A Diffusion-Based Approach for Simulating Forward-in-Time State-Dependent Speciation and Extinction Dynamics.

We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and three-region geographic-state speciation-extinction (GeoSSE) models. We show that the species range state dynamics simulated under tree-based and diffusion-based processes are comparable. We derive a method to infer rate parameters that are compatible with given observed stationary state frequencies and obtain an analytical result to compute stationary state frequencies for a given set of rate parameters. We also describe a procedure to find the time to reach the stationary frequencies of a ClaSSE model using our diffusion-based approach, which we demonstrate using a worked example for a two-region GeoSSE model. Finally, we discuss how the diffusion framework can be applied to formalize relationships between evolutionary patterns and processes under state-dependent diversification scenarios.

A Diffusion-Based Approach for Simulating Forward-in-Time State-Dependent Speciation and Extinction Dynamics.

We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and three-region geographic-state speciation-extinction (GeoSSE) models. We show that the species range state dynamics simulated under tree-based and diffusion-based processes are comparable. We derive a method to infer rate parameters that are compatible with given observed stationary state frequencies and obtain an analytical result to compute stationary state frequencies for a given set of rate parameters. We also describe a procedure to find the time to reach the stationary frequencies of a ClaSSE model using our diffusion-based approach, which we demonstrate using a worked example for a two-region GeoSSE model. Finally, we discuss how the diffusion framework can be applied to formalize relationships between evolutionary patterns and processes under state-dependent diversification scenarios.

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

A global phylogeny of butterflies reveals their evolutionary history, ancestral hosts and biogeographic origins.

Butterflies are a diverse and charismatic insect group that are thought to have evolved with plants and dispersed throughout the world in response to key geological events. However, these hypotheses have not been extensively tested because a comprehensive phylogenetic framework and datasets for butterfly larval hosts and global distributions are lacking. We sequenced 391 genes from nearly 2,300 butterfly species, sampled from 90 countries and 28 specimen collections, to reconstruct a new phylogenomic tree of butterflies representing 92% of all genera. Our phylogeny has strong support for nearly all nodes and demonstrates that at least 36 butterfly tribes require reclassification. Divergence time analyses imply an origin ~100 million years ago for butterflies and indicate that all but one family were present before the K/Pg extinction event. We aggregated larval host datasets and global distribution records and found that butterflies are likely to have first fed on Fabaceae and originated in what is now the Americas. Soon after the Cretaceous Thermal Maximum, butterflies crossed Beringia and diversified in the Palaeotropics. Our results also reveal that most butterfly species are specialists that feed on only one larval host plant family. However, generalist butterflies that consume two or more plant families usually feed on closely related plants.

The build-up of the present-day tropical diversity of tetrapods.

The extraordinary number of species in the tropics when compared to the extra-tropics is probably the most prominent and consistent pattern in biogeography, suggesting that overarching processes regulate this diversity gradient. A major challenge to characterizing which processes are at play relies on quantifying how the frequency and determinants of tropical and extra-tropical speciation, extinction, and dispersal events shaped evolutionary radiations. We address this question by developing and applying spatiotemporal phylogenetic and paleontological models of diversification for tetrapod species incorporating paleoenvironmental variation. Our phylogenetic model results show that area, energy, or species richness did not uniformly affect speciation rates across tetrapods and dispute expectations of a latitudinal gradient in speciation rates. Instead, both neontological and fossil evidence coincide in underscoring the role of extra-tropical extinctions and the outflow of tropical species in shaping biodiversity. These diversification dynamics accurately predict present-day levels of species richness across latitudes and uncover temporal idiosyncrasies but spatial generality across the major tetrapod radiations.

Genomics expands the mammalverse.

Diverse mammal genomes open a new portal to hidden aspects of evolutionary history.

Bayesian inference of admixture graphs on Native American and Arctic populations.

