-population demographic inference, which proved useful with our RAD data. We uncovered that the most ancestral North American population lays in Texas, which further colonized the Central region of the US and more recently the North Eastern coast. Our study confidently rejects a demographic scenario where the North Eastern populations colonized more southern populations until reaching Texas. To estimate the age of divergence between of P. pyralis, which provides deeper insights into the history of the entire species, we assembled a multi-locus phylogenetic data covering the genus Photinus. We uncovered that the phylogenetic node leading to P. pyralis lies at the end of the Miocene. Importantly, modelling the demographic history of North American P. pyralis serves as a null model of nucleotide diversity patterns in a widespread native insect species, which will serve in future studies for the detection of adaptation events in this firefly species, as well as a comparison for future studies of other North American insect taxa.

The rise of grasslands is linked to atmospheric CO2 decline in the late Palaeogene.

Grasslands are predicted to experience a major biodiversity change by the year 2100. A better understanding of how grasslands have responded to past environmental changes will help predict the outcome of current and future environmental changes. Here, we explore the relationship between past atmospheric CO2 and temperature fluctuations and the shifts in diversification rate of Poaceae (grasses) and Asteraceae (daisies), two exceptionally species-rich grassland families (~11,000 and ~23,000 species, respectively). To this end, we develop a Bayesian approach that simultaneously estimates diversification rates through time from time-calibrated phylogenies and correlations between environmental variables and diversification rates. Additionally, we present a statistical approach that incorporates the information of the distribution of missing species in the phylogeny. We find strong evidence supporting a simultaneous increase in diversification rates for grasses and daisies after the most significant reduction of atmospheric CO2 in the Cenozoic (~34 Mya). The fluctuations of paleo-temperatures, however, appear not to have had a significant relationship with the diversification of these grassland families. Overall, our results shed new light on our understanding of the origin of grasslands in the context of past environmental changes.

Relative Time Constraints Improve Molecular Dating.

Dating the tree of life is central to understanding the evolution of life on Earth. Molecular clocks calibrated with fossils represent the state of the art for inferring the ages of major groups. Yet, other information on the timing of species diversification can be used to date the tree of life. For example, horizontal gene transfer events and ancient coevolutionary interactions such as (endo)symbioses occur between contemporaneous species and thus can imply temporal relationships between two nodes in a phylogeny. Temporal constraints from these alternative sources can be particularly helpful when the geological record is sparse, for example, for microorganisms, which represent the majority of extant and extinct biodiversity. Here, we present a new method to combine fossil calibrations and relative age constraints to estimate chronograms. We provide an implementation of relative age constraints in RevBayes that can be combined in a modular manner with the wide range of molecular dating methods available in the software. We use both realistic simulations and empirical datasets of 40 Cyanobacteria and 62 Archaea to evaluate our method. We show that the combination of relative age constraints with fossil calibrations significantly improves the estimation of node ages. [Archaea, Bayesian analysis, cyanobacteria, dating, endosymbiosis, lateral gene transfer, MCMC, molecular clock, phylogenetic dating, relaxed molecular clock, revbayes, tree of life.].

Parallel power posterior analyses for fast computation of marginal likelihoods in phylogenetics.

In Bayesian phylogenetic inference, marginal likelihoods can be estimated using several different methods, including the path-sampling or stepping-stone-sampling algorithms. Both algorithms are computationally demanding because they require a series of power posterior Markov chain Monte Carlo (MCMC) simulations. Here we introduce a general parallelization strategy that distributes the power posterior MCMC simulations and the likelihood computations over available CPUs. Our parallelization strategy can easily be applied to any statistical model despite our primary focus on molecular substitution models in this study. Using two phylogenetic example datasets, we demonstrate that the runtime of the marginal likelihood estimation can be reduced significantly even if only two CPUs are available (an average performance increase of 1.96x). The performance increase is nearly linear with the number of available CPUs. We record a performance increase of 13.3x for cluster nodes with 16 CPUs, representing a substantial reduction to the runtime of marginal likelihood estimations. Hence, our parallelization strategy enables the estimation of marginal likelihoods to complete in a feasible amount of time which previously needed days, weeks or even months. The methods described here are implemented in our open-source software RevBayes which is available from http://www.RevBayes.com.