Admixture graphs are mathematical structures that describe the ancestry of populations in terms of divergence and merging (admixing) of ancestral populations as a graph. An admixture graph consists of a graph topology, branch lengths, and admixture proportions. The branch lengths and admixture proportions can be estimated using numerous numerical optimization methods, but inferring the topology involves a combinatorial search for which no polynomial algorithm is known. In this paper, we present a reversible jump MCMC algorithm for sampling high-probability admixture graphs and show that this approach works well both as a heuristic search for a single best-fitting graph and for summarizing shared features extracted from posterior samples of graphs. We apply the method to 11 Native American and Siberian populations and exploit the shared structure of high-probability graphs to characterize the relationship between Saqqaq, Inuit, Koryaks, and Athabascans. Our analyses show that the Saqqaq is not a good proxy for the previously identified gene flow from Arctic people into the Na-Dene speaking Athabascans.

PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models.

We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, testing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, through its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.

Replicated radiation of a plant clade along a cloud forest archipelago.

Replicated radiations, in which sets of similar forms evolve repeatedly within different regions, can provide powerful insights into parallel evolution and the assembly of functional diversity within communities. Several cases have been described in animals, but in plants we lack well-documented cases of replicated radiation that combine comprehensive phylogenetic and biogeographic analyses, the delimitation of geographic areas within which a set of 'ecomorphs' evolved independently and the identification of potential underlying mechanisms. Here we document the repeated evolution of a set of leaf ecomorphs in a group of neotropical plants. The Oreinotinus lineage within the angiosperm clade Viburnum spread from Mexico to Argentina through disjunct cloud forest environments. In 9 of 11 areas of endemism, species with similar sets of leaf forms evolved in parallel. We reject gene-flow-mediated evolution of similar leaves and show, instead, that species with disparate leaf forms differ in their climatic niches, supporting ecological adaptation as the driver of parallelism. Our identification of a case of replicated radiation in plants sets the stage for comparative analyses of such phenomena across the tree of life.

Phylogenetic inference of where species spread or split across barriers.

SignificanceGeography molds how species evolve in space. Strong geographical barriers to movement, for instance, both inhibit dispersal between regions and allow isolated populations to diverge as new species. Weak barriers, by contrast, permit species range expansion and persistence. These factors present a conundrum: How strong must a barrier be before between-region speciation outpaces dispersal? We designed a phylogenetic model of dispersal, extinction, and speciation that allows regional features to influence rates of biogeographic change and applied it to the neotropical radiation of Anolis lizards. Separation by water induces a threefold steeper barrier to movement than equivalent distances over land. Our model will help biologists detect relationships between evolutionary processes and the spatial contexts in which they operate.

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.

Phylogenetic reconstruction of ancestral ecological networks through time for pierid butterflies and their host plants.

The study of herbivorous insects underpins much of the theory that concerns the evolution of species interactions. In particular, Pieridae butterflies and their host plants have served as a model system for studying evolutionary arms races. To learn more about the coevolution of these two clades, we reconstructed ancestral ecological networks using stochastic mappings that were generated by a phylogenetic model of host-repertoire evolution. We then measured if, when, and how two ecologically important structural features of the ancestral networks (modularity and nestedness) evolved over time. Our study shows that as pierids gained new hosts and formed new modules, a subset of them retained or recolonised the ancestral host(s), preserving connectivity to the original modules. Together, host-range expansions and recolonisations promoted a phase transition in network structure. Our results demonstrate the power of combining network analysis with Bayesian inference of host-repertoire evolution to understand changes in complex species interactions over time.

Modeling Phylogenetic Biome Shifts on a Planet with a Past.