Genome Evolution in Bacteria Isolated from Million-Year-Old Subseafloor Sediment.

Beneath the seafloor, microbial life subsists in isolation from the surface world under persistent energy limitation. The nature and extent of genomic evolution in subseafloor microbes have been unknown. Here, we show that the genomes of Thalassospira bacterial populations cultured from million-year-old subseafloor sediments evolve in clonal populations by point mutation, with a relatively low rate of homologous recombination and elevated numbers of pseudogenes. Ratios of nonsynonymous to synonymous substitutions correlate with the accumulation of pseudogenes, consistent with a role for genetic drift in the subseafloor strains but not in type strains of Thalassospira isolated from the surface world. Consistent with this, pangenome analysis reveals that the subseafloor bacterial genomes have a significantly lower number of singleton genes than the type strains, indicating a reduction in recent gene acquisitions. Numerous insertion-deletion events and pseudogenes were present in a flagellar operon of the subseafloor bacteria, indicating that motility is nonessential in these million-year-old subseafloor sediments. This genomic evolution in subseafloor clonal populations coincided with a phenotypic difference: all subseafloor isolates have a lower rate of growth under laboratory conditions than the Thalassospira xiamenensis type strain. Our findings demonstrate that the long-term physical isolation of Thalassospira, in the absence of recombination, has resulted in clonal populations whereby reduced access to novel genetic material from neighbors has resulted in the fixation of new mutations that accumulate in genomes over millions of years. IMPORTANCE The nature and extent of genomic evolution in subseafloor microbial populations subsisting for millions of years below the seafloor are unknown. Subseafloor populations have ultralow metabolic rates that are hypothesized to restrict reproduction and, consequently, the spread of new traits. Our findings demonstrate that genomes of cultivated bacterial strains from the genus Thalassospira isolated from million-year-old abyssal sediment exhibit greatly reduced levels of homologous recombination, elevated numbers of pseudogenes, and genome-wide evidence of relaxed purifying selection. These substitutions and pseudogenes are fixed into the population, suggesting that the genome evolution of these bacteria has been dominated by genetic drift. Thus, reduced recombination, stemming from long-term physical isolation, resulted in small clonal populations of Thalassospira that have accumulated mutations in their genomes over millions of years.

Locally adaptive Bayesian birth-death model successfully detects slow and rapid rate shifts.

Birth-death processes have given biologists a model-based framework to answer questions about changes in the birth and death rates of lineages in a phylogenetic tree. Therefore birth-death models are central to macroevolutionary as well as phylodynamic analyses. Early approaches to studying temporal variation in birth and death rates using birth-death models faced difficulties due to the restrictive choices of birth and death rate curves through time. Sufficiently flexible time-varying birth-death models are still lacking. We use a piecewise-constant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov random field (HSMRF) prior distributions, to approximate arbitrary changes in birth rate through time. We implement these models in the widely used statistical phylogenetic software platform RevBayes, allowing us to jointly estimate birth-death process parameters, phylogeny, and nuisance parameters in a Bayesian framework. We test both GMRF-based and HSMRF-based models on a variety of simulated diversification scenarios, and then apply them to both a macroevolutionary and an epidemiological dataset. We find that both models are capable of inferring variable birth rates and correctly rejecting variable models in favor of effectively constant models. In general the HSMRF-based model has higher precision than its GMRF counterpart, with little to no loss of accuracy. Applied to a macroevolutionary dataset of the Australian gecko family Pygopodidae (where birth rates are interpretable as speciation rates), the GMRF-based model detects a slow decrease whereas the HSMRF-based model detects a rapid speciation-rate decrease in the last 12 million years. Applied to an infectious disease phylodynamic dataset of sequences from HIV subtype A in Russia and Ukraine (where birth rates are interpretable as the rate of accumulation of new infections), our models detect a strongly elevated rate of infection in the 1990s.

Drift and Directional Selection Are the Evolutionary Forces Driving Gene Expression Divergence in Eye and Brain Tissue of Heliconius Butterflies.

Investigating gene expression evolution over micro- and macroevolutionary timescales will expand our understanding of the role of gene expression in adaptation and speciation. In this study, we characterized the evolutionary forces acting on gene expression levels in eye and brain tissue of five Heliconius butterflies with divergence times of ∼5-12 MYA. We developed and applied Brownian motion (BM) and Ornstein-Uhlenbeck (OU) models to identify genes whose expression levels are evolving through drift, stabilizing selection, or a lineage-specific shift. We found that 81% of the genes evolve under genetic drift. When testing for branch-specific shifts in gene expression, we detected 368 (16%) shift events. Genes showing a shift toward upregulation have significantly lower gene expression variance than those genes showing a shift leading toward downregulation. We hypothesize that directional selection is acting in shifts causing upregulation, since transcription is costly. We further uncovered through simulations that parameter estimation of OU models is biased when using small phylogenies and only becomes reliable with phylogenies having ≥ 50 taxa. Therefore, we developed a new statistical test based on BM to identify highly conserved genes (i.e., evolving under strong stabilizing selection), which comprised 3% of the orthoclusters. In conclusion, we found that drift is the dominant evolutionary force driving gene expression evolution in eye and brain tissue in Heliconius Nevertheless, the higher proportion of genes evolving under directional than under stabilizing selection might reflect species-specific selective pressures on vision and the brain that are necessary to fulfill species-specific requirements.

Early Arrival and Climatically-Linked Geographic Expansion of New World Monkeys from Tiny African Ancestors.

New World Monkeys (NWM) (platyrrhines) are one of the most diverse groups of primates, occupying today a wide range of ecosystems in the American tropics and exhibiting large variations in ecology, morphology, and behavior. Although the relationships among the almost 200 living species are relatively well understood, we lack robust estimates of the timing of origin, ancestral morphology, and geographic range evolution of the clade. Herein, we integrate paleontological and molecular evidence to assess the evolutionary dynamics of extinct and extant platyrrhines. We develop novel analytical frameworks to infer the evolution of body mass, changes in latitudinal ranges through time, and species diversification rates using a phylogenetic tree of living and fossil taxa. Our results show that platyrrhines originated 5-10 million years earlier than previously assumed, dating back to the Middle Eocene. The estimated ancestral platyrrhine was small-weighing 0.4 kg-and matched the size of their presumed African ancestors. As the three platyrrhine families diverged, we recover a rapid change in body mass range. During the Miocene Climatic Optimum, fossil diversity peaked and platyrrhines reached their widest latitudinal range, expanding as far South as Patagonia, favored by warm and humid climate and the lower elevation of the Andes. Finally, global cooling and aridification after the middle Miocene triggered a geographic contraction of NWM and increased their extinction rates. These results unveil the full evolutionary trajectory of an iconic and ecologically important radiation of monkeys and showcase the necessity of integrating fossil and molecular data for reliably estimating evolutionary rates and trends.

Stochastic Character Mapping of State-Dependent Diversification Reveals the Tempo of Evolutionary Decline in Self-Compatible Onagraceae Lineages.

A major goal of evolutionary biology is to identify key evolutionary transitions that correspond with shifts in speciation and extinction rates. Stochastic character mapping has become the primary method used to infer the timing, nature, and number of character state transitions along the branches of a phylogeny. The method is widely employed for standard substitution models of character evolution. However, current approaches cannot be used for models that specifically test the association of character state transitions with shifts in diversification rates such as state-dependent speciation and extinction (SSE) models. Herein, we introduce a new stochastic character mapping algorithm that overcomes these limitations, and apply it to study mating system evolution over a time-calibrated phylogeny of the plant family Onagraceae. Utilizing a hidden state SSE model we tested the association of the loss of self-incompatibility (SI) with shifts in diversification rates. We found that self-compatible lineages have higher extinction rates and lower net-diversification rates compared with self-incompatible lineages. Furthermore, these results provide empirical evidence for the "senescing" diversification rates predicted in highly selfing lineages: our mapped character histories show that the loss of SI is followed by a short-term spike in speciation rates, which declines after a time lag of several million years resulting in negative net-diversification. Lineages that have long been self-compatible, such as Fuchsia and Clarkia, are in a previously unrecognized and ongoing evolutionary decline. Our results demonstrate that stochastic character mapping of SSE models is a powerful tool for examining the timing and nature of both character state transitions and shifts in diversification rates over the phylogeny.