The spatial distribution of biomes has changed considerably over deep time, so the geographical opportunity for an evolutionary lineage to shift into a new biome may depend on how the availability and connectivity of biomes has varied temporally. To better understand how lineages shift between biomes in space and time, we developed a phylogenetic biome shift model in which each lineage shifts between biomes and disperses between regions at rates that depend on the lineage's biome affinity and location relative to the spatial distribution of biomes at any given time. To study the behavior of the biome shift model in an empirical setting, we developed a literature-based representation of paleobiome structure for three mesic forest biomes, six regions, and eight time strata, ranging from the Late Cretaceous (100 Ma) through the present. We then fitted the model to a time-calibrated phylogeny of 119 Viburnum species to compare how the results responded to various realistic or unrealistic assumptions about paleobiome structure. Ancestral biome estimates that account for paleobiome dynamics reconstructed a warm temperate (or tropical) origin of Viburnum, which is consistent with previous fossil-based estimates of ancestral biomes. Imposing unrealistic paleobiome distributions led to ancestral biome estimates that eliminated support for tropical origins, and instead inflated support for cold temperate ancestry throughout the warmer Paleocene and Eocene. The biome shift model we describe is applicable to the study of evolutionary systems beyond Viburnum, and the core mechanisms of our model are extensible to the design of richer phylogenetic models of historical biogeography and/or lineage diversification. We conclude that biome shift models that account for dynamic geographical opportunities are important for inferring ancestral biomes that are compatible with our understanding of Earth history.[Ancestral states; biome shifts; historical biogeography; niche conservatism; phylogenetics].

Joint Phylogenetic Estimation of Geographic Movements and Biome Shifts during the Global Diversification of Viburnum.

Phylogeny, molecular sequences, fossils, biogeography, and biome occupancy are all lines of evidence that reflect the singular evolutionary history of a clade, but they are most often studied separately, by first inferring a fossil-dated molecular phylogeny, then mapping on ancestral ranges and biomes inferred from extant species. Here we jointly model the evolution of biogeographic ranges, biome affinities, and molecular sequences, while incorporating fossils to estimate a dated phylogeny for all of the 163 extant species of the woody plant clade Viburnum (Adoxaceae) that we currently recognize in our ongoing worldwide monographic treatment of the group. Our analyses indicate that while the major Viburnum lineages evolved in the Eocene, the majority of extant species originated since the Miocene. Viburnum radiated first in Asia, in warm, broad-leaved evergreen (lucidophyllous) forests. Within Asia, we infer several early shifts into more tropical forests, and multiple shifts into forests that experience prolonged freezing. From Asia, we infer two early movements into the New World. These two lineages probably first occupied warm temperate forests and adapted later to spreading cold climates. One of these lineages (Porphyrotinus) occupied cloud forests and moved south through the mountains of the Neotropics. Several other movements into North America took place more recently, facilitated by prior adaptations to freezing in the Old World. We also infer four disjunctions between Asia and Europe: the Tinus lineage is the oldest and probably occupied warm forests when it spread, whereas the other three were more recent and in cold-adapted lineages. These results variously contradict published accounts, especially the view that Viburnum radiated initially in cold forests and, accordingly, maintained vessel elements with scalariform perforations. We explored how the location and biome assignments of fossils affected our inference of ancestral areas and biome states. Our results are sensitive to, but not entirely dependent upon, the inclusion of fossil biome data. It will be critical to take advantage of all available lines of evidence to decipher events in the distant past. The joint estimation approach developed here provides cautious hope even when fossil evidence is limited. [Biogeography; biome; combined evidence; fossil pollen; phylogeny; Viburnum.].

Bayesian Inference of Ancestral Host-Parasite Interactions under a Phylogenetic Model of Host Repertoire Evolution.

Intimate ecological interactions, such as those between parasites and their hosts, may persist over long time spans, coupling the evolutionary histories of the lineages involved. Most methods that reconstruct the coevolutionary history of such interactions make the simplifying assumption that parasites have a single host. Many methods also focus on congruence between host and parasite phylogenies, using cospeciation as the null model. However, there is an increasing body of evidence suggesting that the host ranges of parasites are more complex: that host ranges often include more than one host and evolve via gains and losses of hosts rather than through cospeciation alone. Here, we develop a Bayesian approach for inferring coevolutionary history based on a model accommodating these complexities. Specifically, a parasite is assumed to have a host repertoire, which includes both potential hosts and one or more actual hosts. Over time, potential hosts can be added or lost, and potential hosts can develop into actual hosts or vice versa. Thus, host colonization is modeled as a two-step process that may potentially be influenced by host relatedness. We first explore the statistical behavior of our model by simulating evolution of host-parasite interactions under a range of parameter values. We then use our approach, implemented in the program RevBayes, to infer the coevolutionary history between 34 Nymphalini butterfly species and 25 angiosperm families. Our analysis suggests that host relatedness among angiosperm families influences how easily Nymphalini lineages gain new hosts. [Ancestral hosts; coevolution; herbivorous insects; probabilistic modeling.].