Testing the Role of the Red Queen and Court Jester as Drivers of the Macroevolution of Apollo Butterflies.

In macroevolution, the Red Queen (RQ) model posits that biodiversity dynamics depend mainly on species-intrinsic biotic factors such as interactions among species or life-history traits, while the Court Jester (CJ) model states that extrinsic environmental abiotic factors have a stronger role. Until recently, a lack of relevant methodological approaches has prevented the unraveling of contributions from these 2 types of factors to the evolutionary history of a lineage. Herein, we take advantage of the rapid development of new macroevolution models that tie diversification rates to changes in paleoenvironmental (extrinsic) and/or biotic (intrinsic) factors. We inferred a robust and fully-sampled species-level phylogeny, as well as divergence times and ancestral geographic ranges, and related these to the radiation of Apollo butterflies (Parnassiinae) using both extant (molecular) and extinct (fossil/morphological) evidence. We tested whether their diversification dynamics are better explained by an RQ or CJ hypothesis, by assessing whether speciation and extinction were mediated by diversity-dependence (niche filling) and clade-dependent host-plant association (RQ) or by large-scale continuous changes in extrinsic factors such as climate or geology (CJ). For the RQ hypothesis, we found significant differences in speciation rates associated with different host-plants but detected no sign of diversity-dependence. For CJ, the role of Himalayan-Tibetan building was substantial for biogeography but not a driver of high speciation, while positive dependence between warm climate and speciation/extinction was supported by continuously varying maximum-likelihood models. We find that rather than a single factor, the joint effect of multiple factors (biogeography, species traits, environmental drivers, and mass extinction) is responsible for current diversity patterns and that the same factor might act differently across clades, emphasizing the notion of opportunity. This study confirms the importance of the confluence of several factors rather than single explanations in modeling diversification within lineages.

Cladogenetic and Anagenetic Models of Chromosome Number Evolution: A Bayesian Model Averaging Approach.

ABSSTRACT: Chromosome number is a key feature of the higher-order organization of the genome, and changes in chromosome number play a fundamental role in evolution. Dysploid gains and losses in chromosome number, as well as polyploidization events, may drive reproductive isolation and lineage diversification. The recent development of probabilistic models of chromosome number evolution in the groundbreaking work by Mayrose et al. (2010, ChromEvol) have enabled the inference of ancestral chromosome numbers over molecular phylogenies and generated new interest in studying the role of chromosome changes in evolution. However, the ChromEvol approach assumes all changes occur anagenetically (along branches), and does not model events that are specifically cladogenetic. Cladogenetic changes may be expected if chromosome changes result in reproductive isolation. Here we present a new class of models of chromosome number evolution (called ChromoSSE) that incorporate both anagenetic and cladogenetic change. The ChromoSSE models allow us to determine the mode of chromosome number evolution; is chromosome evolution occurring primarily within lineages, primarily at lineage splitting, or in clade-specific combinations of both? Furthermore, we can estimate the location and timing of possible chromosome speciation events over the phylogeny. We implemented ChromoSSE in a Bayesian statistical framework, specifically in the software RevBayes, to accommodate uncertainty in parameter estimates while leveraging the full power of likelihood based methods. We tested ChromoSSE's accuracy with simulations and re-examined chromosomal evolution in Aristolochia, Carex section Spirostachyae, Helianthus, Mimulus sensu lato (s.l.), and Primula section Aleuritia, finding evidence for clade-specific combinations of anagenetic and cladogenetic dysploid and polyploid modes of chromosome evolution. [Anagenetic; Bayes factors; chromosome evolution; chromosome speciation; chromoSSE; cladogenetic; dysploidy; phylogenetic models; polyploidy; reversible-jump Markov chain Monte Carlo; whole genome duplication.].