An Evolutionary Insertion in the Mxra8 Receptor-Binding Site Confers Resistance to Alphavirus Infection and Pathogenesis.

Alphaviruses are emerging, mosquito-transmitted RNA viruses with poorly understood cellular tropism and species selectivity. Mxra8 is a receptor for multiple alphaviruses including chikungunya virus (CHIKV). We discovered that while expression of mouse, rat, chimpanzee, dog, horse, goat, sheep, and human Mxra8 enables alphavirus infection in cell culture, cattle Mxra8 does not. Cattle Mxra8 encodes a 15-amino acid insertion in its ectodomain that prevents Mxra8 binding to CHIKV. Identical insertions are present in zebu, yak, and the extinct auroch. As other Bovinae lineages contain related Mxra8 sequences, this insertion likely occurred at least 5 million years ago. Removing the Mxra8 insertion in Bovinae enhances alphavirus binding and infection, while introducing the insertion into mouse Mxra8 blocks CHIKV binding, prevents infection by multiple alphaviruses in cells, and mitigates CHIKV-induced pathogenesis in mice. Our studies on how this insertion provides resistance to CHIKV infection could facilitate countermeasures that disrupt Mxra8 interactions with alphaviruses.

Interdependent Phenotypic and Biogeographic Evolution Driven by Biotic Interactions.

Biotic interactions are hypothesized to be one of the main processes shaping trait and biogeographic evolution during lineage diversification. Theoretical and empirical evidence suggests that species with similar ecological requirements either spatially exclude each other, by preventing the colonization of competitors or by driving coexisting populations to extinction, or show niche divergence when in sympatry. However, the extent and generality of the effect of interspecific competition in trait and biogeographic evolution has been limited by a dearth of appropriate process-generating models to directly test the effect of biotic interactions. Here, we formulate a phylogenetic parametric model that allows interdependence between trait and biogeographic evolution, thus enabling a direct test of central hypotheses on how biotic interactions shape these evolutionary processes. We adopt a Bayesian data augmentation approach to estimate the joint posterior distribution of trait histories, range histories, and coevolutionary process parameters under this analytically intractable model. Through simulations, we show that our model is capable of distinguishing alternative scenarios of biotic interactions. We apply our model to the radiation of Darwin's finches-a classic example of adaptive divergence-and find limited support for in situ trait divergence in beak size, but stronger evidence for convergence in traits such as beak shape and tarsus length and for competitive exclusion throughout their evolutionary history. These findings are more consistent with presympatric, rather than postsympatric, niche divergence. Our modeling framework opens new possibilities for testing more complex hypotheses about the processes underlying lineage diversification. More generally, it provides a robust probabilistic methodology to model correlated evolution of continuous and discrete characters. [Bayesian; biotic interactions; competition; data augmentation; historical biogeography; trait evolution.].

Granulomatous Mastitis: A Rare Case with Sjogren's Syndrome and Complications.

Granulomatous mastitis (GM) is a rare benign chronic inflammatory process of the breast in reproductive aged females. Although considered idiopathic in many cases, it has been associated with other conditions. Herein we report a highly complex and interesting case of GM in a young female with Sjogren's syndrome. We also review the literature and discuss challenges pertaining to the management of patients with similar risk factors. According to our knowledge, this is the third case documenting the co-occurrence of GM and Sjogren's syndrome. We focus on the challenges and complications of GM in the context of an autoimmune disease. With evidence from our patient's disease course and support from the literature, we advocate the use of corticosteroids for GM to prevent complications in patients with additional risks factors such as an autoimmune disease.